system

Abstract

We provide the system. [Solution] Means of obtaining digital information sources, A means for automatically generating a summary using generative artificial intelligence based on the said information source, The means of listing and providing the generated summary on an electronic marketplace, A means of generating and providing data in a format corresponding to the summary to be purchased, A means for extending the knowledge of artificial intelligence using the said data format, enabling customization and output according to user requests, 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 the 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 the conventional content industry, while there is a need to efficiently obtain information in a short time under the situation where the awareness of time performance is increasing, content producers cannot fully obtain the corresponding price, and the industry itself shows a tendency to shrink. It is difficult to improve this situation and return a legitimate profit to the producer while maximizing the utilization of knowledge obtained from publications. In addition, there is also a lack of means to efficiently summarize the information of a huge number of books and provide it in a customizable form according to the needs of users.

Means for Solving the Problems

[0005] This invention provides a system that acquires digital information sources and automatically generates summaries of them using generative artificial intelligence. The generated summaries are sold on the electronic market, and users can expand the AI's knowledge using data corresponding to the summaries and customize the output in various formats. Furthermore, the volume and format of the generated summaries can be adjusted according to user settings, thereby achieving efficient information acquisition and profit sharing for the publishing industry.

[0006] "Digital information sources" refer to books and materials provided in data formats that can be stored and transmitted electronically.

[0007] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information from linguistic data without human intervention.

[0008] A "summary" refers to a shortened text that concisely summarizes the main content and important points of the original source.

[0009] An "electronic marketplace" refers to an online platform where digital content is bought and sold via the internet.

[0010] "Vector data" refers to a data format that represents information as a set of numbers, making its properties mathematically analyzable.

[0011] A "customizable format" refers to a data structure or specification that allows users to change the content and presentation method according to their needs and purposes.

[0012] "Output" refers to the final result or output provided to the user after processing by this system. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] The system according to the present invention provides a mechanism for automatically generating summaries from digital information sources using artificial intelligence technology and selling them through an electronic marketplace. This system enables the generation of customizable outputs according to user requests and allows for the expansion of artificial intelligence knowledge using data related to purchased summaries.

[0035] The server first accesses a book database from a partner digital content provider via the internet and retrieves information in a predetermined digital format. The retrieved book data is stored on the server and then provided to a generative artificial intelligence system.

[0036] The generative artificial intelligence installed on the server analyzes the acquired book data and summarizes its contents using natural language processing techniques. The generated summary text is converted into vector data using embedding technology, and this data is used to extend the AI's knowledge.

[0037] The terminal provides a user interface, allowing users to access an online marketplace to search, select, and purchase summaries of interest. When a user purchases a summary, the server generates corresponding vector data and provides a download link to the user's terminal.

[0038] Users can use downloaded vector data as training data for artificial intelligence via a dedicated application on their device. Through this process, users can customize their own AI, making the most of the knowledge gained from books and enabling the creation of new knowledge.

[0039] For example, if user A purchases summaries of multiple books on "basic economic theories," they can train an AI based on these summaries, perform comparative analyses of economic theories, and apply them to their own business strategies. Similarly, user B can train an AI on the theme of "literary works," and use this to generate new creative activities that take into account the evolution of themes and characters across different works.

[0040] This system enables the generation of diverse summaries, provides flexible information tailored to the user's purpose, and opens up new possibilities for content.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server retrieves digital book data based on a specified scope via the API of a partner digital content provider. The retrieved data is stored in the server's storage system.

[0044] Step 2:

[0045] The generative artificial intelligence installed on the server reads stored book data, uses natural language processing techniques to extract key points and themes, and generates a text summary. This summary is automatically optimized according to the length and content of the book.

[0046] Step 3:

[0047] The generated summaries are converted into vector data on the server, and the generated vector data is stored in storage. The summaries are then prepared for listing on the electronic marketplace.

[0048] Step 4:

[0049] The device provides a user interface, allowing users to easily search for summaries of interest. Users can filter by topics and genres of interest and select summaries that suit their purpose.

[0050] Step 5:

[0051] The user purchases the summary via their device. Once the purchase is complete, the server selects the vector data for the summary and generates a download link to provide to the user's account.

[0052] Step 6:

[0053] Users download vector data to their devices and use it as training data for AI using a dedicated application. This allows users to cultivate their own artificial intelligence and expand their knowledge on specific topics.

[0054] Step 7:

[0055] Users can request customized output via the device. Based on the user's settings, the device combines AI-generated information and displays it on the screen in an appropriate format.

[0056] (Example 1)

[0057] 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."

[0058] In today's information society, a vast amount of digital information is generated every day. This increase in information volume necessitates that individual users efficiently select and utilize information according to their own interests and needs. However, current technology presents challenges in quickly extracting necessary information from a massive source of data and customizing that information for effective use.

[0059] 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.

[0060] In this invention, the server includes means for acquiring information in digital format, means for automatically generating a summary based on said information using data processing technology, and means for distributing and providing the generated summary to an online marketplace. This enables users to customize and efficiently utilize information according to their own interests.

[0061] "Information in digital format" refers to information in a format that can be stored and transmitted electronically.

[0062] "Data processing technology" is a general term for computational methods and algorithms used to collect, analyze, and process information, and to generate specific deliverables based on that information.

[0063] A "summary" refers to a shortened text or collection of information that extracts the most important elements from the original text.

[0064] An "online marketplace" refers to a platform where goods and services are bought and sold over the internet.

[0065] A "data structure" refers to a format or arrangement method for efficiently storing, retrieving, and processing data.

[0066] A "knowledge processing system" refers to a system for collecting, understanding, learning, storing, and utilizing information.

[0067] "User" refers to an individual or organization that uses the system to search for, retrieve, and utilize information.

[0068] "Numerical data" refers to a data format that represents information as numerical values ​​for processing and calculations.

[0069] A "computer" refers to an electronic device that enables the processing, analysis, and management of data using programs.

[0070] "Training data" refers to a dataset used to train an artificial intelligence model, where the inputs and corresponding results are known.

[0071] This invention provides a method for users to efficiently collect, summarize, and utilize specific information by utilizing an information processing system. Specific embodiments thereof are shown below.

[0072] The server is responsible for collecting information in digital format via the internet. Specifically, it queries information from databases via APIs and retrieves it in digital format. Software technologies used at this stage include RESTful APIs and database management systems. The collected information is securely stored in a database maintained on the server.

[0073] Next, the server uses data processing techniques to summarize the acquired information. This involves the use of generative AI models employing natural language processing techniques. Specifically, machine learning frameworks (e.g., Tensorflow® and PyTorch) are utilized, and models such as BERT and GPT are employed. This extracts key points from the text data and generates a concise summary.

[0074] The generated summaries are converted into numerical format and provided to users through an online marketplace. The terminal provides this information to the user via a user interface. Using the provided interface, users can search, select, and purchase summaries as needed. This process utilizes web browsers and mobile applications.

[0075] Once the purchase is complete, the server generates data in numerical format and provides a download link to the user's device. The user can then use a dedicated application to retrieve this data and utilize it as training data for a knowledge processing system. This enables the customization and training of AI models tailored to individual user needs.

[0076] For example, if a user wants to efficiently learn information about "basic economic theory," they can enter the following prompt into the system:

[0077] "Please generate a book summary about the fundamental theories of economics."

[0078] This allows the system to collect relevant information, generate summaries, and support user learning.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server retrieves digital information from an internet database based on user requests. The input is the search criteria specified by the user, and the output is a list of information matching those criteria. The server communicates with the information source using an API and retrieves the desired data by executing queries. Through this series of operations, the necessary book information is stored on the server.

[0082] Step 2:

[0083] The server stores the acquired book data in an internal database and performs preprocessing for natural language processing. The input is the book data itself, and the output is preprocessed data that is easy for the model to handle. This preprocessing includes text normalization, tokenization, and conversion from unstructured data to structured data.

[0084] Step 3:

[0085] The server generates summaries using preprocessed data and generative AI models such as BERT and GPT. The input is preprocessed text data, and the output is summarized text. Specifically, the AI ​​model identifies the key points of the text and uses them to create a concise summary. This process utilizes machine learning algorithms based on neural networks, aiming for highly accurate summaries by adjusting numerous parameters.

[0086] Step 4:

[0087] The server converts the generated summaries into numerical data using embedding techniques. The input is the summarized text, and the output is numerical data in vector format that represents it. Embedding techniques such as Word2Vec and Doc2Vec are used in this process. The vector data is saved for later data augmentation and model evaluation purposes.

[0088] Step 5:

[0089] The terminal provides an interface that allows the user to access the system. The user can use this interface to search for, select, and, if necessary, purchase the generated summaries. The input is the user's selected summary information, and the output is a list of summaries for which purchase has been confirmed. The interface is intuitive and operates via a web browser.

[0090] Step 6:

[0091] The server generates relevant vector data based on the summaries purchased by the user and provides a download link to the user's device. The input is information about the purchased summaries, and the output is the download link. The user can obtain the data via the link and use it as training data.

[0092] Step 7:

[0093] Users customize their knowledge processing systems using acquired vector data. The input is downloaded numerical data, and the output is a customized AI model. Users can leverage this model to perform information analysis and knowledge expansion tailored to their specific interests and needs.

[0094] (Application Example 1)

[0095] 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."

[0096] In today's information-saturated world, users are required to efficiently acquire necessary knowledge from vast amounts of digital information and utilize it in a useful way. However, existing information delivery systems struggle to quickly summarize diverse forms of information and provide knowledge expansion and utilization that meets user expectations. Furthermore, the lack of sufficient customization to meet individual user needs prevents users from maximizing the value of the information they receive.

[0097] 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.

[0098] In this invention, the server includes means for acquiring digital information, means for automatically generating an information summary using generative artificial intelligence based on said information, and means for listing and providing the generated information summary on an e-commerce platform. This enables users to efficiently acquire necessary knowledge from a vast amount of digital information, customize it in a useful way, and utilize it.

[0099] "Digital information" refers to information formats that are stored or transmitted electronically via computers or the internet.

[0100] "Generative artificial intelligence" is a type of artificial intelligence that can generate new information based on data.

[0101] An "information summary" is a text that condenses the original information and extracts only the important points.

[0102] An "e-commerce platform" is an online marketplace for trading goods and services over the internet.

[0103] A "data format" is a standardized format for structuring or organizing data.

[0104] "User" refers to an individual or organization that uses this system.

[0105] "Recommended content" refers to information and materials suggested by the system that are expected to be useful to the user.

[0106] "Similar content" refers to information that is relevant to the original information summary and is likely to be of interest to the user.

[0107] The system that realizes this invention is characterized by its ability to acquire digital information and automatically generate and provide information summaries. Specifically, it performs the following process.

[0108] The server collects and stores digital information via the internet. This information is efficiently processed using programming languages ​​such as Python and the Flask framework. The collected information is analyzed by generative artificial intelligence, and summaries are generated using natural language processing techniques (e.g., TensorFlow or PyTorch). The generated summaries are vectorized using Word2Vec or BERT, and this is provided as a data format that helps users expand their knowledge.

[0109] The device allows users to search for and select information of interest through its user interface. Users can subscribe to information summaries via the user interface and receive recommendations for similar content based on that summary. Furthermore, they can train their own artificial intelligence systems using the provided vector data.

[0110] For example, if a user searches for information on "the latest AI technology trends," a summary of related digital information will be provided. Similar content, such as applicable AI algorithms and case studies, may also be suggested. Through this convenient process, users can quickly and effectively obtain and utilize the latest information.

[0111] An example of a prompt message could be a specific request such as, "Tell me about current trends in AI technology." This would allow the system to automatically present a summary of relevant information to the user.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server retrieves digital information from digital information providers via the internet. Specifically, it uses APIs to collect book data and news articles. In this process, data in JSON format is provided as input, and the output is stored in a format that is then stored in the server's database.

[0115] Step 2:

[0116] The server automatically generates information summaries using generative artificial intelligence based on stored digital information. The input is the digital information stored in step 1, and the output is generated by extracting important points from the text using natural language processing techniques and producing a summary text. This process involves text analysis using a natural language processing library.

[0117] Step 3:

[0118] The generated summary text is then vectorized. Here, the server converts the input summary text into vector data using embedding techniques such as Word2Vec or BERT. This output can then be used as training data for artificial intelligence.

[0119] Step 4:

[0120] The device allows users to search for and select information of interest through its user interface. Specifically, it takes the prompt phrase "Tell me about current AI technology trends" entered by the user into the search box as input data, retrieves relevant summary information, and outputs it in a displayed format.

[0121] Step 5:

[0122] Users subscribe to the provided information summaries and receive recommendations for similar content based on them. The summary text selected by the user is registered as input in the system, relevant information is searched for, and recommended content is output. A content matching algorithm is used in this step.

[0123] Step 6:

[0124] The terminal supplies vector data to the user's artificial intelligence system, supporting knowledge augmentation. The input is the vector data obtained in step 3, which is added to the user's AI model and used as output. This allows the user to enhance their own AI system.

[0125] 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.

[0126] The system according to the present invention combines artificial intelligence technology and an emotion engine to provide customized information tailored to the user's emotions, thereby realizing a more personalized experience. Specifically, it acquires data from digital information sources and automatically generates summaries using generative artificial intelligence. Furthermore, the added emotion engine recognizes the user's emotional state and performs information processing accordingly.

[0127] The server first acquires digital data of books and then summarizes it using generative artificial intelligence. The generated summaries are then offered on the e-market, but an emotion engine intervenes to adjust the summaries based on user input and emotion recognition via the interface.

[0128] The device receives the user's emotional input and sends it to the server. This emotional data is used to assess the user's current emotional state; for example, if the user is stressed, information with a relaxing effect can be prioritized and displayed. The device also uses feedback received from the emotion engine to display a summary optimized for the user.

[0129] Users can access the online marketplace via an interface on their device and purchase or view summaries of interest. Upon purchase, the server uses an emotion engine to analyze the user's emotional state, generating and providing customized vector data based on that analysis. This data can be used for AI training, enabling the expansion of knowledge tailored to the user's emotions.

[0130] For example, if user A purchases a book summary on "leadership," and the emotion engine analyzes the user's emotions as "low motivation," the summary will be presented in a way that emphasizes content that encourages proactive action. This allows users to obtain information that flexibly responds to their individual emotional state. This system opens up a new dimension in information delivery and provides users with a deeper, more personalized engagement.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The server retrieves data from content providers, who are the source of digital book data, and stores it in storage. This data collection is carried out efficiently via APIs.

[0134] Step 2:

[0135] The server inputs stored book data into a generative artificial intelligence system and generates a summary using natural language processing technology. The generated summary is automatically adjusted based on compression ratio and the main points of the content.

[0136] Step 3:

[0137] The device receives emotional data input from the user and sends this data to the server. Emotional data is collected, for example, through user feedback and choices made during interface operations.

[0138] Step 4:

[0139] The server processes the received emotional data using an emotion engine to analyze the user's emotional state. The analysis results reflect the user's current emotions (joy, stress, anxiety, etc.).

[0140] Step 5:

[0141] The server adjusts the summary based on the analysis results from the emotion engine, optimizing it to match the user's emotional state. For example, when the user is stressed, it will include more content that helps them relax.

[0142] Step 6:

[0143] The terminal displays a customized summary received from the server to the user. Visual effects and navigation are applied according to the user's settings during display.

[0144] Step 7:

[0145] The user reviews the displayed summary and proceeds with the purchase if necessary. Once the purchase is confirmed, the server provides the relevant vector data, which the user can then use as training material for artificial intelligence.

[0146] This series of processes allows users to receive information tailored to their individual emotional state, resulting in a highly customized experience.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] Conventional information delivery systems have struggled to customize content to take into account users' emotional states, making it difficult to provide an information experience tailored to individual users. Furthermore, summaries generated from digital information sources often remain general rather than adequately addressing users' needs and circumstances, highlighting the need for optimal information delivery that aligns with users' interests and requests.

[0150] 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.

[0151] In this invention, the server includes means for acquiring information sources in digital format, means for automatically generating summaries using generative artificial intelligence, and means for analyzing the user's emotional state and adjusting the summaries accordingly. This enables the provision of customized information that responds to the user's emotions, thereby enhancing the individual user experience.

[0152] "Digital information sources" refer to information sources provided in a format that can be processed by a computer, including ebooks and online articles.

[0153] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate content based on given data.

[0154] A "summary" is a way of extracting the essential parts of the original information and expressing them in a shortened form.

[0155] An "electronic marketplace" is a market where digital goods and services are traded online.

[0156] A "data format" refers to a standardized format or protocol used to structure data.

[0157] "Expanding the knowledge of artificial intelligence" means broadening the amount of information and the range of applications of artificial intelligence based on new data and information.

[0158] "User's emotional state" refers to the user's internal emotional state and includes various emotional elements such as stress, joy, and excitement.

[0159] A "terminal" is a device used by users to input information or receive output, and generally refers to computers or smartphones.

[0160] This invention provides a system that presents personalized information in response to the user's emotions. The system consists of a server, a terminal, and a user interface.

[0161] The server first acquires digital information sources from online databases and e-marketplaces. These sources include e-books and web content. The server uses a generative AI model to analyze the acquired information and automatically generate summaries. Open-source platforms and commercial natural language processing AIs are used as AI models. The generated summaries are adjusted based on the user's emotional state. To this end, the server uses an emotion analysis algorithm to evaluate the user's emotional data in real time. This analysis is performed using voice input, text input, or biosensor data provided by the user through their device.

[0162] The device is responsible for collecting emotional data from the user and sending it to the server. The application on the device infers the user's emotional state based on their operation history and input data, and transfers this information to the server's emotional engine. The device also displays a customized summary sent from the server to the user. This summary is tailored to the user's current emotions, making it more engaging for the user.

[0163] Users access the system through their devices. When a user purchases or views summaries of interest, the system provides personalized information tailored to the user's emotions. This allows users to receive content optimized for their emotional state, rather than standardized information.

[0164] For example, if a user requests information about "leadership," the device sends a prompt to the server stating, "In a summary about leadership, please emphasize how to bring about positive change." Based on this prompt, a generative AI model creates a summary, and an emotion engine adjusts it to motivate the user. Finally, the device provides this specially adjusted summary to the user.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server retrieves digital information sources from the e-marketplace. Specifically, it uses APIs to download digital data of required books and articles. This input data is sent to the server in text format. The server stores this digital data in a centrally managed database in preparation for passing it on to a generating AI model.

[0168] Step 2:

[0169] The server uses a generative AI model to automatically generate summaries based on acquired data. Digital data is provided to the AI ​​model as input, along with prompts, and a summary is generated. These prompts include instructions such as, "Summarize the main points of the information source." As output, the server receives the summary text from the AI ​​model.

[0170] Step 3:

[0171] The device collects emotional data from the user. Based on text, voice, and other interaction data entered by the user, it infers the user's emotional state. This data enters the device as input and is analyzed by emotion recognition software. The output is an evaluation result indicating the user's emotional state. This evaluation result is sent to the server.

[0172] Step 4:

[0173] The server uses an emotion engine to analyze the user's emotional state transmitted from the terminal and adjusts the summary to match the user's emotions. Specifically, it receives adjustment instructions for the generated summary from the emotion engine and modifies its content. Emotional state data and the summary are used as input, and the adjusted summary text is obtained as output.

[0174] Step 5:

[0175] The terminal receives the adjusted summary returned from the server and provides it to the user. To display the summary in the UI for user readability, the terminal renders the received summary text in an appropriate GUI format. The adjusted summary is used as input, and the content displayed on the user screen is generated as output.

[0176] Step 6:

[0177] Users view summaries provided on their devices and offer feedback as needed. This feedback is incorporated into the device as new input and used to generate and refine summaries in the future. Through this process, the system analyzes user sentiment more accurately and provides personalized information.

[0178] (Application Example 2)

[0179] 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 device 14 will be referred to as the "terminal."

[0180] Modern digital content consumers demand personalized information tailored to their diverse emotions and needs. However, current systems are unable to adequately customize based on emotions, limiting the improvement of the user experience. Furthermore, the inability to provide emotionally relevant information leads to decreased user satisfaction and makes it difficult to appropriately expand their knowledge.

[0181] 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.

[0182] In this invention, the server includes means for acquiring a data source in digital format, means for automatically generating a summary using generative artificial intelligence based on the data source, means for providing the generated summary to a virtual marketplace, means for processing information based on the user's emotional state and adjusting the summary to correspond to the emotional state, and means for acquiring the user's emotional data and providing a customized summary based on the emotional data. This enables detailed information provision and knowledge personalization that responds to the user's emotions.

[0183] A "digital data source" is a collection of non-physical data used on computers and networks.

[0184] "Generative artificial intelligence" is an artificial intelligence technology that creates new content or summaries based on given data and information.

[0185] "Methods for automatically generating summaries" refer to automated processes for shortening long texts or data, and for extracting and presenting important information.

[0186] A "virtual marketplace" is a platform that enables commercial transactions to take place on the internet, and is a marketplace that does not have a physical location.

[0187] "Emotion-based information processing" is a technology that determines a user's current emotions and changes the content and format of information accordingly.

[0188] "Emotional data" refers to digital information that indicates a user's mental state or emotions, and includes, for example, facial expressions, voice, and input data.

[0189] A "customized summary" is a personalized, abridged version of information created to meet the user's specific needs and emotions.

[0190] To implement this invention, a system involving a server and a user's terminal is primarily used. The server first acquires data sources in digital format. This data is information collected from various content providers and online databases. Next, the server uses generative artificial intelligence to automatically generate a summary based on the acquired data. This generative artificial intelligence, for example, utilizes natural language processing technology to extract important points from large amounts of data and efficiently create a summary.

[0191] The user's device acquires emotional data using voice input and camera sensors. This makes it possible to analyze the user's emotional state from, for example, the tone of their voice and facial expressions. The acquired emotional data is then sent to the server. This emotional data is used in the server's information processing to customize the content and structure of the summary according to the user's emotions. At this time, the emotion engine evaluates the user's emotional state and performs emotion-based optimization.

[0192] For example, if emotional data indicates that a user wants to relax, the server will prioritize providing summaries of relaxing music and videos. Furthermore, if the server determines that the user has a high motivation to learn, it can present summaries of detailed learning content.

[0193] An example of a prompt is, "Generate and present the most appropriate content summary based on the user's emotions." By inputting this prompt into the AI ​​generation model, the system provides the user with a content summary that best suits their current emotional state.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server acquires data sources in digital format. Inputs include information from online databases and content providers. This data is collected and stored in storage, preparing it for subsequent processing.

[0197] Step 2:

[0198] The server supplies the acquired data to a generative artificial intelligence (AI) system, which automatically generates a summary. The input is the data saved in the previous step, and the output is the summarized information. The AI ​​uses natural language processing techniques to select essential information from a large amount of data and summarize it concisely.

[0199] Step 3:

[0200] The user's device acquires emotional data using voice input and the camera. The input is the user's facial expressions and voice, and the output is an emotional state represented by numbers or categories. The device processes this data and calls an emotion engine to analyze the user's emotions.

[0201] Step 4:

[0202] The device sends emotional data to the server. The input is the user's emotional state data, and the output is the analysis result received by the emotion engine on the server. Data is transmitted according to the communication protocol to ensure accurate analysis.

[0203] Step 5:

[0204] The server uses an emotion engine to analyze emotional data and customizes summaries created by generative AI. The input consists of emotional state analysis data and AI-generated summaries, while the output is a customized summary adapted to the user. The server optimizes the content and presentation of the summary to match the user's emotions.

[0205] Step 6:

[0206] The user receives a customized summary through their device. The input is customized summary data from the server, and the output is what is displayed on the device's screen. By reading this, the user can gain an informational experience that resonates with their emotions.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] The system according to the present invention provides a mechanism for automatically generating summaries from digital information sources using artificial intelligence technology and selling them through an electronic marketplace. This system enables the generation of customizable outputs according to user requests and allows for the expansion of artificial intelligence knowledge using data related to purchased summaries.

[0224] The server first accesses a book database from a partner digital content provider via the internet and retrieves information in a predetermined digital format. The retrieved book data is stored on the server and then provided to a generative artificial intelligence system.

[0225] The generative artificial intelligence installed on the server analyzes the acquired book data and summarizes its contents using natural language processing techniques. The generated summary text is converted into vector data using embedding technology, and this data is used to extend the AI's knowledge.

[0226] The terminal provides a user interface, allowing users to access an online marketplace to search, select, and purchase summaries of interest. When a user purchases a summary, the server generates corresponding vector data and provides a download link to the user's terminal.

[0227] Users can use downloaded vector data as training data for artificial intelligence via a dedicated application on their device. Through this process, users can customize their own AI, making the most of the knowledge gained from books and enabling the creation of new knowledge.

[0228] For example, if user A purchases summaries of multiple books on "basic economic theories," they can train an AI based on these summaries, perform comparative analyses of economic theories, and apply them to their own business strategies. Similarly, user B can train an AI on the theme of "literary works," and use this to generate new creative activities that take into account the evolution of themes and characters across different works.

[0229] This system enables the generation of diverse summaries, provides flexible information tailored to the user's purpose, and opens up new possibilities for content.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server retrieves digital book data based on a specified scope via the API of a partner digital content provider. The retrieved data is stored in the server's storage system.

[0233] Step 2:

[0234] The generative artificial intelligence installed on the server reads stored book data, uses natural language processing techniques to extract key points and themes, and generates a text summary. This summary is automatically optimized according to the length and content of the book.

[0235] Step 3:

[0236] The generated summaries are converted into vector data on the server, and the generated vector data is stored in storage. The summaries are then prepared for listing on the electronic marketplace.

[0237] Step 4:

[0238] The device provides a user interface, allowing users to easily search for summaries of interest. Users can filter by topics and genres of interest and select summaries that suit their purpose.

[0239] Step 5:

[0240] The user purchases the summary via their device. Once the purchase is complete, the server selects the vector data for the summary and generates a download link to provide to the user's account.

[0241] Step 6:

[0242] Users download vector data to their devices and use it as training data for AI using a dedicated application. This allows users to cultivate their own artificial intelligence and expand their knowledge on specific topics.

[0243] Step 7:

[0244] Users can request customized output via the device. Based on the user's settings, the device combines AI-generated information and displays it on the screen in an appropriate format.

[0245] (Example 1)

[0246] 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."

[0247] In today's information society, a vast amount of digital information is generated every day. This increase in information volume necessitates that individual users efficiently select and utilize information according to their own interests and needs. However, current technology presents challenges in quickly extracting necessary information from a massive source of data and customizing that information for effective use.

[0248] 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.

[0249] In this invention, the server includes means for acquiring information in digital format, means for automatically generating a summary based on said information using data processing technology, and means for distributing and providing the generated summary to an online marketplace. This enables users to customize and efficiently utilize information according to their own interests.

[0250] "Information in digital format" refers to information in a format that can be stored and transmitted electronically.

[0251] "Data processing technology" is a general term for computational methods and algorithms used to collect, analyze, and process information, and to generate specific deliverables based on that information.

[0252] A "summary" refers to a shortened text or collection of information that extracts the most important elements from the original text.

[0253] An "online marketplace" refers to a platform where goods and services are bought and sold over the internet.

[0254] A "data structure" refers to a format or arrangement method for efficiently storing, retrieving, and processing data.

[0255] A "knowledge processing system" refers to a system for collecting, understanding, learning, storing, and utilizing information.

[0256] "User" refers to an individual or organization that uses the system to search for, retrieve, and utilize information.

[0257] "Numerical data" refers to a data format that represents information as numerical values ​​for processing and calculations.

[0258] A "computer" refers to an electronic device that enables the processing, analysis, and management of data using programs.

[0259] "Training data" refers to a dataset used to train an artificial intelligence model, where the inputs and corresponding results are known.

[0260] This invention provides a method for users to efficiently collect, summarize, and utilize specific information by utilizing an information processing system. Specific embodiments thereof are shown below.

[0261] The server is responsible for collecting information in digital format via the internet. Specifically, it queries information from databases via APIs and retrieves it in digital format. Software technologies used at this stage include RESTful APIs and database management systems. The collected information is securely stored in a database maintained on the server.

[0262] Next, the server uses data processing techniques to summarize the acquired information. This involves the use of generative AI models employing natural language processing techniques. Specifically, machine learning frameworks (e.g., TensorFlow and PyTorch) are utilized, and models such as BERT and GPT are employed. This extracts key points from text data and generates a concise summary.

[0263] The generated summaries are converted into numerical format and provided to users through an online marketplace. The terminal provides this information to the user via a user interface. Using the provided interface, users can search, select, and purchase summaries as needed. This process utilizes web browsers and mobile applications.

[0264] Once the purchase is complete, the server generates data in numerical format and provides a download link to the user's device. The user can then use a dedicated application to retrieve this data and utilize it as training data for a knowledge processing system. This enables the customization and training of AI models tailored to individual user needs.

[0265] For example, if a user wants to efficiently learn information about "basic economic theory," they can enter the following prompt into the system:

[0266] "Please generate a book summary about the fundamental theories of economics."

[0267] This allows the system to collect relevant information, generate summaries, and support user learning.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The server retrieves digital information from an internet database based on user requests. The input is the search criteria specified by the user, and the output is a list of information matching those criteria. The server communicates with the information source using an API and retrieves the desired data by executing queries. Through this series of operations, the necessary book information is stored on the server.

[0271] Step 2:

[0272] The server stores the acquired book data in an internal database and performs preprocessing for natural language processing. The input is the book data itself, and the output is preprocessed data that is easy for the model to handle. This preprocessing includes text normalization, tokenization, and conversion from unstructured data to structured data.

[0273] Step 3:

[0274] The server generates summaries using preprocessed data and generative AI models such as BERT and GPT. The input is preprocessed text data, and the output is summarized text. Specifically, the AI ​​model identifies the key points of the text and uses them to create a concise summary. This process utilizes machine learning algorithms based on neural networks, aiming for highly accurate summaries by adjusting numerous parameters.

[0275] Step 4:

[0276] The server converts the generated summaries into numerical data using embedding techniques. The input is the summarized text, and the output is numerical data in vector format that represents it. Embedding techniques such as Word2Vec and Doc2Vec are used in this process. The vector data is saved for later data augmentation and model evaluation purposes.

[0277] Step 5:

[0278] The terminal provides an interface through which the user can access the system. The user can search for, select, and if necessary, purchase summaries generated using the interface. The input is the summary information selected by the user, and the output is a list of summaries for which the purchase has been finalized. The interface enables intuitive operations and operates on a web browser or the like.

[0279] Step 6:

[0280] Based on the summaries for which the user has completed the purchase, the server generates relevant vector data and provides a download link to the user's terminal. The input is the information of the summaries for which the purchase has been completed, and the output is the download link. The user can obtain the data through the link and use it as training data.

[0281] Step 7:

[0282] The user customizes their knowledge processing system using the obtained vector data. The input is the downloaded numerical data, and the output is the customized AI model. The user can utilize this model to perform information analysis and knowledge expansion according to specific interests and needs.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In modern times of information overload, users are required to efficiently acquire the necessary knowledge from a vast amount of digital information and utilize it in a useful form. However, existing information provision systems have the problem that it is difficult to quickly summarize information in various forms and provide knowledge expansion and utilization that meet users' expectations. Furthermore, due to insufficient customization according to the individual needs of users, there is also the problem that the value of information cannot be maximized.

[0286] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following various means.

[0287] In this invention, the server includes means for acquiring digital information, means for automatically generating an information summary using a generative artificial intelligence based on the information, and means for offering and providing the generated information summary to an e-commerce platform. Thereby, users can efficiently acquire necessary knowledge from a vast amount of digital information and customize and utilize it in a useful form.

[0288] "Digital information" is an information format that is electronically stored or transmitted via a computer or the Internet.

[0289] "Generative artificial intelligence" is a type of artificial intelligence that can generate new information based on data.

[0290] "Information summary" is a text that simplifies the original information and extracts only the important points.

[0291] "E-commerce platform" is an online market for trading goods and services on the Internet.

[0292] "Data format" is a standard format for structuring or organizing data.

[0293] "User" refers to an individual or group using this system.

[0294] "Recommended content" is information and materials that are assumed to be useful to the user and proposed by the system.

[0295] "Similar content" is information that is relevant to the original information summary and is considered to attract the user's interest.

[0296] The system that realizes this invention is characterized by its ability to acquire digital information and automatically generate and provide information summaries. Specifically, it performs the following process.

[0297] The server collects and stores digital information via the internet. This information is efficiently processed using programming languages ​​such as Python and the Flask framework. The collected information is analyzed by generative artificial intelligence, and summaries are generated using natural language processing techniques (e.g., TensorFlow or PyTorch). The generated summaries are vectorized using Word2Vec or BERT, and this is provided as a data format that helps users expand their knowledge.

[0298] The device allows users to search for and select information of interest through its user interface. Users can subscribe to information summaries via the user interface and receive recommendations for similar content based on that summary. Furthermore, they can train their own artificial intelligence systems using the provided vector data.

[0299] For example, if a user searches for information on "the latest AI technology trends," a summary of related digital information will be provided. Similar content, such as applicable AI algorithms and case studies, may also be suggested. Through this convenient process, users can quickly and effectively obtain and utilize the latest information.

[0300] An example of a prompt message could be a specific request such as, "Tell me about current trends in AI technology." This would allow the system to automatically present a summary of relevant information to the user.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The server obtains digital information from a digital information provider via the Internet. As a specific operation, it collects book data and news articles using an API. In this process, data in JSON format is provided as input and output in a format that is saved in the server's database.

[0304] Step 2:

[0305] Based on the saved digital information, the server automatically generates an information summary using a generative artificial intelligence. The input is the digital information saved in Step 1, and the output is produced by extracting important points from the text using natural language processing technology and generating a summary text. In this process, text analysis using a natural language processing library is performed.

[0306] Step 3:

[0307] Vectorize the generated summary text. Here, the server converts the input summary text into vector data using embedding techniques such as Word2Vec or BERT. This output can be used as training data for artificial intelligence.

[0308] Step 4:

[0309] The terminal enables the user to search for and select information that the user is interested in through the user interface. As a specific operation, the prompt text "Tell me about the current trends in AI technology" entered by the user in the search box is used as input data, and the relevant summary information is obtained and output in a form that is displayed.

[0310] Step 5:

[0311] The user subscribes to the provided information summary and receives recommendations for similar content. The summary text selected by the user is registered in the system as input, and relevant information is searched and output as recommended content. In this step, a content matching algorithm is used.

[0312] Step 6:

[0313] The terminal supplies vector data to the user's artificial intelligence system, supporting knowledge augmentation. The input is the vector data obtained in step 3, which is added to the user's AI model and used as output. This allows the user to enhance their own AI system.

[0314] 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.

[0315] The system according to the present invention combines artificial intelligence technology and an emotion engine to provide customized information tailored to the user's emotions, thereby realizing a more personalized experience. Specifically, it acquires data from digital information sources and automatically generates summaries using generative artificial intelligence. Furthermore, the added emotion engine recognizes the user's emotional state and performs information processing accordingly.

[0316] The server first acquires digital data of books and then summarizes it using generative artificial intelligence. The generated summaries are then offered on the e-market, but an emotion engine intervenes to adjust the summaries based on user input and emotion recognition via the interface.

[0317] The device receives the user's emotional input and sends it to the server. This emotional data is used to assess the user's current emotional state; for example, if the user is stressed, information with a relaxing effect can be prioritized and displayed. The device also uses feedback received from the emotion engine to display a summary optimized for the user.

[0318] Users can access the online marketplace via an interface on their device and purchase or view summaries of interest. Upon purchase, the server uses an emotion engine to analyze the user's emotional state, generating and providing customized vector data based on that analysis. This data can be used for AI training, enabling the expansion of knowledge tailored to the user's emotions.

[0319] For example, if user A purchases a book summary on "leadership," and the emotion engine analyzes the user's emotions as "low motivation," the summary will be presented in a way that emphasizes content that encourages proactive action. This allows users to obtain information that flexibly responds to their individual emotional state. This system opens up a new dimension in information delivery and provides users with a deeper, more personalized engagement.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The server retrieves data from content providers, who are the source of digital book data, and stores it in storage. This data collection is carried out efficiently via APIs.

[0323] Step 2:

[0324] The server inputs stored book data into a generative artificial intelligence system and generates a summary using natural language processing technology. The generated summary is automatically adjusted based on compression ratio and the main points of the content.

[0325] Step 3:

[0326] The device receives emotional data input from the user and sends this data to the server. Emotional data is collected, for example, through user feedback and choices made during interface operations.

[0327] Step 4:

[0328] The server processes the received emotional data using an emotion engine to analyze the user's emotional state. The analysis results reflect the user's current emotions (joy, stress, anxiety, etc.).

[0329] Step 5:

[0330] The server adjusts the summary based on the analysis results from the emotion engine, optimizing it to match the user's emotional state. For example, when the user is stressed, it will include more content that helps them relax.

[0331] Step 6:

[0332] The terminal displays a customized summary received from the server to the user. Visual effects and navigation are applied according to the user's settings during display.

[0333] Step 7:

[0334] The user reviews the displayed summary and proceeds with the purchase if necessary. Once the purchase is confirmed, the server provides the relevant vector data, which the user can then use as training material for artificial intelligence.

[0335] This series of processes allows users to receive information tailored to their individual emotional state, resulting in a highly customized experience.

[0336] (Example 2)

[0337] 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".

[0338] Conventional information delivery systems have struggled to customize content to take into account users' emotional states, making it difficult to provide an information experience tailored to individual users. Furthermore, summaries generated from digital information sources often remain general rather than adequately addressing users' needs and circumstances, highlighting the need for optimal information delivery that aligns with users' interests and requests.

[0339] 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.

[0340] In this invention, the server includes means for acquiring information sources in digital format, means for automatically generating summaries using generative artificial intelligence, and means for analyzing the user's emotional state and adjusting the summaries accordingly. This enables the provision of customized information that responds to the user's emotions, thereby enhancing the individual user experience.

[0341] "Digital information sources" refer to information sources provided in a format that can be processed by a computer, including ebooks and online articles.

[0342] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate content based on given data.

[0343] A "summary" is a way of extracting the essential parts of the original information and expressing them in a shortened form.

[0344] An "electronic marketplace" is a market where digital goods and services are traded online.

[0345] A "data format" refers to a standardized format or protocol used to structure data.

[0346] "Expanding the knowledge of artificial intelligence" means broadening the amount of information and the range of applications of artificial intelligence based on new data and information.

[0347] "User's emotional state" refers to the user's internal emotional state and includes various emotional elements such as stress, joy, and excitement.

[0348] A "terminal" is a device used by users to input information or receive output, and generally refers to computers or smartphones.

[0349] This invention provides a system that presents personalized information in response to the user's emotions. The system consists of a server, a terminal, and a user interface.

[0350] The server first acquires digital information sources from online databases and e-marketplaces. These sources include e-books and web content. The server uses a generative AI model to analyze the acquired information and automatically generate summaries. Open-source platforms and commercial natural language processing AIs are used as AI models. The generated summaries are adjusted based on the user's emotional state. To this end, the server uses an emotion analysis algorithm to evaluate the user's emotional data in real time. This analysis is performed using voice input, text input, or biosensor data provided by the user through their device.

[0351] The device is responsible for collecting emotional data from the user and sending it to the server. The application on the device infers the user's emotional state based on their operation history and input data, and transfers this information to the server's emotional engine. The device also displays a customized summary sent from the server to the user. This summary is tailored to the user's current emotions, making it more engaging for the user.

[0352] Users access the system through their devices. When a user purchases or views summaries of interest, the system provides personalized information tailored to the user's emotions. This allows users to receive content optimized for their emotional state, rather than standardized information.

[0353] For example, if a user requests information about "leadership," the device sends a prompt to the server stating, "In a summary about leadership, please emphasize how to bring about positive change." Based on this prompt, a generative AI model creates a summary, and an emotion engine adjusts it to motivate the user. Finally, the device provides this specially adjusted summary to the user.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The server retrieves digital information sources from the e-marketplace. Specifically, it uses APIs to download digital data of required books and articles. This input data is sent to the server in text format. The server stores this digital data in a centrally managed database in preparation for passing it on to a generating AI model.

[0357] Step 2:

[0358] The server uses a generative AI model to automatically generate summaries based on acquired data. Digital data is provided to the AI ​​model as input, along with prompts, and a summary is generated. These prompts include instructions such as, "Summarize the main points of the information source." As output, the server receives the summary text from the AI ​​model.

[0359] Step 3:

[0360] The device collects emotional data from the user. Based on text, voice, and other interaction data entered by the user, it infers the user's emotional state. This data enters the device as input and is analyzed by emotion recognition software. The output is an evaluation result indicating the user's emotional state. This evaluation result is sent to the server.

[0361] Step 4:

[0362] The server uses an emotion engine to analyze the user's emotional state transmitted from the terminal and adjusts the summary to match the user's emotions. Specifically, it receives adjustment instructions for the generated summary from the emotion engine and modifies its content. Emotional state data and the summary are used as input, and the adjusted summary text is obtained as output.

[0363] Step 5:

[0364] The terminal receives the adjusted summary returned from the server and provides it to the user. To display the summary in the UI for user readability, the terminal renders the received summary text in an appropriate GUI format. The adjusted summary is used as input, and the content displayed on the user screen is generated as output.

[0365] Step 6:

[0366] Users view summaries provided on their devices and offer feedback as needed. This feedback is incorporated into the device as new input and used to generate and refine summaries in the future. Through this process, the system analyzes user sentiment more accurately and provides personalized information.

[0367] (Application Example 2)

[0368] 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".

[0369] Modern digital content consumers demand personalized information tailored to their diverse emotions and needs. However, current systems are unable to adequately customize based on emotions, limiting the improvement of the user experience. Furthermore, the inability to provide emotionally relevant information leads to decreased user satisfaction and makes it difficult to appropriately expand their knowledge.

[0370] 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.

[0371] In this invention, the server includes means for acquiring a data source in digital format, means for automatically generating a summary using generative artificial intelligence based on the data source, means for providing the generated summary to a virtual marketplace, means for processing information based on the user's emotional state and adjusting the summary to correspond to the emotional state, and means for acquiring the user's emotional data and providing a customized summary based on the emotional data. This enables detailed information provision and knowledge personalization that responds to the user's emotions.

[0372] A "digital data source" is a collection of non-physical data used on computers and networks.

[0373] "Generative artificial intelligence" is an artificial intelligence technology that creates new content or summaries based on given data and information.

[0374] "Methods for automatically generating summaries" refer to automated processes for shortening long texts or data, and for extracting and presenting important information.

[0375] A "virtual marketplace" is a platform that enables commercial transactions to take place on the internet, and is a marketplace that does not have a physical location.

[0376] "Emotion-based information processing" is a technology that determines a user's current emotions and changes the content and format of information accordingly.

[0377] "Emotional data" refers to digital information that indicates a user's mental state or emotions, and includes, for example, facial expressions, voice, and input data.

[0378] A "customized summary" is a personalized, abridged version of information created to meet the user's specific needs and emotions.

[0379] To implement this invention, a system involving a server and a user's terminal is primarily used. The server first acquires data sources in digital format. This data is information collected from various content providers and online databases. Next, the server uses generative artificial intelligence to automatically generate a summary based on the acquired data. This generative artificial intelligence, for example, utilizes natural language processing technology to extract important points from large amounts of data and efficiently create a summary.

[0380] The user's device acquires emotional data using voice input and camera sensors. This makes it possible to analyze the user's emotional state from, for example, the tone of their voice and facial expressions. The acquired emotional data is then sent to the server. This emotional data is used in the server's information processing to customize the content and structure of the summary according to the user's emotions. At this time, the emotion engine evaluates the user's emotional state and performs emotion-based optimization.

[0381] For example, if emotional data indicates that a user wants to relax, the server will prioritize providing summaries of relaxing music and videos. Furthermore, if the server determines that the user has a high motivation to learn, it can present summaries of detailed learning content.

[0382] An example of a prompt is, "Generate and present the most appropriate content summary based on the user's emotions." By inputting this prompt into the AI ​​generation model, the system provides the user with a content summary that best suits their current emotional state.

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The server acquires data sources in digital format. Inputs include information from online databases and content providers. This data is collected and stored in storage, preparing it for subsequent processing.

[0386] Step 2:

[0387] The server supplies the acquired data to a generative artificial intelligence (AI) system, which automatically generates a summary. The input is the data saved in the previous step, and the output is the summarized information. The AI ​​uses natural language processing techniques to select essential information from a large amount of data and summarize it concisely.

[0388] Step 3:

[0389] The user's device acquires emotional data using voice input and the camera. The input is the user's facial expressions and voice, and the output is an emotional state represented by numbers or categories. The device processes this data and calls an emotion engine to analyze the user's emotions.

[0390] Step 4:

[0391] The device sends emotional data to the server. The input is the user's emotional state data, and the output is the analysis result received by the emotion engine on the server. Data is transmitted according to the communication protocol to ensure accurate analysis.

[0392] Step 5:

[0393] The server uses an emotion engine to analyze emotional data and customizes summaries created by generative AI. The input consists of emotional state analysis data and AI-generated summaries, while the output is a customized summary adapted to the user. The server optimizes the content and presentation of the summary to match the user's emotions.

[0394] Step 6:

[0395] The user receives a customized summary through their device. The input is customized summary data from the server, and the output is what is displayed on the device's screen. By reading this, the user can gain an informational experience that resonates with their emotions.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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".

[0412] The system according to the present invention provides a mechanism for automatically generating summaries from digital information sources using artificial intelligence technology and selling them through an electronic marketplace. This system enables the generation of customizable outputs according to user requests and allows for the expansion of artificial intelligence knowledge using data related to purchased summaries.

[0413] The server first accesses a book database from a partner digital content provider via the internet and retrieves information in a predetermined digital format. The retrieved book data is stored on the server and then provided to a generative artificial intelligence system.

[0414] The generative artificial intelligence installed on the server analyzes the acquired book data and summarizes its contents using natural language processing techniques. The generated summary text is converted into vector data using embedding technology, and this data is used to extend the AI's knowledge.

[0415] The terminal provides a user interface, allowing users to access an online marketplace to search, select, and purchase summaries of interest. When a user purchases a summary, the server generates corresponding vector data and provides a download link to the user's terminal.

[0416] Users can use downloaded vector data as training data for artificial intelligence via a dedicated application on their device. Through this process, users can customize their own AI, making the most of the knowledge gained from books and enabling the creation of new knowledge.

[0417] For example, if user A purchases summaries of multiple books on "basic economic theories," they can train an AI based on these summaries, perform comparative analyses of economic theories, and apply them to their own business strategies. Similarly, user B can train an AI on the theme of "literary works," and use this to generate new creative activities that take into account the evolution of themes and characters across different works.

[0418] This system enables the generation of diverse summaries, provides flexible information tailored to the user's purpose, and opens up new possibilities for content.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server retrieves digital book data based on a specified scope via the API of a partner digital content provider. The retrieved data is stored in the server's storage system.

[0422] Step 2:

[0423] The generative artificial intelligence installed on the server reads stored book data, uses natural language processing techniques to extract key points and themes, and generates a text summary. This summary is automatically optimized according to the length and content of the book.

[0424] Step 3:

[0425] The generated summaries are converted into vector data on the server, and the generated vector data is stored in storage. The summaries are then prepared for listing on the electronic marketplace.

[0426] Step 4:

[0427] The device provides a user interface, allowing users to easily search for summaries of interest. Users can filter by topics and genres of interest and select summaries that suit their purpose.

[0428] Step 5:

[0429] The user purchases the summary via their device. Once the purchase is complete, the server selects the vector data for the summary and generates a download link to provide to the user's account.

[0430] Step 6:

[0431] Users download vector data to their devices and use it as training data for AI using a dedicated application. This allows users to cultivate their own artificial intelligence and expand their knowledge on specific topics.

[0432] Step 7:

[0433] Users can request customized output via the device. Based on the user's settings, the device combines AI-generated information and displays it on the screen in an appropriate format.

[0434] (Example 1)

[0435] 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."

[0436] In today's information society, a vast amount of digital information is generated every day. This increase in information volume necessitates that individual users efficiently select and utilize information according to their own interests and needs. However, current technology presents challenges in quickly extracting necessary information from a massive source of data and customizing that information for effective use.

[0437] 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.

[0438] In this invention, the server includes means for acquiring information in digital format, means for automatically generating a summary based on said information using data processing technology, and means for distributing and providing the generated summary to an online marketplace. This enables users to customize and efficiently utilize information according to their own interests.

[0439] "Information in digital format" refers to information in a format that can be stored and transmitted electronically.

[0440] "Data processing technology" is a general term for computational methods and algorithms used to collect, analyze, and process information, and to generate specific deliverables based on that information.

[0441] A "summary" refers to a shortened text or collection of information that extracts the most important elements from the original text.

[0442] An "online marketplace" refers to a platform where goods and services are bought and sold over the internet.

[0443] A "data structure" refers to a format or arrangement method for efficiently storing, retrieving, and processing data.

[0444] A "knowledge processing system" refers to a system for collecting, understanding, learning, storing, and utilizing information.

[0445] "User" refers to an individual or organization that uses the system to search for, retrieve, and utilize information.

[0446] "Numerical data" refers to a data format that represents information as numerical values ​​for processing and calculations.

[0447] A "computer" refers to an electronic device that enables the processing, analysis, and management of data using programs.

[0448] "Training data" refers to a dataset used to train an artificial intelligence model, where the inputs and corresponding results are known.

[0449] This invention provides a method for users to efficiently collect, summarize, and utilize specific information by utilizing an information processing system. Specific embodiments thereof are shown below.

[0450] The server is responsible for collecting information in digital format via the internet. Specifically, it queries information from databases via APIs and retrieves it in digital format. Software technologies used at this stage include RESTful APIs and database management systems. The collected information is securely stored in a database maintained on the server.

[0451] Next, the server uses data processing techniques to summarize the acquired information. This involves the use of generative AI models employing natural language processing techniques. Specifically, machine learning frameworks (e.g., TensorFlow and PyTorch) are utilized, and models such as BERT and GPT are employed. This extracts key points from text data and generates a concise summary.

[0452] The generated summaries are converted into numerical format and provided to users through an online marketplace. The terminal provides this information to the user via a user interface. Using the provided interface, users can search, select, and purchase summaries as needed. This process utilizes web browsers and mobile applications.

[0453] Once the purchase is complete, the server generates data in numerical format and provides a download link to the user's device. The user can then use a dedicated application to retrieve this data and utilize it as training data for a knowledge processing system. This enables the customization and training of AI models tailored to individual user needs.

[0454] For example, if a user wants to efficiently learn information about "basic economic theory," they can enter the following prompt into the system:

[0455] "Please generate a book summary about the fundamental theories of economics."

[0456] This allows the system to collect relevant information, generate summaries, and support user learning.

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server retrieves digital information from an internet database based on user requests. The input is the search criteria specified by the user, and the output is a list of information matching those criteria. The server communicates with the information source using an API and retrieves the desired data by executing queries. Through this series of operations, the necessary book information is stored on the server.

[0460] Step 2:

[0461] The server stores the acquired book data in an internal database and performs preprocessing for natural language processing. The input is the book data itself, and the output is preprocessed data that is easy for the model to handle. This preprocessing includes text normalization, tokenization, and conversion from unstructured data to structured data.

[0462] Step 3:

[0463] The server generates summaries using preprocessed data and generative AI models such as BERT and GPT. The input is preprocessed text data, and the output is summarized text. Specifically, the AI ​​model identifies the key points of the text and uses them to create a concise summary. This process utilizes machine learning algorithms based on neural networks, aiming for highly accurate summaries by adjusting numerous parameters.

[0464] Step 4:

[0465] The server converts the generated summaries into numerical data using embedding techniques. The input is the summarized text, and the output is numerical data in vector format that represents it. Embedding techniques such as Word2Vec and Doc2Vec are used in this process. The vector data is saved for later data augmentation and model evaluation purposes.

[0466] Step 5:

[0467] The terminal provides an interface that allows the user to access the system. The user can use this interface to search for, select, and, if necessary, purchase the generated summaries. The input is the user's selected summary information, and the output is a list of summaries for which purchase has been confirmed. The interface is intuitive and operates via a web browser.

[0468] Step 6:

[0469] The server generates relevant vector data based on the summaries purchased by the user and provides a download link to the user's device. The input is information about the purchased summaries, and the output is the download link. The user can obtain the data via the link and use it as training data.

[0470] Step 7:

[0471] Users customize their knowledge processing systems using acquired vector data. The input is downloaded numerical data, and the output is a customized AI model. Users can leverage this model to perform information analysis and knowledge expansion tailored to their specific interests and needs.

[0472] (Application Example 1)

[0473] 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."

[0474] In today's information-saturated world, users are required to efficiently acquire necessary knowledge from vast amounts of digital information and utilize it in a useful way. However, existing information delivery systems struggle to quickly summarize diverse forms of information and provide knowledge expansion and utilization that meets user expectations. Furthermore, the lack of sufficient customization to meet individual user needs prevents users from maximizing the value of the information they receive.

[0475] 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.

[0476] In this invention, the server includes means for acquiring digital information, means for automatically generating an information summary using generative artificial intelligence based on said information, and means for listing and providing the generated information summary on an e-commerce platform. This enables users to efficiently acquire necessary knowledge from a vast amount of digital information, customize it in a useful way, and utilize it.

[0477] "Digital information" refers to information formats that are stored or transmitted electronically via computers or the internet.

[0478] "Generative artificial intelligence" is a type of artificial intelligence that can generate new information based on data.

[0479] An "information summary" is a text that condenses the original information and extracts only the important points.

[0480] An "e-commerce platform" is an online marketplace for trading goods and services over the internet.

[0481] A "data format" is a standardized format for structuring or organizing data.

[0482] "User" refers to an individual or organization that uses this system.

[0483] "Recommended content" refers to information and materials suggested by the system that are expected to be useful to the user.

[0484] "Similar content" refers to information that is relevant to the original information summary and is likely to be of interest to the user.

[0485] The system that realizes this invention is characterized by its ability to acquire digital information and automatically generate and provide information summaries. Specifically, it performs the following process.

[0486] The server collects and stores digital information via the internet. This information is efficiently processed using programming languages ​​such as Python and the Flask framework. The collected information is analyzed by generative artificial intelligence, and summaries are generated using natural language processing techniques (e.g., TensorFlow or PyTorch). The generated summaries are vectorized using Word2Vec or BERT, and this is provided as a data format that helps users expand their knowledge.

[0487] The device allows users to search for and select information of interest through its user interface. Users can subscribe to information summaries via the user interface and receive recommendations for similar content based on that summary. Furthermore, they can train their own artificial intelligence systems using the provided vector data.

[0488] For example, if a user searches for information on "the latest AI technology trends," a summary of related digital information will be provided. Similar content, such as applicable AI algorithms and case studies, may also be suggested. Through this convenient process, users can quickly and effectively obtain and utilize the latest information.

[0489] An example of a prompt message could be a specific request such as, "Tell me about current trends in AI technology." This would allow the system to automatically present a summary of relevant information to the user.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The server retrieves digital information from digital information providers via the internet. Specifically, it uses APIs to collect book data and news articles. In this process, data in JSON format is provided as input, and the output is stored in a format that is then stored in the server's database.

[0493] Step 2:

[0494] The server automatically generates information summaries using generative artificial intelligence based on stored digital information. The input is the digital information stored in step 1, and the output is generated by extracting important points from the text using natural language processing techniques and producing a summary text. This process involves text analysis using a natural language processing library.

[0495] Step 3:

[0496] The generated summary text is then vectorized. Here, the server converts the input summary text into vector data using embedding techniques such as Word2Vec or BERT. This output can then be used as training data for artificial intelligence.

[0497] Step 4:

[0498] The device allows users to search for and select information of interest through its user interface. Specifically, it takes the prompt phrase "Tell me about current AI technology trends" entered by the user into the search box as input data, retrieves relevant summary information, and outputs it in a displayed format.

[0499] Step 5:

[0500] Users subscribe to the provided information summaries and receive recommendations for similar content based on them. The summary text selected by the user is registered as input in the system, relevant information is searched for, and recommended content is output. A content matching algorithm is used in this step.

[0501] Step 6:

[0502] The terminal supplies vector data to the user's artificial intelligence system, supporting knowledge augmentation. The input is the vector data obtained in step 3, which is added to the user's AI model and used as output. This allows the user to enhance their own AI system.

[0503] 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.

[0504] The system according to the present invention combines artificial intelligence technology and an emotion engine to provide customized information tailored to the user's emotions, thereby realizing a more personalized experience. Specifically, it acquires data from digital information sources and automatically generates summaries using generative artificial intelligence. Furthermore, the added emotion engine recognizes the user's emotional state and performs information processing accordingly.

[0505] The server first acquires digital data of books and then summarizes it using generative artificial intelligence. The generated summaries are then offered on the e-market, but an emotion engine intervenes to adjust the summaries based on user input and emotion recognition via the interface.

[0506] The device receives the user's emotional input and sends it to the server. This emotional data is used to assess the user's current emotional state; for example, if the user is stressed, information with a relaxing effect can be prioritized and displayed. The device also uses feedback received from the emotion engine to display a summary optimized for the user.

[0507] Users can access the online marketplace via an interface on their device and purchase or view summaries of interest. Upon purchase, the server uses an emotion engine to analyze the user's emotional state, generating and providing customized vector data based on that analysis. This data can be used for AI training, enabling the expansion of knowledge tailored to the user's emotions.

[0508] For example, if user A purchases a book summary on "leadership," and the emotion engine analyzes the user's emotions as "low motivation," the summary will be presented in a way that emphasizes content that encourages proactive action. This allows users to obtain information that flexibly responds to their individual emotional state. This system opens up a new dimension in information delivery and provides users with a deeper, more personalized engagement.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The server retrieves data from content providers, who are the source of digital book data, and stores it in storage. This data collection is carried out efficiently via APIs.

[0512] Step 2:

[0513] The server inputs stored book data into a generative artificial intelligence system and generates a summary using natural language processing technology. The generated summary is automatically adjusted based on compression ratio and the main points of the content.

[0514] Step 3:

[0515] The device receives emotional data input from the user and sends this data to the server. Emotional data is collected, for example, through user feedback and choices made during interface operations.

[0516] Step 4:

[0517] The server processes the received emotional data using an emotion engine to analyze the user's emotional state. The analysis results reflect the user's current emotions (joy, stress, anxiety, etc.).

[0518] Step 5:

[0519] The server adjusts the summary based on the analysis results from the emotion engine, optimizing it to match the user's emotional state. For example, when the user is stressed, it will include more content that helps them relax.

[0520] Step 6:

[0521] The terminal displays a customized summary received from the server to the user. Visual effects and navigation are applied according to the user's settings during display.

[0522] Step 7:

[0523] The user reviews the displayed summary and proceeds with the purchase if necessary. Once the purchase is confirmed, the server provides the relevant vector data, which the user can then use as training material for artificial intelligence.

[0524] This series of processes allows users to receive information tailored to their individual emotional state, resulting in a highly customized experience.

[0525] (Example 2)

[0526] 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."

[0527] Conventional information delivery systems have struggled to customize content to take into account users' emotional states, making it difficult to provide an information experience tailored to individual users. Furthermore, summaries generated from digital information sources often remain general rather than adequately addressing users' needs and circumstances, highlighting the need for optimal information delivery that aligns with users' interests and requests.

[0528] 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.

[0529] In this invention, the server includes means for acquiring information sources in digital format, means for automatically generating summaries using generative artificial intelligence, and means for analyzing the user's emotional state and adjusting the summaries accordingly. This enables the provision of customized information that responds to the user's emotions, thereby enhancing the individual user experience.

[0530] "Digital information sources" refer to information sources provided in a format that can be processed by a computer, including ebooks and online articles.

[0531] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate content based on given data.

[0532] A "summary" is a way of extracting the essential parts of the original information and expressing them in a shortened form.

[0533] An "electronic marketplace" is a market where digital goods and services are traded online.

[0534] A "data format" refers to a standardized format or protocol used to structure data.

[0535] "Expanding the knowledge of artificial intelligence" means broadening the amount of information and the range of applications of artificial intelligence based on new data and information.

[0536] "User's emotional state" refers to the user's internal emotional state and includes various emotional elements such as stress, joy, and excitement.

[0537] A "terminal" is a device used by users to input information or receive output, and generally refers to computers or smartphones.

[0538] This invention provides a system that presents personalized information in response to the user's emotions. The system consists of a server, a terminal, and a user interface.

[0539] The server first acquires digital information sources from online databases and e-marketplaces. These sources include e-books and web content. The server uses a generative AI model to analyze the acquired information and automatically generate summaries. Open-source platforms and commercial natural language processing AIs are used as AI models. The generated summaries are adjusted based on the user's emotional state. To this end, the server uses an emotion analysis algorithm to evaluate the user's emotional data in real time. This analysis is performed using voice input, text input, or biosensor data provided by the user through their device.

[0540] The device is responsible for collecting emotional data from the user and sending it to the server. The application on the device infers the user's emotional state based on their operation history and input data, and transfers this information to the server's emotional engine. The device also displays a customized summary sent from the server to the user. This summary is tailored to the user's current emotions, making it more engaging for the user.

[0541] Users access the system through their devices. When a user purchases or views summaries of interest, the system provides personalized information tailored to the user's emotions. This allows users to receive content optimized for their emotional state, rather than standardized information.

[0542] For example, if a user requests information about "leadership," the device sends a prompt to the server stating, "In a summary about leadership, please emphasize how to bring about positive change." Based on this prompt, a generative AI model creates a summary, and an emotion engine adjusts it to motivate the user. Finally, the device provides this specially adjusted summary to the user.

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The server retrieves digital information sources from the e-marketplace. Specifically, it uses APIs to download digital data of required books and articles. This input data is sent to the server in text format. The server stores this digital data in a centrally managed database in preparation for passing it on to a generating AI model.

[0546] Step 2:

[0547] The server uses a generative AI model to automatically generate summaries based on acquired data. Digital data is provided to the AI ​​model as input, along with prompts, and a summary is generated. These prompts include instructions such as, "Summarize the main points of the information source." As output, the server receives the summary text from the AI ​​model.

[0548] Step 3:

[0549] The device collects emotional data from the user. Based on text, voice, and other interaction data entered by the user, it infers the user's emotional state. This data enters the device as input and is analyzed by emotion recognition software. The output is an evaluation result indicating the user's emotional state. This evaluation result is sent to the server.

[0550] Step 4:

[0551] The server uses an emotion engine to analyze the user's emotional state transmitted from the terminal and adjusts the summary to match the user's emotions. Specifically, it receives adjustment instructions for the generated summary from the emotion engine and modifies its content. Emotional state data and the summary are used as input, and the adjusted summary text is obtained as output.

[0552] Step 5:

[0553] The terminal receives the adjusted summary returned from the server and provides it to the user. To display the summary in the UI for user readability, the terminal renders the received summary text in an appropriate GUI format. The adjusted summary is used as input, and the content displayed on the user screen is generated as output.

[0554] Step 6:

[0555] Users view summaries provided on their devices and offer feedback as needed. This feedback is incorporated into the device as new input and used to generate and refine summaries in the future. Through this process, the system analyzes user sentiment more accurately and provides personalized information.

[0556] (Application Example 2)

[0557] 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."

[0558] Modern digital content consumers demand personalized information tailored to their diverse emotions and needs. However, current systems are unable to adequately customize based on emotions, limiting the improvement of the user experience. Furthermore, the inability to provide emotionally relevant information leads to decreased user satisfaction and makes it difficult to appropriately expand their knowledge.

[0559] 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.

[0560] In this invention, the server includes means for acquiring a data source in digital format, means for automatically generating a summary using generative artificial intelligence based on the data source, means for providing the generated summary to a virtual marketplace, means for processing information based on the user's emotional state and adjusting the summary to correspond to the emotional state, and means for acquiring the user's emotional data and providing a customized summary based on the emotional data. This enables detailed information provision and knowledge personalization that responds to the user's emotions.

[0561] A "digital data source" is a collection of non-physical data used on computers and networks.

[0562] "Generative artificial intelligence" is an artificial intelligence technology that creates new content or summaries based on given data and information.

[0563] "Methods for automatically generating summaries" refer to automated processes for shortening long texts or data, and for extracting and presenting important information.

[0564] A "virtual marketplace" is a platform that enables commercial transactions to take place on the internet, and is a marketplace that does not have a physical location.

[0565] "Emotion-based information processing" is a technology that determines a user's current emotions and changes the content and format of information accordingly.

[0566] "Emotional data" refers to digital information that indicates a user's mental state or emotions, and includes, for example, facial expressions, voice, and input data.

[0567] A "customized summary" is a personalized, abridged version of information created to meet the user's specific needs and emotions.

[0568] To implement this invention, a system involving a server and a user's terminal is primarily used. The server first acquires data sources in digital format. This data is information collected from various content providers and online databases. Next, the server uses generative artificial intelligence to automatically generate a summary based on the acquired data. This generative artificial intelligence, for example, utilizes natural language processing technology to extract important points from large amounts of data and efficiently create a summary.

[0569] The user's device acquires emotional data using voice input and camera sensors. This makes it possible to analyze the user's emotional state from, for example, the tone of their voice and facial expressions. The acquired emotional data is then sent to the server. This emotional data is used in the server's information processing to customize the content and structure of the summary according to the user's emotions. At this time, the emotion engine evaluates the user's emotional state and performs emotion-based optimization.

[0570] For example, if emotional data indicates that a user wants to relax, the server will prioritize providing summaries of relaxing music and videos. Furthermore, if the server determines that the user has a high motivation to learn, it can present summaries of detailed learning content.

[0571] An example of a prompt is, "Generate and present the most appropriate content summary based on the user's emotions." By inputting this prompt into the AI ​​generation model, the system provides the user with a content summary that best suits their current emotional state.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The server acquires data sources in digital format. Inputs include information from online databases and content providers. This data is collected and stored in storage, preparing it for subsequent processing.

[0575] Step 2:

[0576] The server supplies the acquired data to a generative artificial intelligence (AI) system, which automatically generates a summary. The input is the data saved in the previous step, and the output is the summarized information. The AI ​​uses natural language processing techniques to select essential information from a large amount of data and summarize it concisely.

[0577] Step 3:

[0578] The user's device acquires emotional data using voice input and the camera. The input is the user's facial expressions and voice, and the output is an emotional state represented by numbers or categories. The device processes this data and calls an emotion engine to analyze the user's emotions.

[0579] Step 4:

[0580] The device sends emotional data to the server. The input is the user's emotional state data, and the output is the analysis result received by the emotion engine on the server. Data is transmitted according to the communication protocol to ensure accurate analysis.

[0581] Step 5:

[0582] The server uses an emotion engine to analyze emotional data and customizes summaries created by generative AI. The input consists of emotional state analysis data and AI-generated summaries, while the output is a customized summary adapted to the user. The server optimizes the content and presentation of the summary to match the user's emotions.

[0583] Step 6:

[0584] The user receives a customized summary through their device. The input is customized summary data from the server, and the output is what is displayed on the device's screen. By reading this, the user can gain an informational experience that resonates with their emotions.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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".

[0602] The system according to the present invention provides a mechanism for automatically generating summaries from digital information sources using artificial intelligence technology and selling them through an electronic marketplace. This system enables the generation of customizable outputs according to user requests and allows for the expansion of artificial intelligence knowledge using data related to purchased summaries.

[0603] The server first accesses a book database from a partner digital content provider via the internet and retrieves information in a predetermined digital format. The retrieved book data is stored on the server and then provided to a generative artificial intelligence system.

[0604] The generative artificial intelligence installed on the server analyzes the acquired book data and summarizes its contents using natural language processing techniques. The generated summary text is converted into vector data using embedding technology, and this data is used to extend the AI's knowledge.

[0605] The terminal provides a user interface, allowing users to access an online marketplace to search, select, and purchase summaries of interest. When a user purchases a summary, the server generates corresponding vector data and provides a download link to the user's terminal.

[0606] Users can use downloaded vector data as training data for artificial intelligence via a dedicated application on their device. Through this process, users can customize their own AI, making the most of the knowledge gained from books and enabling the creation of new knowledge.

[0607] For example, if user A purchases summaries of multiple books on "basic economic theories," they can train an AI based on these summaries, perform comparative analyses of economic theories, and apply them to their own business strategies. Similarly, user B can train an AI on the theme of "literary works," and use this to generate new creative activities that take into account the evolution of themes and characters across different works.

[0608] This system enables the generation of diverse summaries, provides flexible information tailored to the user's purpose, and opens up new possibilities for content.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The server retrieves digital book data based on a specified scope via the API of a partner digital content provider. The retrieved data is stored in the server's storage system.

[0612] Step 2:

[0613] The generative artificial intelligence installed on the server reads stored book data, uses natural language processing techniques to extract key points and themes, and generates a text summary. This summary is automatically optimized according to the length and content of the book.

[0614] Step 3:

[0615] The generated summaries are converted into vector data on the server, and the generated vector data is stored in storage. The summaries are then prepared for listing on the electronic marketplace.

[0616] Step 4:

[0617] The device provides a user interface, allowing users to easily search for summaries of interest. Users can filter by topics and genres of interest and select summaries that suit their purpose.

[0618] Step 5:

[0619] The user purchases the summary via their device. Once the purchase is complete, the server selects the vector data for the summary and generates a download link to provide to the user's account.

[0620] Step 6:

[0621] Users download vector data to their devices and use it as training data for AI using a dedicated application. This allows users to cultivate their own artificial intelligence and expand their knowledge on specific topics.

[0622] Step 7:

[0623] Users can request customized output via the device. Based on the user's settings, the device combines AI-generated information and displays it on the screen in an appropriate format.

[0624] (Example 1)

[0625] 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".

[0626] In today's information society, a vast amount of digital information is generated every day. This increase in information volume necessitates that individual users efficiently select and utilize information according to their own interests and needs. However, current technology presents challenges in quickly extracting necessary information from a massive source of data and customizing that information for effective use.

[0627] 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.

[0628] In this invention, the server includes means for acquiring information in digital format, means for automatically generating a summary based on said information using data processing technology, and means for distributing and providing the generated summary to an online marketplace. This enables users to customize and efficiently utilize information according to their own interests.

[0629] "Information in digital format" refers to information in a format that can be stored and transmitted electronically.

[0630] "Data processing technology" is a general term for computational methods and algorithms used to collect, analyze, and process information, and to generate specific deliverables based on that information.

[0631] A "summary" refers to a shortened text or collection of information that extracts the most important elements from the original text.

[0632] An "online marketplace" refers to a platform where goods and services are bought and sold over the internet.

[0633] A "data structure" refers to a format or arrangement method for efficiently storing, retrieving, and processing data.

[0634] A "knowledge processing system" refers to a system for collecting, understanding, learning, storing, and utilizing information.

[0635] "User" refers to an individual or organization that uses the system to search for, retrieve, and utilize information.

[0636] "Numerical data" refers to a data format that represents information as numerical values ​​for processing and calculations.

[0637] A "computer" refers to an electronic device that enables the processing, analysis, and management of data using programs.

[0638] "Training data" refers to a dataset used to train an artificial intelligence model, where the inputs and corresponding results are known.

[0639] This invention provides a method for users to efficiently collect, summarize, and utilize specific information by utilizing an information processing system. Specific embodiments thereof are shown below.

[0640] The server is responsible for collecting information in digital format via the internet. Specifically, it queries information from databases via APIs and retrieves it in digital format. Software technologies used at this stage include RESTful APIs and database management systems. The collected information is securely stored in a database maintained on the server.

[0641] Next, the server uses data processing techniques to summarize the acquired information. This involves the use of generative AI models employing natural language processing techniques. Specifically, machine learning frameworks (e.g., TensorFlow and PyTorch) are utilized, and models such as BERT and GPT are employed. This extracts key points from text data and generates a concise summary.

[0642] The generated summaries are converted into numerical format and provided to users through an online marketplace. The terminal provides this information to the user via a user interface. Using the provided interface, users can search, select, and purchase summaries as needed. This process utilizes web browsers and mobile applications.

[0643] Once the purchase is complete, the server generates data in numerical format and provides a download link to the user's device. The user can then use a dedicated application to retrieve this data and utilize it as training data for a knowledge processing system. This enables the customization and training of AI models tailored to individual user needs.

[0644] For example, if a user wants to efficiently learn information about "basic economic theory," they can enter the following prompt into the system:

[0645] "Please generate a book summary about the fundamental theories of economics."

[0646] This allows the system to collect relevant information, generate summaries, and support user learning.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] The server retrieves digital information from an internet database based on user requests. The input is the search criteria specified by the user, and the output is a list of information matching those criteria. The server communicates with the information source using an API and retrieves the desired data by executing queries. Through this series of operations, the necessary book information is stored on the server.

[0650] Step 2:

[0651] The server stores the acquired book data in an internal database and performs preprocessing for natural language processing. The input is the book data itself, and the output is preprocessed data that is easy for the model to handle. This preprocessing includes text normalization, tokenization, and conversion from unstructured data to structured data.

[0652] Step 3:

[0653] The server generates summaries using preprocessed data and generative AI models such as BERT and GPT. The input is preprocessed text data, and the output is summarized text. Specifically, the AI ​​model identifies the key points of the text and uses them to create a concise summary. This process utilizes machine learning algorithms based on neural networks, aiming for highly accurate summaries by adjusting numerous parameters.

[0654] Step 4:

[0655] The server converts the generated summaries into numerical data using embedding techniques. The input is the summarized text, and the output is numerical data in vector format that represents it. Embedding techniques such as Word2Vec and Doc2Vec are used in this process. The vector data is saved for later data augmentation and model evaluation purposes.

[0656] Step 5:

[0657] The terminal provides an interface that allows the user to access the system. The user can use this interface to search for, select, and, if necessary, purchase the generated summaries. The input is the user's selected summary information, and the output is a list of summaries for which purchase has been confirmed. The interface is intuitive and operates via a web browser.

[0658] Step 6:

[0659] The server generates relevant vector data based on the summaries purchased by the user and provides a download link to the user's device. The input is information about the purchased summaries, and the output is the download link. The user can obtain the data via the link and use it as training data.

[0660] Step 7:

[0661] Users customize their knowledge processing systems using acquired vector data. The input is downloaded numerical data, and the output is a customized AI model. Users can leverage this model to perform information analysis and knowledge expansion tailored to their specific interests and needs.

[0662] (Application Example 1)

[0663] 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".

[0664] In today's information-saturated world, users are required to efficiently acquire necessary knowledge from vast amounts of digital information and utilize it in a useful way. However, existing information delivery systems struggle to quickly summarize diverse forms of information and provide knowledge expansion and utilization that meets user expectations. Furthermore, the lack of sufficient customization to meet individual user needs prevents users from maximizing the value of the information they receive.

[0665] 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.

[0666] In this invention, the server includes means for acquiring digital information, means for automatically generating an information summary using generative artificial intelligence based on said information, and means for listing and providing the generated information summary on an e-commerce platform. This enables users to efficiently acquire necessary knowledge from a vast amount of digital information, customize it in a useful way, and utilize it.

[0667] "Digital information" refers to information formats that are stored or transmitted electronically via computers or the internet.

[0668] "Generative artificial intelligence" is a type of artificial intelligence that can generate new information based on data.

[0669] An "information summary" is a text that condenses the original information and extracts only the important points.

[0670] An "e-commerce platform" is an online marketplace for trading goods and services over the internet.

[0671] A "data format" is a standardized format for structuring or organizing data.

[0672] "User" refers to an individual or organization that uses this system.

[0673] "Recommended content" refers to information and materials suggested by the system that are expected to be useful to the user.

[0674] "Similar content" refers to information that is relevant to the original information summary and is likely to be of interest to the user.

[0675] The system that realizes this invention is characterized by its ability to acquire digital information and automatically generate and provide information summaries. Specifically, it performs the following process.

[0676] The server collects and stores digital information via the internet. This information is efficiently processed using programming languages ​​such as Python and the Flask framework. The collected information is analyzed by generative artificial intelligence, and summaries are generated using natural language processing techniques (e.g., TensorFlow or PyTorch). The generated summaries are vectorized using Word2Vec or BERT, and this is provided as a data format that helps users expand their knowledge.

[0677] The device allows users to search for and select information of interest through its user interface. Users can subscribe to information summaries via the user interface and receive recommendations for similar content based on that summary. Furthermore, they can train their own artificial intelligence systems using the provided vector data.

[0678] For example, if a user searches for information on "the latest AI technology trends," a summary of related digital information will be provided. Similar content, such as applicable AI algorithms and case studies, may also be suggested. Through this convenient process, users can quickly and effectively obtain and utilize the latest information.

[0679] An example of a prompt message could be a specific request such as, "Tell me about current trends in AI technology." This would allow the system to automatically present a summary of relevant information to the user.

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] The server retrieves digital information from digital information providers via the internet. Specifically, it uses APIs to collect book data and news articles. In this process, data in JSON format is provided as input, and the output is stored in a format that is then stored in the server's database.

[0683] Step 2:

[0684] The server automatically generates information summaries using generative artificial intelligence based on stored digital information. The input is the digital information stored in step 1, and the output is generated by extracting important points from the text using natural language processing techniques and producing a summary text. This process involves text analysis using a natural language processing library.

[0685] Step 3:

[0686] The generated summary text is then vectorized. Here, the server converts the input summary text into vector data using embedding techniques such as Word2Vec or BERT. This output can then be used as training data for artificial intelligence.

[0687] Step 4:

[0688] The device allows users to search for and select information of interest through its user interface. Specifically, it takes the prompt phrase "Tell me about current AI technology trends" entered by the user into the search box as input data, retrieves relevant summary information, and outputs it in a displayed format.

[0689] Step 5:

[0690] Users subscribe to the provided information summaries and receive recommendations for similar content based on them. The summary text selected by the user is registered as input in the system, relevant information is searched for, and recommended content is output. A content matching algorithm is used in this step.

[0691] Step 6:

[0692] The terminal supplies vector data to the user's artificial intelligence system, supporting knowledge augmentation. The input is the vector data obtained in step 3, which is added to the user's AI model and used as output. This allows the user to enhance their own AI system.

[0693] 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.

[0694] The system according to the present invention combines artificial intelligence technology and an emotion engine to provide customized information tailored to the user's emotions, thereby realizing a more personalized experience. Specifically, it acquires data from digital information sources and automatically generates summaries using generative artificial intelligence. Furthermore, the added emotion engine recognizes the user's emotional state and performs information processing accordingly.

[0695] The server first acquires digital data of books and then summarizes it using generative artificial intelligence. The generated summaries are then offered on the e-market, but an emotion engine intervenes to adjust the summaries based on user input and emotion recognition via the interface.

[0696] The device receives the user's emotional input and sends it to the server. This emotional data is used to assess the user's current emotional state; for example, if the user is stressed, information with a relaxing effect can be prioritized and displayed. The device also uses feedback received from the emotion engine to display a summary optimized for the user.

[0697] Users can access the online marketplace via an interface on their device and purchase or view summaries of interest. Upon purchase, the server uses an emotion engine to analyze the user's emotional state, generating and providing customized vector data based on that analysis. This data can be used for AI training, enabling the expansion of knowledge tailored to the user's emotions.

[0698] For example, if user A purchases a book summary on "leadership," and the emotion engine analyzes the user's emotions as "low motivation," the summary will be presented in a way that emphasizes content that encourages proactive action. This allows users to obtain information that flexibly responds to their individual emotional state. This system opens up a new dimension in information delivery and provides users with a deeper, more personalized engagement.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The server retrieves data from content providers, who are the source of digital book data, and stores it in storage. This data collection is carried out efficiently via APIs.

[0702] Step 2:

[0703] The server inputs stored book data into a generative artificial intelligence system and generates a summary using natural language processing technology. The generated summary is automatically adjusted based on compression ratio and the main points of the content.

[0704] Step 3:

[0705] The device receives emotional data input from the user and sends this data to the server. Emotional data is collected, for example, through user feedback and choices made during interface operations.

[0706] Step 4:

[0707] The server processes the received emotional data using an emotion engine to analyze the user's emotional state. The analysis results reflect the user's current emotions (joy, stress, anxiety, etc.).

[0708] Step 5:

[0709] The server adjusts the summary based on the analysis results from the emotion engine, optimizing it to match the user's emotional state. For example, when the user is stressed, it will include more content that helps them relax.

[0710] Step 6:

[0711] The terminal displays a customized summary received from the server to the user. Visual effects and navigation are applied according to the user's settings during display.

[0712] Step 7:

[0713] The user reviews the displayed summary and proceeds with the purchase if necessary. Once the purchase is confirmed, the server provides the relevant vector data, which the user can then use as training material for artificial intelligence.

[0714] This series of processes allows users to receive information tailored to their individual emotional state, resulting in a highly customized experience.

[0715] (Example 2)

[0716] 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".

[0717] Conventional information delivery systems have struggled to customize content to take into account users' emotional states, making it difficult to provide an information experience tailored to individual users. Furthermore, summaries generated from digital information sources often remain general rather than adequately addressing users' needs and circumstances, highlighting the need for optimal information delivery that aligns with users' interests and requests.

[0718] 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.

[0719] In this invention, the server includes means for acquiring information sources in digital format, means for automatically generating summaries using generative artificial intelligence, and means for analyzing the user's emotional state and adjusting the summaries accordingly. This enables the provision of customized information that responds to the user's emotions, thereby enhancing the individual user experience.

[0720] "Digital information sources" refer to information sources provided in a format that can be processed by a computer, including ebooks and online articles.

[0721] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate content based on given data.

[0722] A "summary" is a way of extracting the essential parts of the original information and expressing them in a shortened form.

[0723] An "electronic marketplace" is a market where digital goods and services are traded online.

[0724] A "data format" refers to a standardized format or protocol used to structure data.

[0725] "Expanding the knowledge of artificial intelligence" means broadening the amount of information and the range of applications of artificial intelligence based on new data and information.

[0726] "User's emotional state" refers to the user's internal emotional state and includes various emotional elements such as stress, joy, and excitement.

[0727] A "terminal" is a device used by users to input information or receive output, and generally refers to computers or smartphones.

[0728] This invention provides a system that presents personalized information in response to the user's emotions. The system consists of a server, a terminal, and a user interface.

[0729] The server first acquires digital information sources from online databases and e-marketplaces. These sources include e-books and web content. The server uses a generative AI model to analyze the acquired information and automatically generate summaries. Open-source platforms and commercial natural language processing AIs are used as AI models. The generated summaries are adjusted based on the user's emotional state. To this end, the server uses an emotion analysis algorithm to evaluate the user's emotional data in real time. This analysis is performed using voice input, text input, or biosensor data provided by the user through their device.

[0730] The device is responsible for collecting emotional data from the user and sending it to the server. The application on the device infers the user's emotional state based on their operation history and input data, and transfers this information to the server's emotional engine. The device also displays a customized summary sent from the server to the user. This summary is tailored to the user's current emotions, making it more engaging for the user.

[0731] Users access the system through their devices. When a user purchases or views summaries of interest, the system provides personalized information tailored to the user's emotions. This allows users to receive content optimized for their emotional state, rather than standardized information.

[0732] For example, if a user requests information about "leadership," the device sends a prompt to the server stating, "In a summary about leadership, please emphasize how to bring about positive change." Based on this prompt, a generative AI model creates a summary, and an emotion engine adjusts it to motivate the user. Finally, the device provides this specially adjusted summary to the user.

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1:

[0735] The server retrieves digital information sources from the e-marketplace. Specifically, it uses APIs to download digital data of required books and articles. This input data is sent to the server in text format. The server stores this digital data in a centrally managed database in preparation for passing it on to a generating AI model.

[0736] Step 2:

[0737] The server uses a generative AI model to automatically generate summaries based on acquired data. Digital data is provided to the AI ​​model as input, along with prompts, and a summary is generated. These prompts include instructions such as, "Summarize the main points of the information source." As output, the server receives the summary text from the AI ​​model.

[0738] Step 3:

[0739] The device collects emotional data from the user. Based on text, voice, and other interaction data entered by the user, it infers the user's emotional state. This data enters the device as input and is analyzed by emotion recognition software. The output is an evaluation result indicating the user's emotional state. This evaluation result is sent to the server.

[0740] Step 4:

[0741] The server uses an emotion engine to analyze the user's emotional state transmitted from the terminal and adjusts the summary to match the user's emotions. Specifically, it receives adjustment instructions for the generated summary from the emotion engine and modifies its content. Emotional state data and the summary are used as input, and the adjusted summary text is obtained as output.

[0742] Step 5:

[0743] The terminal receives the adjusted summary returned from the server and provides it to the user. To display the summary in the UI for user readability, the terminal renders the received summary text in an appropriate GUI format. The adjusted summary is used as input, and the content displayed on the user screen is generated as output.

[0744] Step 6:

[0745] Users view summaries provided on their devices and offer feedback as needed. This feedback is incorporated into the device as new input and used to generate and refine summaries in the future. Through this process, the system analyzes user sentiment more accurately and provides personalized information.

[0746] (Application Example 2)

[0747] 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".

[0748] Modern digital content consumers demand personalized information tailored to their diverse emotions and needs. However, current systems are unable to adequately customize based on emotions, limiting the improvement of the user experience. Furthermore, the inability to provide emotionally relevant information leads to decreased user satisfaction and makes it difficult to appropriately expand their knowledge.

[0749] 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.

[0750] In this invention, the server includes means for acquiring a data source in digital format, means for automatically generating a summary using generative artificial intelligence based on the data source, means for providing the generated summary to a virtual marketplace, means for processing information based on the user's emotional state and adjusting the summary to correspond to the emotional state, and means for acquiring the user's emotional data and providing a customized summary based on the emotional data. This enables detailed information provision and knowledge personalization that responds to the user's emotions.

[0751] A "digital data source" is a collection of non-physical data used on computers and networks.

[0752] "Generative artificial intelligence" is an artificial intelligence technology that creates new content or summaries based on given data and information.

[0753] "Methods for automatically generating summaries" refer to automated processes for shortening long texts or data, and for extracting and presenting important information.

[0754] A "virtual marketplace" is a platform that enables commercial transactions to take place on the internet, and is a marketplace that does not have a physical location.

[0755] "Emotion-based information processing" is a technology that determines a user's current emotions and changes the content and format of information accordingly.

[0756] "Emotional data" refers to digital information that indicates a user's mental state or emotions, and includes, for example, facial expressions, voice, and input data.

[0757] A "customized summary" is a personalized, abridged version of information created to meet the user's specific needs and emotions.

[0758] To implement this invention, a system involving a server and a user's terminal is primarily used. The server first acquires data sources in digital format. This data is information collected from various content providers and online databases. Next, the server uses generative artificial intelligence to automatically generate a summary based on the acquired data. This generative artificial intelligence, for example, utilizes natural language processing technology to extract important points from large amounts of data and efficiently create a summary.

[0759] The user's device acquires emotional data using voice input and camera sensors. This makes it possible to analyze the user's emotional state from, for example, the tone of their voice and facial expressions. The acquired emotional data is then sent to the server. This emotional data is used in the server's information processing to customize the content and structure of the summary according to the user's emotions. At this time, the emotion engine evaluates the user's emotional state and performs emotion-based optimization.

[0760] For example, if emotional data indicates that a user wants to relax, the server will prioritize providing summaries of relaxing music and videos. Furthermore, if the server determines that the user has a high motivation to learn, it can present summaries of detailed learning content.

[0761] An example of a prompt is, "Generate and present the most appropriate content summary based on the user's emotions." By inputting this prompt into the AI ​​generation model, the system provides the user with a content summary that best suits their current emotional state.

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The server acquires data sources in digital format. Inputs include information from online databases and content providers. This data is collected and stored in storage, preparing it for subsequent processing.

[0765] Step 2:

[0766] The server supplies the acquired data to a generative artificial intelligence (AI) system, which automatically generates a summary. The input is the data saved in the previous step, and the output is the summarized information. The AI ​​uses natural language processing techniques to select essential information from a large amount of data and summarize it concisely.

[0767] Step 3:

[0768] The user's device acquires emotional data using voice input and the camera. The input is the user's facial expressions and voice, and the output is an emotional state represented by numbers or categories. The device processes this data and calls an emotion engine to analyze the user's emotions.

[0769] Step 4:

[0770] The device sends emotional data to the server. The input is the user's emotional state data, and the output is the analysis result received by the emotion engine on the server. Data is transmitted according to the communication protocol to ensure accurate analysis.

[0771] Step 5:

[0772] The server uses an emotion engine to analyze emotional data and customizes summaries created by generative AI. The input consists of emotional state analysis data and AI-generated summaries, while the output is a customized summary adapted to the user. The server optimizes the content and presentation of the summary to match the user's emotions.

[0773] Step 6:

[0774] The user receives a customized summary through their device. The input is customized summary data from the server, and the output is what is displayed on the device's screen. By reading this, the user can gain an informational experience that resonates with their emotions.

[0775] 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.

[0776] 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.

[0777] 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 robot 414.

[0778] 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.

[0779] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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."

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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 as being incorporated by reference.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] Means of obtaining digital information sources,

[0799] A means for automatically generating a summary using generative artificial intelligence based on the said information source,

[0800] The means of listing and providing the generated summary on an electronic marketplace,

[0801] A means of generating and providing data in a format corresponding to the summary to be purchased,

[0802] A means for extending the knowledge of artificial intelligence using the said data format, enabling customization and output according to user requests,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, comprising means for providing vector data corresponding to purchased summaries and making it available as training data for artificial intelligence on the user's terminal.

[0806] (Claim 3)

[0807] The system according to claim 1, comprising means for adjusting the volume and format of the summary generated by the generative artificial intelligence according to user settings.

[0808] "Example 1"

[0809] (Claim 1)

[0810] Means of acquiring information in digital format,

[0811] A means for automatically generating a summary using data processing technology based on the said information,

[0812] The means of distributing and providing the generated summaries to online markets,

[0813] A means of generating and providing a data structure based on the summaries to be purchased,

[0814] A means for extending the information of a knowledge processing system using the data structure, enabling configuration and output according to the user's requirements,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, comprising means for providing numerical data corresponding to purchased summaries and making it available on the user's computer as training data for a knowledge processing system.

[0818] (Claim 3)

[0819] The system according to claim 1, comprising means for adjusting the amount and format of summaries generated by data processing technology according to user settings.

[0820] "Application Example 1"

[0821] (Claim 1)

[0822] Means of acquiring digital information,

[0823] A means for automatically generating an information summary using generative artificial intelligence based on the said information,

[0824] The means of listing and providing the generated information summary on an e-commerce platform,

[0825] A means of generating and providing a data format corresponding to the information summary to be purchased,

[0826] A means for extending the knowledge of artificial intelligence using the said data format, and enabling adjustment and output according to the user's requirements,

[0827] A method for displaying recommended content on the user's device and suggesting similar content based on information summaries,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, comprising means for indexing the data format corresponding to the purchased information summary and making it available as training data for artificial intelligence on the user's terminal.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising means for adjusting the quantity and display format of information summaries generated by a generative artificial intelligence according to user settings.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] Means of obtaining digital information sources,

[0836] A means for automatically generating a summary using generative artificial intelligence based on the said information source,

[0837] The means of listing and providing the generated summary on an electronic marketplace,

[0838] A means of generating and providing data in a format corresponding to the summary to be purchased,

[0839] A means for extending the knowledge of artificial intelligence using the said data format, enabling customization and output according to user requests,

[0840] A means of analyzing the user's emotional state and adjusting the summary accordingly,

[0841] A means of using a terminal that displays a summary based on the analysis results of the emotional state,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, comprising means for providing vector data corresponding to purchased summaries and making it available as training data for artificial intelligence on the user's terminal.

[0845] (Claim 3)

[0846] The system according to claim 1, comprising means for adjusting the volume and format of the summary generated by the generative artificial intelligence according to user settings.

[0847] "Application example 2 of combining emotional engines"

[0848] (Claim 1)

[0849] Means for acquiring data sources in digital format,

[0850] A means for automatically generating a summary using generative artificial intelligence based on the said data source,

[0851] A means of providing the generated summary to a virtual marketplace,

[0852] A means for processing information based on the user's emotional state and adjusting a summary corresponding to that emotional state,

[0853] A means for acquiring user sentiment data and providing a customized summary based on said sentiment data,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, comprising means for providing configuration data corresponding to a purchased summary and making it available for use as training data for artificial intelligence on the user's device.

[0857] (Claim 3)

[0858] The system according to claim 1, comprising means for adjusting the volume and format of the summary generated by the generative artificial intelligence according to the user's emotional state. [Explanation of Symbols]

[0859] 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. Means of obtaining digital information sources, A means for automatically generating a summary using generative artificial intelligence based on the said information source, The means of listing and providing the generated summary on an electronic marketplace, A means of generating and providing data in a format corresponding to the summary to be purchased, A means for extending the knowledge of artificial intelligence using the said data format, enabling customization and output according to user requests, A system that includes this.

2. The system according to claim 1, comprising means for providing vector data corresponding to the purchased summaries and making it available as training data for artificial intelligence on the user's terminal.

3. The system according to claim 1, comprising means for adjusting the volume and format of the summary generated by the generative artificial intelligence according to user settings.

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