system

Abstract

We provide the system. [Solution] means of acquiring digital information from information sources, An analysis means for analyzing digital information acquired by the acquisition means and extracting data related to a specific topic, A generation means that filters the data extracted by the analysis means based on the user's interests and generates a personalized feed, A transmission means for sending the feed generated by the generation means to the user's terminal, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern information society, users are exposed to a huge amount of digital information every day. As a result, it has become difficult to efficiently select and obtain necessary information. In addition, there is a lack of means to obtain information that matches each individual's interests and concerns in a short time, and there is a problem that users are likely to miss useful information among the excessive information.

Means for Solving the Problems

[0005] This invention provides a system that extracts data related to a specific topic by acquiring digital information from information sources and analyzing it. Furthermore, it efficiently provides useful information by filtering the extracted data based on the user's interests and generating a personalized feed. Since this generated feed is sent to the user's device, the user can easily access the information they need.

[0006] "Information sources" include various online platforms and databases that provide digital information, news websites, social media, and other similar sources.

[0007] "Digital information" refers to all data, such as documents, images, audio, and video, that can be obtained through electronic means.

[0008] "Means of acquisition" refers to the technical processes and devices used to extract digital information from an information source.

[0009] "Analysis means" refers to processes and technologies that identify, classify, and associate acquired digital information with specific topics.

[0010] "Data" refers to a collection of information that has been extracted and organized through analytical means.

[0011] "Filtering" is the process of selecting data based on specific conditions or criteria and removing unnecessary information.

[0012] A "personalized feed" refers to a collection of information customized according to a user's specific interests, preferences, and behavioral history.

[0013] "Generation method" refers to the algorithms and processes used to create personalized feeds.

[0014] "Transmission means" refers to the communication methods and technologies used to deliver the generated feed to the user's terminal.

[0015] "User" refers to an individual or organization that uses the system.

[0016] "Terminal" refers to an electronic device used by the user to receive and display information.

Brief Description of the Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention constitutes a system that efficiently acquires and analyzes digital information from diverse sources and provides personalized feeds to users. This system mainly consists of a server, terminals, and a user interface.

[0039] Server Role

[0040] The server is the central entity that retrieves digital information from information sources. This process uses news APIs and social networking service APIs to collect information using pre-configured keywords as keys. The server analyzes the retrieved digital information and executes algorithms to extract data relevant to specific topics. This eliminates irrelevant information, and information that matches the user's interests is stored.

[0041] The server further updates the user profile based on the user's interests and behavioral history. This profile is constructed using machine learning techniques to learn from the user's past actions. The updated profile is used for personalized information filtering.

[0042] Terminal role

[0043] The device is responsible for displaying a personalized feed sent from the server to the user. This feed is notified to the user via push notifications and can be viewed through applications or web interfaces. The device also records and sends to the server user interactions, such as reading articles, rating them, and leaving comments.

[0044] User interaction

[0045] Users can access digital information through their devices and view personalized feeds. They can select articles and view summaries. Furthermore, users can change the layout according to their interests and send feedback on specific articles. These interactions are used to further refine user profiles.

[0046] As a concrete example, a server queries a news API using the keyword "technology," and the device sends a push notification to the user along with the results. The user can open the notification on their smartphone and read the latest technology articles that interest them. After this, if the user "likes" the article, that action is sent to the server and will influence future feeds.

[0047] Thus, the present invention is designed so that each element can work together in order to enable users to efficiently acquire information.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The server accesses news APIs and social media APIs at pre-set time intervals to retrieve digital information based on keywords. For example, the server uses the keyword "technology" to collect relevant articles.

[0051] Step 2:

[0052] The server analyzes the acquired digital information and classifies it into specific topics based on the content of articles and posts. The analysis algorithm uses natural language processing technology to evaluate the importance and relevance of the articles.

[0053] Step 3:

[0054] The server filters the information analyzed based on the user's profile. This profile is updated to reflect the user's past behavior and areas of interest. Through this filtering, the most relevant information for the user is selected.

[0055] Step 4:

[0056] The server generates a personalized news feed using filtered information. The generated feed is then ready to be sent to the user's device.

[0057] Step 5:

[0058] The device displays the feed received from the server to the user and notifies them of new information via push notifications. The device displays the news feed on a user interface so that users can easily browse it.

[0059] Step 6:

[0060] Users can view news feeds received through their devices. They can select articles of interest, view summaries, and learn more details.

[0061] Step 7:

[0062] Users take actions such as liking or commenting on articles. This action information is sent to the server as feedback and used to further improve the profile.

[0063] Through these steps, the system efficiently provides information to users and achieves a high level of personalization.

[0064] (Example 1)

[0065] 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."

[0066] In modern society, information is overwhelming, making it difficult for individuals to efficiently acquire information that truly interests them. Traditional information acquisition methods have limitations in providing personalized content, and there is a need for methods that quickly deliver information tailored to the user's interests. Furthermore, to enhance the relevance of acquired information, a feedback loop that takes into account the individual's behavioral history is necessary.

[0067] 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.

[0068] In this invention, the server includes means for collecting electronic information from an information source via an information acquisition device, means for processing the electronic information acquired by the collection means and selecting items related to a specific theme, and a device for creating personalized information by arranging the items selected by the selection means based on the individual's interests. This enables users to quickly acquire highly accurate and customized information that matches their interests.

[0069] An "information acquisition device" is a device that has the function of collecting electronic information from an information source.

[0070] "Electronic information" refers to all types of data that are stored or transmitted in digital format.

[0071] A "specific theme" refers to a particular subject or topic selected when acquiring or processing information.

[0072] "Personalized information" refers to a collection of information customized based on a user's interests and past behavioral history.

[0073] "Personal devices" refer to terminals and devices used by users, including smartphones, tablets, and computers.

[0074] A "personal profile" refers to a collection of information that reflects a user's interests, behavioral history, and preferences.

[0075] This invention comprises a system that efficiently acquires and analyzes digital information from various sources, primarily through a server, terminal, and user interface, and provides personalized feeds to users. The server uses information acquisition devices and utilizes news APIs and SNS APIs to collect electronic information. This includes natural language processing techniques to effectively filter information using subject-based keywords.

[0076] The server processes the collected electronic information and selects items related to specific themes. Natural language processing (NLP) and machine learning algorithms are used for data analysis, which eliminates irrelevant data and extracts highly relevant information. Specific technologies used include data processing libraries in Python and R.

[0077] The server further manages individual profiles and creates personalized information based on the user's interests and behavioral history. Machine learning models update user profiles based on past user behavior data. This process utilizes machine learning libraries such as Scikit-learn and TENSORFLOW®.

[0078] The device plays the role of providing users with personalized information sent from the server. Specifically, the device uses push notification functionality to communicate information to the user, making it viewable through applications and web interfaces. Users can receive notifications using their own devices and access information that interests them.

[0079] As a concrete example, a server queries a news API based on the keyword "technology" and collects relevant news. This information is then delivered to the device via push notification, allowing the user to check the latest technology articles on their smartphone.

[0080] An example of a prompt for a generative AI model might be, "How can we provide articles about the latest technologies based on the user's interests?" Based on this prompt, the AI ​​will provide the necessary information.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server uses information acquisition devices to access news APIs and social networking service APIs to collect electronic information. Keywords such as "technology" are specified as input. In this process, a huge amount of raw data is acquired via APIs, and streaming data is generated as output.

[0084] Step 2:

[0085] The server receives the acquired electronic information and uses natural language processing (NLP) techniques to select data related to a specific theme. The input is the streaming data obtained in step 1, and through the data analysis process, it extracts relevant articles and posts, generating an organized dataset as output.

[0086] Step 3:

[0087] The server processes the organized dataset using a generative AI model to create personalized information based on individual interests. This step utilizes the user's profile information and the dataset obtained in step 2 as input. A machine learning algorithm is executed, generating a customized information feed as output.

[0088] Step 4:

[0089] The device receives a feed sent from the server and delivers information to the user via push notifications. The input is the feed created in step 3. The device displays the notification and, as output, provides the user with a means to access articles and information of interest.

[0090] Step 5:

[0091] Users can view information received via their devices, read articles, rate them, or leave comments. Each action is recorded on the device as input. These interactions are sent to the server as output and used as data to help with future profile updates and information feed generation.

[0092] (Application Example 1)

[0093] 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."

[0094] In today's information-saturated world, users are required to access information of interest quickly and accurately. However, finding relevant information from the vast amount of data is not easy and currently requires time and effort. Furthermore, while users access information using a variety of devices, the information provided is not always optimized for each device.

[0095] 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.

[0096] In this invention, the server includes an acquisition means for acquiring digital information from an information source, an analysis means for analyzing the digital information acquired by the acquisition means to extract data related to a specific topic, and an update means for acquiring user evaluations and reactions and updating the user's profile based on them. This makes it possible to generate personalized feeds based on user interests and provide them quickly in a format optimized for portable information devices.

[0097] "Information sources" refer to the various data providers from which digital information is obtained.

[0098] "Digital information" refers to news articles, blog posts, and social media posts that are expressed in electronic format.

[0099] "Means of acquisition" refers to the methods and mechanisms for collecting digital information from information sources.

[0100] "Analysis means" refers to methods or mechanisms for analyzing acquired digital information and selecting data related to a specific topic.

[0101] "Generation means" refers to methods and mechanisms that filter information according to the user's interests and create personalized feeds.

[0102] "Portable information devices" refer to portable information terminals such as smartphones and tablets.

[0103] "Transmission method" refers to the method or mechanism for delivering the generated feed to users.

[0104] "Update methods" refer to methods and mechanisms that improve profiles based on user evaluations and feedback, thereby enhancing the accuracy of personalization.

[0105] A "profile" is a unique piece of information data created based on a user's interests and behavioral history.

[0106] A "feed" is a stream of information that organizes and continuously provides analyzed digital information to users.

[0107] The system for implementing this invention consists primarily of a server, a terminal, and a user interface. The server is programmed, for example, using Python and the Django framework, and retrieves digital information from information sources. This retrieval is performed via various news APIs and social networking APIs. For information analysis, neural network libraries such as TensorFlow are used to extract data related to specific topics and reduce noisy information. Through this process, data tailored to the user's interests is accumulated on the server.

[0108] The device is designed as a smartphone or tablet application and receives personalized feeds generated by the server. When a user views the feed and rates or comments on articles, the device sends that information back to the server, updating the user's profile. Through this cyclical process, the feed becomes increasingly tailored to the user's interests.

[0109] Users can quickly access the latest information through their mobile devices. Even if they have multiple topics of interest, the system automatically adjusts the information accordingly, allowing users to acquire information efficiently. Furthermore, push notifications to smartphones enable immediate viewing of new feeds.

[0110] For example, if a user expresses interest in "technology," the system will gather the latest information related to AI technology and digital gadgets, tailored to that interest, and provide a personalized feed through the mobile app. The prompt to the generative AI model at this time would be something like, "Retrieve news articles on the latest AI technology and add them to the user's feed."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server retrieves digital information from news APIs and social networking service APIs. Input is pre-configured keywords, and data obtained by sending API requests is output. At this time, an API client library is used to accurately collect the necessary data.

[0114] Step 2:

[0115] The server analyzes the acquired digital information. The input is the data collected in step 1, and the output is important data related to a specific topic. Here, TensorFlow is used to analyze patterns in the data and remove irrelevant information.

[0116] Step 3:

[0117] The server filters the data based on the analysis results to fit the user's interests and generates a personalized feed. The input is the output data from step 2, and a feed based on the user profile is output. The filtering process is optimized using a generative AI model.

[0118] Step 4:

[0119] The server sends the generated personalized feed to the device. The input is the feed from step 3, which is displayed as a feed on the device. Here, push notifications are enabled to quickly deliver the latest information to the user.

[0120] Step 5:

[0121] The device displays the received personalized feed to the user. The input is the feed sent from the server, and the output is a visual presentation to the user. Information is provided in an easy-to-understand and visually appealing format through the UI.

[0122] Step 6:

[0123] Users view information within the feed and provide ratings and comments. The input is the articles being fed, and the output is rating and comment data. User ratings are recorded to help generate future feeds.

[0124] Step 7:

[0125] The server incorporates user ratings and comments into the user profile. The input is the data obtained in step 6, and the output is the updated profile data. This data will be used for future feed generation.

[0126] 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.

[0127] This invention combines an emotion engine with a system that acquires digital information from information sources and provides it to users as a personalized feed. The emotion engine has the function of analyzing the user's emotional state and adjusting the content of the feed accordingly. This invention mainly consists of a server, a terminal, and a user interface.

[0128] Server Role

[0129] The server has the function of regularly collecting digital information from news sites and social media. The collected information is analyzed and classified based on specific topics. This analysis process uses natural language processing technology to evaluate the information and confirm its relevance to topics.

[0130] The server further uses an emotion engine to infer the user's emotional state. This emotion engine analyzes emotions from the user's content creation (e.g., comments and posts), voice, and images to identify the user's current psychological state. This information, along with the user profile and behavioral history, is used to personalize the feed.

[0131] Terminal role

[0132] The device is responsible for notifying and displaying personalized feeds provided by the server to the user. The feeds are adjusted according to the user's emotional state, and content appropriate to a specific emotion is highlighted on the device.

[0133] For example, if a user is identified as experiencing feelings of joy, the device will prioritize displaying positive news and interesting articles. Conversely, if a user is determined to be experiencing stress, it will provide relaxing content and information that helps reduce stress.

[0134] User interaction

[0135] Users can access personalized feeds at any time via their devices. As users read and rate articles, new sentiment data and interest information are collected and sent to the server. This data influences the generation of future feeds.

[0136] For example, when a user views content and rates it as "fun," the emotion engine recognizes that emotion and adjusts future feeds to include more content that evokes similar feelings.

[0137] Thus, the present invention enables the provision of information tailored to the user's emotions and behavior, thereby realizing a more enriching user experience.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The server regularly collects digital information from news sites and social media. Based on specific topics, it retrieves information using news APIs and web scraping techniques. For example, the server collects the latest articles related to technology.

[0141] Step 2:

[0142] The server analyzes the collected digital information and extracts important data. It performs natural language processing on the text to classify articles based on their relevance and topic. Then, it filters out irrelevant articles.

[0143] Step 3:

[0144] The server activates an emotion engine to identify the user's emotional state. It analyzes user-generated content and feedback, and uses that data to infer the user's current emotions.

[0145] Step 4:

[0146] The server filters the analysis results based on the user's profile information. In this process, it combines the user's past behavioral history with their current emotional state to select personalized information.

[0147] Step 5:

[0148] The server generates a personalized feed using filtered information and sends it to the device. The priority of the information provided is adjusted according to the user's emotional state.

[0149] Step 6:

[0150] The device displays a personalized feed received from the server to the user. The feed is configured to highlight relaxing information, for example, if the user is currently feeling stressed.

[0151] Step 7:

[0152] Users browse feeds displayed on their devices, read articles that interest them, and enter their thoughts and opinions. These interactions are sent back to the server and used to update the user's sentiment profile and behavioral history.

[0153] This process allows the system to provide information that takes into account the user's emotional state, resulting in a more personalized content experience.

[0154] (Example 2)

[0155] 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".

[0156] In today's information society, users are exposed to a vast amount of information, making it difficult to efficiently acquire information that is useful and of interest to them. Furthermore, there is a lack of systems that present information while taking into account the user's momentary emotional state, so there is a need to provide personalized feedback that responds to the user's emotions.

[0157] 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.

[0158] In this invention, the server includes a collection means for collecting data from information sources, an analysis means for analyzing the data acquired by the collection means and extracting information related to a specific classification, and a generation means for adjusting the information extracted by the analysis means based on the user's emotional state and generating personalized content. As a result, the user can receive information optimized for their emotional state at that moment, enabling them to acquire more meaningful information.

[0159] "Collection means" refers to technologies that have the function of acquiring data from information sources.

[0160] "Analysis means" refers to techniques for processing collected data and identifying and extracting information related to a specific classification.

[0161] "Generation means" refers to technology that has the function of adjusting information analyzed based on the user's emotional state to create personalized content.

[0162] "Transmission means" refers to technology that has the function of notifying the user's device of the generated content.

[0163] "Emotional state" refers to information that indicates the user's current psychological reactions and feelings.

[0164] "Personalized content" refers to information that is customized according to the user's specific needs and circumstances.

[0165] The server periodically acquires digital information using web scraping techniques and APIs for information gathering. News sites and social media serve as sources of information. This data is analyzed using natural language processing techniques and associated with specific topics. Specifically, the information is classified through topic modeling and keyword extraction.

[0166] The analyzed information is further processed by an emotion engine. This engine analyzes the user's past posts, comments, browsing history, and audio data to infer the user's emotional state. Text and audio analysis software is used for emotion analysis, and emotion tags such as positive, negative, and neutral are generated.

[0167] Based on the generated sentiment data and analyzed information, the server generates content tailored to the user. In this process, a generative AI model is used to create a personalized feed. An example of a prompt given to the generative AI is, "Find news that matches the user's current sentiment state."

[0168] The device is responsible for notifying the user of personalized feeds sent from the server. The feeds are displayed with content that matches the user's emotional state highlighted. For example, a user who is feeling stressed will be shown music and articles that help with relaxation.

[0169] Users can view feeds through their devices and rate the content. These interactions are stored on the server side as new sentiment data and used to generate future feeds. This feedback loop enables the provision of more relevant information to users.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The server periodically collects digital information from news sites and social media using web scraping techniques and APIs. The input is raw text data obtained from each information source. The output is the collected data stored in a primary database.

[0173] Step 2:

[0174] The server analyzes the collected data using natural language processing techniques. Specifically, it uses topic modeling to extract key categories and topics from the text data. The input is the raw text data obtained in step 1, and the output is data categorized by topic.

[0175] Step 3:

[0176] The server analyzes the user's emotional state using an emotion engine based on the user's past behavior data, posts, and comments. This analysis includes text analysis and voice analysis. The input is the user's behavior history data, and the output is an estimated emotion tag (positive, negative, neutral, etc.).

[0177] Step 4:

[0178] The server generates personalized feeds using a generative AI model based on the analyzed information and the estimated user's emotional state. Specifically, it takes the prompt "Find news that matches the current user's emotional state" as input to the AI ​​model and generates a feed as output.

[0179] Step 5:

[0180] The device receives a personalized feed sent from the server, notifies the user, and displays it. The input is the feed generated in step 4, and the output is a customized news feed displayed on the user's screen. The display method includes visual highlights and designs that correspond to the user's emotional state.

[0181] Step 6:

[0182] Users view feeds through their devices and rate each piece of content. These ratings (e.g., feedback such as "fun" or "informative") are sent back to the server and used for sentiment analysis and feed generation in the future. The input is user rating data, and the output is updated sentiment data and feedback data.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] Traditional information distribution systems often provide feeds without considering the user's emotional state, resulting in a lack of content tailored to the user's psychological condition. This leads to a challenge in improving user satisfaction.

[0186] 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.

[0187] In this invention, the server includes an acquisition means for acquiring digital information from an information source, an analysis means for analyzing the digital information acquired by the acquisition means and extracting data related to a specific topic, and an emotion analysis means for analyzing the user's emotional state and adjusting the data based on that emotion. This makes it possible to provide a personalized feed that corresponds to the user's emotional state.

[0188] "Means of acquisition" refers to a device or process equipped with the function of collecting digital information from an information source.

[0189] "Analysis means" refers to a device or process that has the function of analyzing acquired digital information and extracting data related to a specific topic.

[0190] "Emotional analysis means" refers to a device or process that analyzes the emotional state of a user and adjusts the data based on those emotions.

[0191] "Generation means" refers to a device or process that has the function of filtering adjusted data based on user interests and creating a personalized feed.

[0192] "Transmission means" refers to a device or process that has the function of sending the generated feed to the user's device.

[0193] The system that realizes this invention mainly consists of a server, a terminal, and a user.

[0194] The server operates using information acquisition means, analysis means, and sentiment analysis means. The information acquisition means periodically collects digital information from news sites and social media on the internet. The analysis means analyzes the collected information using natural language processing techniques and extracts data based on relevant topics. The sentiment analysis means the server analyzes text input and behavioral history from users to infer their emotional state. This sentiment analysis can also utilize voice and image analysis techniques.

[0195] The device receives a feed sent from the server and displays it to the user. The personalized feed is generated by a generation mechanism according to the user's interests and emotional state, and the device has the function to visually highlight and display it. For example, if the emotion analysis mechanism determines that the user is feeling stressed, the device will prioritize displaying relaxing content and information that helps reduce stress.

[0196] Users can view the feeds provided via their devices and provide ratings and feedback. This feedback is sent to the server and incorporated into the feed generation process for future updates.

[0197] For example, if a user enters "I'm a little tired today" into their device, the system recognizes that emotion and adjusts its feed to display music and videos suitable for relaxation. The generative AI model can apply this information using prompts such as the following:

[0198] "Please generate a suitable feed when a user types 'I'm a little tired today.' We'll particularly focus on relaxation and stress-relief content."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The server uses information acquisition methods to obtain digital information from external sources. This involves data collection via APIs, temporarily storing data from news sites and social media. Data from the information sources is provided as input, and a collection of raw digital information is obtained as output.

[0202] Step 2:

[0203] The server uses analysis tools to analyze the acquired digital information based on natural language processing techniques and divide it into related topics. The input is the digital information obtained in step 1, and the output is a dataset classified by topic. This operation applies text analysis algorithms to perform keyword extraction and topic modeling.

[0204] Step 3:

[0205] The server analyzes the user's input text and past behavioral history to determine the user's emotional state using emotion analysis tools. The input consists of text and audio data from the user, and the output is the inferred emotional state. This analysis uses an emotion AI model to extract emotions from the text and audio.

[0206] Step 4:

[0207] The server uses a generation mechanism to personalize the feed based on the emotional state obtained in the previous step and the user's interests. The input is data organized by topic and the user's emotional state, and the output is a customized feed. A data filtering algorithm is used to appropriately select information based on emotions and interests.

[0208] Step 5:

[0209] The device receives a personalized feed sent from the server via a transmission method and displays it to the user. The input is the personalized feed from the server, and the output is a visual display of the feed for the user. The feed is displayed through the user interface, and the user is notified using push notifications.

[0210] Step 6:

[0211] Users view feeds via their devices and provide feedback. This feedback is returned to the server and influences future feed generation. Input is the user's browsing information and feedback, and output is an updated user profile. User ratings and comments are incorporated as feedback into the feed generation engine.

[0212] 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.

[0213] 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 those described above. 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 shown 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention constitutes a system that efficiently acquires and analyzes digital information from diverse sources and provides personalized feeds to users. This system mainly consists of a server, terminals, and a user interface.

[0229] Server Role

[0230] The server is the central entity that retrieves digital information from information sources. This process uses news APIs and social networking service APIs to collect information using pre-configured keywords as keys. The server analyzes the retrieved digital information and executes algorithms to extract data relevant to specific topics. This eliminates irrelevant information, and information that matches the user's interests is stored.

[0231] The server further updates the user profile based on the user's interests and behavioral history. This profile is constructed using machine learning techniques to learn from the user's past actions. The updated profile is used for personalized information filtering.

[0232] Terminal role

[0233] The device is responsible for displaying a personalized feed sent from the server to the user. This feed is notified to the user via push notifications and can be viewed through applications or web interfaces. The device also records and sends to the server user interactions, such as reading articles, rating them, and leaving comments.

[0234] User interaction

[0235] Users can access digital information through their devices and view personalized feeds. They can select articles and view summaries. Furthermore, users can change the layout according to their interests and send feedback on specific articles. These interactions are used to further refine user profiles.

[0236] As a concrete example, a server queries a news API using the keyword "technology," and the device sends a push notification to the user along with the results. The user can open the notification on their smartphone and read the latest technology articles that interest them. After this, if the user "likes" the article, that action is sent to the server and will influence future feeds.

[0237] Thus, the present invention is designed so that each element can work together in order to enable users to efficiently acquire information.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The server accesses news APIs and social media APIs at pre-set time intervals to retrieve digital information based on keywords. For example, the server uses the keyword "technology" to collect relevant articles.

[0241] Step 2:

[0242] The server analyzes the acquired digital information and classifies it into specific topics based on the content of articles and posts. The analysis algorithm uses natural language processing technology to evaluate the importance and relevance of the articles.

[0243] Step 3:

[0244] The server filters the information analyzed based on the user's profile. This profile is updated to reflect the user's past behavior and areas of interest. Through this filtering, the most relevant information for the user is selected.

[0245] Step 4:

[0246] The server generates a personalized news feed using filtered information. The generated feed is then ready to be sent to the user's device.

[0247] Step 5:

[0248] The device displays the feed received from the server to the user and notifies them of new information via push notifications. The device displays the news feed on a user interface so that users can easily browse it.

[0249] Step 6:

[0250] Users can view news feeds received through their devices. They can select articles of interest, view summaries, and learn more details.

[0251] Step 7:

[0252] Users take actions such as liking or commenting on articles. This action information is sent to the server as feedback and used to further improve the profile.

[0253] Through these steps, the system efficiently provides information to users and achieves a high level of personalization.

[0254] (Example 1)

[0255] 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."

[0256] In modern society, information is overwhelming, making it difficult for individuals to efficiently acquire information that truly interests them. Traditional information acquisition methods have limitations in providing personalized content, and there is a need for methods that quickly deliver information tailored to the user's interests. Furthermore, to enhance the relevance of acquired information, a feedback loop that takes into account the individual's behavioral history is necessary.

[0257] 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.

[0258] In this invention, the server includes means for collecting electronic information from an information source via an information acquisition device, means for processing the electronic information acquired by the collection means and selecting items related to a specific theme, and a device for creating personalized information by arranging the items selected by the selection means based on the individual's interests. This enables users to quickly acquire highly accurate and customized information that matches their interests.

[0259] An "information acquisition device" is a device that has the function of collecting electronic information from an information source.

[0260] "Electronic information" refers to all types of data that are stored or transmitted in digital format.

[0261] A "specific theme" refers to a particular subject or topic selected when acquiring or processing information.

[0262] "Personalized information" refers to a collection of information customized based on a user's interests and past behavioral history.

[0263] "Personal devices" refer to terminals and devices used by users, including smartphones, tablets, and computers.

[0264] A "personal profile" refers to a collection of information that reflects a user's interests, behavioral history, and preferences.

[0265] This invention comprises a system that efficiently acquires and analyzes digital information from various sources, primarily through a server, terminal, and user interface, and provides personalized feeds to users. The server uses information acquisition devices and utilizes news APIs and SNS APIs to collect electronic information. This includes natural language processing techniques to effectively filter information using subject-based keywords.

[0266] The server processes the collected electronic information and selects items related to specific themes. Natural language processing (NLP) and machine learning algorithms are used for data analysis, which eliminates irrelevant data and extracts highly relevant information. Specific technologies used include data processing libraries in Python and R.

[0267] The server further manages individual profiles and creates personalized information based on the user's interests and behavioral history. Machine learning models update user profiles based on past user behavior data. This process utilizes machine learning libraries such as Scikit-learn and TensorFlow.

[0268] The device plays the role of providing users with personalized information sent from the server. Specifically, the device uses push notification functionality to communicate information to the user, making it viewable through applications and web interfaces. Users can receive notifications using their own devices and access information that interests them.

[0269] As a concrete example, a server queries a news API based on the keyword "technology" and collects relevant news. This information is then delivered to the device via push notification, allowing the user to check the latest technology articles on their smartphone.

[0270] An example of a prompt for a generative AI model might be, "How can we provide articles about the latest technologies based on the user's interests?" Based on this prompt, the AI ​​will provide the necessary information.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The server uses information acquisition devices to access news APIs and social networking service APIs to collect electronic information. Keywords such as "technology" are specified as input. In this process, a huge amount of raw data is acquired via APIs, and streaming data is generated as output.

[0274] Step 2:

[0275] The server receives the acquired electronic information and uses natural language processing (NLP) techniques to select data related to a specific theme. The input is the streaming data obtained in step 1, and through the data analysis process, it extracts relevant articles and posts, generating an organized dataset as output.

[0276] Step 3:

[0277] The server processes the organized dataset using a generative AI model to create personalized information based on individual interests. This step utilizes the user's profile information and the dataset obtained in step 2 as input. A machine learning algorithm is executed, generating a customized information feed as output.

[0278] Step 4:

[0279] The terminal receives the feed sent from the server and distributes information to the user through push notifications. As input, there is the feed created in step 3. The terminal displays the notification and, as output, provides the user with means to access articles and information of interest.

[0280] Step 5:

[0281] The user can view the information received via the terminal, read, evaluate, or comment on the article. Each operation is recorded by the terminal as input. These interactions are sent to the server as output and used as data useful for future profile updates and information feed generation.

[0282] (Application Example 1)

[0283] 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".

[0284] In modern times when information is flooding, users are required to quickly and accurately access information of interest. However, it is not easy to find information relevant to oneself from a vast amount of information, and currently, it takes time and effort. Also, users obtain information using various devices, but the information optimized for each device is not always provided.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0286] In this invention, the server includes an acquisition means for acquiring digital information from an information source, an analysis means for analyzing the digital information acquired by the acquisition means and extracting data related to a specific topic, and an update means for acquiring the evaluation and reaction of the user and updating the user profile based thereon. Thereby, it becomes possible to generate a personalized feed based on the interests of the user and provide it quickly in a format optimized for portable information devices.

[0287] The "information source" refers to various data providers from which digital information is acquired.

[0288] The "digital information" refers to news articles, blog posts, SNS posts, etc. expressed in electronic form.

[0289] The "acquisition means" refers to a method or mechanism for collecting digital information from an information source.

[0290] The "analysis means" refers to a method or mechanism for analyzing the acquired digital information and selecting data related to a specific topic.

[0291] The "generation means" refers to a method or mechanism for filtering information according to the interests of the user and creating an individualized feed.

[0292] The "portable information device" refers to a portable information terminal such as a smartphone or a tablet.

[0293] The "transmission means" refers to a method or mechanism for delivering the generated feed to the user.

[0294] The "update means" refers to a method or mechanism for improving the profile based on the evaluation and reaction of the user and enhancing the personalization accuracy.

[0295] The "profile" refers to personal specific information data constructed based on the interests and behavior history of the user.

[0296] A "feed" is a stream of information that organizes and continuously provides analyzed digital information to users.

[0297] The system for implementing this invention consists primarily of a server, a terminal, and a user interface. The server is programmed, for example, using Python and the Django framework, and retrieves digital information from information sources. This retrieval is performed via various news APIs and social networking APIs. For information analysis, neural network libraries such as TensorFlow are used to extract data related to specific topics and reduce noisy information. Through this process, data tailored to the user's interests is accumulated on the server.

[0298] The device is designed as a smartphone or tablet application and receives personalized feeds generated by the server. When a user views the feed and rates or comments on articles, the device sends that information back to the server, updating the user's profile. Through this cyclical process, the feed becomes increasingly tailored to the user's interests.

[0299] Users can quickly access the latest information through their mobile devices. Even if they have multiple topics of interest, the system automatically adjusts the information accordingly, allowing users to acquire information efficiently. Furthermore, push notifications to smartphones enable immediate viewing of new feeds.

[0300] For example, if a user expresses interest in "technology," the system will gather the latest information related to AI technology and digital gadgets, tailored to that interest, and provide a personalized feed through the mobile app. The prompt to the generative AI model at this time would be something like, "Retrieve news articles on the latest AI technology and add them to the user's feed."

[0301] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0302] Step 1:

[0303] The server obtains digital information from news APIs and SNS APIs. The input is a pre-set keyword, and the data obtained by sending an API request is output. At this time, the necessary data is accurately collected using an API client library.

[0304] Step 2:

[0305] The server analyzes the obtained digital information. The input is the data collected in Step 1, and the output is important data related to a specific topic. Here, TensorFlow is used to analyze the patterns in the data and remove low-relevance information.

[0306] Step 3:

[0307] The server filters the data based on the analysis results to fit the user's interests and generates a personalized feed. The input is the output data of Step 2, and a feed based on the user profile is output. The generation AI model is utilized to optimize the filtering process.

[0308] Step 4:

[0309] The server sends the generated personalized feed to the terminal. The input is the feed of Step 3, and it is displayed as a feed on the terminal. Here, the push notification function is enabled to quickly deliver the latest information to the user.

[0310] Step 5:

[0311] The device displays the received personalized feed to the user. The input is the feed sent from the server, and the output is a visual presentation to the user. Information is provided in an easy-to-understand and visually appealing format through the UI.

[0312] Step 6:

[0313] Users view information within the feed and provide ratings and comments. The input is the articles being fed, and the output is rating and comment data. User ratings are recorded to help generate future feeds.

[0314] Step 7:

[0315] The server incorporates user ratings and comments into the user profile. The input is the data obtained in step 6, and the output is the updated profile data. This data will be used for future feed generation.

[0316] 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.

[0317] This invention combines an emotion engine with a system that acquires digital information from information sources and provides it to users as a personalized feed. The emotion engine has the function of analyzing the user's emotional state and adjusting the content of the feed accordingly. This invention mainly consists of a server, a terminal, and a user interface.

[0318] Server Role

[0319] The server has the function of regularly collecting digital information from news sites and social media. The collected information is analyzed and classified based on specific topics. This analysis process uses natural language processing technology to evaluate the information and confirm its relevance to topics.

[0320] The server further uses an emotion engine to infer the user's emotional state. This emotion engine analyzes emotions from the user's content creation (e.g., comments and posts), voice, and images to identify the user's current psychological state. This information, along with the user profile and behavioral history, is used to personalize the feed.

[0321] Terminal role

[0322] The device is responsible for notifying and displaying personalized feeds provided by the server to the user. The feeds are adjusted according to the user's emotional state, and content appropriate to a specific emotion is highlighted on the device.

[0323] For example, if a user is identified as experiencing feelings of joy, the device will prioritize displaying positive news and interesting articles. Conversely, if a user is determined to be experiencing stress, it will provide relaxing content and information that helps reduce stress.

[0324] User interaction

[0325] Users can access personalized feeds at any time via their devices. As users read and rate articles, new sentiment data and interest information are collected and sent to the server. This data influences the generation of future feeds.

[0326] For example, when a user views content and rates it as "fun," the emotion engine recognizes that emotion and adjusts future feeds to include more content that evokes similar feelings.

[0327] Thus, the present invention enables the provision of information tailored to the user's emotions and behavior, thereby realizing a more enriching user experience.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The server regularly collects digital information from news sites and social media. Based on specific topics, it retrieves information using news APIs and web scraping techniques. For example, the server collects the latest articles related to technology.

[0331] Step 2:

[0332] The server analyzes the collected digital information and extracts important data. It performs natural language processing on the text to classify articles based on their relevance and topic. Then, it filters out irrelevant articles.

[0333] Step 3:

[0334] The server activates an emotion engine to identify the user's emotional state. It analyzes user-generated content and feedback, and uses that data to infer the user's current emotions.

[0335] Step 4:

[0336] The server filters the analysis results based on the user's profile information. In this process, it combines the user's past behavioral history with their current emotional state to select personalized information.

[0337] Step 5:

[0338] The server generates a personalized feed using filtered information and sends it to the device. The priority of the information provided is adjusted according to the user's emotional state.

[0339] Step 6:

[0340] The device displays a personalized feed received from the server to the user. The feed is configured to highlight relaxing information, for example, if the user is currently feeling stressed.

[0341] Step 7:

[0342] Users browse feeds displayed on their devices, read articles that interest them, and enter their thoughts and opinions. These interactions are sent back to the server and used to update the user's sentiment profile and behavioral history.

[0343] This process allows the system to provide information that takes into account the user's emotional state, resulting in a more personalized content experience.

[0344] (Example 2)

[0345] 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".

[0346] In today's information society, users are exposed to a vast amount of information, making it difficult to efficiently acquire information that is useful and of interest to them. Furthermore, there is a lack of systems that present information while taking into account the user's momentary emotional state, so there is a need to provide personalized feedback that responds to the user's emotions.

[0347] 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.

[0348] In this invention, the server includes a collection means for collecting data from information sources, an analysis means for analyzing the data acquired by the collection means and extracting information related to a specific classification, and a generation means for adjusting the information extracted by the analysis means based on the user's emotional state and generating personalized content. As a result, the user can receive information optimized for their emotional state at that moment, enabling them to acquire more meaningful information.

[0349] "Collection means" refers to technologies that have the function of acquiring data from information sources.

[0350] "Analysis means" refers to techniques for processing collected data and identifying and extracting information related to a specific classification.

[0351] "Generation means" refers to technology that has the function of adjusting information analyzed based on the user's emotional state to create personalized content.

[0352] "Transmission means" refers to technology that has the function of notifying the user's device of the generated content.

[0353] "Emotional state" refers to information that indicates the user's current psychological reactions and feelings.

[0354] "Personalized content" refers to information that is customized according to the user's specific needs and circumstances.

[0355] The server periodically acquires digital information using web scraping techniques and APIs for information gathering. News sites and social media serve as sources of information. This data is analyzed using natural language processing techniques and associated with specific topics. Specifically, the information is classified through topic modeling and keyword extraction.

[0356] The analyzed information is further processed by an emotion engine. This engine analyzes the user's past posts, comments, browsing history, and audio data to infer the user's emotional state. Text and audio analysis software is used for emotion analysis, and emotion tags such as positive, negative, and neutral are generated.

[0357] Based on the generated sentiment data and analyzed information, the server generates content tailored to the user. In this process, a generative AI model is used to create a personalized feed. An example of a prompt given to the generative AI is, "Find news that matches the user's current sentiment state."

[0358] The device is responsible for notifying the user of personalized feeds sent from the server. The feeds are displayed with content that matches the user's emotional state highlighted. For example, a user who is feeling stressed will be shown music and articles that help with relaxation.

[0359] Users can view feeds through their devices and rate the content. These interactions are stored on the server side as new sentiment data and used to generate future feeds. This feedback loop enables the provision of more relevant information to users.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The server periodically collects digital information from news sites and social media using web scraping techniques and APIs. The input is raw text data obtained from each information source. The output is the collected data stored in a primary database.

[0363] Step 2:

[0364] The server analyzes the collected data using natural language processing techniques. Specifically, it uses topic modeling to extract key categories and topics from the text data. The input is the raw text data obtained in step 1, and the output is data categorized by topic.

[0365] Step 3:

[0366] The server analyzes the user's emotional state using an emotion engine based on the user's past behavior data, posts, and comments. This analysis includes text analysis and voice analysis. The input is the user's behavior history data, and the output is an estimated emotion tag (positive, negative, neutral, etc.).

[0367] Step 4:

[0368] The server generates personalized feeds using a generative AI model based on the analyzed information and the estimated user's emotional state. Specifically, it takes the prompt "Find news that matches the current user's emotional state" as input to the AI ​​model and generates a feed as output.

[0369] Step 5:

[0370] The device receives a personalized feed sent from the server, notifies the user, and displays it. The input is the feed generated in step 4, and the output is a customized news feed displayed on the user's screen. The display method includes visual highlights and designs that correspond to the user's emotional state.

[0371] Step 6:

[0372] Users view feeds through their devices and rate each piece of content. These ratings (e.g., feedback such as "fun" or "informative") are sent back to the server and used for sentiment analysis and feed generation in the future. The input is user rating data, and the output is updated sentiment data and feedback data.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] Traditional information distribution systems often provide feeds without considering the user's emotional state, resulting in a lack of content tailored to the user's psychological condition. This leads to a challenge in improving user satisfaction.

[0376] 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.

[0377] In this invention, the server includes an acquisition means for acquiring digital information from an information source, an analysis means for analyzing the digital information acquired by the acquisition means and extracting data related to a specific topic, and an emotion analysis means for analyzing the user's emotional state and adjusting the data based on that emotion. This makes it possible to provide a personalized feed that corresponds to the user's emotional state.

[0378] "Means of acquisition" refers to a device or process equipped with the function of collecting digital information from an information source.

[0379] "Analysis means" refers to a device or process that has the function of analyzing acquired digital information and extracting data related to a specific topic.

[0380] "Emotional analysis means" refers to a device or process that analyzes the emotional state of a user and adjusts the data based on those emotions.

[0381] "Generation means" refers to a device or process that has the function of filtering adjusted data based on user interests and creating a personalized feed.

[0382] "Transmission means" refers to a device or process that has the function of sending the generated feed to the user's device.

[0383] The system that realizes this invention mainly consists of a server, a terminal, and a user.

[0384] The server operates using information acquisition means, analysis means, and sentiment analysis means. The information acquisition means periodically collects digital information from news sites and social media on the internet. The analysis means analyzes the collected information using natural language processing techniques and extracts data based on relevant topics. The sentiment analysis means the server analyzes text input and behavioral history from users to infer their emotional state. This sentiment analysis can also utilize voice and image analysis techniques.

[0385] The device receives a feed sent from the server and displays it to the user. The personalized feed is generated by a generation mechanism according to the user's interests and emotional state, and the device has the function to visually highlight and display it. For example, if the emotion analysis mechanism determines that the user is feeling stressed, the device will prioritize displaying relaxing content and information that helps reduce stress.

[0386] Users can view the feeds provided via their devices and provide ratings and feedback. This feedback is sent to the server and incorporated into the feed generation process for future updates.

[0387] For example, if a user enters "I'm a little tired today" into their device, the system recognizes that emotion and adjusts its feed to display music and videos suitable for relaxation. The generative AI model can apply this information using prompts such as the following:

[0388] "Please generate a suitable feed when a user types 'I'm a little tired today.' We'll particularly focus on relaxation and stress-relief content."

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The server uses information acquisition methods to obtain digital information from external sources. This involves data collection via APIs, temporarily storing data from news sites and social media. Data from the information sources is provided as input, and a collection of raw digital information is obtained as output.

[0392] Step 2:

[0393] The server uses analysis tools to analyze the acquired digital information based on natural language processing techniques and divide it into related topics. The input is the digital information obtained in step 1, and the output is a dataset classified by topic. This operation applies text analysis algorithms to perform keyword extraction and topic modeling.

[0394] Step 3:

[0395] The server analyzes the user's input text and past behavioral history to determine the user's emotional state using emotion analysis tools. The input consists of text and audio data from the user, and the output is the inferred emotional state. This analysis uses an emotion AI model to extract emotions from the text and audio.

[0396] Step 4:

[0397] The server uses a generation mechanism to personalize the feed based on the emotional state obtained in the previous step and the user's interests. The input is data organized by topic and the user's emotional state, and the output is a customized feed. A data filtering algorithm is used to appropriately select information based on emotions and interests.

[0398] Step 5:

[0399] The device receives a personalized feed sent from the server via a transmission method and displays it to the user. The input is the personalized feed from the server, and the output is a visual display of the feed for the user. The feed is displayed through the user interface, and the user is notified using push notifications.

[0400] Step 6:

[0401] Users view feeds via their devices and provide feedback. This feedback is returned to the server and influences future feed generation. Input is the user's browsing information and feedback, and output is an updated user profile. User ratings and comments are incorporated as feedback into the feed generation engine.

[0402] 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.

[0403] 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 those described above. 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 shown 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] This invention constitutes a system that efficiently acquires and analyzes digital information from diverse sources and provides personalized feeds to users. This system mainly consists of a server, terminals, and a user interface.

[0419] Server Role

[0420] The server is the central entity that retrieves digital information from information sources. This process uses news APIs and social networking service APIs to collect information using pre-configured keywords as keys. The server analyzes the retrieved digital information and executes algorithms to extract data relevant to specific topics. This eliminates irrelevant information, and information that matches the user's interests is stored.

[0421] The server further updates the user profile based on the user's interests and behavioral history. This profile is constructed using machine learning techniques to learn from the user's past actions. The updated profile is used for personalized information filtering.

[0422] Terminal role

[0423] The device is responsible for displaying a personalized feed sent from the server to the user. This feed is notified to the user via push notifications and can be viewed through applications or web interfaces. The device also records and sends to the server user interactions, such as reading articles, rating them, and leaving comments.

[0424] User interaction

[0425] Users can access digital information through their devices and view personalized feeds. They can select articles and view summaries. Furthermore, users can change the layout according to their interests and send feedback on specific articles. These interactions are used to further refine user profiles.

[0426] As a concrete example, a server queries a news API using the keyword "technology," and the device sends a push notification to the user along with the results. The user can open the notification on their smartphone and read the latest technology articles that interest them. After this, if the user "likes" the article, that action is sent to the server and will influence future feeds.

[0427] Thus, the present invention is designed so that each element can work together in order to enable users to efficiently acquire information.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The server accesses news APIs and social media APIs at pre-set time intervals to retrieve digital information based on keywords. For example, the server uses the keyword "technology" to collect relevant articles.

[0431] Step 2:

[0432] The server analyzes the acquired digital information and classifies it into specific topics based on the content of articles and posts. The analysis algorithm uses natural language processing technology to evaluate the importance and relevance of the articles.

[0433] Step 3:

[0434] The server filters the information analyzed based on the user's profile. This profile is updated to reflect the user's past behavior and areas of interest. Through this filtering, the most relevant information for the user is selected.

[0435] Step 4:

[0436] The server generates a personalized news feed using filtered information. The generated feed is then ready to be sent to the user's device.

[0437] Step 5:

[0438] The device displays the feed received from the server to the user and notifies them of new information via push notifications. The device displays the news feed on a user interface so that users can easily browse it.

[0439] Step 6:

[0440] Users can view news feeds received through their devices. They can select articles of interest, view summaries, and learn more details.

[0441] Step 7:

[0442] Users take actions such as liking or commenting on articles. This action information is sent to the server as feedback and used to further improve the profile.

[0443] Through these steps, the system efficiently provides information to users and achieves a high level of personalization.

[0444] (Example 1)

[0445] 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."

[0446] In modern society, information is overwhelming, making it difficult for individuals to efficiently acquire information that truly interests them. Traditional information acquisition methods have limitations in providing personalized content, and there is a need for methods that quickly deliver information tailored to the user's interests. Furthermore, to enhance the relevance of acquired information, a feedback loop that takes into account the individual's behavioral history is necessary.

[0447] 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.

[0448] In this invention, the server includes means for collecting electronic information from an information source via an information acquisition device, means for processing the electronic information acquired by the collection means and selecting items related to a specific theme, and a device for creating personalized information by arranging the items selected by the selection means based on the individual's interests. This enables users to quickly acquire highly accurate and customized information that matches their interests.

[0449] An "information acquisition device" is a device that has the function of collecting electronic information from an information source.

[0450] "Electronic information" refers to all types of data that are stored or transmitted in digital format.

[0451] A "specific theme" refers to a particular subject or topic selected when acquiring or processing information.

[0452] "Personalized information" refers to a collection of information customized based on a user's interests and past behavioral history.

[0453] "Personal devices" refer to terminals and devices used by users, including smartphones, tablets, and computers.

[0454] A "personal profile" refers to a collection of information that reflects a user's interests, behavioral history, and preferences.

[0455] This invention comprises a system that efficiently acquires and analyzes digital information from various sources, primarily through a server, terminal, and user interface, and provides personalized feeds to users. The server uses information acquisition devices and utilizes news APIs and SNS APIs to collect electronic information. This includes natural language processing techniques to effectively filter information using subject-based keywords.

[0456] The server processes the collected electronic information and selects items related to specific themes. Natural language processing (NLP) and machine learning algorithms are used for data analysis, which eliminates irrelevant data and extracts highly relevant information. Specific technologies used include data processing libraries in Python and R.

[0457] The server further manages individual profiles and creates personalized information based on the user's interests and behavioral history. Machine learning models update user profiles based on past user behavior data. This process utilizes machine learning libraries such as Scikit-learn and TensorFlow.

[0458] The device plays the role of providing users with personalized information sent from the server. Specifically, the device uses push notification functionality to communicate information to the user, making it viewable through applications and web interfaces. Users can receive notifications using their own devices and access information that interests them.

[0459] As a concrete example, a server queries a news API based on the keyword "technology" and collects relevant news. This information is then delivered to the device via push notification, allowing the user to check the latest technology articles on their smartphone.

[0460] An example of a prompt for a generative AI model might be, "How can we provide articles about the latest technologies based on the user's interests?" Based on this prompt, the AI ​​will provide the necessary information.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The server uses information acquisition devices to access news APIs and social networking service APIs to collect electronic information. Keywords such as "technology" are specified as input. In this process, a huge amount of raw data is acquired via APIs, and streaming data is generated as output.

[0464] Step 2:

[0465] The server receives the acquired electronic information and uses natural language processing (NLP) techniques to select data related to a specific theme. The input is the streaming data obtained in step 1, and through the data analysis process, it extracts relevant articles and posts, generating an organized dataset as output.

[0466] Step 3:

[0467] The server processes the organized dataset using a generative AI model to create personalized information based on individual interests. This step utilizes the user's profile information and the dataset obtained in step 2 as input. A machine learning algorithm is executed, generating a customized information feed as output.

[0468] Step 4:

[0469] The device receives a feed sent from the server and delivers information to the user via push notifications. The input is the feed created in step 3. The device displays the notification and, as output, provides the user with a means to access articles and information of interest.

[0470] Step 5:

[0471] Users can view information received via their devices, read articles, rate them, or leave comments. Each action is recorded on the device as input. These interactions are sent to the server as output and used as data to help with future profile updates and information feed generation.

[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 access information of interest quickly and accurately. However, finding relevant information from the vast amount of data is not easy and currently requires time and effort. Furthermore, while users access information using a variety of devices, the information provided is not always optimized for each device.

[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 an acquisition means for acquiring digital information from an information source, an analysis means for analyzing the digital information acquired by the acquisition means to extract data related to a specific topic, and an update means for acquiring user evaluations and reactions and updating the user's profile based on them. This makes it possible to generate personalized feeds based on user interests and provide them quickly in a format optimized for portable information devices.

[0477] "Information sources" refer to the various data providers from which digital information is obtained.

[0478] "Digital information" refers to news articles, blog posts, and social media posts that are expressed in electronic format.

[0479] "Means of acquisition" refers to the methods and mechanisms for collecting digital information from information sources.

[0480] "Analysis means" refers to methods or mechanisms for analyzing acquired digital information and selecting data related to a specific topic.

[0481] "Generation means" refers to methods and mechanisms that filter information according to the user's interests and create personalized feeds.

[0482] "Portable information devices" refer to portable information terminals such as smartphones and tablets.

[0483] "Transmission method" refers to the method or mechanism for delivering the generated feed to users.

[0484] "Update methods" refer to methods and mechanisms that improve profiles based on user evaluations and feedback, thereby enhancing the accuracy of personalization.

[0485] A "profile" is a unique piece of information data created based on a user's interests and behavioral history.

[0486] A "feed" is a stream of information that organizes and continuously provides analyzed digital information to users.

[0487] The system for implementing this invention consists primarily of a server, a terminal, and a user interface. The server is programmed, for example, using Python and the Django framework, and retrieves digital information from information sources. This retrieval is performed via various news APIs and social networking APIs. For information analysis, neural network libraries such as TensorFlow are used to extract data related to specific topics and reduce noisy information. Through this process, data tailored to the user's interests is accumulated on the server.

[0488] The device is designed as a smartphone or tablet application and receives personalized feeds generated by the server. When a user views the feed and rates or comments on articles, the device sends that information back to the server, updating the user's profile. Through this cyclical process, the feed becomes increasingly tailored to the user's interests.

[0489] Users can quickly access the latest information through their mobile devices. Even if they have multiple topics of interest, the system automatically adjusts the information accordingly, allowing users to acquire information efficiently. Furthermore, push notifications to smartphones enable immediate viewing of new feeds.

[0490] For example, if a user expresses interest in "technology," the system will gather the latest information related to AI technology and digital gadgets, tailored to that interest, and provide a personalized feed through the mobile app. The prompt to the generative AI model at this time would be something like, "Retrieve news articles on the latest AI technology and add them to the user's feed."

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The server retrieves digital information from news APIs and social networking service APIs. Input is pre-configured keywords, and data obtained by sending API requests is output. At this time, an API client library is used to accurately collect the necessary data.

[0494] Step 2:

[0495] The server analyzes the acquired digital information. The input is the data collected in step 1, and the output is important data related to a specific topic. Here, TensorFlow is used to analyze patterns in the data and remove irrelevant information.

[0496] Step 3:

[0497] The server filters the data based on the analysis results to fit the user's interests and generates a personalized feed. The input is the output data from step 2, and a feed based on the user profile is output. The filtering process is optimized using a generative AI model.

[0498] Step 4:

[0499] The server sends the generated personalized feed to the device. The input is the feed from step 3, which is displayed as a feed on the device. Here, push notifications are enabled to quickly deliver the latest information to the user.

[0500] Step 5:

[0501] The device displays the received personalized feed to the user. The input is the feed sent from the server, and the output is a visual presentation to the user. Information is provided in an easy-to-understand and visually appealing format through the UI.

[0502] Step 6:

[0503] Users view information within the feed and provide ratings and comments. The input is the articles being fed, and the output is rating and comment data. User ratings are recorded to help generate future feeds.

[0504] Step 7:

[0505] The server incorporates user ratings and comments into the user profile. The input is the data obtained in step 6, and the output is the updated profile data. This data will be used for future feed generation.

[0506] 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.

[0507] This invention combines an emotion engine with a system that acquires digital information from information sources and provides it to users as a personalized feed. The emotion engine has the function of analyzing the user's emotional state and adjusting the content of the feed accordingly. This invention mainly consists of a server, a terminal, and a user interface.

[0508] Server Role

[0509] The server has the function of regularly collecting digital information from news sites and social media. The collected information is analyzed and classified based on specific topics. This analysis process uses natural language processing technology to evaluate the information and confirm its relevance to topics.

[0510] The server further uses an emotion engine to infer the user's emotional state. This emotion engine analyzes emotions from the user's content creation (e.g., comments and posts), voice, and images to identify the user's current psychological state. This information, along with the user profile and behavioral history, is used to personalize the feed.

[0511] Terminal role

[0512] The device is responsible for notifying and displaying personalized feeds provided by the server to the user. The feeds are adjusted according to the user's emotional state, and content appropriate to a specific emotion is highlighted on the device.

[0513] For example, if a user is identified as experiencing feelings of joy, the device will prioritize displaying positive news and interesting articles. Conversely, if a user is determined to be experiencing stress, it will provide relaxing content and information that helps reduce stress.

[0514] User interaction

[0515] Users can access personalized feeds at any time via their devices. As users read and rate articles, new sentiment data and interest information are collected and sent to the server. This data influences the generation of future feeds.

[0516] For example, when a user views content and rates it as "fun," the emotion engine recognizes that emotion and adjusts future feeds to include more content that evokes similar feelings.

[0517] Thus, the present invention enables the provision of information tailored to the user's emotions and behavior, thereby realizing a more enriching user experience.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The server regularly collects digital information from news sites and social media. Based on specific topics, it retrieves information using news APIs and web scraping techniques. For example, the server collects the latest articles related to technology.

[0521] Step 2:

[0522] The server analyzes the collected digital information and extracts important data. It performs natural language processing on the text to classify articles based on their relevance and topic. Then, it filters out irrelevant articles.

[0523] Step 3:

[0524] The server activates an emotion engine to identify the user's emotional state. It analyzes user-generated content and feedback, and uses that data to infer the user's current emotions.

[0525] Step 4:

[0526] The server filters the analysis results based on the user's profile information. In this process, it combines the user's past behavioral history with their current emotional state to select personalized information.

[0527] Step 5:

[0528] The server generates a personalized feed using filtered information and sends it to the device. The priority of the information provided is adjusted according to the user's emotional state.

[0529] Step 6:

[0530] The device displays a personalized feed received from the server to the user. The feed is configured to highlight relaxing information, for example, if the user is currently feeling stressed.

[0531] Step 7:

[0532] Users browse feeds displayed on their devices, read articles that interest them, and enter their thoughts and opinions. These interactions are sent back to the server and used to update the user's sentiment profile and behavioral history.

[0533] This process allows the system to provide information that takes into account the user's emotional state, resulting in a more personalized content experience.

[0534] (Example 2)

[0535] 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."

[0536] In today's information society, users are exposed to a vast amount of information, making it difficult to efficiently acquire information that is useful and of interest to them. Furthermore, there is a lack of systems that present information while taking into account the user's momentary emotional state, so there is a need to provide personalized feedback that responds to the user's emotions.

[0537] 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.

[0538] In this invention, the server includes a collection means for collecting data from information sources, an analysis means for analyzing the data acquired by the collection means and extracting information related to a specific classification, and a generation means for adjusting the information extracted by the analysis means based on the user's emotional state and generating personalized content. As a result, the user can receive information optimized for their emotional state at that moment, enabling them to acquire more meaningful information.

[0539] "Collection means" refers to technologies that have the function of acquiring data from information sources.

[0540] "Analysis means" refers to techniques for processing collected data and identifying and extracting information related to a specific classification.

[0541] "Generation means" refers to technology that has the function of adjusting information analyzed based on the user's emotional state to create personalized content.

[0542] "Transmission means" refers to technology that has the function of notifying the user's device of the generated content.

[0543] "Emotional state" refers to information that indicates the user's current psychological reactions and feelings.

[0544] "Personalized content" refers to information that is customized according to the user's specific needs and circumstances.

[0545] The server periodically acquires digital information using web scraping techniques and APIs for information gathering. News sites and social media serve as sources of information. This data is analyzed using natural language processing techniques and associated with specific topics. Specifically, the information is classified through topic modeling and keyword extraction.

[0546] The analyzed information is further processed by an emotion engine. This engine analyzes the user's past posts, comments, browsing history, and audio data to infer the user's emotional state. Text and audio analysis software is used for emotion analysis, and emotion tags such as positive, negative, and neutral are generated.

[0547] Based on the generated sentiment data and analyzed information, the server generates content tailored to the user. In this process, a generative AI model is used to create a personalized feed. An example of a prompt given to the generative AI is, "Find news that matches the user's current sentiment state."

[0548] The device is responsible for notifying the user of personalized feeds sent from the server. The feeds are displayed with content that matches the user's emotional state highlighted. For example, a user who is feeling stressed will be shown music and articles that help with relaxation.

[0549] Users can view feeds through their devices and rate the content. These interactions are stored on the server side as new sentiment data and used to generate future feeds. This feedback loop enables the provision of more relevant information to users.

[0550] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0551] Step 1:

[0552] The server periodically collects digital information from news sites and social media using web scraping techniques and APIs. The input is raw text data obtained from each information source. The output is the collected data stored in a primary database.

[0553] Step 2:

[0554] The server analyzes the collected data using natural language processing techniques. Specifically, it uses topic modeling to extract key categories and topics from the text data. The input is the raw text data obtained in step 1, and the output is data categorized by topic.

[0555] Step 3:

[0556] The server analyzes the user's emotional state using an emotion engine based on the user's past behavior data, posts, and comments. This analysis includes text analysis and voice analysis. The input is the user's behavior history data, and the output is an estimated emotion tag (positive, negative, neutral, etc.).

[0557] Step 4:

[0558] The server generates personalized feeds using a generative AI model based on the analyzed information and the estimated user's emotional state. Specifically, it takes the prompt "Find news that matches the current user's emotional state" as input to the AI ​​model and generates a feed as output.

[0559] Step 5:

[0560] The device receives a personalized feed sent from the server, notifies the user, and displays it. The input is the feed generated in step 4, and the output is a customized news feed displayed on the user's screen. The display method includes visual highlights and designs that correspond to the user's emotional state.

[0561] Step 6:

[0562] Users view feeds through their devices and rate each piece of content. These ratings (e.g., feedback such as "fun" or "informative") are sent back to the server and used for sentiment analysis and feed generation in the future. The input is user rating data, and the output is updated sentiment data and feedback data.

[0563] (Application Example 2)

[0564] 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."

[0565] Traditional information distribution systems often provide feeds without considering the user's emotional state, resulting in a lack of content tailored to the user's psychological condition. This leads to a challenge in improving user satisfaction.

[0566] 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.

[0567] In this invention, the server includes an acquisition means for acquiring digital information from an information source, an analysis means for analyzing the digital information acquired by the acquisition means and extracting data related to a specific topic, and an emotion analysis means for analyzing the user's emotional state and adjusting the data based on that emotion. This makes it possible to provide a personalized feed that corresponds to the user's emotional state.

[0568] "Means of acquisition" refers to a device or process equipped with the function of collecting digital information from an information source.

[0569] "Analysis means" refers to a device or process that has the function of analyzing acquired digital information and extracting data related to a specific topic.

[0570] "Emotional analysis means" refers to a device or process that analyzes the emotional state of a user and adjusts the data based on those emotions.

[0571] "Generation means" refers to a device or process that has the function of filtering adjusted data based on user interests and creating a personalized feed.

[0572] "Transmission means" refers to a device or process that has the function of sending the generated feed to the user's device.

[0573] The system that realizes this invention mainly consists of a server, a terminal, and a user.

[0574] The server operates using information acquisition means, analysis means, and sentiment analysis means. The information acquisition means periodically collects digital information from news sites and social media on the internet. The analysis means analyzes the collected information using natural language processing techniques and extracts data based on relevant topics. The sentiment analysis means the server analyzes text input and behavioral history from users to infer their emotional state. This sentiment analysis can also utilize voice and image analysis techniques.

[0575] The device receives a feed sent from the server and displays it to the user. The personalized feed is generated by a generation mechanism according to the user's interests and emotional state, and the device has the function to visually highlight and display it. For example, if the emotion analysis mechanism determines that the user is feeling stressed, the device will prioritize displaying relaxing content and information that helps reduce stress.

[0576] Users can view the feeds provided via their devices and provide ratings and feedback. This feedback is sent to the server and incorporated into the feed generation process for future updates.

[0577] For example, if a user enters "I'm a little tired today" into their device, the system recognizes that emotion and adjusts its feed to display music and videos suitable for relaxation. The generative AI model can apply this information using prompts such as the following:

[0578] "Please generate a suitable feed when a user types 'I'm a little tired today.' We'll particularly focus on relaxation and stress-relief content."

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] The server uses information acquisition methods to obtain digital information from external sources. This involves data collection via APIs, temporarily storing data from news sites and social media. Data from the information sources is provided as input, and a collection of raw digital information is obtained as output.

[0582] Step 2:

[0583] The server uses analysis tools to analyze the acquired digital information based on natural language processing techniques and divide it into related topics. The input is the digital information obtained in step 1, and the output is a dataset classified by topic. This operation applies text analysis algorithms to perform keyword extraction and topic modeling.

[0584] Step 3:

[0585] The server analyzes the user's input text and past behavioral history to determine the user's emotional state using emotion analysis tools. The input consists of text and audio data from the user, and the output is the inferred emotional state. This analysis uses an emotion AI model to extract emotions from the text and audio.

[0586] Step 4:

[0587] The server uses a generation mechanism to personalize the feed based on the emotional state obtained in the previous step and the user's interests. The input is data organized by topic and the user's emotional state, and the output is a customized feed. A data filtering algorithm is used to appropriately select information based on emotions and interests.

[0588] Step 5:

[0589] The device receives a personalized feed sent from the server via a transmission method and displays it to the user. The input is the personalized feed from the server, and the output is a visual display of the feed for the user. The feed is displayed through the user interface, and the user is notified using push notifications.

[0590] Step 6:

[0591] Users view feeds via their devices and provide feedback. This feedback is returned to the server and influences future feed generation. Input is the user's browsing information and feedback, and output is an updated user profile. User ratings and comments are incorporated as feedback into the feed generation engine.

[0592] 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.

[0593] 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 those described above. 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 shown 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.

[0594] 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.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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".

[0609] This invention constitutes a system that efficiently acquires and analyzes digital information from diverse sources and provides personalized feeds to users. This system mainly consists of a server, terminals, and a user interface.

[0610] Server Role

[0611] The server is the central entity that retrieves digital information from information sources. This process uses news APIs and social networking service APIs to collect information using pre-configured keywords as keys. The server analyzes the retrieved digital information and executes algorithms to extract data relevant to specific topics. This eliminates irrelevant information, and information that matches the user's interests is stored.

[0612] The server further updates the user profile based on the user's interests and behavioral history. This profile is constructed using machine learning techniques to learn from the user's past actions. The updated profile is used for personalized information filtering.

[0613] Terminal role

[0614] The device is responsible for displaying a personalized feed sent from the server to the user. This feed is notified to the user via push notifications and can be viewed through applications or web interfaces. The device also records and sends to the server user interactions, such as reading articles, rating them, and leaving comments.

[0615] User interaction

[0616] Users can access digital information through their devices and view personalized feeds. They can select articles and view summaries. Furthermore, users can change the layout according to their interests and send feedback on specific articles. These interactions are used to further refine user profiles.

[0617] As a concrete example, a server queries a news API using the keyword "technology," and the device sends a push notification to the user along with the results. The user can open the notification on their smartphone and read the latest technology articles that interest them. After this, if the user "likes" the article, that action is sent to the server and will influence future feeds.

[0618] Thus, the present invention is designed so that each element can work together in order to enable users to efficiently acquire information.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The server accesses news APIs and social media APIs at pre-set time intervals to retrieve digital information based on keywords. For example, the server uses the keyword "technology" to collect relevant articles.

[0622] Step 2:

[0623] The server analyzes the acquired digital information and classifies it into specific topics based on the content of articles and posts. The analysis algorithm uses natural language processing technology to evaluate the importance and relevance of the articles.

[0624] Step 3:

[0625] The server filters the information analyzed based on the user's profile. This profile is updated to reflect the user's past behavior and areas of interest. Through this filtering, the most relevant information for the user is selected.

[0626] Step 4:

[0627] The server generates a personalized news feed using filtered information. The generated feed is then ready to be sent to the user's device.

[0628] Step 5:

[0629] The device displays the feed received from the server to the user and notifies them of new information via push notifications. The device displays the news feed on a user interface so that users can easily browse it.

[0630] Step 6:

[0631] Users can view news feeds received through their devices. They can select articles of interest, view summaries, and learn more details.

[0632] Step 7:

[0633] Users take actions such as liking or commenting on articles. This action information is sent to the server as feedback and used to further improve the profile.

[0634] Through these steps, the system efficiently provides information to users and achieves a high level of personalization.

[0635] (Example 1)

[0636] 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".

[0637] In modern society, information is overwhelming, making it difficult for individuals to efficiently acquire information that truly interests them. Traditional information acquisition methods have limitations in providing personalized content, and there is a need for methods that quickly deliver information tailored to the user's interests. Furthermore, to enhance the relevance of acquired information, a feedback loop that takes into account the individual's behavioral history is necessary.

[0638] 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.

[0639] In this invention, the server includes means for collecting electronic information from an information source via an information acquisition device, means for processing the electronic information acquired by the collection means and selecting items related to a specific theme, and a device for creating personalized information by arranging the items selected by the selection means based on the individual's interests. This enables users to quickly acquire highly accurate and customized information that matches their interests.

[0640] An "information acquisition device" is a device that has the function of collecting electronic information from an information source.

[0641] "Electronic information" refers to all types of data that are stored or transmitted in digital format.

[0642] A "specific theme" refers to a particular subject or topic selected when acquiring or processing information.

[0643] "Personalized information" refers to a collection of information customized based on a user's interests and past behavioral history.

[0644] "Personal devices" refer to terminals and devices used by users, including smartphones, tablets, and computers.

[0645] A "personal profile" refers to a collection of information that reflects a user's interests, behavioral history, and preferences.

[0646] This invention comprises a system that efficiently acquires and analyzes digital information from various sources, primarily through a server, terminal, and user interface, and provides personalized feeds to users. The server uses information acquisition devices and utilizes news APIs and SNS APIs to collect electronic information. This includes natural language processing techniques to effectively filter information using subject-based keywords.

[0647] The server processes the collected electronic information and selects items related to specific themes. Natural language processing (NLP) and machine learning algorithms are used for data analysis, which eliminates irrelevant data and extracts highly relevant information. Specific technologies used include data processing libraries in Python and R.

[0648] The server further manages individual profiles and creates personalized information based on the user's interests and behavioral history. Machine learning models update user profiles based on past user behavior data. This process utilizes machine learning libraries such as Scikit-learn and TensorFlow.

[0649] The device plays the role of providing users with personalized information sent from the server. Specifically, the device uses push notification functionality to communicate information to the user, making it viewable through applications and web interfaces. Users can receive notifications using their own devices and access information that interests them.

[0650] As a concrete example, a server queries a news API based on the keyword "technology" and collects relevant news. This information is then delivered to the device via push notification, allowing the user to check the latest technology articles on their smartphone.

[0651] An example of a prompt for a generative AI model might be, "How can we provide articles about the latest technologies based on the user's interests?" Based on this prompt, the AI ​​will provide the necessary information.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The server uses information acquisition devices to access news APIs and social networking service APIs to collect electronic information. Keywords such as "technology" are specified as input. In this process, a huge amount of raw data is acquired via APIs, and streaming data is generated as output.

[0655] Step 2:

[0656] The server receives the acquired electronic information and uses natural language processing (NLP) techniques to select data related to a specific theme. The input is the streaming data obtained in step 1, and through the data analysis process, it extracts relevant articles and posts, generating an organized dataset as output.

[0657] Step 3:

[0658] The server processes the organized dataset using a generative AI model to create personalized information based on individual interests. This step utilizes the user's profile information and the dataset obtained in step 2 as input. A machine learning algorithm is executed, generating a customized information feed as output.

[0659] Step 4:

[0660] The device receives a feed sent from the server and delivers information to the user via push notifications. The input is the feed created in step 3. The device displays the notification and, as output, provides the user with a means to access articles and information of interest.

[0661] Step 5:

[0662] Users can view information received via their devices, read articles, rate them, or leave comments. Each action is recorded on the device as input. These interactions are sent to the server as output and used as data to help with future profile updates and information feed generation.

[0663] (Application Example 1)

[0664] 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".

[0665] In today's information-saturated world, users are required to access information of interest quickly and accurately. However, finding relevant information from the vast amount of data is not easy and currently requires time and effort. Furthermore, while users access information using a variety of devices, the information provided is not always optimized for each device.

[0666] 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.

[0667] In this invention, the server includes an acquisition means for acquiring digital information from an information source, an analysis means for analyzing the digital information acquired by the acquisition means to extract data related to a specific topic, and an update means for acquiring user evaluations and reactions and updating the user's profile based on them. This makes it possible to generate personalized feeds based on user interests and provide them quickly in a format optimized for portable information devices.

[0668] "Information sources" refer to the various data providers from which digital information is obtained.

[0669] "Digital information" refers to news articles, blog posts, and social media posts that are expressed in electronic format.

[0670] "Means of acquisition" refers to the methods and mechanisms for collecting digital information from information sources.

[0671] "Analysis means" refers to methods or mechanisms for analyzing acquired digital information and selecting data related to a specific topic.

[0672] "Generation means" refers to methods and mechanisms that filter information according to the user's interests and create personalized feeds.

[0673] "Portable information devices" refer to portable information terminals such as smartphones and tablets.

[0674] "Transmission method" refers to the method or mechanism for delivering the generated feed to users.

[0675] "Update methods" refer to methods and mechanisms that improve profiles based on user evaluations and feedback, thereby enhancing the accuracy of personalization.

[0676] A "profile" is a unique piece of information data created based on a user's interests and behavioral history.

[0677] A "feed" is a stream of information that organizes and continuously provides analyzed digital information to users.

[0678] The system for implementing this invention consists primarily of a server, a terminal, and a user interface. The server is programmed, for example, using Python and the Django framework, and retrieves digital information from information sources. This retrieval is performed via various news APIs and social networking APIs. For information analysis, neural network libraries such as TensorFlow are used to extract data related to specific topics and reduce noisy information. Through this process, data tailored to the user's interests is accumulated on the server.

[0679] The device is designed as a smartphone or tablet application and receives personalized feeds generated by the server. When a user views the feed and rates or comments on articles, the device sends that information back to the server, updating the user's profile. Through this cyclical process, the feed becomes increasingly tailored to the user's interests.

[0680] Users can quickly access the latest information through their mobile devices. Even if they have multiple topics of interest, the system automatically adjusts the information accordingly, allowing users to acquire information efficiently. Furthermore, push notifications to smartphones enable immediate viewing of new feeds.

[0681] For example, if a user expresses interest in "technology," the system will gather the latest information related to AI technology and digital gadgets, tailored to that interest, and provide a personalized feed through the mobile app. The prompt to the generative AI model at this time would be something like, "Retrieve news articles on the latest AI technology and add them to the user's feed."

[0682] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0683] Step 1:

[0684] The server retrieves digital information from news APIs and social networking service APIs. Input is pre-configured keywords, and data obtained by sending API requests is output. At this time, an API client library is used to accurately collect the necessary data.

[0685] Step 2:

[0686] The server analyzes the acquired digital information. The input is the data collected in step 1, and the output is important data related to a specific topic. Here, TensorFlow is used to analyze patterns in the data and remove irrelevant information.

[0687] Step 3:

[0688] The server filters the data based on the analysis results to fit the user's interests and generates a personalized feed. The input is the output data from step 2, and a feed based on the user profile is output. The filtering process is optimized using a generative AI model.

[0689] Step 4:

[0690] The server sends the generated personalized feed to the device. The input is the feed from step 3, which is displayed as a feed on the device. Here, push notifications are enabled to quickly deliver the latest information to the user.

[0691] Step 5:

[0692] The device displays the received personalized feed to the user. The input is the feed sent from the server, and the output is a visual presentation to the user. Information is provided in an easy-to-understand and visually appealing format through the UI.

[0693] Step 6:

[0694] Users view information within the feed and provide ratings and comments. The input is the articles being fed, and the output is rating and comment data. User ratings are recorded to help generate future feeds.

[0695] Step 7:

[0696] The server incorporates user ratings and comments into the user profile. The input is the data obtained in step 6, and the output is the updated profile data. This data will be used for future feed generation.

[0697] 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.

[0698] This invention combines an emotion engine with a system that acquires digital information from information sources and provides it to users as a personalized feed. The emotion engine has the function of analyzing the user's emotional state and adjusting the content of the feed accordingly. This invention mainly consists of a server, a terminal, and a user interface.

[0699] Server Role

[0700] The server has the function of regularly collecting digital information from news sites and social media. The collected information is analyzed and classified based on specific topics. This analysis process uses natural language processing technology to evaluate the information and confirm its relevance to topics.

[0701] The server further uses an emotion engine to infer the user's emotional state. This emotion engine analyzes emotions from the user's content creation (e.g., comments and posts), voice, and images to identify the user's current psychological state. This information, along with the user profile and behavioral history, is used to personalize the feed.

[0702] Terminal role

[0703] The device is responsible for notifying and displaying personalized feeds provided by the server to the user. The feeds are adjusted according to the user's emotional state, and content appropriate to a specific emotion is highlighted on the device.

[0704] For example, if a user is identified as experiencing feelings of joy, the device will prioritize displaying positive news and interesting articles. Conversely, if a user is determined to be experiencing stress, it will provide relaxing content and information that helps reduce stress.

[0705] User interaction

[0706] Users can access personalized feeds at any time via their devices. As users read and rate articles, new sentiment data and interest information are collected and sent to the server. This data influences the generation of future feeds.

[0707] For example, when a user views content and rates it as "fun," the emotion engine recognizes that emotion and adjusts future feeds to include more content that evokes similar feelings.

[0708] Thus, the present invention enables the provision of information tailored to the user's emotions and behavior, thereby realizing a more enriching user experience.

[0709] The following describes the processing flow.

[0710] Step 1:

[0711] The server regularly collects digital information from news sites and social media. Based on specific topics, it retrieves information using news APIs and web scraping techniques. For example, the server collects the latest articles related to technology.

[0712] Step 2:

[0713] The server analyzes the collected digital information and extracts important data. It performs natural language processing on the text to classify articles based on their relevance and topic. Then, it filters out irrelevant articles.

[0714] Step 3:

[0715] The server activates an emotion engine to identify the user's emotional state. It analyzes user-generated content and feedback, and uses that data to infer the user's current emotions.

[0716] Step 4:

[0717] The server filters the analysis results based on the user's profile information. In this process, it combines the user's past behavioral history with their current emotional state to select personalized information.

[0718] Step 5:

[0719] The server generates a personalized feed using filtered information and sends it to the device. The priority of the information provided is adjusted according to the user's emotional state.

[0720] Step 6:

[0721] The device displays a personalized feed received from the server to the user. The feed is configured to highlight relaxing information, for example, if the user is currently feeling stressed.

[0722] Step 7:

[0723] Users browse feeds displayed on their devices, read articles that interest them, and enter their thoughts and opinions. These interactions are sent back to the server and used to update the user's sentiment profile and behavioral history.

[0724] This process allows the system to provide information that takes into account the user's emotional state, resulting in a more personalized content experience.

[0725] (Example 2)

[0726] 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".

[0727] In today's information society, users are exposed to a vast amount of information, making it difficult to efficiently acquire information that is useful and of interest to them. Furthermore, there is a lack of systems that present information while taking into account the user's momentary emotional state, so there is a need to provide personalized feedback that responds to the user's emotions.

[0728] 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.

[0729] In this invention, the server includes a collection means for collecting data from information sources, an analysis means for analyzing the data acquired by the collection means and extracting information related to a specific classification, and a generation means for adjusting the information extracted by the analysis means based on the user's emotional state and generating personalized content. As a result, the user can receive information optimized for their emotional state at that moment, enabling them to acquire more meaningful information.

[0730] "Collection means" refers to technologies that have the function of acquiring data from information sources.

[0731] "Analysis means" refers to techniques for processing collected data and identifying and extracting information related to a specific classification.

[0732] "Generation means" refers to technology that has the function of adjusting information analyzed based on the user's emotional state to create personalized content.

[0733] "Transmission means" refers to technology that has the function of notifying the user's device of the generated content.

[0734] "Emotional state" refers to information that indicates the user's current psychological reactions and feelings.

[0735] "Personalized content" refers to information that is customized according to the user's specific needs and circumstances.

[0736] The server periodically acquires digital information using web scraping techniques and APIs for information gathering. News sites and social media serve as sources of information. This data is analyzed using natural language processing techniques and associated with specific topics. Specifically, the information is classified through topic modeling and keyword extraction.

[0737] The analyzed information is further processed by an emotion engine. This engine analyzes the user's past posts, comments, browsing history, and audio data to infer the user's emotional state. Text and audio analysis software is used for emotion analysis, and emotion tags such as positive, negative, and neutral are generated.

[0738] Based on the generated sentiment data and analyzed information, the server generates content tailored to the user. In this process, a generative AI model is used to create a personalized feed. An example of a prompt given to the generative AI is, "Find news that matches the user's current sentiment state."

[0739] The device is responsible for notifying the user of personalized feeds sent from the server. The feeds are displayed with content that matches the user's emotional state highlighted. For example, a user who is feeling stressed will be shown music and articles that help with relaxation.

[0740] Users can view feeds through their devices and rate the content. These interactions are stored on the server side as new sentiment data and used to generate future feeds. This feedback loop enables the provision of more relevant information to users.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The server periodically collects digital information from news sites and social media using web scraping techniques and APIs. The input is raw text data obtained from each information source. The output is the collected data stored in a primary database.

[0744] Step 2:

[0745] The server analyzes the collected data using natural language processing techniques. Specifically, it uses topic modeling to extract key categories and topics from the text data. The input is the raw text data obtained in step 1, and the output is data categorized by topic.

[0746] Step 3:

[0747] The server analyzes the user's emotional state using an emotion engine based on the user's past behavior data, posts, and comments. This analysis includes text analysis and voice analysis. The input is the user's behavior history data, and the output is an estimated emotion tag (positive, negative, neutral, etc.).

[0748] Step 4:

[0749] The server generates personalized feeds using a generative AI model based on the analyzed information and the estimated user's emotional state. Specifically, it takes the prompt "Find news that matches the current user's emotional state" as input to the AI ​​model and generates a feed as output.

[0750] Step 5:

[0751] The device receives a personalized feed sent from the server, notifies the user, and displays it. The input is the feed generated in step 4, and the output is a customized news feed displayed on the user's screen. The display method includes visual highlights and designs that correspond to the user's emotional state.

[0752] Step 6:

[0753] Users view feeds through their devices and rate each piece of content. These ratings (e.g., feedback such as "fun" or "informative") are sent back to the server and used for sentiment analysis and feed generation in the future. The input is user rating data, and the output is updated sentiment data and feedback data.

[0754] (Application Example 2)

[0755] 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".

[0756] Traditional information distribution systems often provide feeds without considering the user's emotional state, resulting in a lack of content tailored to the user's psychological condition. This leads to a challenge in improving user satisfaction.

[0757] 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.

[0758] In this invention, the server includes an acquisition means for acquiring digital information from an information source, an analysis means for analyzing the digital information acquired by the acquisition means and extracting data related to a specific topic, and an emotion analysis means for analyzing the user's emotional state and adjusting the data based on that emotion. This makes it possible to provide a personalized feed that corresponds to the user's emotional state.

[0759] "Means of acquisition" refers to a device or process equipped with the function of collecting digital information from an information source.

[0760] "Analysis means" refers to a device or process that has the function of analyzing acquired digital information and extracting data related to a specific topic.

[0761] "Emotional analysis means" refers to a device or process that analyzes the emotional state of a user and adjusts the data based on those emotions.

[0762] "Generation means" refers to a device or process that has the function of filtering adjusted data based on user interests and creating a personalized feed.

[0763] "Transmission means" refers to a device or process that has the function of sending the generated feed to the user's device.

[0764] The system that realizes this invention mainly consists of a server, a terminal, and a user.

[0765] The server operates using information acquisition means, analysis means, and sentiment analysis means. The information acquisition means periodically collects digital information from news sites and social media on the internet. The analysis means analyzes the collected information using natural language processing techniques and extracts data based on relevant topics. The sentiment analysis means the server analyzes text input and behavioral history from users to infer their emotional state. This sentiment analysis can also utilize voice and image analysis techniques.

[0766] The device receives a feed sent from the server and displays it to the user. The personalized feed is generated by a generation mechanism according to the user's interests and emotional state, and the device has the function to visually highlight and display it. For example, if the emotion analysis mechanism determines that the user is feeling stressed, the device will prioritize displaying relaxing content and information that helps reduce stress.

[0767] Users can view the feeds provided via their devices and provide ratings and feedback. This feedback is sent to the server and incorporated into the feed generation process for future updates.

[0768] For example, if a user enters "I'm a little tired today" into their device, the system recognizes that emotion and adjusts its feed to display music and videos suitable for relaxation. The generative AI model can apply this information using prompts such as the following:

[0769] "Please generate a suitable feed when a user types 'I'm a little tired today.' We'll particularly focus on relaxation and stress-relief content."

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The server uses information acquisition methods to obtain digital information from external sources. This involves data collection via APIs, temporarily storing data from news sites and social media. Data from the information sources is provided as input, and a collection of raw digital information is obtained as output.

[0773] Step 2:

[0774] The server uses analysis tools to analyze the acquired digital information based on natural language processing techniques and divide it into related topics. The input is the digital information obtained in step 1, and the output is a dataset classified by topic. This operation applies text analysis algorithms to perform keyword extraction and topic modeling.

[0775] Step 3:

[0776] The server analyzes the user's input text and past behavioral history to determine the user's emotional state using emotion analysis tools. The input consists of text and audio data from the user, and the output is the inferred emotional state. This analysis uses an emotion AI model to extract emotions from the text and audio.

[0777] Step 4:

[0778] The server uses a generation mechanism to personalize the feed based on the emotional state obtained in the previous step and the user's interests. The input is data organized by topic and the user's emotional state, and the output is a customized feed. A data filtering algorithm is used to appropriately select information based on emotions and interests.

[0779] Step 5:

[0780] The device receives a personalized feed sent from the server via a transmission method and displays it to the user. The input is the personalized feed from the server, and the output is a visual display of the feed for the user. The feed is displayed through the user interface, and the user is notified using push notifications.

[0781] Step 6:

[0782] Users view feeds via their devices and provide feedback. This feedback is returned to the server and influences future feed generation. Input is the user's browsing information and feedback, and output is an updated user profile. User ratings and comments are incorporated as feedback into the feed generation engine.

[0783] 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.

[0784] 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 those described above. 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 shown 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.

[0785] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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."

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0804] The following is further disclosed regarding the embodiments described above.

[0805] (Claim 1)

[0806] means of acquiring digital information from information sources,

[0807] An analysis means for analyzing digital information acquired by the acquisition means and extracting data related to a specific topic,

[0808] A generation means that filters the data extracted by the analysis means based on the user's interests and generates a personalized feed,

[0809] A transmission means for sending the feed generated by the generation means to the user's terminal,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, wherein the user's interests are updated based on the user's behavioral history.

[0813] (Claim 3)

[0814] The system according to claim 1, wherein the transmission means has a function to send push notifications to the user's terminal.

[0815] "Example 1"

[0816] (Claim 1)

[0817] Means for collecting electronic information from an information source via an information acquisition device,

[0818] A means for processing electronic information acquired by the aforementioned collection means and selecting items related to a specific theme,

[0819] A device that creates personalized information by organizing items selected by the aforementioned selection means based on an individual's interests,

[0820] A means for sending the aforementioned created information to a personal device,

[0821] A means for recording the viewing and reactions to the aforementioned information and updating an individual's profile,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the individual's interests are updated based on the individual's past usage history.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the sending means has a function to send notifications to an individual's device.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] means of acquiring digital information from information sources,

[0830] An analysis means for analyzing digital information acquired by the acquisition means and extracting data related to a specific topic,

[0831] A generation means that filters the data extracted by the analysis means based on the user's interests and generates a personalized feed,

[0832] A transmission means for transmitting the feed generated by the generation means to the user's portable information device,

[0833] An update mechanism that obtains user ratings and feedback and updates the user profile based on them,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein the updating means refines the profile based on the user's behavior history and contributes to feed generation.

[0837] (Claim 3)

[0838] The system according to claim 1, wherein the transmission means has a function to send push notifications to the user's portable information device, and is applicable to a content distribution service.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] Data collection methods for gathering data from information sources,

[0842] An analysis means for analyzing data acquired by the aforementioned collection means and extracting information related to a specific classification,

[0843] A generation means that adjusts the information extracted by the analysis means based on the user's emotional state and generates personalized content,

[0844] A transmission means for notifying the user's device of the content generated by the generation means,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the user's emotional state is updated based on behavioral history and emotion analysis data.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the transmission means has a function of providing visual emphasis to the user's device.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] means of acquiring digital information from information sources,

[0853] An analysis means for analyzing digital information acquired by the acquisition means and extracting data related to a specific topic,

[0854] An emotion analysis means that analyzes the emotional state of the user and adjusts the data based on that emotion,

[0855] A generation means that filters the data adjusted by the aforementioned sentiment analysis means based on the user's interests and generates a personalized feed,

[0856] A transmission means for transmitting the feed generated by the generation means to the user's device,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the user's interests are updated based on the user's emotional state and behavioral history.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the transmission means has a function to send push notifications to the user's device and highlights content that corresponds to the user's emotions. [Explanation of Symbols]

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

Claims

1. means of acquiring digital information from information sources, An analysis means for analyzing digital information acquired by the acquisition means and extracting data related to a specific topic, A generation means that filters the data extracted by the analysis means based on the user's interests and generates a personalized feed, A transmission means for sending the feed generated by the generation means to the user's terminal, A system that includes this.

2. The system according to claim 1, wherein the user's interests are updated based on the user's behavioral history.

3. The system according to claim 1, wherein the transmission means has a function to send push notifications to the user's terminal.

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