system

Abstract

Provide a system. A method for collecting news articles, A means of sentiment analysis of collected news articles; A means of extracting positive news articles based on sentiment analysis; a means of summarizing positive news articles; means for setting and storing user profile information; means for selecting positive news articles based on the stored profile information; and means for providing the selected news articles to a user.

Description

[Technical Field]

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

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

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

[0004] Current news sites and news feeds often use algorithms that display negative news at the top of the list, limiting users' opportunities to receive positive news. This often leads to stress and anxiety. Furthermore, there is a lack of customization to provide news that matches the user's individual interests and preferences. It is necessary to solve these issues and provide a customizable news delivery system that allows users to receive information from a positive perspective. [Means for solving the problem]

[0005] The present invention provides a system including means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, and means for providing the selected news articles to the user, thereby enabling the user to always receive news that matches their individual interests from a positive perspective and reducing the stress and anxiety that accompanies it.

[0006] "Means for collecting news articles" refers to system components such as programs and APIs for obtaining article data from news sources on the Internet.

[0007] "Sentiment analysis means" refers to algorithms or programs that use natural language processing techniques to analyze the content of news articles and classify the content as positive, negative, or neutral.

[0008] "Means for extracting positive news articles" refers to a program or processing flow for selecting news articles that are judged to be positive based on the results of sentiment analysis.

[0009] "Summarization methods" refers to technologies and programs that use generative AI or other summarization algorithms to concisely summarize the extracted positive news article content.

[0010] "Means for setting and storing user profile information" refers to a system component that records information about preferences and interests entered by a user using a terminal and stores the information in a database or the like.

[0011] "Means for selecting news articles based on profile information" refers to a program or algorithm that references stored user profile information and selects positive news articles that match that information.

[0012] "Means for providing news stories to a user" refers to a system component for displaying or communicating selected positive news stories to a user via a device, web application, or the like. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

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

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

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

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0034] This system is designed to enable users to receive news from a positive perspective, and a series of processes, such as news article collection, sentiment analysis, summary generation, user profile setting and storage, and news selection and provision, are carried out by the server.

[0035] News article collection

[0036] The server collects the latest news articles from news sources. For example, it uses a news API to obtain news article data using the following procedure. A system is built to periodically collect the latest news using an endpoint and authentication key to access the API.

[0037] Conducting sentiment analysis

[0038] The server performs sentiment analysis on the collected news articles. Using natural language processing technology, it analyzes the content of the articles and classifies them as positive, negative, or neutral. This allows only positive news articles that are useful to the user to proceed to the next step.

[0039] Summary Generation

[0040] The server extracts positive news articles based on sentiment analysis and summarizes them using generative AI technology and summarization algorithms to concisely summarize the article content, allowing users to quickly and effectively obtain information.

[0041] Setting and saving a user profile

[0042] Users can access the system through their devices and set news categories based on their interests and preferences. The server then stores the user's input in a database. The stored profile information is then used to customize each user's individual news feed.

[0043] News selection and provision

[0044] The server selects summaries of positive news articles that match the user's interests based on the user's profile information. The selected news is formatted as a feed and provided to the user via their device. The user can then view the customized positive news on their device.

[0045] Specific examples

[0046] For example, the server collects 50 article data from a news API at 8:00 a.m. Next, it uses natural language processing technology to perform sentiment analysis on the articles and extracts 30 positive articles. The articles are then summarized using generative AI to create 30 summary articles. User A uses his device to set his interests in "technology" and "health" in his profile. The server stores the profile information set by User A in a database and, based on this, selects summary articles related to "technology" and "health" as a feed. As a result, User A can receive positive news that matches his interests through a customized feed displayed on his device.

[0047] This system allows users to efficiently obtain positive information that matches their interests, helping to reduce daily stress and anxiety.

[0048] The processing flow will be explained below.

[0049] Step 1:

[0050] The server collects the latest news articles from news sources. Specifically, it uses a news API to obtain news article data. It uses an authentication key for the API and performs a process to collect, for example, 50 articles at a fixed time every day.

[0051] Step 2:

[0052] The server uses natural language processing technology to analyze the sentiment of collected news articles. For example, it uses sentiment analysis libraries such as TextBlob and VADER to classify each article as positive, negative, or neutral. Only articles judged as positive are stored in a list.

[0053] Step 3:

[0054] The server then performs a summarization process on the positively identified news articles, using generative AI and summarization algorithms to concisely summarize the content of each article, which is then stored in a list.

[0055] Step 4:

[0056] Users access the system using their terminals and configure their news feed settings, such as selecting categories of interest (technology, health, entertainment, etc.), and sending this information to the server. The server then stores the selected data as the user's profile information.

[0057] Step 5:

[0058] The server stores the user's profile information in a database. The stored profile information is used to customize the news feed. For example, if user A is interested in "technology" and "health," this preference is recorded as profile information.

[0059] Step 6:

[0060] The server selects relevant articles from the summarized positive news articles based on the user's profile information. The selection criteria are news articles that match the user-specified categories. This generates a list of positive news articles tailored to the user's interests.

[0061] Step 7:

[0062] The server then provides the selected news articles to the user's device, allowing the user to view a customized positive news feed from their device. For example, news items displayed as a feed are formatted to show the title and summary.

[0063] In this way, the server, the terminal, and the user work together to realize a mechanism for providing a news feed customized from a positive perspective.

[0064] Example 1

[0065] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0066] In modern society, a huge amount of news articles are available on the Internet, many of which contain negative content. Exposure to such negative news can increase users' psychological burden and cause stress and anxiety. Furthermore, there is a lack of systems in place that can efficiently collect news in categories of interest to users and provide only positive articles based on that information. There is a need to address these issues.

[0067] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0068] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for providing the selected news articles to the user, means for summarizing the news articles using a generative AI model, and means for filtering news articles based on the user's setting information. This allows the user to be provided with only positive news articles that match their interests and preferences, thereby reducing daily stress and anxiety.

[0069] A "news article" is a piece of writing that reports events or information collected from the internet or other sources.

[0070] "Means of collection" refers to the functionality for obtaining news articles from news sources on the Internet.

[0071] "Means for sentiment analysis" refers to a function for analyzing the content of a news article and identifying its emotional nature.

[0072] "Positive news articles" refer to news articles that are determined to have a positive sentiment as a result of sentiment analysis.

[0073] "Means for extracting" refers to a function for selecting target data based on specific criteria.

[0074] "Means for summarizing" refers to the function of concisely summarizing the contents of a news article.

[0075] "User Profile Information" means data about a user's interests and preferences that is used to customize news delivery.

[0076] "Means for setting and saving" refers to the functionality that allows a user to input profile information and save that information.

[0077] "Means for selection" refers to the functionality for selecting appropriate news articles based on stored profile information.

[0078] "Means for providing" refers to a function for displaying selected news articles to a user.

[0079] A "generative AI model" refers to a computer model that uses artificial intelligence technology to create new data and information.

[0080] "Filtering means" refers to a function for filtering data based on specific criteria.

[0081] The system of this invention is designed to enable users to receive news from a positive perspective. A series of processes, including news article collection, sentiment analysis, summary generation, user profile setting and storage, and news selection and provision, are performed by the server.

[0082] The server collects the latest news articles from news sources on the Internet. For this purpose, the server leverages APIs to obtain news data. For example, it uses the NewsAPI.org endpoint and API key to periodically collect the latest news in JSON format.

[0083] The server then performs sentiment analysis on the collected news articles, using NLP libraries (e.g., NLTK, spaCy) and sentiment analysis models (e.g., TextBlob, Google®'s Sentiment Analysis API) to classify articles as positive, negative, or neutral.

[0084] For news articles that are judged to be positive as a result of sentiment analysis, the server summarizes them. Generative AI techniques (e.g., OpenAI's GPT-3) can be used to concisely summarize the article's content. Other summarization algorithms (e.g., TextRank, BERT Summarizer) may also be used.

[0085] Users access the system through their terminals and set news categories based on their interests and preferences. The information entered by the user during this setting process is stored by the server in a database (e.g., MySQL (registered trademark), MongoDB). The saved profile information is used to customize the news feed later.

[0086] The server selects positive news articles that match the user's interests from the summarized articles based on the user's profile information. The selected articles are formatted as a feed and provided to the user via the device. The user can then view the customized positive news on the device.

[0087] As a concrete example, the server collects 50 news article data from a news API at 8:00 every morning and performs sentiment analysis to extract 30 positive articles. Next, it uses generative AI to summarize these positive articles and create 30 summary articles. User A uses his device to set his profile as being interested in "technology" and "health." Based on this profile information, the server selects summary articles related to "technology" and "health" as a feed and provides them to User A. As a result, User A can receive positive news that matches his interests through his device.

[0088] An example prompt is:

[0089] "Collect and summarize positive stories from today's latest news. News categories based on user interests: Technology and Health."

[0090] This system allows users to efficiently obtain positive information that is tailored to their interests, helping to reduce daily stress and anxiety.

[0091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0092] Step 1: Gather news articles

[0093] explanation:

[0094] The server collects the latest news articles from news sources. Specifically, the server periodically sends requests to news APIs on the Internet to obtain news article data.

[0095] input:

[0096] News API endpoint and authentication key

[0097] output:

[0098] News article data in JSON format

[0099] Specific behavior:

[0100] For example, the server accesses the NewsAPI.org endpoint at 8:00 AM and retrieves 50 news articles, giving the server the latest news article information.

[0101] Step 2: Conducting sentiment analysis

[0102] explanation:

[0103] The server performs sentiment analysis on the collected news articles. It uses natural language processing techniques to analyze the text of each article and identify its emotional nature.

[0104] input:

[0105] News article text data

[0106] output:

[0107] Sentiment analysis results (positive, negative, neutral classification)

[0108] Specific behavior:

[0109] For example, the server uses TextBlob to calculate sentiment scores for 50 news articles and classify them as positive, negative, or neutral, allowing the server to identify positive news articles.

[0110] Step 3: Extracting positive news articles

[0111] explanation:

[0112] The server extracts only positive news articles based on the results of sentiment analysis.

[0113] input:

[0114] Sentiment analysis results

[0115] output:

[0116] Positive news articles

[0117] Specific behavior:

[0118] The server extracts news articles that are judged as positive by sentiment analysis and excludes other news articles. For example, if 30 out of 50 articles are judged as positive, these 30 news articles will be extracted.

[0119] Step 4: Generate a summary of the news article

[0120] explanation:

[0121] The server summarizes positive news articles, using a generative AI model to concisely summarize the article content.

[0122] input:

[0123] Positive news articles

[0124] output:

[0125] Summarized news articles

[0126] Specific behavior:

[0127] For example, the server uses GPT-3 to summarize 30 positive news articles, helping users quickly understand the news content.

[0128] Step 5: Set up and save your user profile

[0129] explanation:

[0130] Users set news categories through their terminals, and the server stores the information.

[0131] input:

[0132] User input information (interests and preferred news categories)

[0133] output:

[0134] Stored User Profile Information

[0135] Specific behavior:

[0136] For example, User A selects the categories "Technology" and "Health" on his device. The server stores this information in a database.

[0137] Step 6: News selection and delivery

[0138] explanation:

[0139] Based on the user's profile information, the server selects and provides summarized positive news articles that match the user's interests.

[0140] input:

[0141] Summarized news articles, user profile information

[0142] output:

[0143] News feeds provided to users

[0144] Specific behavior:

[0145] For example, the server selects summary articles related to "technology" and "health" based on User A's profile information and sends them to the device. User A can then browse the positive news that interests them through a customized feed displayed on the device.

[0146] Through this series of processes, the system allows users to reduce stress and anxiety in their daily lives and efficiently obtain positive information.

[0147] (Application example 1)

[0148] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0149] In today's world, people have access to a wide range of news articles, but many of them contain negative content, which can increase stress and anxiety. Furthermore, there is a lack of systems that efficiently provide news articles that match users' interests and preferences, which means that users spend a lot of time obtaining the information that is most relevant to them. Furthermore, effective news delivery methods using visual devices such as smart glasses and head-mounted displays have yet to be established.

[0150] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0151] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for customizing news articles based on the user's interests and preferences, and means for providing the customized news articles to the user via the visual terminal, thereby enabling the user to effectively obtain positive news articles based on their interests and preferences and obtain information through the visual terminal while reducing stress and anxiety.

[0152] "Means of collecting news articles" refers to technologies such as APIs and web crawlers that regularly collect the latest articles from news sources on the Internet.

[0153] "Means for sentiment analysis" is a function that uses natural language processing technology to classify the content of news articles into positive, negative, or neutral.

[0154] "Method for extracting positive news articles" is a process for selecting only articles that contain positive emotions from the collected and analyzed news articles.

[0155] "A means of summarizing positive news articles" is a function that concisely summarizes selected positive news articles using generative AI technology and summarization algorithms.

[0156] "Means for establishing and storing user profile information" refers to a database or interface that allows users to register their interests and preferences and store them in the system.

[0157] A "positive news story selection method" is a process that selects news stories that match a user's interests and preferences based on a stored user profile.

[0158] The "means for customizing news articles" is a function that provides news articles in an appropriate format that are individually suited to the user based on the user's profile information.

[0159] "Means of providing to users via visual terminals" refers to technology that displays news articles through wearable devices such as smart glasses or head-mounted displays, providing them visually to users.

[0160] To implement this invention, we will build a system that collects news articles, analyzes sentiment, summarizes them, sets and saves user profiles, and selects and provides articles. The specific implementation method of the system is shown below.

[0161] The server uses the news API to collect the latest news articles from news sources on the Internet. It periodically retrieves news article data using an endpoint and authentication key to access the API. This data is stored in storage for further processing.

[0162] Next, the server performs sentiment analysis on the collected news articles using natural language processing technology. For sentiment analysis, it uses a common natural language processing library (e.g., Transformers) to classify the content of the article as positive, negative, or neutral. Through this process, only positive news articles that are useful to users are allowed to proceed to the next step.

[0163] Positive news articles are summarized using generative AI techniques and summarization algorithms (e.g., Transformer generative AI models), and the summarized articles are converted into a format that can be delivered to users as needed and managed within the system.

[0164] Users access the system through their devices and set news categories based on their interests and preferences. This profile information is stored in a database and used to customize each user's personal news feed.

[0165] Based on the saved profile information, the system selects summaries of positive news articles that match the user's interests. The selected news is formatted as a feed and delivered to the user via their device (smartphone, smart glasses, head-mounted display), allowing the user to browse customized positive news.

[0166] For example, the server collects 50 article data from a news API at 8:00 a.m. It then uses natural language processing technology to perform sentiment analysis and extract 30 positive articles. The articles are then summarized using generative AI to create 30 summary articles. User A uses their device to set their profile as being interested in "technology" and "health." The server stores the profile information set by User A in a database and, based on this, selects summary articles related to "technology" and "health" as a feed. As a result, User A can receive positive news that matches their interests through a customized feed displayed on their device.

[0167] An example of a prompt to input to a generative AI model is as follows:

[0168] text

[0169] Recent technological advancements have been astounding, with artificial intelligence playing an increasingly important role. For example, new AI solutions are...

[0170] By inputting this prompt into a generative AI model, a summary appropriate to the prompt can be obtained.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] The server collects the latest articles from news sources on the Internet. Specifically, it accesses the news API and retrieves the latest news data using the endpoint and authentication key. This data is sent to the server in JSON format. The data retrieved from the news API is temporarily stored in storage.

[0174] Input: News API endpoint and authentication key

[0175] Output: News article data in JSON format

[0176] Step 2:

[0177] The server performs sentiment analysis on the collected news articles using natural language processing techniques. Specifically, it extracts the text of the news articles and classifies them into positive, negative, or neutral sentiment using an NLP library (e.g., Transformers). This classification result is tagged with each news article.

[0178] Input: News article data in JSON format

[0179] Output: News article data with sentiment tags

[0180] Step 3:

[0181] The server extracts positive news articles based on sentiment analysis, filters out only those tagged as positive, and creates a list for further processing steps.

[0182] Input: sentiment-tagged news article data

[0183] Output: A list of positive news articles

[0184] Step 4:

[0185] The server summarizes positive news articles. Using generative AI techniques (e.g., Transformer generative AI models), it concisely summarizes the content of each news article. The generated summaries are stored as a single text field.

[0186] Input: A list of positive news articles

[0187] Output: A list of summarized positive news articles

[0188] Step 5:

[0189] Users can set news categories based on their interests and preferences through their devices. They input and set the selected category information as a user profile. This information is sent to the server and stored in a database.

[0190] Input: Category information about your interests and preferences

[0191] Output: User profile information stored in a database

[0192] Step 6:

[0193] The server selects summarized positive news articles that match the user's interests based on the stored user profile information, and generates a customized news feed by applying appropriate filtering and matching based on the user profile information.

[0194] Input: List of summarized positive news articles, user profile information

[0195] Output: A customized news feed

[0196] Step 7:

[0197] The server provides selected news articles to users through their devices, displaying them on visual devices such as smart glasses or head-mounted displays. Users can use these visual devices to browse customized positive news in real time.

[0198] Input: Customized News Feed

[0199] Output: News article displayed on terminal

[0200] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0201] This system is designed to help users receive news from a positive perspective. It collects news articles, analyzes sentiment, generates summaries, sets and saves user profiles, selects and delivers news, and incorporates an emotion engine that recognizes users' emotions to further customize the content of the news feed.

[0202] News article collection

[0203] The server collects the latest news articles from news sources on the Internet using an API. For example, it periodically retrieves news articles using an authentication key for the news API. This allows you to always have the latest current information.

[0204] Conducting sentiment analysis

[0205] The server performs sentiment analysis on the collected news articles using natural language processing technology. It analyzes the content of the articles and classifies them as positive, negative, or neutral. For sentiment analysis, it uses sentiment analysis libraries such as TextBlob and VADER. Articles that are determined to be positive proceed to the next processing step.

[0206] Summary Generation

[0207] The server summarizes the extracted positive news articles based on sentiment analysis, and uses generative AI and summarization algorithms to concisely summarize the article content, allowing users to quickly and effectively grasp the main points of the news.

[0208] Setting and saving a user profile

[0209] Users access the system using their terminals and configure their news feed settings, such as selecting categories of interest (e.g., technology, health, entertainment), and submitting them to the server, which stores the user's profile information in a database and uses it to customize their news feed.

[0210] Introducing the Emotion Engine

[0211] The emotion engine uses techniques to analyze the user's facial expressions and voice to recognize the user's emotional state. For example, it uses a camera to capture the user's facial expressions and uses an expression analysis algorithm to determine the emotion, or it collects the user's voice through a microphone and analyzes the tone and rate of the voice to determine the emotion.

[0212] News selection and provision

[0213] The server combines the user's profile information with the user's emotional state as recognized by the emotion engine to select appropriate summaries of positive news articles. If the user is feeling stressed, the server will prioritize news articles with particularly positive content.

[0214] Specific examples

[0215] For example, the server collects 50 articles from the news API at 8:00 a.m., extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his device to set his interests in "technology" and "health" in his profile, and the server stores this information in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his interests and emotional state.

[0216] This system allows users to efficiently obtain positive information that is tailored to their interests and emotional state, helping to reduce daily stress and anxiety.

[0217] The processing flow will be explained below.

[0218] Step 1:

[0219] The server collects the latest news articles from news sources on the Internet. Specifically, it uses a news API to periodically send requests to a configured endpoint to obtain data on the latest news articles. This allows new information to be collected automatically every day.

[0220] Step 2:

[0221] The server performs sentiment analysis on the collected news articles using natural language processing technology. For example, it uses a sentiment analysis library to analyze the text of each news article and classify it as positive, negative, or neutral. Only news articles judged as positive are stored in a list.

[0222] Step 3:

[0223] The server uses generative AI to generate summaries for news articles that are positively identified, and the summaries are stored in a database along with the original articles, allowing users to quickly grasp key information.

[0224] Step 4:

[0225] Users access the system using their devices and configure their news feeds to reflect their preferences and interests. Specifically, they select news categories (e.g., technology, health, entertainment, etc.) and send the configuration information to the server.

[0226] Step 5:

[0227] The server stores user profile information in a database, which is used as the basis for customizing a user's individual news feed.

[0228] Step 6:

[0229] The user uses the device's camera and microphone to have the emotion engine recognize their emotional state. The device's camera captures the user's facial expressions, and an expression analysis algorithm is used to determine the user's emotions (e.g., joy, sadness, stress). The user's voice is also analyzed via the microphone, and the emotional state is assessed based on the tone and rate of the voice.

[0230] Step 7:

[0231] The server combines the user's profile information with the emotional state obtained by the emotion engine to select appropriate summaries of positive news articles. For example, if the user is feeling stressed, it will prioritize news articles that will help them relax.

[0232] Step 8:

[0233] The server provides selected news articles to the user's device, where the user can view a customized news feed. The news feed displays titles and summaries, and the user can click on articles of interest to read more.

[0234] As a concrete example, the server collects 50 articles from the news API at 8:00 a.m., performs sentiment analysis to extract 30 positive articles, and generates summaries. User A uses a device to set his / her profile as interested in "technology" and "health," and the sentiment engine detects User A's stress. In this case, the server selects particularly relaxing, positive news related to technology and health and provides it to User A's device. User A can receive news that matches his / her interests and emotional state.

[0235] In this way, the system provides a more personalized positive news feed based on the user's profile information and real-time emotional state.

[0236] Example 2

[0237] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0238] In modern society, negative news can increase psychological stress and anxiety when users consume news. Users also face the challenge of finding positive news that suits them from the vast amount of information available. There is a need for a system that can solve these problems, reduce daily stress and anxiety, and enable users to efficiently obtain positive news.

[0239] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0240] In this invention, the server includes means for collecting news articles, means for performing sentiment analysis, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for recognizing the user's emotional state, means for selecting positive news articles based on the saved profile information and emotional state, and means for providing the selected news articles to the user, thereby enabling the user to efficiently obtain positive news that matches their interests and emotional state and reduce daily stress and anxiety.

[0241] A "news article" is a written piece of information that reports current events or happenings.

[0242] "Means of collection" refers to a function for automatically acquiring article data from news sources on the Internet.

[0243] "Sentiment analysis" is a process that uses natural language processing technology to analyze emotional tendencies from text data and classify them as positive, negative, neutral, etc.

[0244] A "positive news article" is a news article that is classified as positive as a result of sentiment analysis.

[0245] "Means for summarizing" is a function that succinctly summarizes the contents of a news article.

[0246] "User profile information" refers to data about interest categories and personal preferences that a user provides to the system.

[0247] "Means for setting and saving" refers to a function for storing user-selected categories and setting information within the system.

[0248] "Means for recognizing emotional state" refers to technology that analyzes data on the user's facial expressions and voice to determine their emotional state at that time.

[0249] A "selection means" is a function that selects appropriate news articles based on stored profile information and a perceived emotional state.

[0250] The "means of providing" is a function for delivering selected news articles to users.

[0251] This system is designed to help users receive news from a positive perspective. The system operates by combining the following steps: news article collection, sentiment analysis, summary generation, user profile creation and storage, emotion recognition engine to determine the user's emotional state, and news selection and provision. A detailed implementation example is shown below.

[0252] News article collection

[0253] The server periodically collects the latest news articles from news sources on the Internet using an API. For example, every morning at 8:00, it sends an HTTP request to a specific news API using an authentication key to retrieve the latest 50 news articles. The collected article data is stored in an internal database.

[0254] Conducting sentiment analysis

[0255] The server performs sentiment analysis on the collected news articles. It uses natural language processing techniques such as TextBlob and VADER to classify each news article as positive, negative, or neutral, allowing it to extract positive news articles that are appropriate for the user.

[0256] Summary Generation

[0257] The server summarizes the news articles that are identified as positive. It uses a generative AI model or summarization algorithm (e.g., using a generative AI model) to concisely summarize the article's content. An example prompt might be, "Classify the following news articles as positive, negative, or neutral, and generate summaries of the positive articles."

[0258] Setting and saving a user profile

[0259] Users access the system using an internet-connected device to configure their news feed preferences, for example by selecting categories of interest such as "technology" or "health," and then submitting their preferences to the server, which stores this information in a database.

[0260] Introducing the Emotion Engine

[0261] The server runs an emotion engine to recognize the user's emotional state. It uses a camera to capture the user's facial expressions and uses facial expression analysis algorithms (e.g., OpenCV or DeepFace) to determine the emotion. It also collects audio data through a microphone and analyzes the tone and rate of the audio to determine the emotional state.

[0262] News selection and provision

[0263] The server selects appropriate summaries of positive news articles based on the user's stored profile information and the user's emotional state as recognized by the emotion engine. For example, if the server determines that the user is feeling stressed, it will prioritize positive news articles that are particularly relaxing and provide them as a feed.

[0264] Specific examples

[0265] For example, at 8:00 a.m., the server collects 50 articles from the news API, extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his or her device to set his or her interests in "technology" and "health" in his or her profile and sends this information to the server. This information is stored in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his or her emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his or her interests and emotional state.

[0266] This system allows users to efficiently obtain positive information that matches their interests and emotional state, helping to reduce daily stress and anxiety.

[0267] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0268] Step 1: Gather news articles

[0269] The server uses an API to collect the latest news articles from news sources on the Internet. Specifically, it sends an HTTP request to a specific news API using an authentication key. For example, a request is sent every morning at 8:00 to retrieve the latest 50 news articles. The input is news article data retrieved from the API, and the output is raw data stored on the server.

[0270] Step 2: Conducting sentiment analysis

[0271] The server performs sentiment analysis on the collected news articles. Specifically, it uses natural language processing libraries such as TextBlob and VADER to analyze each news article. For example, TextBlob is used to calculate the sentiment score of the article text and classify it as positive, negative, or neutral. The input is the news article text, and the output is the classification result including the sentiment score.

[0272] Step 3: Extracting positive news articles

[0273] The server extracts news articles that are judged to be positive based on the results of sentiment analysis. Specifically, it filters only articles that are deemed positive based on the classification results of sentiment analysis. The input is the classification results including sentiment scores, and the output is a list of positive news articles.

[0274] Step 4: Summarize the positive news article

[0275] The server summarizes the extracted positive news articles. Specifically, it uses a generative AI model or summarization algorithm (e.g., using a generative AI model) to concisely summarize the article's content. For example, it sends a prompt to the generative AI model saying, "Please summarize the following news article," and obtains the summarized text. The input is the full text of the positive news article, and the output is the summarized text.

[0276] Step 5: Set up and save your user profile

[0277] A user accesses the system using a terminal and configures his or her news feed. Specifically, the user selects categories of interest (e.g., "technology" or "health") in a web form and sends it to the server. The server stores the received profile information in a database. The input is the user's selected categories and preferences, and the output is the profile information stored in the database.

[0278] Step 6: Implementing the Emotion Engine

[0279] The server runs an emotion engine to recognize the user's emotional state. Specifically, it uses the device's camera to capture the user's facial expression and uses an expression analysis algorithm (e.g., OpenCV or DeepFace) to determine the emotion. It also analyzes audio data collected through the microphone and determines the emotion from the tone and rate of the voice. The input is the user's facial expression data and audio data, and the output is the emotional state as a result of the analysis.

[0280] Step 7: Select and deliver news

[0281] The server selects the most appropriate positive news articles based on the user's profile information and recognized emotional state. Specifically, it references the profile information and emotional data from the database to filter and rank the most appropriate news articles. For users who are feeling stressed, it prioritizes articles that are particularly relaxing. The input is positive news articles, profile information, and emotional state, and the output is a list of selected news articles.

[0282] This processing step allows users to efficiently receive positive news that matches their interests and emotional state, and can also reduce daily stress and anxiety.

[0283] (Application example 2)

[0284] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0285] Conventional news delivery systems provide generic news articles uniformly without fully considering the user's emotional state or interests, which means that the information users receive is not always pleasant or useful. As a result, users may feel dissatisfied or stressed when browsing the news. Furthermore, the quality of the customer experience in physical stores has not improved due to a lack of technology that can recognize customers' emotional state in real time and provide appropriate information. Therefore, there is a need for a system that can provide appropriate and positive news and product information according to the user's emotional state and interests.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0287] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for using an emotion engine to recognize the user's emotional state, means for adjusting the news articles based on the emotional state recognized by the emotion engine, and means for providing the selected news articles to the user, thereby making it possible to provide positive news information and product information according to the user's emotional state and interests.

[0288] "Means for collecting news articles" refers to the function of obtaining article data from news sources on the Internet using an API.

[0289] "Means for sentiment analysis of collected news articles" refers to a function that uses natural language processing technology to classify the sentiment of an article and determine whether it is positive, negative, or neutral.

[0290] "Means for extracting positive news articles based on sentiment analysis" is a function that selects news articles that are determined to be positive as a result of sentiment analysis.

[0291] The "means for summarizing positive news articles" is a function that succinctly summarizes the content of news articles that are judged to be positive.

[0292] "Means for setting and saving user profile information" refers to the ability for users to input their interest categories and personal settings into the system and record that information in a database.

[0293] The "means for selecting positive news articles based on stored profile information" is a function that uses a user's profile information stored in the database to select the most appropriate positive news articles for that user.

[0294] "Means for using an emotion engine to recognize the user's emotional state" refers to a technology for analyzing the user's facial expressions and voice to determine the user's emotional state.

[0295] The "means for adjusting news articles based on the emotional state recognized by the emotion engine" is a function for changing the content and order of news articles provided depending on the emotional state of the user.

[0296] "Means for providing selected news articles to users" refers to a function that displays or provides audio guidance of the final selected news articles on the user's terminal or through a robot in a physical store.

[0297] The system of the present invention is designed to enable users to receive news from a positive perspective. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the preferred embodiments of the present invention.

[0298] News article collection

[0299] The server collects news articles from multiple news sources on the Internet. This collection is done using an API (Application Programming Interface) to efficiently retrieve article data. The API is called periodically, and the latest news articles are stored on the server.

[0300] Conducting sentiment analysis

[0301] The server then performs sentiment analysis on the collected news articles. This analysis uses natural language processing techniques. For example, it uses sentiment analysis libraries such as TextBlob or VADER to classify the content of each article as positive, negative, or neutral. Once the sentiment analysis is complete, news articles that are determined to be positive proceed to the next processing step.

[0302] Summary Generation

[0303] Once a news article is positively identified, it is processed for summary generation, using generative AI and summarization algorithms to concisely summarize the article's content, allowing users to quickly grasp the main points of the news. This process eliminates redundant information, leaving only the key points.

[0304] Setting and saving a user profile

[0305] Users access the system using their own devices and select categories of interest (e.g., technology, health, entertainment, etc.). User profile information is stored on the server and used to select news articles later.

[0306] Introducing the Emotion Engine

[0307] While a user is browsing the news, the emotion engine analyzes the user's emotional state in real time. The emotion engine captures the user's facial expressions through a camera and uses an expression analysis algorithm to determine their emotions. It also collects voice data through a microphone and analyzes the tone and speed of the voice to determine emotions. This analysis uses facial and voice recognition technologies.

[0308] News selection and provision

[0309] Finally, the server selects appropriate summaries of positive news articles based on the user's profile information and the emotional state recognized by the emotion engine. For example, if the server determines that the user is feeling stressed, it will prioritize positive news articles that will help them relax. The selected news articles are then displayed on the user's device or provided via a robot in a physical store.

[0310] Specific examples

[0311] For example, the server collects 50 articles from the news API at 8:00 a.m., extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his device to set his interests in "technology" and "health" in his profile, and the server stores this information in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his interests and emotional state.

[0312] Example of generative AI model and prompt

[0313] To improve the customer experience in physical stores, the system is installed on robots placed in physical stores and uses prompts such as:

[0314] "This in-store robot analyzes customers' faces and voices to provide positive health news and in-store product recommendations based on their emotional state."

[0315] "It uses facial and voice recognition to determine whether the customer is relaxed or stressed and then presents information that is appropriate for that state."

[0316] In this way, users can have a personal and positive experience when they visit the store.

[0317] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0318] Step 1:

[0319] The server collects news articles from news sources on the Internet using APIs. The input is the news API endpoint and API key, and the output is a list of the latest news articles. Specifically, the server periodically calls the API and stores the retrieved news article data in an internal database.

[0320] Step 2:

[0321] The server receives a list of news articles and performs sentiment analysis on each article. The input is a list of collected news articles, and the output is a list of news articles classified as positive, negative, or neutral. Specifically, the server uses sentiment analysis libraries such as TextBlob and VADER to analyze the content of each news article and calculate a sentiment score.

[0322] Step 3:

[0323] The server extracts news articles that are judged to be positive based on the results of sentiment analysis. The input is a list of news articles classified as positive, negative, or neutral, and the output is a list of positive news articles. Specifically, the server selects news articles from the list that have a sentiment score above a certain level.

[0324] Step 4:

[0325] The server generates summaries for news articles that are judged to be positive. The input is a list of positive news articles, and the output is a list of summarized positive news articles. Specifically, the server uses generative AI and summarization algorithms to extract and summarize the key points of each news article.

[0326] Step 5:

[0327] Users use their own devices to input, set, and save profile information. The input is the user's selected interest categories and personal settings, and the output is the set profile information being saved in the server's database. Specifically, users input information through the UI and send it to the server.

[0328] Step 6:

[0329] The server selects positive news articles based on user profile information. The input is a list of summarized positive news articles and the user's profile information, and the output is a list of news articles that are most suitable for the user. Specifically, the server references the profile information in the database and selects articles in categories that interest the user.

[0330] Step 7:

[0331] The user's device or a robot in a physical store recognizes the user's emotional state in real time. The input is image data and voice data of the user's face, and the output is the user's current emotional state. Specifically, the system uses a camera and microphone to analyze emotions using facial and voice recognition technology.

[0332] Step 8:

[0333] The server adjusts the news articles based on the emotional state recognized by the emotion engine. The input is the user's emotional state and a list of selected news articles, and the output is the adjusted list of news articles. Specifically, the server changes the priority of positive news articles according to the user's emotional state.

[0334] Step 9:

[0335] The selected news articles are provided to the user through the user's device or a robot in a brick-and-mortar store. The input is a tailored list of news articles, and the output is the news articles that are displayed or spoken to the user. In concrete terms, the device or robot provides the news articles to the user in an appropriate format.

[0336] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0337] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0338] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0339] [Second embodiment]

[0340] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0341] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0342] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0343] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0344] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0345] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0346] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0347] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0348] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0349] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0350] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0351] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0352] This system is designed to enable users to receive news from a positive perspective, and a series of processes, such as news article collection, sentiment analysis, summary generation, user profile setting and storage, and news selection and provision, are carried out by the server.

[0353] News article collection

[0354] The server collects the latest news articles from news sources. For example, it uses a news API to obtain news article data using the following procedure. A system is built to periodically collect the latest news using an endpoint and authentication key to access the API.

[0355] Conducting sentiment analysis

[0356] The server performs sentiment analysis on the collected news articles. Using natural language processing technology, it analyzes the content of the articles and classifies them as positive, negative, or neutral. This allows only positive news articles that are useful to the user to proceed to the next step.

[0357] Summary Generation

[0358] The server extracts positive news articles based on sentiment analysis and summarizes them using generative AI technology and summarization algorithms to concisely summarize the article content, allowing users to quickly and effectively obtain information.

[0359] Setting and saving a user profile

[0360] Users can access the system through their devices and set news categories based on their interests and preferences. The server then stores the user's input in a database. The stored profile information is then used to customize each user's individual news feed.

[0361] News selection and provision

[0362] The server selects summaries of positive news articles that match the user's interests based on the user's profile information. The selected news is formatted as a feed and provided to the user via their device. The user can then view the customized positive news on their device.

[0363] Specific examples

[0364] For example, the server collects 50 article data from a news API at 8:00 a.m. Next, it uses natural language processing technology to perform sentiment analysis on the articles and extracts 30 positive articles. The articles are then summarized using generative AI to create 30 summary articles. User A uses his device to set his interests in "technology" and "health" in his profile. The server stores the profile information set by User A in a database and, based on this, selects summary articles related to "technology" and "health" as a feed. As a result, User A can receive positive news that matches his interests through a customized feed displayed on his device.

[0365] This system allows users to efficiently obtain positive information that matches their interests, helping to reduce daily stress and anxiety.

[0366] The processing flow will be explained below.

[0367] Step 1:

[0368] The server collects the latest news articles from news sources. Specifically, it uses a news API to obtain news article data. It uses an authentication key for the API and performs a process to collect, for example, 50 articles at a fixed time every day.

[0369] Step 2:

[0370] The server uses natural language processing technology to analyze the sentiment of collected news articles. For example, it uses sentiment analysis libraries such as TextBlob and VADER to classify each article as positive, negative, or neutral. Only articles judged as positive are stored in a list.

[0371] Step 3:

[0372] The server then performs a summarization process on the positively identified news articles, using generative AI and summarization algorithms to concisely summarize the content of each article, which is then stored in a list.

[0373] Step 4:

[0374] Users access the system using their terminals and configure their news feed settings, such as selecting categories of interest (technology, health, entertainment, etc.), and sending this information to the server. The server then stores the selected data as the user's profile information.

[0375] Step 5:

[0376] The server stores the user's profile information in a database. The stored profile information is used to customize the news feed. For example, if user A is interested in "technology" and "health," this preference is recorded as profile information.

[0377] Step 6:

[0378] The server selects relevant articles from the summarized positive news articles based on the user's profile information. The selection criteria are news articles that match the user-specified categories. This generates a list of positive news articles tailored to the user's interests.

[0379] Step 7:

[0380] The server then provides the selected news articles to the user's device, allowing the user to view a customized positive news feed from their device. For example, news items displayed as a feed are formatted to show the title and summary.

[0381] In this way, the server, the terminal, and the user work together to realize a mechanism for providing a news feed customized from a positive perspective.

[0382] Example 1

[0383] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0384] In modern society, a huge amount of news articles are available on the Internet, many of which contain negative content. Exposure to such negative news can increase users' psychological burden and cause stress and anxiety. Furthermore, there is a lack of systems in place that can efficiently collect news in categories of interest to users and provide only positive articles based on that information. There is a need to address these issues.

[0385] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0386] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for providing the selected news articles to the user, means for summarizing the news articles using a generative AI model, and means for filtering news articles based on the user's setting information. This allows the user to be provided with only positive news articles that match their interests and preferences, thereby reducing daily stress and anxiety.

[0387] A "news article" is a piece of writing that reports events or information collected from the internet or other sources.

[0388] "Means of collection" refers to the functionality for obtaining news articles from news sources on the Internet.

[0389] "Means for sentiment analysis" refers to a function for analyzing the content of a news article and identifying its emotional nature.

[0390] "Positive news articles" refer to news articles that are determined to have a positive sentiment as a result of sentiment analysis.

[0391] "Means for extracting" refers to a function for selecting target data based on specific criteria.

[0392] "Means for summarizing" refers to the function of concisely summarizing the contents of a news article.

[0393] "User Profile Information" means data about a user's interests and preferences that is used to customize news delivery.

[0394] "Means for setting and saving" refers to the functionality that allows a user to input profile information and save that information.

[0395] "Means for selection" refers to the functionality for selecting appropriate news articles based on stored profile information.

[0396] "Means for providing" refers to a function for displaying selected news articles to a user.

[0397] A "generative AI model" refers to a computer model that uses artificial intelligence technology to create new data and information.

[0398] "Filtering means" refers to a function for filtering data based on specific criteria.

[0399] The system of this invention is designed to enable users to receive news from a positive perspective. A series of processes, including news article collection, sentiment analysis, summary generation, user profile setting and storage, and news selection and provision, are performed by the server.

[0400] The server collects the latest news articles from news sources on the Internet. For this purpose, the server leverages APIs to obtain news data. For example, it uses the NewsAPI.org endpoint and API key to periodically collect the latest news in JSON format.

[0401] The server then performs sentiment analysis on the collected news articles, using NLP libraries (e.g., NLTK, spaCy) and sentiment analysis models (e.g., TextBlob, Google's Sentiment Analysis API) to classify articles as positive, negative, or neutral.

[0402] For news articles that are judged to be positive as a result of sentiment analysis, the server summarizes them. This can be done using generative AI techniques (e.g., OpenAI's GPT-3) to concisely summarize the article's content, or it can use other summarization algorithms (e.g., TextRank, BERT Summarizer).

[0403] Users access the system through their devices and set up news categories based on their interests and preferences. The information entered by the user during this setting process is stored by the server in a database (e.g., MySQL, MongoDB). The saved profile information is then used to customize the news feed.

[0404] The server selects positive news articles that match the user's interests from the summarized articles based on the user's profile information. The selected articles are formatted as a feed and provided to the user via the device. The user can then view the customized positive news on the device.

[0405] As a concrete example, the server collects 50 news article data from a news API at 8:00 every morning and performs sentiment analysis to extract 30 positive articles. Next, it uses generative AI to summarize these positive articles and create 30 summary articles. User A uses his device to set his profile as being interested in "technology" and "health." Based on this profile information, the server selects summary articles related to "technology" and "health" as a feed and provides them to User A. As a result, User A can receive positive news that matches his interests through his device.

[0406] An example prompt is:

[0407] "Collect and summarize positive stories from today's latest news. News categories based on user interests: Technology and Health."

[0408] This system allows users to efficiently obtain positive information that is tailored to their interests, helping to reduce daily stress and anxiety.

[0409] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0410] Step 1: Gather news articles

[0411] explanation:

[0412] The server collects the latest news articles from news sources. Specifically, the server periodically sends requests to news APIs on the Internet to obtain news article data.

[0413] input:

[0414] News API endpoint and authentication key

[0415] output:

[0416] News article data in JSON format

[0417] Specific behavior:

[0418] For example, the server accesses the NewsAPI.org endpoint at 8:00 AM and retrieves 50 news articles, giving the server the latest news article information.

[0419] Step 2: Conducting sentiment analysis

[0420] explanation:

[0421] The server performs sentiment analysis on the collected news articles. It uses natural language processing techniques to analyze the text of each article and identify its emotional nature.

[0422] input:

[0423] News article text data

[0424] output:

[0425] Sentiment analysis results (positive, negative, neutral classification)

[0426] Specific behavior:

[0427] For example, the server uses TextBlob to calculate sentiment scores for 50 news articles and classify them as positive, negative, or neutral, allowing the server to identify positive news articles.

[0428] Step 3: Extracting positive news articles

[0429] explanation:

[0430] The server extracts only positive news articles based on the results of sentiment analysis.

[0431] input:

[0432] Sentiment analysis results

[0433] output:

[0434] Positive news articles

[0435] Specific behavior:

[0436] The server extracts news articles that are judged as positive by sentiment analysis and excludes other news articles. For example, if 30 out of 50 articles are judged as positive, these 30 news articles will be extracted.

[0437] Step 4: Generate a summary of the news article

[0438] explanation:

[0439] The server summarizes positive news articles, using a generative AI model to concisely summarize the article content.

[0440] input:

[0441] Positive news articles

[0442] output:

[0443] Summarized news articles

[0444] Specific behavior:

[0445] For example, the server uses GPT-3 to summarize 30 positive news articles, helping users quickly understand the news content.

[0446] Step 5: Set up and save your user profile

[0447] explanation:

[0448] Users set news categories through their terminals, and the server stores the information.

[0449] input:

[0450] User input information (interests and preferred news categories)

[0451] output:

[0452] Stored User Profile Information

[0453] Specific behavior:

[0454] For example, User A selects the categories "Technology" and "Health" on his device. The server stores this information in a database.

[0455] Step 6: News selection and delivery

[0456] explanation:

[0457] Based on the user's profile information, the server selects and provides summarized positive news articles that match the user's interests.

[0458] input:

[0459] Summarized news articles, user profile information

[0460] output:

[0461] News feeds provided to users

[0462] Specific behavior:

[0463] For example, the server selects summary articles related to "technology" and "health" based on User A's profile information and sends them to the device. User A can then browse the positive news that interests them through a customized feed displayed on the device.

[0464] Through this series of processes, the system allows users to reduce stress and anxiety in their daily lives and efficiently obtain positive information.

[0465] (Application example 1)

[0466] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0467] In today's world, people have access to a wide range of news articles, but many of them contain negative content, which can increase stress and anxiety. Furthermore, there is a lack of systems that efficiently provide news articles that match users' interests and preferences, which means that users spend a lot of time obtaining the information that is most relevant to them. Furthermore, effective news delivery methods using visual devices such as smart glasses and head-mounted displays have yet to be established.

[0468] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0469] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for customizing news articles based on the user's interests and preferences, and means for providing the customized news articles to the user via the visual terminal, thereby enabling the user to effectively obtain positive news articles based on their interests and preferences and obtain information through the visual terminal while reducing stress and anxiety.

[0470] "Means of collecting news articles" refers to technologies such as APIs and web crawlers that regularly collect the latest articles from news sources on the Internet.

[0471] "Means for sentiment analysis" is a function that uses natural language processing technology to classify the content of news articles into positive, negative, or neutral.

[0472] "Method for extracting positive news articles" is a process for selecting only articles that contain positive emotions from the collected and analyzed news articles.

[0473] "A means of summarizing positive news articles" is a function that concisely summarizes selected positive news articles using generative AI technology and summarization algorithms.

[0474] "Means for establishing and storing user profile information" refers to a database or interface that allows users to register their interests and preferences and store them in the system.

[0475] A "positive news story selection method" is a process that selects news stories that match a user's interests and preferences based on a stored user profile.

[0476] The "means for customizing news articles" is a function that provides news articles in an appropriate format that are individually suited to the user based on the user's profile information.

[0477] "Means of providing to users via visual terminals" refers to technology that displays news articles through wearable devices such as smart glasses or head-mounted displays, providing them visually to users.

[0478] To implement this invention, we will build a system that collects news articles, analyzes sentiment, summarizes them, sets and saves user profiles, and selects and provides articles. The specific implementation method of the system is shown below.

[0479] The server uses the news API to collect the latest news articles from news sources on the Internet. It periodically retrieves news article data using an endpoint and authentication key to access the API. This data is stored in storage for further processing.

[0480] Next, the server performs sentiment analysis on the collected news articles using natural language processing technology. For sentiment analysis, it uses a common natural language processing library (e.g., Transformers) to classify the content of the article as positive, negative, or neutral. Through this process, only positive news articles that are useful to users are allowed to proceed to the next step.

[0481] Positive news articles are summarized using generative AI techniques and summarization algorithms (e.g., Transformer generative AI models), and the summarized articles are converted into a format that can be delivered to users as needed and managed within the system.

[0482] Users access the system through their devices and set news categories based on their interests and preferences. This profile information is stored in a database and used to customize each user's personal news feed.

[0483] Based on the saved profile information, the system selects summaries of positive news articles that match the user's interests. The selected news is formatted as a feed and delivered to the user via their device (smartphone, smart glasses, head-mounted display), allowing the user to browse customized positive news.

[0484] For example, the server collects 50 article data from a news API at 8:00 a.m. It then uses natural language processing technology to perform sentiment analysis and extract 30 positive articles. The articles are then summarized using generative AI to create 30 summary articles. User A uses their device to set their profile as being interested in "technology" and "health." The server stores the profile information set by User A in a database and, based on this, selects summary articles related to "technology" and "health" as a feed. As a result, User A can receive positive news that matches their interests through a customized feed displayed on their device.

[0485] An example of a prompt to input to a generative AI model is as follows:

[0486] text

[0487] Recent technological advancements have been astounding, with artificial intelligence playing an increasingly important role. For example, new AI solutions are...

[0488] By inputting this prompt into a generative AI model, a summary appropriate to the prompt can be obtained.

[0489] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0490] Step 1:

[0491] The server collects the latest articles from news sources on the Internet. Specifically, it accesses the news API and retrieves the latest news data using the endpoint and authentication key. This data is sent to the server in JSON format. The data retrieved from the news API is temporarily stored in storage.

[0492] Input: News API endpoint and authentication key

[0493] Output: News article data in JSON format

[0494] Step 2:

[0495] The server performs sentiment analysis on the collected news articles using natural language processing techniques. Specifically, it extracts the text of the news articles and classifies them into positive, negative, or neutral sentiment using an NLP library (e.g., Transformers). This classification result is tagged with each news article.

[0496] Input: News article data in JSON format

[0497] Output: News article data with sentiment tags

[0498] Step 3:

[0499] The server extracts positive news articles based on sentiment analysis, filters out only those tagged as positive, and creates a list for further processing steps.

[0500] Input: sentiment-tagged news article data

[0501] Output: A list of positive news articles

[0502] Step 4:

[0503] The server summarizes positive news articles. Using generative AI techniques (e.g., Transformer generative AI models), it concisely summarizes the content of each news article. The generated summaries are stored as a single text field.

[0504] Input: A list of positive news articles

[0505] Output: A list of summarized positive news articles

[0506] Step 5:

[0507] Users can set news categories based on their interests and preferences through their devices. They input and set the selected category information as a user profile. This information is sent to the server and stored in a database.

[0508] Input: Category information about your interests and preferences

[0509] Output: User profile information stored in a database

[0510] Step 6:

[0511] The server selects summarized positive news articles that match the user's interests based on the stored user profile information, and generates a customized news feed by applying appropriate filtering and matching based on the user profile information.

[0512] Input: List of summarized positive news articles, user profile information

[0513] Output: A customized news feed

[0514] Step 7:

[0515] The server provides selected news articles to users through their devices, displaying them on visual devices such as smart glasses or head-mounted displays. Users can use these visual devices to browse customized positive news in real time.

[0516] Input: Customized News Feed

[0517] Output: News article displayed on terminal

[0518] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0519] This system is designed to help users receive news from a positive perspective. It collects news articles, analyzes sentiment, generates summaries, sets and saves user profiles, selects and delivers news, and incorporates an emotion engine that recognizes users' emotions to further customize the content of the news feed.

[0520] News article collection

[0521] The server collects the latest news articles from news sources on the Internet using an API. For example, it periodically retrieves news articles using an authentication key for the news API. This allows you to always have the latest current information.

[0522] Conducting sentiment analysis

[0523] The server performs sentiment analysis on the collected news articles using natural language processing technology. It analyzes the content of the articles and classifies them as positive, negative, or neutral. For sentiment analysis, it uses sentiment analysis libraries such as TextBlob and VADER. Articles that are determined to be positive proceed to the next processing step.

[0524] Summary Generation

[0525] The server summarizes the extracted positive news articles based on sentiment analysis, and uses generative AI and summarization algorithms to concisely summarize the article content, allowing users to quickly and effectively grasp the main points of the news.

[0526] Setting and saving a user profile

[0527] Users access the system using their terminals and configure their news feed settings, such as selecting categories of interest (e.g., technology, health, entertainment), and submitting them to the server, which stores the user's profile information in a database and uses it to customize their news feed.

[0528] Introducing the Emotion Engine

[0529] The emotion engine uses techniques to analyze the user's facial expressions and voice to recognize the user's emotional state. For example, it uses a camera to capture the user's facial expressions and uses an expression analysis algorithm to determine the emotion, or it collects the user's voice through a microphone and analyzes the tone and rate of the voice to determine the emotion.

[0530] News selection and provision

[0531] The server combines the user's profile information with the user's emotional state as recognized by the emotion engine to select appropriate summaries of positive news articles. If the user is feeling stressed, the server will prioritize news articles with particularly positive content.

[0532] Specific examples

[0533] For example, the server collects 50 articles from the news API at 8:00 a.m., extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his device to set his interests in "technology" and "health" in his profile, and the server stores this information in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his interests and emotional state.

[0534] This system allows users to efficiently obtain positive information that is tailored to their interests and emotional state, helping to reduce daily stress and anxiety.

[0535] The processing flow will be explained below.

[0536] Step 1:

[0537] The server collects the latest news articles from news sources on the Internet. Specifically, it uses a news API to periodically send requests to a configured endpoint to obtain data on the latest news articles. This allows new information to be collected automatically every day.

[0538] Step 2:

[0539] The server performs sentiment analysis on the collected news articles using natural language processing technology. For example, it uses a sentiment analysis library to analyze the text of each news article and classify it as positive, negative, or neutral. Only news articles judged as positive are stored in a list.

[0540] Step 3:

[0541] The server uses generative AI to generate summaries for news articles that are positively identified, and the summaries are stored in a database along with the original articles, allowing users to quickly grasp key information.

[0542] Step 4:

[0543] Users access the system using their devices and configure their news feeds to reflect their preferences and interests. Specifically, they select news categories (e.g., technology, health, entertainment, etc.) and send the configuration information to the server.

[0544] Step 5:

[0545] The server stores user profile information in a database, which is used as the basis for customizing a user's individual news feed.

[0546] Step 6:

[0547] The user uses the device's camera and microphone to have the emotion engine recognize their emotional state. The device's camera captures the user's facial expressions, and an expression analysis algorithm is used to determine the user's emotions (e.g., joy, sadness, stress). The user's voice is also analyzed via the microphone, and the emotional state is assessed based on the tone and rate of the voice.

[0548] Step 7:

[0549] The server combines the user's profile information with the emotional state obtained by the emotion engine to select appropriate summaries of positive news articles. For example, if the user is feeling stressed, it will prioritize news articles that will help them relax.

[0550] Step 8:

[0551] The server provides selected news articles to the user's device, where the user can view a customized news feed. The news feed displays titles and summaries, and the user can click on articles of interest to read more.

[0552] As a concrete example, the server collects 50 articles from the news API at 8:00 a.m., performs sentiment analysis to extract 30 positive articles, and generates summaries. User A uses a device to set his / her profile as interested in "technology" and "health," and the sentiment engine detects User A's stress. In this case, the server selects particularly relaxing, positive news related to technology and health and provides it to User A's device. User A can receive news that matches his / her interests and emotional state.

[0553] In this way, the system provides a more personalized positive news feed based on the user's profile information and real-time emotional state.

[0554] Example 2

[0555] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0556] In modern society, negative news can increase psychological stress and anxiety when users consume news. Users also face the challenge of finding positive news that suits them from the vast amount of information available. There is a need for a system that can solve these problems, reduce daily stress and anxiety, and enable users to efficiently obtain positive news.

[0557] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0558] In this invention, the server includes means for collecting news articles, means for performing sentiment analysis, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for recognizing the user's emotional state, means for selecting positive news articles based on the saved profile information and emotional state, and means for providing the selected news articles to the user, thereby enabling the user to efficiently obtain positive news that matches their interests and emotional state and reduce daily stress and anxiety.

[0559] A "news article" is a written piece of information that reports current events or happenings.

[0560] "Means of collection" refers to a function for automatically acquiring article data from news sources on the Internet.

[0561] "Sentiment analysis" is a process that uses natural language processing technology to analyze emotional tendencies from text data and classify them as positive, negative, neutral, etc.

[0562] A "positive news article" is a news article that is classified as positive as a result of sentiment analysis.

[0563] "Means for summarizing" is a function that succinctly summarizes the contents of a news article.

[0564] "User profile information" refers to data about interest categories and personal preferences that a user provides to the system.

[0565] "Means for setting and saving" refers to a function for storing user-selected categories and setting information within the system.

[0566] "Means for recognizing emotional state" refers to technology that analyzes data on the user's facial expressions and voice to determine their emotional state at that time.

[0567] A "selection means" is a function that selects appropriate news articles based on stored profile information and a perceived emotional state.

[0568] The "means of providing" is a function for delivering selected news articles to users.

[0569] This system is designed to help users receive news from a positive perspective. The system operates by combining the following steps: news article collection, sentiment analysis, summary generation, user profile creation and storage, emotion recognition engine to determine the user's emotional state, and news selection and provision. A detailed implementation example is shown below.

[0570] News article collection

[0571] The server periodically collects the latest news articles from news sources on the Internet using an API. For example, every morning at 8:00, it sends an HTTP request to a specific news API using an authentication key to retrieve the latest 50 news articles. The collected article data is stored in an internal database.

[0572] Conducting sentiment analysis

[0573] The server performs sentiment analysis on the collected news articles. It uses natural language processing techniques such as TextBlob and VADER to classify each news article as positive, negative, or neutral, allowing it to extract positive news articles that are appropriate for the user.

[0574] Summary Generation

[0575] The server summarizes the news articles that are identified as positive. It uses a generative AI model or summarization algorithm (e.g., using a generative AI model) to concisely summarize the article's content. An example prompt might be, "Classify the following news articles as positive, negative, or neutral, and generate summaries of the positive articles."

[0576] Setting and saving a user profile

[0577] Users access the system using an internet-connected device to configure their news feed preferences, for example by selecting categories of interest such as "technology" or "health," and then submitting their preferences to the server, which stores this information in a database.

[0578] Introducing the Emotion Engine

[0579] The server runs an emotion engine to recognize the user's emotional state. It uses a camera to capture the user's facial expressions and uses facial expression analysis algorithms (e.g., OpenCV or DeepFace) to determine the emotion. It also collects audio data through a microphone and analyzes the tone and rate of the audio to determine the emotional state.

[0580] News selection and provision

[0581] The server selects appropriate summaries of positive news articles based on the user's stored profile information and the user's emotional state as recognized by the emotion engine. For example, if the server determines that the user is feeling stressed, it will prioritize positive news articles that are particularly relaxing and provide them as a feed.

[0582] Specific examples

[0583] For example, at 8:00 a.m., the server collects 50 articles from the news API, extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his or her device to set his or her interests in "technology" and "health" in his or her profile and sends this information to the server. This information is stored in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his or her emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his or her interests and emotional state.

[0584] This system allows users to efficiently obtain positive information that matches their interests and emotional state, helping to reduce daily stress and anxiety.

[0585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0586] Step 1: Gather news articles

[0587] The server uses an API to collect the latest news articles from news sources on the Internet. Specifically, it sends an HTTP request to a specific news API using an authentication key. For example, a request is sent every morning at 8:00 to retrieve the latest 50 news articles. The input is news article data retrieved from the API, and the output is raw data stored on the server.

[0588] Step 2: Conducting sentiment analysis

[0589] The server performs sentiment analysis on the collected news articles. Specifically, it uses natural language processing libraries such as TextBlob and VADER to analyze each news article. For example, TextBlob is used to calculate the sentiment score of the article text and classify it as positive, negative, or neutral. The input is the news article text, and the output is the classification result including the sentiment score.

[0590] Step 3: Extracting positive news articles

[0591] The server extracts news articles that are judged to be positive based on the results of sentiment analysis. Specifically, it filters only articles that are deemed positive based on the classification results of sentiment analysis. The input is the classification results including sentiment scores, and the output is a list of positive news articles.

[0592] Step 4: Summarize the positive news article

[0593] The server summarizes the extracted positive news articles. Specifically, it uses a generative AI model or summarization algorithm (e.g., using a generative AI model) to concisely summarize the article's content. For example, it sends a prompt to the generative AI model saying, "Please summarize the following news article," and obtains the summarized text. The input is the full text of the positive news article, and the output is the summarized text.

[0594] Step 5: Set up and save your user profile

[0595] A user accesses the system using a terminal and configures his or her news feed. Specifically, the user selects categories of interest (e.g., "technology" or "health") in a web form and sends it to the server. The server stores the received profile information in a database. The input is the user's selected categories and preferences, and the output is the profile information stored in the database.

[0596] Step 6: Implementing the Emotion Engine

[0597] The server runs an emotion engine to recognize the user's emotional state. Specifically, it uses the device's camera to capture the user's facial expression and uses an expression analysis algorithm (e.g., OpenCV or DeepFace) to determine the emotion. It also analyzes audio data collected through the microphone and determines the emotion from the tone and rate of the voice. The input is the user's facial expression data and audio data, and the output is the emotional state as a result of the analysis.

[0598] Step 7: Select and deliver news

[0599] The server selects the most appropriate positive news articles based on the user's profile information and recognized emotional state. Specifically, it references the profile information and emotional data from the database to filter and rank the most appropriate news articles. For users who are feeling stressed, it prioritizes articles that are particularly relaxing. The input is positive news articles, profile information, and emotional state, and the output is a list of selected news articles.

[0600] This processing step allows users to efficiently receive positive news that matches their interests and emotional state, and can also reduce daily stress and anxiety.

[0601] (Application example 2)

[0602] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0603] Conventional news delivery systems provide generic news articles uniformly without fully considering the user's emotional state or interests, which means that the information users receive is not always pleasant or useful. As a result, users may feel dissatisfied or stressed when browsing the news. Furthermore, the quality of the customer experience in physical stores has not improved due to a lack of technology that can recognize customers' emotional state in real time and provide appropriate information. Therefore, there is a need for a system that can provide appropriate and positive news and product information according to the user's emotional state and interests.

[0604] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0605] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for using an emotion engine to recognize the user's emotional state, means for adjusting the news articles based on the emotional state recognized by the emotion engine, and means for providing the selected news articles to the user, thereby making it possible to provide positive news information and product information according to the user's emotional state and interests.

[0606] "Means for collecting news articles" refers to the function of obtaining article data from news sources on the Internet using an API.

[0607] "Means for sentiment analysis of collected news articles" refers to a function that uses natural language processing technology to classify the sentiment of an article and determine whether it is positive, negative, or neutral.

[0608] "Means for extracting positive news articles based on sentiment analysis" is a function that selects news articles that are determined to be positive as a result of sentiment analysis.

[0609] The "means for summarizing positive news articles" is a function that succinctly summarizes the content of news articles that are judged to be positive.

[0610] "Means for setting and saving user profile information" refers to the ability for users to input their interest categories and personal settings into the system and record that information in a database.

[0611] The "means for selecting positive news articles based on stored profile information" is a function that uses a user's profile information stored in the database to select the most appropriate positive news articles for that user.

[0612] "Means for using an emotion engine to recognize the user's emotional state" refers to a technology for analyzing the user's facial expressions and voice to determine the user's emotional state.

[0613] The "means for adjusting news articles based on the emotional state recognized by the emotion engine" is a function for changing the content and order of news articles provided depending on the emotional state of the user.

[0614] "Means for providing selected news articles to users" refers to a function that displays or provides audio guidance of the final selected news articles on the user's terminal or through a robot in a physical store.

[0615] The system of the present invention is designed to enable users to receive news from a positive perspective. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the preferred embodiments of the present invention.

[0616] News article collection

[0617] The server collects news articles from multiple news sources on the Internet. This collection is done using an API (Application Programming Interface) to efficiently retrieve article data. The API is called periodically, and the latest news articles are stored on the server.

[0618] Conducting sentiment analysis

[0619] The server then performs sentiment analysis on the collected news articles. This analysis uses natural language processing techniques. For example, it uses sentiment analysis libraries such as TextBlob or VADER to classify the content of each article as positive, negative, or neutral. Once the sentiment analysis is complete, news articles that are determined to be positive proceed to the next processing step.

[0620] Summary Generation

[0621] Once a news article is positively identified, it is processed for summary generation, using generative AI and summarization algorithms to concisely summarize the article's content, allowing users to quickly grasp the main points of the news. This process eliminates redundant information, leaving only the key points.

[0622] Setting and saving a user profile

[0623] Users access the system using their own devices and select categories of interest (e.g., technology, health, entertainment, etc.). User profile information is stored on the server and used to select news articles later.

[0624] Introducing the Emotion Engine

[0625] While a user is browsing the news, the emotion engine analyzes the user's emotional state in real time. The emotion engine captures the user's facial expressions through a camera and uses an expression analysis algorithm to determine their emotions. It also collects voice data through a microphone and analyzes the tone and speed of the voice to determine emotions. This analysis uses facial and voice recognition technologies.

[0626] News selection and provision

[0627] Finally, the server selects appropriate summaries of positive news articles based on the user's profile information and the emotional state recognized by the emotion engine. For example, if the server determines that the user is feeling stressed, it will prioritize positive news articles that will help them relax. The selected news articles are then displayed on the user's device or provided via a robot in a physical store.

[0628] Specific examples

[0629] For example, the server collects 50 articles from the news API at 8:00 a.m., extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his device to set his interests in "technology" and "health" in his profile, and the server stores this information in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his interests and emotional state.

[0630] Example of generative AI model and prompt

[0631] To improve the customer experience in physical stores, the system is installed on robots placed in physical stores and uses prompts such as:

[0632] "This in-store robot analyzes customers' faces and voices to provide positive health news and in-store product recommendations based on their emotional state."

[0633] "It uses facial and voice recognition to determine whether the customer is relaxed or stressed and then presents information that is appropriate for that state."

[0634] In this way, users can have a personal and positive experience when they visit the store.

[0635] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0636] Step 1:

[0637] The server collects news articles from news sources on the Internet using APIs. The input is the news API endpoint and API key, and the output is a list of the latest news articles. Specifically, the server periodically calls the API and stores the retrieved news article data in an internal database.

[0638] Step 2:

[0639] The server receives a list of news articles and performs sentiment analysis on each article. The input is a list of collected news articles, and the output is a list of news articles classified as positive, negative, or neutral. Specifically, the server uses sentiment analysis libraries such as TextBlob and VADER to analyze the content of each news article and calculate a sentiment score.

[0640] Step 3:

[0641] The server extracts news articles that are judged to be positive based on the results of sentiment analysis. The input is a list of news articles classified as positive, negative, or neutral, and the output is a list of positive news articles. Specifically, the server selects news articles from the list that have a sentiment score above a certain level.

[0642] Step 4:

[0643] The server generates summaries for news articles that are judged to be positive. The input is a list of positive news articles, and the output is a list of summarized positive news articles. Specifically, the server uses generative AI and summarization algorithms to extract and summarize the key points of each news article.

[0644] Step 5:

[0645] Users use their own devices to input, set, and save profile information. The input is the user's selected interest categories and personal settings, and the output is the set profile information being saved in the server's database. Specifically, users input information through the UI and send it to the server.

[0646] Step 6:

[0647] The server selects positive news articles based on user profile information. The input is a list of summarized positive news articles and the user's profile information, and the output is a list of news articles that are most suitable for the user. Specifically, the server references the profile information in the database and selects articles in categories that interest the user.

[0648] Step 7:

[0649] The user's device or a robot in a physical store recognizes the user's emotional state in real time. The input is image data and voice data of the user's face, and the output is the user's current emotional state. Specifically, the system uses a camera and microphone to analyze emotions using facial and voice recognition technology.

[0650] Step 8:

[0651] The server adjusts the news articles based on the emotional state recognized by the emotion engine. The input is the user's emotional state and a list of selected news articles, and the output is the adjusted list of news articles. Specifically, the server changes the priority of positive news articles according to the user's emotional state.

[0652] Step 9:

[0653] The selected news articles are provided to the user through the user's device or a robot in a brick-and-mortar store. The input is a tailored list of news articles, and the output is the news articles that are displayed or spoken to the user. In concrete terms, the device or robot provides the news articles to the user in an appropriate format.

[0654] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0655] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0656] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0657] [Third embodiment]

[0658] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0659] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0660] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0661] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0662] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0663] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0664] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0665] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0666] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0667] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0668] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0669] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0670] This system is designed to enable users to receive news from a positive perspective, and a series of processes, such as news article collection, sentiment analysis, summary generation, user profile setting and storage, and news selection and provision, are carried out by the server.

[0671] News article collection

[0672] The server collects the latest news articles from news sources. For example, it uses a news API to obtain news article data using the following procedure. A system is built to periodically collect the latest news using an endpoint and authentication key to access the API.

[0673] Conducting sentiment analysis

[0674] The server performs sentiment analysis on the collected news articles. Using natural language processing technology, it analyzes the content of the articles and classifies them as positive, negative, or neutral. This allows only positive news articles that are useful to the user to proceed to the next step.

[0675] Summary Generation

[0676] The server extracts positive news articles based on sentiment analysis and summarizes them using generative AI technology and summarization algorithms to concisely summarize the article content, allowing users to quickly and effectively obtain information.

[0677] Setting and saving a user profile

[0678] Users can access the system through their devices and set news categories based on their interests and preferences. The server then stores the user's input in a database. The stored profile information is then used to customize each user's individual news feed.

[0679] News selection and provision

[0680] The server selects summaries of positive news articles that match the user's interests based on the user's profile information. The selected news is formatted as a feed and provided to the user via their device. The user can then view the customized positive news on their device.

[0681] Specific examples

[0682] For example, the server collects 50 article data from a news API at 8:00 a.m. Next, it uses natural language processing technology to perform sentiment analysis on the articles and extracts 30 positive articles. The articles are then summarized using generative AI to create 30 summary articles. User A uses his device to set his interests in "technology" and "health" in his profile. The server stores the profile information set by User A in a database and, based on this, selects summary articles related to "technology" and "health" as a feed. As a result, User A can receive positive news that matches his interests through a customized feed displayed on his device.

[0683] This system allows users to efficiently obtain positive information that matches their interests, helping to reduce daily stress and anxiety.

[0684] The processing flow will be explained below.

[0685] Step 1:

[0686] The server collects the latest news articles from news sources. Specifically, it uses a news API to obtain news article data. It uses an authentication key for the API and performs a process to collect, for example, 50 articles at a fixed time every day.

[0687] Step 2:

[0688] The server uses natural language processing technology to analyze the sentiment of collected news articles. For example, it uses sentiment analysis libraries such as TextBlob and VADER to classify each article as positive, negative, or neutral. Only articles judged as positive are stored in a list.

[0689] Step 3:

[0690] The server then performs a summarization process on the positively identified news articles, using generative AI and summarization algorithms to concisely summarize the content of each article, which is then stored in a list.

[0691] Step 4:

[0692] Users access the system using their terminals and configure their news feed settings, such as selecting categories of interest (technology, health, entertainment, etc.), and sending this information to the server. The server then stores the selected data as the user's profile information.

[0693] Step 5:

[0694] The server stores the user's profile information in a database. The stored profile information is used to customize the news feed. For example, if user A is interested in "technology" and "health," this preference is recorded as profile information.

[0695] Step 6:

[0696] The server selects relevant articles from the summarized positive news articles based on the user's profile information. The selection criteria are news articles that match the user-specified categories. This generates a list of positive news articles tailored to the user's interests.

[0697] Step 7:

[0698] The server then provides the selected news articles to the user's device, allowing the user to view a customized positive news feed from their device. For example, news items displayed as a feed are formatted to show the title and summary.

[0699] In this way, the server, the terminal, and the user work together to realize a mechanism for providing a news feed customized from a positive perspective.

[0700] Example 1

[0701] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0702] In modern society, a huge amount of news articles are available on the Internet, many of which contain negative content. Exposure to such negative news can increase users' psychological burden and cause stress and anxiety. Furthermore, there is a lack of systems in place that can efficiently collect news in categories of interest to users and provide only positive articles based on that information. There is a need to address these issues.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0704] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for providing the selected news articles to the user, means for summarizing the news articles using a generative AI model, and means for filtering news articles based on the user's setting information. This allows the user to be provided with only positive news articles that match their interests and preferences, thereby reducing daily stress and anxiety.

[0705] A "news article" is a piece of writing that reports events or information collected from the internet or other sources.

[0706] "Means of collection" refers to the functionality for obtaining news articles from news sources on the Internet.

[0707] "Means for sentiment analysis" refers to a function for analyzing the content of a news article and identifying its emotional nature.

[0708] "Positive news articles" refer to news articles that are determined to have a positive sentiment as a result of sentiment analysis.

[0709] "Means for extracting" refers to a function for selecting target data based on specific criteria.

[0710] "Means for summarizing" refers to the function of concisely summarizing the contents of a news article.

[0711] "User Profile Information" means data about a user's interests and preferences that is used to customize news delivery.

[0712] "Means for setting and saving" refers to the functionality that allows a user to input profile information and save that information.

[0713] "Means for selection" refers to the functionality for selecting appropriate news articles based on stored profile information.

[0714] "Means for providing" refers to a function for displaying selected news articles to a user.

[0715] A "generative AI model" refers to a computer model that uses artificial intelligence technology to create new data and information.

[0716] "Filtering means" refers to a function for filtering data based on specific criteria.

[0717] The system of this invention is designed to enable users to receive news from a positive perspective. A series of processes, including news article collection, sentiment analysis, summary generation, user profile setting and storage, and news selection and provision, are performed by the server.

[0718] The server collects the latest news articles from news sources on the Internet. For this purpose, the server leverages APIs to obtain news data. For example, it uses the NewsAPI.org endpoint and API key to periodically collect the latest news in JSON format.

[0719] The server then performs sentiment analysis on the collected news articles, using NLP libraries (e.g., NLTK, spaCy) and sentiment analysis models (e.g., TextBlob, Google's Sentiment Analysis API) to classify articles as positive, negative, or neutral.

[0720] For news articles that are judged to be positive as a result of sentiment analysis, the server summarizes them. This can be done using generative AI techniques (e.g., OpenAI's GPT-3) to concisely summarize the article's content, or it can use other summarization algorithms (e.g., TextRank, BERT Summarizer).

[0721] Users access the system through their devices and set up news categories based on their interests and preferences. The information entered by the user during this setting process is stored by the server in a database (e.g., MySQL, MongoDB). The saved profile information is then used to customize the news feed.

[0722] The server selects positive news articles that match the user's interests from the summarized articles based on the user's profile information. The selected articles are formatted as a feed and provided to the user via the device. The user can then view the customized positive news on the device.

[0723] As a concrete example, the server collects 50 news article data from a news API at 8:00 every morning and performs sentiment analysis to extract 30 positive articles. Next, it uses generative AI to summarize these positive articles and create 30 summary articles. User A uses his device to set his profile as being interested in "technology" and "health." Based on this profile information, the server selects summary articles related to "technology" and "health" as a feed and provides them to User A. As a result, User A can receive positive news that matches his interests through his device.

[0724] An example prompt is:

[0725] "Collect and summarize positive stories from today's latest news. News categories based on user interests: Technology and Health."

[0726] This system allows users to efficiently obtain positive information that is tailored to their interests, helping to reduce daily stress and anxiety.

[0727] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0728] Step 1: Gather news articles

[0729] explanation:

[0730] The server collects the latest news articles from news sources. Specifically, the server periodically sends requests to news APIs on the Internet to obtain news article data.

[0731] input:

[0732] News API endpoint and authentication key

[0733] output:

[0734] News article data in JSON format

[0735] Specific behavior:

[0736] For example, the server accesses the NewsAPI.org endpoint at 8:00 AM and retrieves 50 news articles, giving the server the latest news article information.

[0737] Step 2: Conducting sentiment analysis

[0738] explanation:

[0739] The server performs sentiment analysis on the collected news articles. It uses natural language processing techniques to analyze the text of each article and identify its emotional nature.

[0740] input:

[0741] News article text data

[0742] output:

[0743] Sentiment analysis results (positive, negative, neutral classification)

[0744] Specific behavior:

[0745] For example, the server uses TextBlob to calculate sentiment scores for 50 news articles and classify them as positive, negative, or neutral, allowing the server to identify positive news articles.

[0746] Step 3: Extracting positive news articles

[0747] explanation:

[0748] The server extracts only positive news articles based on the results of sentiment analysis.

[0749] input:

[0750] Sentiment analysis results

[0751] output:

[0752] Positive news articles

[0753] Specific behavior:

[0754] The server extracts news articles that are judged as positive by sentiment analysis and excludes other news articles. For example, if 30 out of 50 articles are judged as positive, these 30 news articles will be extracted.

[0755] Step 4: Generate a summary of the news article

[0756] explanation:

[0757] The server summarizes positive news articles, using a generative AI model to concisely summarize the article content.

[0758] input:

[0759] Positive news articles

[0760] output:

[0761] Summarized news articles

[0762] Specific behavior:

[0763] For example, the server uses GPT-3 to summarize 30 positive news articles, helping users quickly understand the news content.

[0764] Step 5: Set up and save your user profile

[0765] explanation:

[0766] Users set news categories through their terminals, and the server stores the information.

[0767] input:

[0768] User input information (interests and preferred news categories)

[0769] output:

[0770] Stored User Profile Information

[0771] Specific behavior:

[0772] For example, User A selects the categories "Technology" and "Health" on his device. The server stores this information in a database.

[0773] Step 6: News selection and delivery

[0774] explanation:

[0775] Based on the user's profile information, the server selects and provides summarized positive news articles that match the user's interests.

[0776] input:

[0777] Summarized news articles, user profile information

[0778] output:

[0779] News feeds provided to users

[0780] Specific behavior:

[0781] For example, the server selects summary articles related to "technology" and "health" based on User A's profile information and sends them to the device. User A can then browse the positive news that interests them through a customized feed displayed on the device.

[0782] Through this series of processes, the system allows users to reduce stress and anxiety in their daily lives and efficiently obtain positive information.

[0783] (Application example 1)

[0784] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0785] In today's world, people have access to a wide range of news articles, but many of them contain negative content, which can increase stress and anxiety. Furthermore, there is a lack of systems that efficiently provide news articles that match users' interests and preferences, which means that users spend a lot of time obtaining the information that is most relevant to them. Furthermore, effective news delivery methods using visual devices such as smart glasses and head-mounted displays have yet to be established.

[0786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0787] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for customizing news articles based on the user's interests and preferences, and means for providing the customized news articles to the user via the visual terminal, thereby enabling the user to effectively obtain positive news articles based on their interests and preferences and obtain information through the visual terminal while reducing stress and anxiety.

[0788] "Means of collecting news articles" refers to technologies such as APIs and web crawlers that regularly collect the latest articles from news sources on the Internet.

[0789] "Means for sentiment analysis" is a function that uses natural language processing technology to classify the content of news articles into positive, negative, or neutral.

[0790] "Method for extracting positive news articles" is a process for selecting only articles that contain positive emotions from the collected and analyzed news articles.

[0791] "A means of summarizing positive news articles" is a function that concisely summarizes selected positive news articles using generative AI technology and summarization algorithms.

[0792] "Means for establishing and storing user profile information" refers to a database or interface that allows users to register their interests and preferences and store them in the system.

[0793] A "positive news story selection method" is a process that selects news stories that match a user's interests and preferences based on a stored user profile.

[0794] The "means for customizing news articles" is a function that provides news articles in an appropriate format that are individually suited to the user based on the user's profile information.

[0795] "Means of providing to users via visual terminals" refers to technology that displays news articles through wearable devices such as smart glasses or head-mounted displays, providing them visually to users.

[0796] To implement this invention, we will build a system that collects news articles, analyzes sentiment, summarizes them, sets and saves user profiles, and selects and provides articles. The specific implementation method of the system is shown below.

[0797] The server uses the news API to collect the latest news articles from news sources on the Internet. It periodically retrieves news article data using an endpoint and authentication key to access the API. This data is stored in storage for further processing.

[0798] Next, the server performs sentiment analysis on the collected news articles using natural language processing technology. For sentiment analysis, it uses a common natural language processing library (e.g., Transformers) to classify the content of the article as positive, negative, or neutral. Through this process, only positive news articles that are useful to users are allowed to proceed to the next step.

[0799] Positive news articles are summarized using generative AI techniques and summarization algorithms (e.g., Transformer generative AI models), and the summarized articles are converted into a format that can be delivered to users as needed and managed within the system.

[0800] Users access the system through their devices and set news categories based on their interests and preferences. This profile information is stored in a database and used to customize each user's personal news feed.

[0801] Based on the saved profile information, the system selects summaries of positive news articles that match the user's interests. The selected news is formatted as a feed and delivered to the user via their device (smartphone, smart glasses, head-mounted display), allowing the user to browse customized positive news.

[0802] For example, the server collects 50 article data from a news API at 8:00 a.m. It then uses natural language processing technology to perform sentiment analysis and extract 30 positive articles. The articles are then summarized using generative AI to create 30 summary articles. User A uses their device to set their profile as being interested in "technology" and "health." The server stores the profile information set by User A in a database and, based on this, selects summary articles related to "technology" and "health" as a feed. As a result, User A can receive positive news that matches their interests through a customized feed displayed on their device.

[0803] An example of a prompt to input to a generative AI model is as follows:

[0804] text

[0805] Recent technological advancements have been astounding, with artificial intelligence playing an increasingly important role. For example, new AI solutions are...

[0806] By inputting this prompt into a generative AI model, a summary appropriate to the prompt can be obtained.

[0807] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0808] Step 1:

[0809] The server collects the latest articles from news sources on the Internet. Specifically, it accesses the news API and retrieves the latest news data using the endpoint and authentication key. This data is sent to the server in JSON format. The data retrieved from the news API is temporarily stored in storage.

[0810] Input: News API endpoint and authentication key

[0811] Output: News article data in JSON format

[0812] Step 2:

[0813] The server performs sentiment analysis on the collected news articles using natural language processing techniques. Specifically, it extracts the text of the news articles and classifies them into positive, negative, or neutral sentiment using an NLP library (e.g., Transformers). This classification result is tagged with each news article.

[0814] Input: News article data in JSON format

[0815] Output: News article data with sentiment tags

[0816] Step 3:

[0817] The server extracts positive news articles based on sentiment analysis, filters out only those tagged as positive, and creates a list for further processing steps.

[0818] Input: sentiment-tagged news article data

[0819] Output: A list of positive news articles

[0820] Step 4:

[0821] The server summarizes positive news articles. Using generative AI techniques (e.g., Transformer generative AI models), it concisely summarizes the content of each news article. The generated summaries are stored as a single text field.

[0822] Input: A list of positive news articles

[0823] Output: A list of summarized positive news articles

[0824] Step 5:

[0825] Users can set news categories based on their interests and preferences through their devices. They input and set the selected category information as a user profile. This information is sent to the server and stored in a database.

[0826] Input: Category information about your interests and preferences

[0827] Output: User profile information stored in a database

[0828] Step 6:

[0829] The server selects summarized positive news articles that match the user's interests based on the stored user profile information, and generates a customized news feed by applying appropriate filtering and matching based on the user profile information.

[0830] Input: List of summarized positive news articles, user profile information

[0831] Output: A customized news feed

[0832] Step 7:

[0833] The server provides selected news articles to users through their devices, displaying them on visual devices such as smart glasses or head-mounted displays. Users can use these visual devices to browse customized positive news in real time.

[0834] Input: Customized News Feed

[0835] Output: News article displayed on terminal

[0836] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0837] This system is designed to help users receive news from a positive perspective. It collects news articles, analyzes sentiment, generates summaries, sets and saves user profiles, selects and delivers news, and incorporates an emotion engine that recognizes users' emotions to further customize the content of the news feed.

[0838] News article collection

[0839] The server collects the latest news articles from news sources on the Internet using an API. For example, it periodically retrieves news articles using an authentication key for the news API. This allows you to always have the latest current information.

[0840] Conducting sentiment analysis

[0841] The server performs sentiment analysis on the collected news articles using natural language processing technology. It analyzes the content of the articles and classifies them as positive, negative, or neutral. For sentiment analysis, it uses sentiment analysis libraries such as TextBlob and VADER. Articles that are determined to be positive proceed to the next processing step.

[0842] Summary Generation

[0843] The server summarizes the extracted positive news articles based on sentiment analysis, and uses generative AI and summarization algorithms to concisely summarize the article content, allowing users to quickly and effectively grasp the main points of the news.

[0844] Setting and saving a user profile

[0845] Users access the system using their terminals and configure their news feed settings, such as selecting categories of interest (e.g., technology, health, entertainment), and submitting them to the server, which stores the user's profile information in a database and uses it to customize their news feed.

[0846] Introducing the Emotion Engine

[0847] The emotion engine uses techniques to analyze the user's facial expressions and voice to recognize the user's emotional state. For example, it uses a camera to capture the user's facial expressions and uses an expression analysis algorithm to determine the emotion, or it collects the user's voice through a microphone and analyzes the tone and rate of the voice to determine the emotion.

[0848] News selection and provision

[0849] The server combines the user's profile information with the user's emotional state as recognized by the emotion engine to select appropriate summaries of positive news articles. If the user is feeling stressed, the server will prioritize news articles with particularly positive content.

[0850] Specific examples

[0851] For example, the server collects 50 articles from the news API at 8:00 a.m., extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his device to set his interests in "technology" and "health" in his profile, and the server stores this information in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his interests and emotional state.

[0852] This system allows users to efficiently obtain positive information that is tailored to their interests and emotional state, helping to reduce daily stress and anxiety.

[0853] The processing flow will be explained below.

[0854] Step 1:

[0855] The server collects the latest news articles from news sources on the Internet. Specifically, it uses a news API to periodically send requests to a configured endpoint to obtain data on the latest news articles. This allows new information to be collected automatically every day.

[0856] Step 2:

[0857] The server performs sentiment analysis on the collected news articles using natural language processing technology. For example, it uses a sentiment analysis library to analyze the text of each news article and classify it as positive, negative, or neutral. Only news articles judged as positive are stored in a list.

[0858] Step 3:

[0859] The server uses generative AI to generate summaries for news articles that are positively identified, and the summaries are stored in a database along with the original articles, allowing users to quickly grasp key information.

[0860] Step 4:

[0861] Users access the system using their devices and configure their news feeds to reflect their preferences and interests. Specifically, they select news categories (e.g., technology, health, entertainment, etc.) and send the configuration information to the server.

[0862] Step 5:

[0863] The server stores user profile information in a database, which is used as the basis for customizing a user's individual news feed.

[0864] Step 6:

[0865] The user uses the device's camera and microphone to have the emotion engine recognize their emotional state. The device's camera captures the user's facial expressions, and an expression analysis algorithm is used to determine the user's emotions (e.g., joy, sadness, stress). The user's voice is also analyzed via the microphone, and the emotional state is assessed based on the tone and rate of the voice.

[0866] Step 7:

[0867] The server combines the user's profile information with the emotional state obtained by the emotion engine to select appropriate summaries of positive news articles. For example, if the user is feeling stressed, it will prioritize news articles that will help them relax.

[0868] Step 8:

[0869] The server provides selected news articles to the user's device, where the user can view a customized news feed. The news feed displays titles and summaries, and the user can click on articles of interest to read more.

[0870] As a concrete example, the server collects 50 articles from the news API at 8:00 a.m., performs sentiment analysis to extract 30 positive articles, and generates summaries. User A uses a device to set his / her profile as interested in "technology" and "health," and the sentiment engine detects User A's stress. In this case, the server selects particularly relaxing, positive news related to technology and health and provides it to User A's device. User A can receive news that matches his / her interests and emotional state.

[0871] In this way, the system provides a more personalized positive news feed based on the user's profile information and real-time emotional state.

[0872] Example 2

[0873] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0874] In modern society, negative news can increase psychological stress and anxiety when users consume news. Users also face the challenge of finding positive news that suits them from the vast amount of information available. There is a need for a system that can solve these problems, reduce daily stress and anxiety, and enable users to efficiently obtain positive news.

[0875] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0876] In this invention, the server includes means for collecting news articles, means for performing sentiment analysis, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for recognizing the user's emotional state, means for selecting positive news articles based on the saved profile information and emotional state, and means for providing the selected news articles to the user, thereby enabling the user to efficiently obtain positive news that matches their interests and emotional state and reduce daily stress and anxiety.

[0877] A "news article" is a written piece of information that reports current events or happenings.

[0878] "Means of collection" refers to a function for automatically acquiring article data from news sources on the Internet.

[0879] "Sentiment analysis" is a process that uses natural language processing technology to analyze emotional tendencies from text data and classify them as positive, negative, neutral, etc.

[0880] A "positive news article" is a news article that is classified as positive as a result of sentiment analysis.

[0881] "Means for summarizing" is a function that succinctly summarizes the contents of a news article.

[0882] "User profile information" refers to data about interest categories and personal preferences that a user provides to the system.

[0883] "Means for setting and saving" refers to a function for storing user-selected categories and setting information within the system.

[0884] "Means for recognizing emotional state" refers to technology that analyzes data on the user's facial expressions and voice to determine their emotional state at that time.

[0885] A "selection means" is a function that selects appropriate news articles based on stored profile information and a perceived emotional state.

[0886] The "means of providing" is a function for delivering selected news articles to users.

[0887] This system is designed to help users receive news from a positive perspective. The system operates by combining the following steps: news article collection, sentiment analysis, summary generation, user profile creation and storage, emotion recognition engine to determine the user's emotional state, and news selection and provision. A detailed implementation example is shown below.

[0888] News article collection

[0889] The server periodically collects the latest news articles from news sources on the Internet using an API. For example, every morning at 8:00, it sends an HTTP request to a specific news API using an authentication key to retrieve the latest 50 news articles. The collected article data is stored in an internal database.

[0890] Conducting sentiment analysis

[0891] The server performs sentiment analysis on the collected news articles. It uses natural language processing techniques such as TextBlob and VADER to classify each news article as positive, negative, or neutral, allowing it to extract positive news articles that are appropriate for the user.

[0892] Summary Generation

[0893] The server summarizes the news articles that are identified as positive. It uses a generative AI model or summarization algorithm (e.g., using a generative AI model) to concisely summarize the article's content. An example prompt might be, "Classify the following news articles as positive, negative, or neutral, and generate summaries of the positive articles."

[0894] Setting and saving a user profile

[0895] Users access the system using an internet-connected device to configure their news feed preferences, for example by selecting categories of interest such as "technology" or "health," and then submitting their preferences to the server, which stores this information in a database.

[0896] Introducing the Emotion Engine

[0897] The server runs an emotion engine to recognize the user's emotional state. It uses a camera to capture the user's facial expressions and uses facial expression analysis algorithms (e.g., OpenCV or DeepFace) to determine the emotion. It also collects audio data through a microphone and analyzes the tone and rate of the audio to determine the emotional state.

[0898] News selection and provision

[0899] The server selects appropriate summaries of positive news articles based on the user's stored profile information and the user's emotional state as recognized by the emotion engine. For example, if the server determines that the user is feeling stressed, it will prioritize positive news articles that are particularly relaxing and provide them as a feed.

[0900] Specific examples

[0901] For example, at 8:00 a.m., the server collects 50 articles from the news API, extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his or her device to set his or her interests in "technology" and "health" in his or her profile and sends this information to the server. This information is stored in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his or her emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his or her interests and emotional state.

[0902] This system allows users to efficiently obtain positive information that matches their interests and emotional state, helping to reduce daily stress and anxiety.

[0903] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0904] Step 1: Gather news articles

[0905] The server uses an API to collect the latest news articles from news sources on the Internet. Specifically, it sends an HTTP request to a specific news API using an authentication key. For example, a request is sent every morning at 8:00 to retrieve the latest 50 news articles. The input is news article data retrieved from the API, and the output is raw data stored on the server.

[0906] Step 2: Conducting sentiment analysis

[0907] The server performs sentiment analysis on the collected news articles. Specifically, it uses natural language processing libraries such as TextBlob and VADER to analyze each news article. For example, TextBlob is used to calculate the sentiment score of the article text and classify it as positive, negative, or neutral. The input is the news article text, and the output is the classification result including the sentiment score.

[0908] Step 3: Extracting positive news articles

[0909] The server extracts news articles that are judged to be positive based on the results of sentiment analysis. Specifically, it filters only articles that are deemed positive based on the classification results of sentiment analysis. The input is the classification results including sentiment scores, and the output is a list of positive news articles.

[0910] Step 4: Summarize the positive news article

[0911] The server summarizes the extracted positive news articles. Specifically, it uses a generative AI model or summarization algorithm (e.g., using a generative AI model) to concisely summarize the article's content. For example, it sends a prompt to the generative AI model saying, "Please summarize the following news article," and obtains the summarized text. The input is the full text of the positive news article, and the output is the summarized text.

[0912] Step 5: Set up and save your user profile

[0913] A user accesses the system using a terminal and configures his or her news feed. Specifically, the user selects categories of interest (e.g., "technology" or "health") in a web form and sends it to the server. The server stores the received profile information in a database. The input is the user's selected categories and preferences, and the output is the profile information stored in the database.

[0914] Step 6: Implementing the Emotion Engine

[0915] The server runs an emotion engine to recognize the user's emotional state. Specifically, it uses the device's camera to capture the user's facial expression and uses an expression analysis algorithm (e.g., OpenCV or DeepFace) to determine the emotion. It also analyzes audio data collected through the microphone and determines the emotion from the tone and rate of the voice. The input is the user's facial expression data and audio data, and the output is the emotional state as a result of the analysis.

[0916] Step 7: Select and deliver news

[0917] The server selects the most appropriate positive news articles based on the user's profile information and recognized emotional state. Specifically, it references the profile information and emotional data from the database to filter and rank the most appropriate news articles. For users who are feeling stressed, it prioritizes articles that are particularly relaxing. The input is positive news articles, profile information, and emotional state, and the output is a list of selected news articles.

[0918] This processing step allows users to efficiently receive positive news that matches their interests and emotional state, and can also reduce daily stress and anxiety.

[0919] (Application example 2)

[0920] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0921] Conventional news delivery systems provide generic news articles uniformly without fully considering the user's emotional state or interests, which means that the information users receive is not always pleasant or useful. As a result, users may feel dissatisfied or stressed when browsing the news. Furthermore, the quality of the customer experience in physical stores has not improved due to a lack of technology that can recognize customers' emotional state in real time and provide appropriate information. Therefore, there is a need for a system that can provide appropriate and positive news and product information according to the user's emotional state and interests.

[0922] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0923] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for using an emotion engine to recognize the user's emotional state, means for adjusting the news articles based on the emotional state recognized by the emotion engine, and means for providing the selected news articles to the user, thereby making it possible to provide positive news information and product information according to the user's emotional state and interests.

[0924] "Means for collecting news articles" refers to the function of obtaining article data from news sources on the Internet using an API.

[0925] "Means for sentiment analysis of collected news articles" refers to a function that uses natural language processing technology to classify the sentiment of an article and determine whether it is positive, negative, or neutral.

[0926] "Means for extracting positive news articles based on sentiment analysis" is a function that selects news articles that are determined to be positive as a result of sentiment analysis.

[0927] The "means for summarizing positive news articles" is a function that succinctly summarizes the content of news articles that are judged to be positive.

[0928] "Means for setting and saving user profile information" refers to the ability for users to input their interest categories and personal settings into the system and record that information in a database.

[0929] The "means for selecting positive news articles based on stored profile information" is a function that uses a user's profile information stored in the database to select the most appropriate positive news articles for that user.

[0930] "Means for using an emotion engine to recognize the user's emotional state" refers to a technology for analyzing the user's facial expressions and voice to determine the user's emotional state.

[0931] The "means for adjusting news articles based on the emotional state recognized by the emotion engine" is a function for changing the content and order of news articles provided depending on the emotional state of the user.

[0932] "Means for providing selected news articles to users" refers to a function that displays or provides audio guidance of the final selected news articles on the user's terminal or through a robot in a physical store.

[0933] The system of the present invention is designed to enable users to receive news from a positive perspective. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the preferred embodiments of the present invention.

[0934] News article collection

[0935] The server collects news articles from multiple news sources on the Internet. This collection is done using an API (Application Programming Interface) to efficiently retrieve article data. The API is called periodically, and the latest news articles are stored on the server.

[0936] Conducting sentiment analysis

[0937] The server then performs sentiment analysis on the collected news articles. This analysis uses natural language processing techniques. For example, it uses sentiment analysis libraries such as TextBlob or VADER to classify the content of each article as positive, negative, or neutral. Once the sentiment analysis is complete, news articles that are determined to be positive proceed to the next processing step.

[0938] Summary Generation

[0939] Once a news article is positively identified, it is processed for summary generation, using generative AI and summarization algorithms to concisely summarize the article's content, allowing users to quickly grasp the main points of the news. This process eliminates redundant information, leaving only the key points.

[0940] Setting and saving a user profile

[0941] Users access the system using their own devices and select categories of interest (e.g., technology, health, entertainment, etc.). User profile information is stored on the server and used to select news articles later.

[0942] Introducing the Emotion Engine

[0943] While a user is browsing the news, the emotion engine analyzes the user's emotional state in real time. The emotion engine captures the user's facial expressions through a camera and uses an expression analysis algorithm to determine their emotions. It also collects voice data through a microphone and analyzes the tone and speed of the voice to determine emotions. This analysis uses facial and voice recognition technologies.

[0944] News selection and provision

[0945] Finally, the server selects appropriate summaries of positive news articles based on the user's profile information and the emotional state recognized by the emotion engine. For example, if the server determines that the user is feeling stressed, it will prioritize positive news articles that will help them relax. The selected news articles are then displayed on the user's device or provided via a robot in a physical store.

[0946] Specific examples

[0947] For example, the server collects 50 articles from the news API at 8:00 a.m., extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his device to set his interests in "technology" and "health" in his profile, and the server stores this information in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his interests and emotional state.

[0948] Example of generative AI model and prompt

[0949] To improve the customer experience in physical stores, the system is installed on robots placed in physical stores and uses prompts such as:

[0950] "This in-store robot analyzes customers' faces and voices to provide positive health news and in-store product recommendations based on their emotional state."

[0951] "It uses facial and voice recognition to determine whether the customer is relaxed or stressed and then presents information that is appropriate for that state."

[0952] In this way, users can have a personal and positive experience when they visit the store.

[0953] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0954] Step 1:

[0955] The server collects news articles from news sources on the Internet using APIs. The input is the news API endpoint and API key, and the output is a list of the latest news articles. Specifically, the server periodically calls the API and stores the retrieved news article data in an internal database.

[0956] Step 2:

[0957] The server receives a list of news articles and performs sentiment analysis on each article. The input is a list of collected news articles, and the output is a list of news articles classified as positive, negative, or neutral. Specifically, the server uses sentiment analysis libraries such as TextBlob and VADER to analyze the content of each news article and calculate a sentiment score.

[0958] Step 3:

[0959] The server extracts news articles that are judged to be positive based on the results of sentiment analysis. The input is a list of news articles classified as positive, negative, or neutral, and the output is a list of positive news articles. Specifically, the server selects news articles from the list that have a sentiment score above a certain level.

[0960] Step 4:

[0961] The server generates summaries for news articles that are judged to be positive. The input is a list of positive news articles, and the output is a list of summarized positive news articles. Specifically, the server uses generative AI and summarization algorithms to extract and summarize the key points of each news article.

[0962] Step 5:

[0963] Users use their own devices to input, set, and save profile information. The input is the user's selected interest categories and personal settings, and the output is the set profile information being saved in the server's database. Specifically, users input information through the UI and send it to the server.

[0964] Step 6:

[0965] The server selects positive news articles based on user profile information. The input is a list of summarized positive news articles and the user's profile information, and the output is a list of news articles that are most suitable for the user. Specifically, the server references the profile information in the database and selects articles in categories that interest the user.

[0966] Step 7:

[0967] The user's device or a robot in a physical store recognizes the user's emotional state in real time. The input is image data and voice data of the user's face, and the output is the user's current emotional state. Specifically, the system uses a camera and microphone to analyze emotions using facial and voice recognition technology.

[0968] Step 8:

[0969] The server adjusts the news articles based on the emotional state recognized by the emotion engine. The input is the user's emotional state and a list of selected news articles, and the output is the adjusted list of news articles. Specifically, the server changes the priority of positive news articles according to the user's emotional state.

[0970] Step 9:

[0971] The selected news articles are provided to the user through the user's device or a robot in a brick-and-mortar store. The input is a tailored list of news articles, and the output is the news articles that are displayed or spoken to the user. In concrete terms, the device or robot provides the news articles to the user in an appropriate format.

[0972] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0973] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0974] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0975] [Fourth embodiment]

[0976] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0977] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0978] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0979] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0980] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0981] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0982] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0983] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0984] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0985] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0986] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0987] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0988] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0989] This system is designed to enable users to receive news from a positive perspective, and a series of processes, such as news article collection, sentiment analysis, summary generation, user profile setting and storage, and news selection and provision, are carried out by the server.

[0990] News article collection

[0991] The server collects the latest news articles from news sources. For example, it uses a news API to obtain news article data using the following procedure. A system is built to periodically collect the latest news using an endpoint and authentication key to access the API.

[0992] Conducting sentiment analysis

[0993] The server performs sentiment analysis on the collected news articles. Using natural language processing technology, it analyzes the content of the articles and classifies them as positive, negative, or neutral. This allows only positive news articles that are useful to the user to proceed to the next step.

[0994] Summary Generation

[0995] The server extracts positive news articles based on sentiment analysis and summarizes them using generative AI technology and summarization algorithms to concisely summarize the article content, allowing users to quickly and effectively obtain information.

[0996] Setting and saving a user profile

[0997] Users can access the system through their devices and set news categories based on their interests and preferences. The server then stores the user's input in a database. The stored profile information is then used to customize each user's individual news feed.

[0998] News selection and provision

[0999] The server selects summaries of positive news articles that match the user's interests based on the user's profile information. The selected news is formatted as a feed and provided to the user via their device. The user can then view the customized positive news on their device.

[1000] Specific examples

[1001] For example, the server collects 50 article data from a news API at 8:00 a.m. Next, it uses natural language processing technology to perform sentiment analysis on the articles and extracts 30 positive articles. The articles are then summarized using generative AI to create 30 summary articles. User A uses his device to set his interests in "technology" and "health" in his profile. The server stores the profile information set by User A in a database and, based on this, selects summary articles related to "technology" and "health" as a feed. As a result, User A can receive positive news that matches his interests through a customized feed displayed on his device.

[1002] This system allows users to efficiently obtain positive information that matches their interests, helping to reduce daily stress and anxiety.

[1003] The processing flow will be explained below.

[1004] Step 1:

[1005] The server collects the latest news articles from news sources. Specifically, it uses a news API to obtain news article data. It uses an authentication key for the API and performs a process to collect, for example, 50 articles at a fixed time every day.

[1006] Step 2:

[1007] The server uses natural language processing technology to analyze the sentiment of collected news articles. For example, it uses sentiment analysis libraries such as TextBlob and VADER to classify each article as positive, negative, or neutral. Only articles judged as positive are stored in a list.

[1008] Step 3:

[1009] The server then performs a summarization process on the positively identified news articles, using generative AI and summarization algorithms to concisely summarize the content of each article, which is then stored in a list.

[1010] Step 4:

[1011] Users access the system using their terminals and configure their news feed settings, such as selecting categories of interest (technology, health, entertainment, etc.), and sending this information to the server. The server then stores the selected data as the user's profile information.

[1012] Step 5:

[1013] The server stores the user's profile information in a database. The stored profile information is used to customize the news feed. For example, if user A is interested in "technology" and "health," this preference is recorded as profile information.

[1014] Step 6:

[1015] The server selects relevant articles from the summarized positive news articles based on the user's profile information. The selection criteria are news articles that match the user-specified categories. This generates a list of positive news articles tailored to the user's interests.

[1016] Step 7:

[1017] The server then provides the selected news articles to the user's device, allowing the user to view a customized positive news feed from their device. For example, news items displayed as a feed are formatted to show the title and summary.

[1018] In this way, the server, the terminal, and the user work together to realize a mechanism for providing a news feed customized from a positive perspective.

[1019] Example 1

[1020] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1021] In modern society, a huge amount of news articles are available on the Internet, many of which contain negative content. Exposure to such negative news can increase users' psychological burden and cause stress and anxiety. Furthermore, there is a lack of systems in place that can efficiently collect news in categories of interest to users and provide only positive articles based on that information. There is a need to address these issues.

[1022] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1023] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for providing the selected news articles to the user, means for summarizing the news articles using a generative AI model, and means for filtering news articles based on the user's setting information. This allows the user to be provided with only positive news articles that match their interests and preferences, thereby reducing daily stress and anxiety.

[1024] A "news article" is a piece of writing that reports events or information collected from the internet or other sources.

[1025] "Means of collection" refers to the functionality for obtaining news articles from news sources on the Internet.

[1026] "Means for sentiment analysis" refers to a function for analyzing the content of a news article and identifying its emotional nature.

[1027] "Positive news articles" refer to news articles that are determined to have a positive sentiment as a result of sentiment analysis.

[1028] "Means for extracting" refers to a function for selecting target data based on specific criteria.

[1029] "Means for summarizing" refers to the function of concisely summarizing the contents of a news article.

[1030] "User Profile Information" means data about a user's interests and preferences that is used to customize news delivery.

[1031] "Means for setting and saving" refers to the functionality that allows a user to input profile information and save that information.

[1032] "Means for selection" refers to the functionality for selecting appropriate news articles based on stored profile information.

[1033] "Means for providing" refers to a function for displaying selected news articles to a user.

[1034] A "generative AI model" refers to a computer model that uses artificial intelligence technology to create new data and information.

[1035] "Filtering means" refers to a function for filtering data based on specific criteria.

[1036] The system of this invention is designed to enable users to receive news from a positive perspective. A series of processes, including news article collection, sentiment analysis, summary generation, user profile setting and storage, and news selection and provision, are performed by the server.

[1037] The server collects the latest news articles from news sources on the Internet. For this purpose, the server leverages APIs to obtain news data. For example, it uses the NewsAPI.org endpoint and API key to periodically collect the latest news in JSON format.

[1038] The server then performs sentiment analysis on the collected news articles, using NLP libraries (e.g., NLTK, spaCy) and sentiment analysis models (e.g., TextBlob, Google's Sentiment Analysis API) to classify articles as positive, negative, or neutral.

[1039] For news articles that are judged to be positive as a result of sentiment analysis, the server summarizes them. This can be done using generative AI techniques (e.g., OpenAI's GPT-3) to concisely summarize the article's content, or it can use other summarization algorithms (e.g., TextRank, BERT Summarizer).

[1040] Users access the system through their devices and set up news categories based on their interests and preferences. The information entered by the user during this setting process is stored by the server in a database (e.g., MySQL, MongoDB). The saved profile information is then used to customize the news feed.

[1041] The server selects positive news articles that match the user's interests from the summarized articles based on the user's profile information. The selected articles are formatted as a feed and provided to the user via the device. The user can then view the customized positive news on the device.

[1042] As a concrete example, the server collects 50 news article data from a news API at 8:00 every morning and performs sentiment analysis to extract 30 positive articles. Next, it uses generative AI to summarize these positive articles and create 30 summary articles. User A uses his device to set his profile as being interested in "technology" and "health." Based on this profile information, the server selects summary articles related to "technology" and "health" as a feed and provides them to User A. As a result, User A can receive positive news that matches his interests through his device.

[1043] An example prompt is:

[1044] "Collect and summarize positive stories from today's latest news. News categories based on user interests: Technology and Health."

[1045] This system allows users to efficiently obtain positive information that is tailored to their interests, helping to reduce daily stress and anxiety.

[1046] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1047] Step 1: Gather news articles

[1048] explanation:

[1049] The server collects the latest news articles from news sources. Specifically, the server periodically sends requests to news APIs on the Internet to obtain news article data.

[1050] input:

[1051] News API endpoint and authentication key

[1052] output:

[1053] News article data in JSON format

[1054] Specific behavior:

[1055] For example, the server accesses the NewsAPI.org endpoint at 8:00 AM and retrieves 50 news articles, giving the server the latest news article information.

[1056] Step 2: Conducting sentiment analysis

[1057] explanation:

[1058] The server performs sentiment analysis on the collected news articles. It uses natural language processing techniques to analyze the text of each article and identify its emotional nature.

[1059] input:

[1060] News article text data

[1061] output:

[1062] Sentiment analysis results (positive, negative, neutral classification)

[1063] Specific behavior:

[1064] For example, the server uses TextBlob to calculate sentiment scores for 50 news articles and classify them as positive, negative, or neutral, allowing the server to identify positive news articles.

[1065] Step 3: Extracting positive news articles

[1066] explanation:

[1067] The server extracts only positive news articles based on the results of sentiment analysis.

[1068] input:

[1069] Sentiment analysis results

[1070] output:

[1071] Positive news articles

[1072] Specific behavior:

[1073] The server extracts news articles that are judged as positive by sentiment analysis and excludes other news articles. For example, if 30 out of 50 articles are judged as positive, these 30 news articles will be extracted.

[1074] Step 4: Generate a summary of the news article

[1075] explanation:

[1076] The server summarizes positive news articles, using a generative AI model to concisely summarize the article content.

[1077] input:

[1078] Positive news articles

[1079] output:

[1080] Summarized news articles

[1081] Specific behavior:

[1082] For example, the server uses GPT-3 to summarize 30 positive news articles, helping users quickly understand the news content.

[1083] Step 5: Set up and save your user profile

[1084] explanation:

[1085] Users set news categories through their terminals, and the server stores the information.

[1086] input:

[1087] User input information (interests and preferred news categories)

[1088] output:

[1089] Stored User Profile Information

[1090] Specific behavior:

[1091] For example, User A selects the categories "Technology" and "Health" on his device. The server stores this information in a database.

[1092] Step 6: News selection and delivery

[1093] explanation:

[1094] Based on the user's profile information, the server selects and provides summarized positive news articles that match the user's interests.

[1095] input:

[1096] Summarized news articles, user profile information

[1097] output:

[1098] News feeds provided to users

[1099] Specific behavior:

[1100] For example, the server selects summary articles related to "technology" and "health" based on User A's profile information and sends them to the device. User A can then browse the positive news that interests them through a customized feed displayed on the device.

[1101] Through this series of processes, the system allows users to reduce stress and anxiety in their daily lives and efficiently obtain positive information.

[1102] (Application example 1)

[1103] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1104] In today's world, people have access to a wide range of news articles, but many of them contain negative content, which can increase stress and anxiety. Furthermore, there is a lack of systems that efficiently provide news articles that match users' interests and preferences, which means that users spend a lot of time obtaining the information that is most relevant to them. Furthermore, effective news delivery methods using visual devices such as smart glasses and head-mounted displays have yet to be established.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1106] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for customizing news articles based on the user's interests and preferences, and means for providing the customized news articles to the user via the visual terminal, thereby enabling the user to effectively obtain positive news articles based on their interests and preferences and obtain information through the visual terminal while reducing stress and anxiety.

[1107] "Means of collecting news articles" refers to technologies such as APIs and web crawlers that regularly collect the latest articles from news sources on the Internet.

[1108] "Means for sentiment analysis" is a function that uses natural language processing technology to classify the content of news articles into positive, negative, or neutral.

[1109] "Method for extracting positive news articles" is a process for selecting only articles that contain positive emotions from the collected and analyzed news articles.

[1110] "A means of summarizing positive news articles" is a function that concisely summarizes selected positive news articles using generative AI technology and summarization algorithms.

[1111] "Means for establishing and storing user profile information" refers to a database or interface that allows users to register their interests and preferences and store them in the system.

[1112] A "positive news story selection method" is a process that selects news stories that match a user's interests and preferences based on a stored user profile.

[1113] The "means for customizing news articles" is a function that provides news articles in an appropriate format that are individually suited to the user based on the user's profile information.

[1114] "Means of providing to users via visual terminals" refers to technology that displays news articles through wearable devices such as smart glasses or head-mounted displays, providing them visually to users.

[1115] To implement this invention, we will build a system that collects news articles, analyzes sentiment, summarizes them, sets and saves user profiles, and selects and provides articles. The specific implementation method of the system is shown below.

[1116] The server uses the news API to collect the latest news articles from news sources on the Internet. It periodically retrieves news article data using an endpoint and authentication key to access the API. This data is stored in storage for further processing.

[1117] Next, the server performs sentiment analysis on the collected news articles using natural language processing technology. For sentiment analysis, it uses a common natural language processing library (e.g., Transformers) to classify the content of the article as positive, negative, or neutral. Through this process, only positive news articles that are useful to users are allowed to proceed to the next step.

[1118] Positive news articles are summarized using generative AI techniques and summarization algorithms (e.g., Transformer generative AI models), and the summarized articles are converted into a format that can be delivered to users as needed and managed within the system.

[1119] Users access the system through their devices and set news categories based on their interests and preferences. This profile information is stored in a database and used to customize each user's personal news feed.

[1120] Based on the saved profile information, the system selects summaries of positive news articles that match the user's interests. The selected news is formatted as a feed and delivered to the user via their device (smartphone, smart glasses, head-mounted display), allowing the user to browse customized positive news.

[1121] For example, the server collects 50 article data from a news API at 8:00 a.m. It then uses natural language processing technology to perform sentiment analysis and extract 30 positive articles. The articles are then summarized using generative AI to create 30 summary articles. User A uses their device to set their profile as being interested in "technology" and "health." The server stores the profile information set by User A in a database and, based on this, selects summary articles related to "technology" and "health" as a feed. As a result, User A can receive positive news that matches their interests through a customized feed displayed on their device.

[1122] An example of a prompt to input to a generative AI model is as follows:

[1123] text

[1124] Recent technological advancements have been astounding, with artificial intelligence playing an increasingly important role. For example, new AI solutions are...

[1125] By inputting this prompt into a generative AI model, a summary appropriate to the prompt can be obtained.

[1126] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1127] Step 1:

[1128] The server collects the latest articles from news sources on the Internet. Specifically, it accesses the news API and retrieves the latest news data using the endpoint and authentication key. This data is sent to the server in JSON format. The data retrieved from the news API is temporarily stored in storage.

[1129] Input: News API endpoint and authentication key

[1130] Output: News article data in JSON format

[1131] Step 2:

[1132] The server performs sentiment analysis on the collected news articles using natural language processing techniques. Specifically, it extracts the text of the news articles and classifies them into positive, negative, or neutral sentiment using an NLP library (e.g., Transformers). This classification result is tagged with each news article.

[1133] Input: News article data in JSON format

[1134] Output: News article data with sentiment tags

[1135] Step 3:

[1136] The server extracts positive news articles based on sentiment analysis, filters out only those tagged as positive, and creates a list for further processing steps.

[1137] Input: sentiment-tagged news article data

[1138] Output: A list of positive news articles

[1139] Step 4:

[1140] The server summarizes positive news articles. Using generative AI techniques (e.g., Transformer generative AI models), it concisely summarizes the content of each news article. The generated summaries are stored as a single text field.

[1141] Input: A list of positive news articles

[1142] Output: A list of summarized positive news articles

[1143] Step 5:

[1144] Users can set news categories based on their interests and preferences through their devices. They input and set the selected category information as a user profile. This information is sent to the server and stored in a database.

[1145] Input: Category information about your interests and preferences

[1146] Output: User profile information stored in a database

[1147] Step 6:

[1148] The server selects summarized positive news articles that match the user's interests based on the stored user profile information, and generates a customized news feed by applying appropriate filtering and matching based on the user profile information.

[1149] Input: List of summarized positive news articles, user profile information

[1150] Output: A customized news feed

[1151] Step 7:

[1152] The server provides selected news articles to users through their devices, displaying them on visual devices such as smart glasses or head-mounted displays. Users can use these visual devices to browse customized positive news in real time.

[1153] Input: Customized News Feed

[1154] Output: News article displayed on terminal

[1155] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1156] This system is designed to help users receive news from a positive perspective. It collects news articles, analyzes sentiment, generates summaries, sets and saves user profiles, selects and delivers news, and incorporates an emotion engine that recognizes users' emotions to further customize the content of the news feed.

[1157] News article collection

[1158] The server collects the latest news articles from news sources on the Internet using an API. For example, it periodically retrieves news articles using an authentication key for the news API. This allows you to always have the latest current information.

[1159] Conducting sentiment analysis

[1160] The server performs sentiment analysis on the collected news articles using natural language processing technology. It analyzes the content of the articles and classifies them as positive, negative, or neutral. For sentiment analysis, it uses sentiment analysis libraries such as TextBlob and VADER. Articles that are determined to be positive proceed to the next processing step.

[1161] Summary Generation

[1162] The server summarizes the extracted positive news articles based on sentiment analysis, and uses generative AI and summarization algorithms to concisely summarize the article content, allowing users to quickly and effectively grasp the main points of the news.

[1163] Setting and saving a user profile

[1164] Users access the system using their terminals and configure their news feed settings, such as selecting categories of interest (e.g., technology, health, entertainment), and submitting them to the server, which stores the user's profile information in a database and uses it to customize their news feed.

[1165] Introducing the Emotion Engine

[1166] The emotion engine uses techniques to analyze the user's facial expressions and voice to recognize the user's emotional state. For example, it uses a camera to capture the user's facial expressions and uses an expression analysis algorithm to determine the emotion, or it collects the user's voice through a microphone and analyzes the tone and rate of the voice to determine the emotion.

[1167] News selection and provision

[1168] The server combines the user's profile information with the user's emotional state as recognized by the emotion engine to select appropriate summaries of positive news articles. If the user is feeling stressed, the server will prioritize news articles with particularly positive content.

[1169] Specific examples

[1170] For example, the server collects 50 articles from the news API at 8:00 a.m., extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his device to set his interests in "technology" and "health" in his profile, and the server stores this information in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his interests and emotional state.

[1171] This system allows users to efficiently obtain positive information that is tailored to their interests and emotional state, helping to reduce daily stress and anxiety.

[1172] The processing flow will be explained below.

[1173] Step 1:

[1174] The server collects the latest news articles from news sources on the Internet. Specifically, it uses a news API to periodically send requests to a configured endpoint to obtain data on the latest news articles. This allows new information to be collected automatically every day.

[1175] Step 2:

[1176] The server performs sentiment analysis on the collected news articles using natural language processing technology. For example, it uses a sentiment analysis library to analyze the text of each news article and classify it as positive, negative, or neutral. Only news articles judged as positive are stored in a list.

[1177] Step 3:

[1178] The server uses generative AI to generate summaries for news articles that are positively identified, and the summaries are stored in a database along with the original articles, allowing users to quickly grasp key information.

[1179] Step 4:

[1180] Users access the system using their devices and configure their news feeds to reflect their preferences and interests. Specifically, they select news categories (e.g., technology, health, entertainment, etc.) and send the configuration information to the server.

[1181] Step 5:

[1182] The server stores user profile information in a database, which is used as the basis for customizing a user's individual news feed.

[1183] Step 6:

[1184] The user uses the device's camera and microphone to have the emotion engine recognize their emotional state. The device's camera captures the user's facial expressions, and an expression analysis algorithm is used to determine the user's emotions (e.g., joy, sadness, stress). The user's voice is also analyzed via the microphone, and the emotional state is assessed based on the tone and rate of the voice.

[1185] Step 7:

[1186] The server combines the user's profile information with the emotional state obtained by the emotion engine to select appropriate summaries of positive news articles. For example, if the user is feeling stressed, it will prioritize news articles that will help them relax.

[1187] Step 8:

[1188] The server provides selected news articles to the user's device, where the user can view a customized news feed. The news feed displays titles and summaries, and the user can click on articles of interest to read more.

[1189] As a concrete example, the server collects 50 articles from the news API at 8:00 a.m., performs sentiment analysis to extract 30 positive articles, and generates summaries. User A uses a device to set his / her profile as interested in "technology" and "health," and the sentiment engine detects User A's stress. In this case, the server selects particularly relaxing, positive news related to technology and health and provides it to User A's device. User A can receive news that matches his / her interests and emotional state.

[1190] In this way, the system provides a more personalized positive news feed based on the user's profile information and real-time emotional state.

[1191] Example 2

[1192] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1193] In modern society, negative news can increase psychological stress and anxiety when users consume news. Users also face the challenge of finding positive news that suits them from the vast amount of information available. There is a need for a system that can solve these problems, reduce daily stress and anxiety, and enable users to efficiently obtain positive news.

[1194] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1195] In this invention, the server includes means for collecting news articles, means for performing sentiment analysis, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for recognizing the user's emotional state, means for selecting positive news articles based on the saved profile information and emotional state, and means for providing the selected news articles to the user, thereby enabling the user to efficiently obtain positive news that matches their interests and emotional state and reduce daily stress and anxiety.

[1196] A "news article" is a written piece of information that reports current events or happenings.

[1197] "Means of collection" refers to a function for automatically acquiring article data from news sources on the Internet.

[1198] "Sentiment analysis" is a process that uses natural language processing technology to analyze emotional tendencies from text data and classify them as positive, negative, neutral, etc.

[1199] A "positive news article" is a news article that is classified as positive as a result of sentiment analysis.

[1200] "Means for summarizing" is a function that succinctly summarizes the contents of a news article.

[1201] "User profile information" refers to data about interest categories and personal preferences that a user provides to the system.

[1202] "Means for setting and saving" refers to a function for storing user-selected categories and setting information within the system.

[1203] "Means for recognizing emotional state" refers to technology that analyzes data on the user's facial expressions and voice to determine their emotional state at that time.

[1204] A "selection means" is a function that selects appropriate news articles based on stored profile information and a perceived emotional state.

[1205] The "means of providing" is a function for delivering selected news articles to users.

[1206] This system is designed to help users receive news from a positive perspective. The system operates by combining the following steps: news article collection, sentiment analysis, summary generation, user profile creation and storage, emotion recognition engine to determine the user's emotional state, and news selection and provision. A detailed implementation example is shown below.

[1207] News article collection

[1208] The server periodically collects the latest news articles from news sources on the Internet using an API. For example, every morning at 8:00, it sends an HTTP request to a specific news API using an authentication key to retrieve the latest 50 news articles. The collected article data is stored in an internal database.

[1209] Conducting sentiment analysis

[1210] The server performs sentiment analysis on the collected news articles. It uses natural language processing techniques such as TextBlob and VADER to classify each news article as positive, negative, or neutral, allowing it to extract positive news articles that are appropriate for the user.

[1211] Summary Generation

[1212] The server summarizes the news articles that are identified as positive. It uses a generative AI model or summarization algorithm (e.g., using a generative AI model) to concisely summarize the article's content. An example prompt might be, "Classify the following news articles as positive, negative, or neutral, and generate summaries of the positive articles."

[1213] Setting and saving a user profile

[1214] Users access the system using an internet-connected device to configure their news feed preferences, for example by selecting categories of interest such as "technology" or "health," and then submitting their preferences to the server, which stores this information in a database.

[1215] Introducing the Emotion Engine

[1216] The server runs an emotion engine to recognize the user's emotional state. It uses a camera to capture the user's facial expressions and uses facial expression analysis algorithms (e.g., OpenCV or DeepFace) to determine the emotion. It also collects audio data through a microphone and analyzes the tone and rate of the audio to determine the emotional state.

[1217] News selection and provision

[1218] The server selects appropriate summaries of positive news articles based on the user's stored profile information and the user's emotional state as recognized by the emotion engine. For example, if the server determines that the user is feeling stressed, it will prioritize positive news articles that are particularly relaxing and provide them as a feed.

[1219] Specific examples

[1220] For example, at 8:00 a.m., the server collects 50 articles from the news API, extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his or her device to set his or her interests in "technology" and "health" in his or her profile and sends this information to the server. This information is stored in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his or her emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his or her interests and emotional state.

[1221] This system allows users to efficiently obtain positive information that matches their interests and emotional state, helping to reduce daily stress and anxiety.

[1222] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1223] Step 1: Gather news articles

[1224] The server uses an API to collect the latest news articles from news sources on the Internet. Specifically, it sends an HTTP request to a specific news API using an authentication key. For example, a request is sent every morning at 8:00 to retrieve the latest 50 news articles. The input is news article data retrieved from the API, and the output is raw data stored on the server.

[1225] Step 2: Conducting sentiment analysis

[1226] The server performs sentiment analysis on the collected news articles. Specifically, it uses natural language processing libraries such as TextBlob and VADER to analyze each news article. For example, TextBlob is used to calculate the sentiment score of the article text and classify it as positive, negative, or neutral. The input is the news article text, and the output is the classification result including the sentiment score.

[1227] Step 3: Extracting positive news articles

[1228] The server extracts news articles that are judged to be positive based on the results of sentiment analysis. Specifically, it filters only articles that are deemed positive based on the classification results of sentiment analysis. The input is the classification results including sentiment scores, and the output is a list of positive news articles.

[1229] Step 4: Summarize the positive news article

[1230] The server summarizes the extracted positive news articles. Specifically, it uses a generative AI model or summarization algorithm (e.g., using a generative AI model) to concisely summarize the article's content. For example, it sends a prompt to the generative AI model saying, "Please summarize the following news article," and obtains the summarized text. The input is the full text of the positive news article, and the output is the summarized text.

[1231] Step 5: Set up and save your user profile

[1232] A user accesses the system using a terminal and configures his or her news feed. Specifically, the user selects categories of interest (e.g., "technology" or "health") in a web form and sends it to the server. The server stores the received profile information in a database. The input is the user's selected categories and preferences, and the output is the profile information stored in the database.

[1233] Step 6: Implementing the Emotion Engine

[1234] The server runs an emotion engine to recognize the user's emotional state. Specifically, it uses the device's camera to capture the user's facial expression and uses an expression analysis algorithm (e.g., OpenCV or DeepFace) to determine the emotion. It also analyzes audio data collected through the microphone and determines the emotion from the tone and rate of the voice. The input is the user's facial expression data and audio data, and the output is the emotional state as a result of the analysis.

[1235] Step 7: Select and deliver news

[1236] The server selects the most appropriate positive news articles based on the user's profile information and recognized emotional state. Specifically, it references the profile information and emotional data from the database to filter and rank the most appropriate news articles. For users who are feeling stressed, it prioritizes articles that are particularly relaxing. The input is positive news articles, profile information, and emotional state, and the output is a list of selected news articles.

[1237] This processing step allows users to efficiently receive positive news that matches their interests and emotional state, and can also reduce daily stress and anxiety.

[1238] (Application example 2)

[1239] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1240] Conventional news delivery systems provide generic news articles uniformly without fully considering the user's emotional state or interests, which means that the information users receive is not always pleasant or useful. As a result, users may feel dissatisfied or stressed when browsing the news. Furthermore, the quality of the customer experience in physical stores has not improved due to a lack of technology that can recognize customers' emotional state in real time and provide appropriate information. Therefore, there is a need for a system that can provide appropriate and positive news and product information according to the user's emotional state and interests.

[1241] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1242] In this invention, the server includes means for collecting news articles, means for sentiment analysis of the collected news articles, means for extracting positive news articles based on the sentiment analysis, means for summarizing the positive news articles, means for setting and saving user profile information, means for selecting positive news articles based on the saved profile information, means for using an emotion engine to recognize the user's emotional state, means for adjusting the news articles based on the emotional state recognized by the emotion engine, and means for providing the selected news articles to the user, thereby making it possible to provide positive news information and product information according to the user's emotional state and interests.

[1243] "Means for collecting news articles" refers to the function of obtaining article data from news sources on the Internet using an API.

[1244] "Means for sentiment analysis of collected news articles" refers to a function that uses natural language processing technology to classify the sentiment of an article and determine whether it is positive, negative, or neutral.

[1245] "Means for extracting positive news articles based on sentiment analysis" is a function that selects news articles that are determined to be positive as a result of sentiment analysis.

[1246] The "means for summarizing positive news articles" is a function that succinctly summarizes the content of news articles that are judged to be positive.

[1247] "Means for setting and saving user profile information" refers to the ability for users to input their interest categories and personal settings into the system and record that information in a database.

[1248] The "means for selecting positive news articles based on stored profile information" is a function that uses a user's profile information stored in the database to select the most appropriate positive news articles for that user.

[1249] "Means for using an emotion engine to recognize the user's emotional state" refers to a technology for analyzing the user's facial expressions and voice to determine the user's emotional state.

[1250] The "means for adjusting news articles based on the emotional state recognized by the emotion engine" is a function for changing the content and order of news articles provided depending on the emotional state of the user.

[1251] "Means for providing selected news articles to users" refers to a function that displays or provides audio guidance of the final selected news articles on the user's terminal or through a robot in a physical store.

[1252] The system of the present invention is designed to enable users to receive news from a positive perspective. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following describes in detail the preferred embodiments of the present invention.

[1253] News article collection

[1254] The server collects news articles from multiple news sources on the Internet. This collection is done using an API (Application Programming Interface) to efficiently retrieve article data. The API is called periodically, and the latest news articles are stored on the server.

[1255] Conducting sentiment analysis

[1256] The server then performs sentiment analysis on the collected news articles. This analysis uses natural language processing techniques. For example, it uses sentiment analysis libraries such as TextBlob or VADER to classify the content of each article as positive, negative, or neutral. Once the sentiment analysis is complete, news articles that are determined to be positive proceed to the next processing step.

[1257] Summary Generation

[1258] Once a news article is positively identified, it is processed for summary generation, using generative AI and summarization algorithms to concisely summarize the article's content, allowing users to quickly grasp the main points of the news. This process eliminates redundant information, leaving only the key points.

[1259] Setting and saving a user profile

[1260] Users access the system using their own devices and select categories of interest (e.g., technology, health, entertainment, etc.). User profile information is stored on the server and used to select news articles later.

[1261] Introducing the Emotion Engine

[1262] While a user is browsing the news, the emotion engine analyzes the user's emotional state in real time. The emotion engine captures the user's facial expressions through a camera and uses an expression analysis algorithm to determine their emotions. It also collects voice data through a microphone and analyzes the tone and speed of the voice to determine emotions. This analysis uses facial and voice recognition technologies.

[1263] News selection and provision

[1264] Finally, the server selects appropriate summaries of positive news articles based on the user's profile information and the emotional state recognized by the emotion engine. For example, if the server determines that the user is feeling stressed, it will prioritize positive news articles that will help them relax. The selected news articles are then displayed on the user's device or provided via a robot in a physical store.

[1265] Specific examples

[1266] For example, the server collects 50 articles from the news API at 8:00 a.m., extracts 30 positive news articles based on sentiment analysis, and generates summaries. User A uses his device to set his interests in "technology" and "health" in his profile, and the server stores this information in a database. At the same time, while User A is browsing the news, the emotion engine analyzes User A's facial expressions and voice to determine his emotional state. If it determines that User A is feeling stressed, the server will prioritize positive news that is particularly relaxing and provide it as a feed. In this way, User A can receive news that matches his interests and emotional state.

[1267] Example of generative AI model and prompt

[1268] To improve the customer experience in physical stores, the system is installed on robots placed in physical stores and uses prompts such as:

[1269] "This in-store robot analyzes customers' faces and voices to provide positive health news and in-store product recommendations based on their emotional state."

[1270] "It uses facial and voice recognition to determine whether the customer is relaxed or stressed and then presents information that is appropriate for that state."

[1271] In this way, users can have a personal and positive experience when they visit the store.

[1272] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1273] Step 1:

[1274] The server collects news articles from news sources on the Internet using APIs. The input is the news API endpoint and API key, and the output is a list of the latest news articles. Specifically, the server periodically calls the API and stores the retrieved news article data in an internal database.

[1275] Step 2:

[1276] The server receives a list of news articles and performs sentiment analysis on each article. The input is a list of collected news articles, and the output is a list of news articles classified as positive, negative, or neutral. Specifically, the server uses sentiment analysis libraries such as TextBlob and VADER to analyze the content of each news article and calculate a sentiment score.

[1277] Step 3:

[1278] The server extracts news articles that are judged to be positive based on the results of sentiment analysis. The input is a list of news articles classified as positive, negative, or neutral, and the output is a list of positive news articles. Specifically, the server selects news articles from the list that have a sentiment score above a certain level.

[1279] Step 4:

[1280] The server generates summaries for news articles that are judged to be positive. The input is a list of positive news articles, and the output is a list of summarized positive news articles. Specifically, the server uses generative AI and summarization algorithms to extract and summarize the key points of each news article.

[1281] Step 5:

[1282] Users use their own devices to input, set, and save profile information. The input is the user's selected interest categories and personal settings, and the output is the set profile information being saved in the server's database. Specifically, users input information through the UI and send it to the server.

[1283] Step 6:

[1284] The server selects positive news articles based on user profile information. The input is a list of summarized positive news articles and the user's profile information, and the output is a list of news articles that are most suitable for the user. Specifically, the server references the profile information in the database and selects articles in categories that interest the user.

[1285] Step 7:

[1286] The user's device or a robot in a physical store recognizes the user's emotional state in real time. The input is image data and voice data of the user's face, and the output is the user's current emotional state. Specifically, the system uses a camera and microphone to analyze emotions using facial and voice recognition technology.

[1287] Step 8:

[1288] The server adjusts the news articles based on the emotional state recognized by the emotion engine. The input is the user's emotional state and a list of selected news articles, and the output is the adjusted list of news articles. Specifically, the server changes the priority of positive news articles according to the user's emotional state.

[1289] Step 9:

[1290] The selected news articles are provided to the user through the user's device or a robot in a brick-and-mortar store. The input is a tailored list of news articles, and the output is the news articles that are displayed or spoken to the user. In concrete terms, the device or robot provides the news articles to the user in an appropriate format.

[1291] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1292] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1293] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1294] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1295] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1296] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1297] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1298] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1299] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1300] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1301] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1302] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1303] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1304] 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.

[1305] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1306] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1307] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1308] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1309] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1310] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1311] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1312] The following is further disclosed regarding the above embodiment.

[1313] (Claim 1)

[1314] a means of collecting news articles;

[1315] A means of sentiment analysis of collected news articles;

[1316] A means of extracting positive news articles based on sentiment analysis;

[1317] a means of summarizing positive news articles;

[1318] means for setting and storing user profile information;

[1319] means for selecting positive news articles based on the stored profile information;

[1320] A system including a means for providing selected news articles to a user.

[1321] (Claim 2)

[1322] 2. The system of claim 1, wherein the means for collecting news articles uses an API for obtaining article data from news sources on the Internet.

[1323] (Claim 3)

[1324] 10. The system of claim 1, wherein the means for performing sentiment analysis uses natural language processing techniques to classify the sentiment of the article.

[1325] "Example 1"

[1326] (Claim 1)

[1327] a means of collecting news articles;

[1328] A means of sentiment analysis of collected news articles;

[1329] A means of extracting positive news articles based on sentiment analysis;

[1330] a means of summarizing positive news articles;

[1331] means for setting and storing user profile information;

[1332] means for selecting positive news articles based on the stored profile information;

[1333] means for providing selected news articles to a user;

[1334] A means for summarizing news articles using a generative AI model; and

[1335] A system including means for filtering news articles based on user preferences.

[1336] (Claim 2)

[1337] 2. The system of claim 1, wherein the means for collecting news articles uses an API for obtaining article data from news sources on the Internet.

[1338] (Claim 3)

[1339] 10. The system of claim 1, wherein the means for performing sentiment analysis uses natural language processing techniques to classify the sentiment of the article.

[1340] "Application Example 1"

[1341] (Claim 1)

[1342] a means of collecting news articles;

[1343] A means of sentiment analysis of collected news articles;

[1344] A means of extracting positive news articles based on sentiment analysis;

[1345] a means of summarizing positive news articles;

[1346] means for setting and storing user profile information;

[1347] means for selecting positive news articles based on the stored profile information;

[1348] means for providing selected news articles to a user;

[1349] a means for customizing news articles based on a user's interests and preferences;

[1350] A system including a means for providing customized news articles to a user via a visual terminal.

[1351] (Claim 2)

[1352] 2. The system of claim 1, wherein the means for collecting news articles uses an API for obtaining article data from news sources on the Internet.

[1353] (Claim 3)

[1354] 10. The system of claim 1, wherein the means for performing sentiment analysis uses natural language processing techniques to classify the sentiment of the article.

[1355] "Example 2: Combining Emotion Engines"

[1356] (Claim 1)

[1357] a means of collecting news articles;

[1358] A means of sentiment analysis of collected news articles;

[1359] A means of extracting positive news articles based on sentiment analysis;

[1360] a means of summarizing positive news articles;

[1361] means for setting and storing user profile information;

[1362] means for recognizing the emotional state of a user;

[1363] means for selecting positive news articles based on the stored profile information and emotional state;

[1364] A system including a means for providing selected news articles to a user.

[1365] (Claim 2)

[1366] 10. The system of claim 1, wherein the means for collecting news articles uses an application program interface for obtaining article data from news sources on the Internet.

[1367] (Claim 3)

[1368] 10. The system of claim 1, wherein the means for performing sentiment analysis uses natural language processing techniques to classify the sentiment of the article.

[1369] "Application example 2 when combining emotion engines"

[1370] (Claim 1)

[1371] a means of collecting news articles;

[1372] A means of sentiment analysis of collected news articles;

[1373] A means of extracting positive news articles based on sentiment analysis;

[1374] a means of summarizing positive news articles;

[1375] means for setting and storing user profile information;

[1376] means for selecting positive news articles based on the stored profile information;

[1377] means for using an emotion engine to recognize an emotional state of a user;

[1378] means for adjusting the news article based on the emotional state recognized by the emotion engine;

[1379] A system including a means for providing selected news articles to a user.

[1380] (Claim 2)

[1381] 2. The system of claim 1, wherein the means for collecting news articles uses an API for obtaining article data from news sources on the Internet.

[1382] (Claim 3)

[1383] 10. The system of claim 1, wherein the means for performing sentiment analysis uses natural language processing techniques to classify the sentiment of the article. [Explanation of symbols]

[1384] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of collecting news articles; A means of sentiment analysis of collected news articles; A means of extracting positive news articles based on sentiment analysis; a means of summarizing positive news articles; means for setting and storing user profile information; means for selecting positive news articles based on the stored profile information; and means for providing the selected news articles to a user.

2. 2. The system of claim 1, wherein the means for collecting news articles uses an API for acquiring article data from news sources on the Internet.

3. 10. The system of claim 1, wherein the means for performing sentiment analysis uses natural language processing techniques to classify the sentiment of the article.

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