System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting input information from a user terminal used by a user and measuring vocabulary and Chinese character ability of the user by an AI, a means for rewriting a news article published in the world by the AI based on the measurement result, and a means for preferentially displaying an article based on a word frequently used by the user and an interested field.SELECTED DRAWING: Figure 1

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] Conventional news articles and information provision systems often use advanced vocabulary and technical terms, making them difficult to understand, especially for users with limited vocabulary or kanji ability. This creates an information gap, preventing users from accessing necessary information or enjoying articles in their favorite fields. The present invention aims to eliminate this information gap by adjusting the difficulty level of information according to the user's vocabulary and kanji ability. [Means for solving the problem]

[0005] This invention provides a means for AI to collect input information from a user's device and measure the user's vocabulary and kanji ability. Based on the results, the AI ​​rewrites publicly available news articles in a way that is easy for the user to understand. It also includes a means for preferentially displaying articles based on the user's frequently used words and areas of interest. These means make it easier for users to obtain information appropriate to their own vocabulary and kanji ability.

[0006] A "terminal" is a device used by a user, such as a computer or smartphone.

[0007] "Input information" refers to text data and answer data to options provided by the user via the terminal.

[0008] "Vocabulary" refers to the number and depth of words a user can understand and use.

[0009] "Kanji ability" refers to the number and depth of kanji that a user can understand and use.

[0010] "AI" is an abbreviation for artificial intelligence, which refers to the software and algorithms that computers use to perform specific tasks.

[0011] "Measurement" is the process of assessing or calculating a particular parameter or capability.

[0012] A "news article" is a current affairs or news item disseminated to the public.

[0013] "Generative AI" refers to artificial intelligence that automatically generates new text or content based on specific data or instructions.

[0014] "Rewrite" means to change the original text into another expression.

[0015] "Priority display" refers to placing content in a more prominent position than other information based on specific criteria.

[0016] "Hobbies and preferences" refers to a user's interests, concerns, and preferences. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

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

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

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

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0032] 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.

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

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system for providing personalized news articles based on a user's vocabulary and kanji ability. An embodiment of the system will be described in detail below.

[0039] Program processing explanation

[0040] 1. User registration and initial settings

[0041] User: Opens the application for the first time and enters the required information on the account creation page.

[0042] Terminal: Sends the entered information to the server.

[0043] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[0044] On the device: Present the initial setup survey to the user and collect their responses.

[0045] 2. Vocabulary and Kanji ability assessment

[0046] User: View the quizzes and sentences provided in the application and enter their answers.

[0047] Device: Sends quiz answer data to the server.

[0048] Server: Receives the response data and uses an AI model to analyze and measure the user's vocabulary and kanji ability. The results are stored in a database.

[0049] 3. Personalize your news articles

[0050] Server: Gets the latest news articles from an external news API.

[0051] Server: Reflecting each user's vocabulary and kanji ability, the server uses generative AI to rewrite news articles into expressions that are easy for the user to understand. For example, it converts the term "fiscal policy" into "how the government spends money."

[0052] Server: Determines the ranking of news articles based on user interest data.

[0053] Server: Generates a personalized list of news articles and sends it to the user's device.

[0054] 4. View articles and gather feedback

[0055] On your device: Show personalized news articles to your users.

[0056] User: Read the article and provide feedback on comprehension and satisfaction.

[0057] Device: Collects user feedback and sends it to the server.

[0058] Server: Stores the feedback in a database and uses this data to improve the generative AI model.

[0059] Specific examples

[0060] Article personalization examples

[0061] 3.1. Server: Get the economy-related news article "Japan's GDP grows."

[0062] 3.2. Server: User A's vocabulary and kanji ability are intermediate, so convert "GDP" to "gross domestic product" and "growth" to "got up."

[0063] 3.3. Server: Since user A is interested in economics, place this article at the top of the priority display list.

[0064] 3.4. Server: Generates a personalized article list and sends it to the device.

[0065] 4.1. Device: Display the personalized article "Japan's GDP has increased" to User A.

[0066] 4.2. User: Reads the article and gives feedback that it was easy to understand.

[0067] 4.3. Terminal: Display the feedback form and collect User A's input.

[0068] 4.5. Server: Analyzes the feedback data and uses it to improve the generative AI model.

[0069] As described above, the present invention aims to eliminate the information gap by providing news articles that correspond to the user's vocabulary and kanji ability, thereby improving the user's ease of accepting information and level of interest.

[0070] The processing flow will be explained below.

[0071] Program processing details

[0072] Step 1:

[0073] User: Opens the application for the first time and enters the required information on the account creation page.

[0074] Step 2:

[0075] Terminal: Sends the entered information to the server.

[0076] Step 3:

[0077] Server: Creates an account based on the provided user information and saves it in the database.

[0078] Server: Generates the initial survey and sends it to the device.

[0079] Step 4:

[0080] On the device: Present the initial setup survey to the user and collect their responses.

[0081] Step 5:

[0082] User: Complete the initial setup survey and send the answers to your device.

[0083] Step 6:

[0084] Terminal: Sends the response data to the server.

[0085] Step 7:

[0086] Server: Receives the response data and uses the AI ​​model to perform an initial vocabulary assessment and initial interest settings for the user.

[0087] Step 8:

[0088] User: View the quizzes and sentences provided in the application and enter answers to them.

[0089] Step 9:

[0090] Terminal: Collects user quiz answer data and sends it to the server.

[0091] Step 10:

[0092] Server: Receives the response data and uses an AI model to measure the user's vocabulary and kanji ability.

[0093] Server: Stores the measurement results in a database.

[0094] Step 11:

[0095] Server: Gets the latest news articles from an external news API.

[0096] Step 12:

[0097] Server: Based on each user's vocabulary data, generative AI is used to rewrite news articles into expressions that are easy for the user to understand.

[0098] For example, convert "GDP" to "gross domestic product" and "growth" to "increased."

[0099] Step 13:

[0100] Server: Determines the ranking of news articles based on user interest data.

[0101] Step 14:

[0102] Server: Generates a personalized list of news articles and sends it to the device.

[0103] Step 15:

[0104] On your device: Show personalized news articles to your users.

[0105] Step 16:

[0106] User: Read the article and provide feedback on comprehension and satisfaction.

[0107] Step 17:

[0108] Terminal: Display a feedback form and collect user input.

[0109] Step 18:

[0110] Device: Sends feedback data to the server.

[0111] Step 19:

[0112] Server: Receives feedback and stores it in a database.

[0113] Server: Analyzes the feedback data and uses it to improve the generative AI model.

[0114] Through the above steps, the system of the present invention aims to provide personalized information tailored to the user's vocabulary and kanji ability, thereby eliminating the information gap.

[0115] Example 1

[0116] 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."

[0117] In modern society, a huge amount of news articles are updated daily, but not all of this information is easy to understand for all users. In particular, users with different vocabulary and kanji abilities have difficulty understanding news articles that contain technical terms and difficult vocabulary, resulting in differences in how easily they accept information. As a result, certain user groups may not be able to obtain sufficient information, and the information gap may widen. In addition, articles are not sufficiently personalized based on users' interests, making it difficult for users to find articles that will interest them. Therefore, there is a need for a system that provides easy-to-understand news articles that suit each user's vocabulary and kanji ability.

[0118] 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.

[0119] In this invention, the server includes means for collecting input information from the user's device, means for an AI to measure the user's vocabulary and kanji ability, means for the generation AI to rewrite publicly available news articles based on the results of the measurement, means for preferentially displaying articles based on the user's frequently used words and areas of interest, and means for collecting feedback from the user and improving the AI ​​model. This enables users to receive easy-to-understand news articles that are suited to their vocabulary and kanji ability, making it easier for them to access information that is of high interest to them.

[0120] "User's device" refers to a device such as a computer, smartphone, or tablet used by a user.

[0121] "Input information" refers to information that a user enters through a device, including name, email address, password, survey responses, quiz and assignment responses, etc.

[0122] "Vocabulary" refers to the range and number of words a user can understand and use.

[0123] "Kanji ability" refers to the range and number of kanji characters that a user can understand and use.

[0124] "AI" is an abbreviation for artificial intelligence, and specifically refers to computer programs and algorithms that analyze user input and evaluate vocabulary and kanji ability.

[0125] "Public news articles" refers to the latest economic, social, political, and other news articles obtained from external news providers.

[0126] "Generative AI" refers to a generative artificial intelligence model that rewrites news articles into expressions that suit the user's vocabulary and kanji ability.

[0127] "Frequent words" refer to words or phrases that a user has frequently used or searched for in the past.

[0128] "Areas of interest" refers to news categories and topics that users have expressed a high level of interest in through surveys, usage history, etc.

[0129] "Feedback" refers to opinions and ratings regarding understanding and satisfaction that users provide after reading a news article.

[0130] "AI model improvement" refers to the process of utilizing collected feedback data to improve the performance of a generative AI model.

[0131] The present invention is a system that provides personalized news articles based on the user's vocabulary and kanji ability. To implement this system, the following specific steps are taken.

[0132] User registration and initial settings

[0133] First, the user installs the application and, when using it for the first time, enters required information such as name, email address, and password on the account creation page. The device sends the entered information to the server. The server receives this information and creates a new account. Specifically, it stores this information in a database (e.g., MySQL (registered trademark)). Next, the server generates an initial setup questionnaire for the user and sends it to the device. The device displays the questionnaire to the user and prompts the user to enter their answers. The answered information is sent from the device to the server and stored in the database.

[0134] Vocabulary and Kanji ability assessment

[0135] The user views quizzes and sentences provided within the application and enters their answers. The device sends this answer data to the server. The server uses a generative AI model (e.g., GPT-3 (registered trademark)) to analyze the received answer data. At this time, the server sends the following prompt to the generative AI model: "Based on the user's answers, please evaluate this user's vocabulary and kanji ability." The generative AI model evaluates the user's vocabulary and kanji ability based on this prompt. The evaluation results are stored in a database by the server.

[0136] Personalized news articles

[0137] Next, the server retrieves the latest news articles using an external news API (e.g., newsAPI.org). These retrieved news articles are rewritten to suit each user's vocabulary and kanji ability. The server then uses a generative AI model to send a prompt such as, "The user's vocabulary is intermediate. Please simplify the wording of this article." This prompt converts the wording of the article. For example, it converts "fiscal policy" to "how the government uses money." In this way, articles that have been rewritten to be easier for the user to understand are ranked based on the user's interest data. Finally, a personalized list of news articles is generated and sent to the device.

[0138] View articles and gather feedback

[0139] The device displays a personalized list of news articles sent from the server to the user. The user reads the articles and provides feedback on their understanding and satisfaction. The feedback is sent to the server via the device. The server stores the received feedback in a database and uses it to improve the generative AI model.

[0140] Specific examples

[0141] In personalizing a news article, the server retrieves an economic news article titled "Japan's GDP grows."

[0142] If User A's vocabulary and kanji ability are intermediate, the generation AI will convert "GDP" to "gross domestic product" and "growth" to "risen."

[0143] User A is very interested in economics, so this article will be placed high on the priority display list.

[0144] This personalized article list is sent to the device and displayed to User A.

[0145] This invention provides news articles tailored to the individual abilities of users, facilitating their understanding and acceptance of information, thereby realizing a system that alleviates the digital divide and efficiently provides information tailored to the user's interests.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] User registration and initial settings

[0149] User: The user opens the application and enters the required information on the account creation page, such as name, email address, and password.

[0150] Input: User information such as name, email address, and password.

[0151] Terminal: The terminal sends the entered information to the server.

[0152] Output: User information sent to the server.

[0153] Server: The server creates a new account based on the received user information, stores this information in a database, and generates an initial setup survey and sends it to the device.

[0154] Input: User information sent from the device.

[0155] Output: Saved user account, generated survey.

[0156] Terminal: The terminal displays the survey to the user and prompts the user to enter their answers.

[0157] Input: Initial Setup Survey.

[0158] User: The user completes an initial setup questionnaire and enters the answers into the device.

[0159] Output: User's survey responses.

[0160] Terminal: The terminal sends the survey responses to the server.

[0161] Input: User's survey response information.

[0162] Output: Survey responses sent to the server.

[0163] Step 2:

[0164] Vocabulary and Kanji ability assessment

[0165] User: The user enters answers to quizzes and assignments.

[0166] Input: Quiz or assignment, user's answer.

[0167] Terminal: The terminal sends the user's response data to the server.

[0168] Output: The response data sent to the server.

[0169] Server: Based on the received answer data, the server generates prompt sentences to evaluate the user's vocabulary and kanji ability, and sends these prompt sentences to the generative AI model.

[0170] Input: User response data.

[0171] Output: A prompt to the generative AI model.

[0172] Generative AI model: The generative AI model evaluates the user's vocabulary and kanji ability based on the prompt sentence.

[0173] Output: The evaluation result.

[0174] Server: The server stores the evaluation results received from the generative AI model in a database.

[0175] Input: Evaluation results from a generative AI model.

[0176] Output: Evaluation results stored in a database.

[0177] Step 3:

[0178] Personalized news articles

[0179] Server: The server retrieves the latest news articles from an external news API.

[0180] Input: News API request.

[0181] Output: News article data.

[0182] Server: The server uses a generative AI model to generate prompts for rewriting the retrieved news articles based on the user's vocabulary and kanji ability. Example prompt: "The user has intermediate vocabulary, so please simplify the wording of this article."

[0183] Input: News article, user vocabulary and kanji proficiency data.

[0184] Output: A prompt to the generative AI model.

[0185] Generative AI model: Based on a prompt, the generative AI model rewrites news articles in a way that is easy for users to understand.

[0186] Output: A rewritten news article.

[0187] Server: The server determines the order in which news articles to display based on the user's interest data, generates a personalized news article list, and sends it to the device.

[0188] Input: rewritten news articles, user interest data.

[0189] Output: A personalized list of news articles.

[0190] Step 4:

[0191] View articles and gather feedback

[0192] Device: The device displays a personalized list of news articles to the user.

[0193] Input: A list of news articles sent by the server.

[0194] User: The user reads the article and provides feedback on their understanding and satisfaction.

[0195] Output: User feedback data.

[0196] Device: The device sends the user's feedback to the server.

[0197] Input: User feedback data.

[0198] Server: The server stores the received feedback data in a database and uses it to improve the generative AI model.

[0199] Output: Feedback data stored in a database.

[0200] (Application example 1)

[0201] 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."

[0202] Conventional advertising systems do not personalize ads based on the user's vocabulary or writing ability, which results in ads that are difficult for users to understand. This reduces the effectiveness of ads and makes it difficult for them to attract users' interest.

[0203] 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.

[0204] In this invention, the server includes means for collecting input information from the user's terminal and measuring the user's vocabulary and writing ability with an AI, means for the generation AI to rewrite advertising information based on the measurement results, and means for preferentially displaying advertisements based on the user's frequently used words and areas of interest. This allows advertisements to be optimized according to the user's level of understanding, making it possible to more effectively attract the user's interest and attention.

[0205] Key Word Definitions

[0206] "User" refers to any individual or group of people who use the System.

[0207] "User terminal" refers to an electronic device that allows a user to access and operate the system.

[0208] "Input information" refers to data entered by a user into a terminal and transmitted to a server.

[0209] "Vocabulary" refers to the range of words a user can understand and use.

[0210] "Character ability" refers to the range of characters, kana notations, and kanji characters that a user can understand and use.

[0211] "AI" refers to an information processing system that uses artificial intelligence technology.

[0212] "Advertising information" refers to information intended to promote the sale of products or advertise services.

[0213] "Generative AI" refers to a technology that uses artificial intelligence to generate sentences based on the user's vocabulary and writing ability.

[0214] A "word" refers to the smallest unit of expression in language.

[0215] A "field" refers to an area related to a particular subject or theme.

[0216] "Means" refers to the process or method for achieving a goal.

[0217] A "system" refers to a set of mechanisms in which multiple components work together to achieve a specific function.

[0218] patent specification

[0219] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below.

[0220] First, the user launches the application on their device and creates an account. When creating an account, they enter the necessary information. The device then sends the entered information to the server.

[0221] The server creates an account based on the received user information, stores this data in a database, and then generates questionnaires and quizzes to measure the user's vocabulary and writing ability as an initial setting, and sends them to the user's device.

[0222] Users answer these initial setup quizzes and surveys. The device sends the user's response data to the server. The server then analyzes the response data using an AI model to measure the user's vocabulary and writing ability. The analysis results are stored in a database.

[0223] The server then retrieves the latest ad information from an external ad API. Based on the user's vocabulary and writing ability measurements, the server uses a generative AI model to rewrite the ad information into language that is easier for the user to understand. For example, it converts technical jargon and difficult vocabulary into simpler language. This rewriting process uses advanced natural language processing models such as GPT-4 (registered trademark).

[0224] The server then determines the order in which ads are displayed based on the user's past behavior and areas of interest. Once a properly personalized ad list is generated, it is sent to the user's device.

[0225] The device displays this personalized ad list to the user. The user views the ads and provides feedback on their content. The device collects this feedback data and sends it to the server. The server uses this feedback data to continuously improve the accuracy of the generative AI model.

[0226] The specific hardware and software used are Python and Flask for the server, MySQL for database management, GPT-4 API for the generative AI model, and React Native for the front-end application.

[0227] Specific examples

[0228] Below are some specific examples of ad personalization:

[0229] Original ad copy

[0230] "The latest smartphone is now available. Its AI camera and 5G compatibility will change your everyday life."

[0231] Personalized ad text

[0232] "New smartphone released with AI camera and high-speed internet"

[0233] Prompt Sentence Examples

[0234] "If the user has an intermediate level of vocabulary, personalize the original ad copy, 'The latest smartphone is here. It changes your daily life with its AI camera and 5G connectivity,' in simpler terms."

[0235] In this way, the present invention realizes maximizing the effectiveness of advertising by appropriately personalizing advertising information in accordance with the user's vocabulary and writing ability.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Program processing steps

[0238] Step 1:

[0239] The user launches the application on their device and creates an account.

[0240] Input: User information from your device (username, password, email address, etc.)

[0241] Output: User information sent to the server

[0242] Specific operation: The terminal sends the entered user information to the server.

[0243] Step 2:

[0244] The server creates an account based on the received user information and stores it in the database.

[0245] Input: User information sent to the server

[0246] Output: Account information stored in the database

[0247] Specific operation: The server receives the user information and saves it as a new record in the database (MySQL).

[0248] Step 3:

[0249] As an initial setting, the server generates questionnaires and quizzes to measure vocabulary and writing ability, and sends them to the user's device.

[0250] Input: New user registration information

[0251] Output: Initial setup quiz and survey

[0252] Specific operation: The server generates quizzes and surveys and sends them to the user's device.

[0253] Step 4:

[0254] Users complete default quizzes and surveys.

[0255] Input: Quizzes and surveys

[0256] Output: User's answer

[0257] Specific operation: The user answers the displayed quiz or survey and sends it to their device.

[0258] Step 5:

[0259] The terminal sends the user's response data to the server.

[0260] Input: User response data

[0261] Output: Response data sent to the server

[0262] Specific operation: The user device sends the response data to the server.

[0263] Step 6:

[0264] The server receives the response data, analyzes the user's vocabulary and writing ability using a generative AI model, and stores the results in a database.

[0265] Input: User response data

[0266] Output: Vocabulary and writing ability measurements

[0267] Specific operation: The server analyzes the response data, measures vocabulary and writing ability, and stores the results in a database.

[0268] Step 7:

[0269] The server retrieves the latest ad information from an external ad API.

[0270] Input: External Ads API

[0271] Output: Retrieved advertising information

[0272] Specific operation: The server periodically calls the external advertising API to obtain the latest advertising information.

[0273] Step 8:

[0274] The server uses a generative AI model to personalize advertising information based on the user's vocabulary and writing ability.

[0275] Input: Vocabulary and writing ability test results, advertising information

[0276] Output: Personalized advertising information

[0277] Specific operation: The server uses a generative AI model (GPT-4) to rewrite advertising information into expressions that are easy for users to understand.

[0278] Step 9:

[0279] The server determines the order in which ads are displayed based on the user's past behavioral data and areas of interest.

[0280] Input: User behavior data, interests, personalized advertising

[0281] Output: Prioritized ad list

[0282] Specific operation: The server determines the ad ranking based on the user's behavioral data and areas of interest, and generates an ad list.

[0283] Step 10:

[0284] The server transmits the personalized advertisement list to the user terminal.

[0285] Input: Prioritized Ad List

[0286] Output: Ad list sent to the user's device

[0287] Specific operation: The server sends a personalized ad list to the user's device.

[0288] Step 11:

[0289] The device displays a personalized list of ads to the user.

[0290] Input: Personalized Ad List

[0291] Output: The ad shown to the user

[0292] Specific operation: The user device receives the advertisement list and displays it to the user.

[0293] Step 12:

[0294] Users view ads and provide feedback.

[0295] Enter: Advertisement

[0296] Output: User feedback

[0297] What happens: Users view the ad and provide feedback on comprehension and interest.

[0298] Step 13:

[0299] The user terminal collects feedback data and transmits it to the server.

[0300] Input: User feedback

[0301] Output: Feedback data sent to the server

[0302] Specific operation: The user device collects feedback data and sends it to the server.

[0303] Step 14:

[0304] The server uses the feedback data to improve the accuracy of the generative AI model.

[0305] Input: Feedback data

[0306] Output: An updated generative AI model

[0307] What it does: The server analyzes the feedback data to improve the accuracy of the generative AI model (GPT-4) and help with future ad personalization.

[0308] 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.

[0309] The present invention provides a system that personalizes news articles based on a user's vocabulary and kanji ability, and further recognizes the user's emotions to provide appropriate content. The following describes an embodiment of the system in detail.

[0310] Program processing explanation

[0311] 1. User registration and initial settings

[0312] User: Opens the application for the first time and enters the required information on the account creation page.

[0313] Terminal: Sends the entered information to the server.

[0314] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[0315] On the device: Present the initial setup survey to the user and collect their responses.

[0316] 2. Vocabulary and Kanji ability assessment

[0317] User: View the quizzes and sentences provided in the application and enter their answers.

[0318] Device: Sends quiz answer data to the server.

[0319] Server: Receives the response data and uses an AI model to analyze and measure the user's vocabulary and kanji ability. The results are stored in a database.

[0320] 3. Emotion recognition using the emotion engine

[0321] User: Enter text or voice input.

[0322] Terminal: Sends user input data (text or voice) to the server.

[0323] Server: The emotion engine analyzes the input data and recognizes the user's emotions. For example, if the user is sad or happy, it records the emotion label.

[0324] Server: Stores the analysis results in a database.

[0325] 4. Personalize your news articles

[0326] Server: Gets the latest news articles from an external news API.

[0327] Server: Using each user's vocabulary data, the server uses generative AI to rewrite news articles into expressions that are easier for the user to understand. For example, it converts "GDP" to "gross domestic product" and "growth" to "risen."

[0328] Server: Determines the order in which news articles are displayed based on user interest data and emotion recognition results.

[0329] For example, if a user is feeling sad, encouraging content or positive news articles will be prioritized.

[0330] Server: Generates a personalized list of news articles and sends it to the device.

[0331] 5. View articles and gather feedback

[0332] On your device: Show personalized news articles to your users.

[0333] User: Read the article and provide feedback on comprehension and satisfaction.

[0334] Terminal: Display a feedback form and collect user input.

[0335] Device: Sends feedback data to the server.

[0336] Server: Stores the feedback in a database and uses this data to improve the generative AI model.

[0337] Specific examples

[0338] Example of article personalization based on sentiment

[0339] 3.1. Server: Receives the text "I'm not feeling very well today" entered by User A on his smartphone.

[0340] 3.2. Server: The emotion engine analyzes this input data and recognizes that User A has the emotion "sad."

[0341] 4.1. Server: Get the latest news article "Economic Growth Outlook".

[0342] 4.2. Server: User A's vocabulary data is intermediate, so convert "economic growth" to "economic rise."

[0343] 4.3. Server: Since User A is feeling "sad," it is decided to prioritize displaying articles with encouraging and positive content.

[0344] 4.4. Server: Generates a personalized article list and sends it to the device.

[0345] 5.1. Device: Show personalized article with positive content to User A.

[0346] 5.2. User: Reads the article and gives feedback saying that it "inspired me."

[0347] 5.3. Terminal: Collects feedback and sends it to the server.

[0348] 5.4. Server: Stores the feedback and uses it to improve the generative AI model.

[0349] As described above, the present invention aims to eliminate the digital divide and improve the user experience by providing information that takes into account the user's vocabulary, kanji ability, and even emotional state, and by providing content that is easy for users to understand and appropriate.

[0350] The processing flow will be explained below.

[0351] Program processing details

[0352] Step 1:

[0353] User: Opens the application for the first time and enters the required information on the account creation page.

[0354] Step 2:

[0355] Terminal: Sends the entered information to the server.

[0356] Step 3:

[0357] Server: Creates an account based on the provided user information and saves it in the database.

[0358] Server: Generates the initial setup survey and sends it to the device.

[0359] Step 4:

[0360] On the device: Present the initial setup survey to the user and collect their responses.

[0361] Step 5:

[0362] User: Complete the initial setup survey and send the answers to your device.

[0363] Step 6:

[0364] Terminal: Sends the response data to the server.

[0365] Step 7:

[0366] Server: Receives the response data and uses the AI ​​model to perform an initial vocabulary assessment and initial interest settings for the user.

[0367] Step 8:

[0368] User: View the quizzes and sentences provided in the application and enter answers to them.

[0369] Step 9:

[0370] Terminal: Collects user quiz answer data and sends it to the server.

[0371] Step 10:

[0372] Server: Receives the response data and uses an AI model to measure the user's vocabulary and kanji ability.

[0373] Server: Stores the measurement results in a database.

[0374] Step 11:

[0375] User: Express their feelings through text input or voice input.

[0376] Step 12:

[0377] Device: Sends the user's text and voice data to the server.

[0378] Step 13:

[0379] Server: The emotion engine analyzes the input data and recognizes the user's emotions.

[0380] For example, the text "I'm tired today" is analyzed and the emotion label "tired" is assigned.

[0381] Step 14:

[0382] Server: Stores the sentiment analysis results in a database.

[0383] Step 15:

[0384] Server: Gets the latest news articles from an external news API.

[0385] Step 16:

[0386] Server: Based on each user's vocabulary data, generative AI is used to rewrite news articles into expressions that are easy for the user to understand.

[0387] For example, convert "GDP" to "gross domestic product" and "growth" to "increased."

[0388] Step 17:

[0389] Server: Determines the order in which news articles are displayed based on user interest data and emotion recognition results.

[0390] For example, if a user is feeling "tired," positive articles related to energy recovery will be displayed first.

[0391] Step 18:

[0392] Server: Generates a personalized list of news articles and sends it to the device.

[0393] Step 19:

[0394] On your device: Show personalized news articles to your users.

[0395] Step 20:

[0396] User: Read the article and provide feedback on comprehension and satisfaction.

[0397] Step 21:

[0398] Terminal: Display a feedback form and collect user input.

[0399] Step 22:

[0400] Device: Sends feedback data to the server.

[0401] Step 23:

[0402] Server: Receives feedback and stores it in a database.

[0403] Server: Improves the generative AI model based on feedback data.

[0404] Through the above steps, the system of the present invention aims to provide personalized information based on the user's vocabulary, kanji ability, and emotional state, and to provide appropriate content that is easy for the user to understand.

[0405] Example 2

[0406] 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."

[0407] Conventional news article delivery systems lacked personalization based on the user's vocabulary and kanji ability, making it difficult to understand articles. Furthermore, because they provided information without taking the user's emotional state into consideration, content that did not match the user's emotions was sometimes displayed, resulting in a lack of improvement in the user experience. This could widen the information gap.

[0408] 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.

[0409] In this invention, the server includes means for collecting input information from the user's terminal, means for measuring the user's vocabulary and kanji ability by an AI, means for rewriting publicly available news articles by a generation AI based on the measurement results, means for preferentially displaying articles based on the user's frequently used words and areas of interest, means for recognizing the user's emotional state, and means for preferentially displaying appropriate news articles based on the recognized emotions. This makes it possible to personalize news articles according to the user's vocabulary and kanji ability and provide content that takes emotions into consideration.

[0410] "User's device" refers to a device used by a user to input and view information, including smartphones, tablets, and personal computers.

[0411] "Input information" refers to data provided by a user through a terminal, and includes various types of information such as text, audio, and images.

[0412] "Vocabulary" refers to the range and depth of words a user can understand and use.

[0413] "Kanji ability" refers to the range of kanji that a user can understand and use, and the level of understanding of those kanji.

[0414] "AI" or "artificial intelligence" refers to algorithms and technologies used to measure and analyze a user's vocabulary and kanji skills.

[0415] "Public news stories" refers to current public news content obtained from external news services.

[0416] "Generative AI" refers to a generative model that uses natural language processing to rewrite news articles in a way that is easy for users to understand.

[0417] "User's frequently used words" refers to words and phrases that the user frequently uses in their past operation history and input content.

[0418] "Areas of interest" refers to the topics or categories that a user is interested in, and are determined based on past browsing history and survey responses.

[0419] "Emotional state" refers to the emotion the user is currently feeling, and is expressed by different emotion labels such as joy, sadness, anger, etc.

[0420] "Means for preferentially displaying appropriate news articles based on emotions" refers to the process of selecting the news articles that best fit the emotional state of the user as recognized by the emotion engine and adjusting the display order.

[0421] The present invention is a system that personalizes news articles based on a user's vocabulary and kanji ability, and further recognizes the user's emotions to provide appropriate content. Specific embodiments of the system are described below.

[0422] Program processing and the hardware and software used

[0423] 1. User registration and initial settings

[0424] When using the application for the first time, the user opens the application and enters the required information on the account creation page. The device sends that information to the server. The server creates an account based on the provided information and stores it in a database. It also generates an initial setup questionnaire and sends it to the device. The user answers the questionnaire, and the device collects the answers and sends them to the server.

[0425] The hardware and software used include devices such as smartphones and PCs, cloud-based database servers, and survey generation software.

[0426] 2. Vocabulary and Kanji ability assessment

[0427] Users view quizzes and sentences provided on the application and enter their answers. The device then sends the answer data to the server. The server then analyzes the answer data using an AI model (e.g., OpenAI (registered trademark) GPT-3) to measure the user's vocabulary and kanji ability. The measurement results are stored in a database.

[0428] 3. Emotion recognition using the emotion engine

[0429] Users input their understanding and satisfaction with news articles using text or voice. The device sends the input data to a server. The server uses an emotion engine (e.g., Google® Cloud Natural Language) to analyze the input data and recognize the user's emotional state. The results are stored in a database.

[0430] 4. Personalize your news articles

[0431] The server retrieves the latest news articles from an external news API (e.g., NewsAPI). Based on each user's vocabulary data, it uses generative AI (e.g., OpenAI GPT-3) to rewrite the news articles into a format that is easy for the user to understand. Furthermore, it determines the display order based on the user's interest data and emotion recognition results. For example, if the user is sad, it prioritizes positive news. A personalized list of news articles is generated and sent to the device.

[0432] 5. View articles and gather feedback

[0433] The device displays personalized news articles to the user, who then reads the article and provides feedback on their understanding and satisfaction. The device collects the feedback and sends it to a server, which stores it in a database and uses it to improve the generative AI model.

[0434] Specific examples

[0435] For example, consider a scenario where a user is using an app for the first time.

[0436] 1. Users create an account and answer a short survey for initial setup, including questions like, "What is your favorite news genre?"

[0437] 2. To measure vocabulary and kanji ability, users take a quiz to answer the meaning of "economic growth," and the results are sent to the server. The AI ​​model determines the user's level of understanding as "intermediate."

[0438] 3. Emotion recognition: if a user types "I'm a little sad today," the emotion engine will analyze this and record it as "sad."

[0439] 4. In news article personalization, the server converts the retrieved news article "economic growth outlook" into "economic upturn" based on the user's vocabulary, and further prioritizes positive content for "sad" users.

[0440] 5. When displaying articles, a personalized article titled "Economic Growth" is displayed to the user, and if the user provides feedback such as "This article cheered me up," that information is sent to the server and reflected in future content provision.

[0441] The system can provide optimal news articles based on the user's vocabulary, kanji ability, and even emotional state, and the model is continually improved based on feedback, further enhancing the user experience.

[0442] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0443] Step 1: User registration and initial setup

[0444] Input: A user launches an application for the first time and enters information such as their name, email address, and password.

[0445] Operation and data processing: The device sends the user's input information to the server. The server receives the input information and stores it in a database as account information. The server then generates an initial setup questionnaire and sends it to the device.

[0446] Output: The server generates a new account ID and survey data and sends them to the device. The device displays the survey to the user, who answers it. The user's answers are then sent from the device to the server again and saved.

[0447] Step 2: Vocabulary and Kanji Ability Assessment

[0448] Input: The user answers a quiz or statement.

[0449] Operation and data processing: The device sends the user's answers to the server. The server analyzes the answers using an AI model (e.g., OpenAI GPT-3). The analysis results are used to measure the user's vocabulary and kanji ability. The server then stores the results in a database.

[0450] Output: The server generates evaluation data on the user's vocabulary and Kanji ability and records it in a database.

[0451] Step 3: Emotion recognition by the emotion engine

[0452] Input: Users input their understanding and satisfaction with a news article using text or voice.

[0453] Operation and data processing: The device sends this input data to the server. The server analyzes the input data using an emotion engine (e.g., Google Cloud Natural Language). As a result, the server recognizes the user's emotional state. The server stores the recognized emotion results in a database.

[0454] Output: The server generates an emotion label for the user (e.g., happy, sad) and records it in a database.

[0455] Step 4: Personalize your news articles

[0456] Input: Latest news articles retrieved by the server from an external news API (e.g., NewsAPI), along with the user's vocabulary, kanji ability, emotional state, and interest data.

[0457] Operation and data processing: Based on each user's vocabulary data, the server uses generative AI (e.g., OpenAI GPT-3) to rewrite the retrieved news articles into a format that is easy for the user to understand. In addition, the server takes into account the user's emotional state and interest data to determine the display order of news articles.

[0458] Output: The server generates a personalized list of news articles and sends it to the device.

[0459] Step 5: View the article and gather feedback

[0460] Input: A personalized list of news articles sent by the server.

[0461] Operation and Data Processing: The device displays personalized news articles to the user. The user reads the articles and provides feedback on their understanding and satisfaction. The device then sends this feedback to the server.

[0462] Output: The server stores the received feedback in a database and uses it to improve the generative AI model in the future.

[0463] (Application example 2)

[0464] 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."

[0465] Conventional news delivery systems provide news articles without considering the user's vocabulary, kanji ability, or emotional state, which often results in content that is not suited to the user's understanding or interests. Furthermore, content that is uninteresting to the user or information that does not match their emotional state is displayed, resulting in a lower level of satisfaction in the reader experience. This can cause users to lose interest in the news, and for news providers, it can also cause a problem of reduced effectiveness in conveying information.

[0466] 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.

[0467] In this invention, the server includes means for collecting input information from the user's terminal and using AI to measure the user's vocabulary and kanji ability, means for the generation AI to rewrite publicly available news articles based on the results of the measurement, means for preferentially displaying articles based on the user's frequently used words and areas of interest, and means for recognizing the user's emotions and adjusting the content of the news articles according to their emotional state. This makes it possible to provide news content that is easy for the user to understand and takes into consideration their emotions.

[0468] "User device" refers to the electronic device used by the user to view news articles, including smartphones and tablets.

[0469] "Input information" refers to data provided by the user via the device, and includes text, audio, quiz answers, etc.

[0470] "AI" is an abbreviation for Artificial Intelligence, and refers to technology for analyzing and measuring users' vocabulary and kanji ability.

[0471] "Generative AI" refers to artificial intelligence technology that automatically converts news articles and text data into a form that is easy for users to understand.

[0472] "Emotional state" refers to the user's current mood or emotion, and includes states such as "sad," "happy," and "anxious."

[0473] "Emotion recognition" refers to the process of analyzing a user's text or voice input data to identify their emotions.

[0474] "News Article" means a piece of information obtained from an external news source, including domestic and international events and social issues.

[0475] "Rewriting" refers to using generative AI to convert the content of a news article to suit the user's vocabulary and kanji ability.

[0476] "Terminology" refers to difficult words and expressions used in a specific field that are difficult for general users to understand.

[0477] "Areas" refer to words that users frequently use or areas of interest, such as "economy," "sports," and "entertainment."

[0478] "Adjusting" refers to changing the content and display order of news articles depending on the user's emotional state.

[0479] MODE FOR CARRYING OUT THE INVENTION

[0480] System Overview

[0481] This invention is a system that provides news articles that are easy for users to understand and that respond to their emotions. The system collects input information from the user's device, measures their vocabulary and kanji ability based on that information using AI, and then rewrites the news article using a generation AI. It also recognizes the user's emotions and adjusts the content of the article according to their emotional state.

[0482] Hardware and software used

[0483] The system includes the following hardware and software:

[0484] Hardware

[0485] User's device (smartphone, tablet, etc.)

[0486] Server (processes and stores data)

[0487] software

[0488] Analysis Library (TextBlob)

[0489] Emotion Recognition Model (Hugging Face's transformers)

[0490] Web API (retrieving news articles)

[0491] Process Overview

[0492] 1. User registration and initial settings

[0493] User: Opens the application, enters the required information, and creates an account.

[0494] Device: The entered information is sent to the server, which stores the user information, then generates an initial setup questionnaire and sends it to the device.

[0495] User: Answers the survey and the device sends the answers to the server.

[0496] 2. Vocabulary and Kanji ability assessment

[0497] User: Views quizzes and statements and enters answers to them.

[0498] Device: Sends quiz answer data to the server, which analyzes the data and measures the user's vocabulary and kanji ability.

[0499] 3. Emotional Recognition

[0500] User: Enter text or voice input.

[0501] Device: Sends input data to the server, which uses an emotion engine to recognize the user's emotions.

[0502] 4. Personalize your news articles

[0503] Server: The latest news articles obtained from an external news API are rewritten using a generative AI based on the user's vocabulary data. In addition, the display order of articles is determined based on the user's emotion recognition results.

[0504] Server: Generates a personalized list of news articles and sends it to the device.

[0505] Specific examples

[0506] 1. The server receives the text "I'm not feeling very well today" entered by user A on his smartphone.

[0507] 2. The server uses the emotion engine to recognize that User A has the emotion "sad."

[0508] 3. The server retrieves the "economic growth outlook" from an external news API.

[0509] 4. The server converts "economic growth" to "economic rise" based on User A's vocabulary data (intermediate level).

[0510] 5. The server sees that User A is feeling sad, so it displays a news article with an encouraging message added. For example, it displays "The economy may continue to improve. Cheer up!"

[0511] 6. User A reads the article and gives feedback saying, "It's encouraging."

[0512] Prompt Sentence Examples

[0513] The user types, "I'm not feeling very good today." The emotion recognition engine analyzes this text and identifies the user's emotion as "sad." Please rewrite the following news article, "Economic Growth Outlook," to fit the user's vocabulary level (intermediate level), and add an encouraging message.

[0514] This will enable news articles to be provided that meet the individual needs of users, improving the user experience.

[0515] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0516] Step 1:

[0517] User registration and initial settings

[0518] User: Opens the application and enters the required information (name, email address, password, etc.) on the account creation page.

[0519] Terminal: Sends the entered information to the server.

[0520] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[0521] User: Answers an initial setup questionnaire, and the device sends the answers to the server, which collects basic information and initial data about the user.

[0522] Step 2:

[0523] Vocabulary and Kanji ability assessment

[0524] User: View the quizzes and sentences presented in the application and enter the answers.

[0525] Device: Sends quiz answer data to the server.

[0526] Server: Analyzes the received response data and uses an AI model to measure the user's vocabulary and kanji ability. The results are stored in a database. For example, it determines how well the user understands words such as "GDP" and "growth."

[0527] Step 3:

[0528] Emotion recognition

[0529] User: Enter text or voice input.

[0530] Terminal: Sends input data (text or voice) to the server.

[0531] Server: The emotion engine analyzes the input data and recognizes the user's emotions. For example, it analyzes the input text "I'm not feeling very good today" and assigns an emotion label such as "sad." The analysis results are stored in a database.

[0532] Step 4:

[0533] Personalized news articles

[0534] Server: Gets the latest news articles from an external news API.

[0535] Server: Using each user's vocabulary data, the server uses generative AI to rewrite news articles into expressions that are easier for the user to understand. For example, it converts "economic growth" into "economic growth" and "GDP" into "gross domestic product."

[0536] Server: Adjust the order and content of articles based on the user's emotion recognition results. For example, if the user is expressing sadness, encouragement and positive articles will be displayed first.

[0537] Server: Generates a tailored list of news articles and sends it to the device.

[0538] Step 5:

[0539] View articles and gather feedback

[0540] Device: Display personalized news articles to users. For example, present an article to User A saying, "The economy may continue to improve. Stay strong!"

[0541] User: Read the article and provide feedback on comprehension and satisfaction.

[0542] Terminal: Displays a feedback form and collects input from the user, then sends this input data to the server.

[0543] Server: The feedback data is stored in a database and used to improve the generative AI model and emotion engine. For example, if a user gives feedback that they felt energized, this information is stored and used for future personalization.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] [Second embodiment]

[0548] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0549] 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.

[0550] 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).

[0551] 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.

[0552] 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.

[0553] 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).

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] In the smart glasses 214, the 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.

[0559] 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."

[0560] The present invention is a system for providing personalized news articles based on a user's vocabulary and kanji ability. An embodiment of the system will be described in detail below.

[0561] Program processing explanation

[0562] 1. User registration and initial settings

[0563] User: Opens the application for the first time and enters the required information on the account creation page.

[0564] Terminal: Sends the entered information to the server.

[0565] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[0566] On the device: Present the initial setup survey to the user and collect their responses.

[0567] 2. Vocabulary and Kanji ability assessment

[0568] User: View the quizzes and sentences provided in the application and enter their answers.

[0569] Device: Sends quiz answer data to the server.

[0570] Server: Receives the response data and uses an AI model to analyze and measure the user's vocabulary and kanji ability. The results are stored in a database.

[0571] 3. Personalize your news articles

[0572] Server: Gets the latest news articles from an external news API.

[0573] Server: Reflecting each user's vocabulary and kanji ability, the server uses generative AI to rewrite news articles into expressions that are easy for the user to understand. For example, it converts the term "fiscal policy" into "how the government spends money."

[0574] Server: Determines the ranking of news articles based on user interest data.

[0575] Server: Generates a personalized list of news articles and sends it to the user's device.

[0576] 4. View articles and gather feedback

[0577] On your device: Show personalized news articles to your users.

[0578] User: Read the article and provide feedback on comprehension and satisfaction.

[0579] Device: Collects user feedback and sends it to the server.

[0580] Server: Stores the feedback in a database and uses this data to improve the generative AI model.

[0581] Specific examples

[0582] Article personalization examples

[0583] 3.1. Server: Get the economy-related news article "Japan's GDP grows."

[0584] 3.2. Server: User A's vocabulary and kanji ability are intermediate, so convert "GDP" to "gross domestic product" and "growth" to "got up."

[0585] 3.3. Server: Since user A is interested in economics, place this article at the top of the priority display list.

[0586] 3.4. Server: Generates a personalized article list and sends it to the device.

[0587] 4.1. Device: Display the personalized article "Japan's GDP has increased" to User A.

[0588] 4.2. User: Reads the article and gives feedback that it was easy to understand.

[0589] 4.3. Terminal: Display the feedback form and collect User A's input.

[0590] 4.5. Server: Analyzes the feedback data and uses it to improve the generative AI model.

[0591] As described above, the present invention aims to eliminate the information gap by providing news articles that correspond to the user's vocabulary and kanji ability, thereby improving the user's ease of accepting information and level of interest.

[0592] The processing flow will be explained below.

[0593] Program processing details

[0594] Step 1:

[0595] User: Opens the application for the first time and enters the required information on the account creation page.

[0596] Step 2:

[0597] Terminal: Sends the entered information to the server.

[0598] Step 3:

[0599] Server: Creates an account based on the provided user information and saves it in the database.

[0600] Server: Generates the initial survey and sends it to the device.

[0601] Step 4:

[0602] On the device: Present the initial setup survey to the user and collect their responses.

[0603] Step 5:

[0604] User: Complete the initial setup survey and send the answers to your device.

[0605] Step 6:

[0606] Terminal: Sends the response data to the server.

[0607] Step 7:

[0608] Server: Receives the response data and uses the AI ​​model to perform an initial vocabulary assessment and initial interest settings for the user.

[0609] Step 8:

[0610] User: View the quizzes and sentences provided in the application and enter answers to them.

[0611] Step 9:

[0612] Terminal: Collects user quiz answer data and sends it to the server.

[0613] Step 10:

[0614] Server: Receives the response data and uses an AI model to measure the user's vocabulary and kanji ability.

[0615] Server: Stores the measurement results in a database.

[0616] Step 11:

[0617] Server: Gets the latest news articles from an external news API.

[0618] Step 12:

[0619] Server: Based on each user's vocabulary data, generative AI is used to rewrite news articles into expressions that are easy for the user to understand.

[0620] For example, convert "GDP" to "gross domestic product" and "growth" to "increased."

[0621] Step 13:

[0622] Server: Determines the ranking of news articles based on user interest data.

[0623] Step 14:

[0624] Server: Generates a personalized list of news articles and sends it to the device.

[0625] Step 15:

[0626] On your device: Show personalized news articles to your users.

[0627] Step 16:

[0628] User: Read the article and provide feedback on comprehension and satisfaction.

[0629] Step 17:

[0630] Terminal: Display a feedback form and collect user input.

[0631] Step 18:

[0632] Device: Sends feedback data to the server.

[0633] Step 19:

[0634] Server: Receives feedback and stores it in a database.

[0635] Server: Analyzes the feedback data and uses it to improve the generative AI model.

[0636] Through the above steps, the system of the present invention aims to provide personalized information tailored to the user's vocabulary and kanji ability, thereby eliminating the information gap.

[0637] Example 1

[0638] 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."

[0639] In modern society, a huge amount of news articles are updated daily, but not all of this information is easy to understand for all users. In particular, users with different vocabulary and kanji abilities have difficulty understanding news articles that contain technical terms and difficult vocabulary, resulting in differences in how easily they accept information. As a result, certain user groups may not be able to obtain sufficient information, and the information gap may widen. In addition, articles are not sufficiently personalized based on users' interests, making it difficult for users to find articles that will interest them. Therefore, there is a need for a system that provides easy-to-understand news articles that suit each user's vocabulary and kanji ability.

[0640] 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.

[0641] In this invention, the server includes means for collecting input information from the user's device, means for an AI to measure the user's vocabulary and kanji ability, means for the generation AI to rewrite publicly available news articles based on the results of the measurement, means for preferentially displaying articles based on the user's frequently used words and areas of interest, and means for collecting feedback from the user and improving the AI ​​model. This enables users to receive easy-to-understand news articles that are suited to their vocabulary and kanji ability, making it easier for them to access information that is of high interest to them.

[0642] "User's device" refers to a device such as a computer, smartphone, or tablet used by a user.

[0643] "Input information" refers to information that a user enters through a device, including name, email address, password, survey responses, quiz and assignment responses, etc.

[0644] "Vocabulary" refers to the range and number of words a user can understand and use.

[0645] "Kanji ability" refers to the range and number of kanji characters that a user can understand and use.

[0646] "AI" is an abbreviation for artificial intelligence, and specifically refers to computer programs and algorithms that analyze user input and evaluate vocabulary and kanji ability.

[0647] "Public news articles" refers to the latest economic, social, political, and other news articles obtained from external news providers.

[0648] "Generative AI" refers to a generative artificial intelligence model that rewrites news articles into expressions that suit the user's vocabulary and kanji ability.

[0649] "Frequent words" refer to words or phrases that a user has frequently used or searched for in the past.

[0650] "Areas of interest" refers to news categories and topics that users have expressed a high level of interest in through surveys, usage history, etc.

[0651] "Feedback" refers to opinions and ratings regarding understanding and satisfaction that users provide after reading a news article.

[0652] "AI model improvement" refers to the process of utilizing collected feedback data to improve the performance of a generative AI model.

[0653] The present invention is a system that provides personalized news articles based on the user's vocabulary and kanji ability. To implement this system, the following specific steps are taken.

[0654] User registration and initial settings

[0655] First, the user installs the application and, when using it for the first time, enters required information such as name, email address, and password on the account creation page. The device sends the entered information to the server. The server receives this information and creates a new account. Specifically, it stores this information in a database (e.g., MySQL). Next, the server generates an initial setup questionnaire for the user and sends it to the device. The device displays the questionnaire to the user and prompts the user to enter their answers. The answered information is sent from the device to the server and stored in the database.

[0656] Vocabulary and Kanji ability assessment

[0657] The user views quizzes and sentences provided within the application and enters their answers. The device sends this answer data to the server. The server uses a generative AI model (e.g., GPT-3) to analyze the received answer data. At this time, the server sends the following prompt to the generative AI model: "Based on the user's answers, please evaluate this user's vocabulary and kanji ability." The generative AI model evaluates the user's vocabulary and kanji ability based on this prompt. The evaluation results are stored in a database by the server.

[0658] Personalized news articles

[0659] Next, the server retrieves the latest news articles using an external news API (e.g., newsAPI.org). These retrieved news articles are rewritten to suit each user's vocabulary and kanji ability. The server then uses a generative AI model to send a prompt such as, "The user's vocabulary is intermediate. Please simplify the wording of this article." This prompt converts the wording of the article. For example, it converts "fiscal policy" to "how the government uses money." In this way, articles that have been rewritten to be easier for the user to understand are ranked based on the user's interest data. Finally, a personalized list of news articles is generated and sent to the device.

[0660] View articles and gather feedback

[0661] The device displays a personalized list of news articles sent from the server to the user. The user reads the articles and provides feedback on their understanding and satisfaction. The feedback is sent to the server via the device. The server stores the received feedback in a database and uses it to improve the generative AI model.

[0662] Specific examples

[0663] In personalizing a news article, the server retrieves an economic news article titled "Japan's GDP grows."

[0664] If User A's vocabulary and kanji ability are intermediate, the generation AI will convert "GDP" to "gross domestic product" and "growth" to "risen."

[0665] User A is very interested in economics, so this article will be placed high on the priority display list.

[0666] This personalized article list is sent to the device and displayed to User A.

[0667] This invention provides news articles tailored to the individual abilities of users, facilitating their understanding and acceptance of information, thereby realizing a system that alleviates the digital divide and efficiently provides information tailored to the user's interests.

[0668] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0669] Step 1:

[0670] User registration and initial settings

[0671] User: The user opens the application and enters the required information on the account creation page, such as name, email address, and password.

[0672] Input: User information such as name, email address, and password.

[0673] Terminal: The terminal sends the entered information to the server.

[0674] Output: User information sent to the server.

[0675] Server: The server creates a new account based on the received user information, stores this information in a database, and generates an initial setup survey and sends it to the device.

[0676] Input: User information sent from the device.

[0677] Output: Saved user account, generated survey.

[0678] Terminal: The terminal displays the survey to the user and prompts the user to enter their answers.

[0679] Input: Initial Setup Survey.

[0680] User: The user completes an initial setup questionnaire and enters the answers into the device.

[0681] Output: User's survey responses.

[0682] Terminal: The terminal sends the survey responses to the server.

[0683] Input: User's survey response information.

[0684] Output: Survey responses sent to the server.

[0685] Step 2:

[0686] Vocabulary and Kanji ability assessment

[0687] User: The user enters answers to quizzes and assignments.

[0688] Input: Quiz or assignment, user's answer.

[0689] Terminal: The terminal sends the user's response data to the server.

[0690] Output: The response data sent to the server.

[0691] Server: Based on the received answer data, the server generates prompt sentences to evaluate the user's vocabulary and kanji ability, and sends these prompt sentences to the generative AI model.

[0692] Input: User response data.

[0693] Output: A prompt to the generative AI model.

[0694] Generative AI model: The generative AI model evaluates the user's vocabulary and kanji ability based on the prompt sentence.

[0695] Output: The evaluation result.

[0696] Server: The server stores the evaluation results received from the generative AI model in a database.

[0697] Input: Evaluation results from a generative AI model.

[0698] Output: Evaluation results stored in a database.

[0699] Step 3:

[0700] Personalized news articles

[0701] Server: The server retrieves the latest news articles from an external news API.

[0702] Input: News API request.

[0703] Output: News article data.

[0704] Server: The server uses a generative AI model to generate prompts for rewriting the retrieved news articles based on the user's vocabulary and kanji ability. Example prompt: "The user has intermediate vocabulary, so please simplify the wording of this article."

[0705] Input: News article, user vocabulary and kanji proficiency data.

[0706] Output: A prompt to the generative AI model.

[0707] Generative AI model: Based on a prompt, the generative AI model rewrites news articles in a way that is easy for users to understand.

[0708] Output: A rewritten news article.

[0709] Server: The server determines the order in which news articles to display based on the user's interest data, generates a personalized news article list, and sends it to the device.

[0710] Input: rewritten news articles, user interest data.

[0711] Output: A personalized list of news articles.

[0712] Step 4:

[0713] View articles and gather feedback

[0714] Device: The device displays a personalized list of news articles to the user.

[0715] Input: A list of news articles sent by the server.

[0716] User: The user reads the article and provides feedback on their understanding and satisfaction.

[0717] Output: User feedback data.

[0718] Device: The device sends the user's feedback to the server.

[0719] Input: User feedback data.

[0720] Server: The server stores the received feedback data in a database and uses it to improve the generative AI model.

[0721] Output: Feedback data stored in a database.

[0722] (Application example 1)

[0723] 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."

[0724] Conventional advertising systems do not personalize ads based on the user's vocabulary or writing ability, which results in ads that are difficult for users to understand. This reduces the effectiveness of ads and makes it difficult for them to attract users' interest.

[0725] 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.

[0726] In this invention, the server includes means for collecting input information from the user's terminal and measuring the user's vocabulary and writing ability with an AI, means for the generation AI to rewrite advertising information based on the measurement results, and means for preferentially displaying advertisements based on the user's frequently used words and areas of interest. This allows advertisements to be optimized according to the user's level of understanding, making it possible to more effectively attract the user's interest and attention.

[0727] Key Word Definitions

[0728] "User" refers to any individual or group of people who use the System.

[0729] "User terminal" refers to an electronic device that allows a user to access and operate the system.

[0730] "Input information" refers to data entered by a user into a terminal and transmitted to a server.

[0731] "Vocabulary" refers to the range of words a user can understand and use.

[0732] "Character ability" refers to the range of characters, kana notations, and kanji characters that a user can understand and use.

[0733] "AI" refers to an information processing system that uses artificial intelligence technology.

[0734] "Advertising information" refers to information intended to promote the sale of products or advertise services.

[0735] "Generative AI" refers to a technology that uses artificial intelligence to generate sentences based on the user's vocabulary and writing ability.

[0736] A "word" refers to the smallest unit of expression in language.

[0737] A "field" refers to an area related to a particular subject or theme.

[0738] "Means" refers to the process or method for achieving a goal.

[0739] A "system" refers to a set of mechanisms in which multiple components work together to achieve a specific function.

[0740] patent specification

[0741] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below.

[0742] First, the user launches the application on their device and creates an account. When creating an account, they enter the necessary information. The device then sends the entered information to the server.

[0743] The server creates an account based on the received user information, stores this data in a database, and then generates questionnaires and quizzes to measure the user's vocabulary and writing ability as an initial setting, and sends them to the user's device.

[0744] Users answer these initial setup quizzes and surveys. The device sends the user's response data to the server. The server then analyzes the response data using an AI model to measure the user's vocabulary and writing ability. The analysis results are stored in a database.

[0745] The server then retrieves the latest ad information from an external ad API. Based on the user's vocabulary and writing ability measurements, the server uses a generative AI model to rewrite the ad information into language that is easier for the user to understand. For example, it converts technical jargon and difficult vocabulary into simpler language. This rewriting process uses advanced natural language processing models such as GPT-4.

[0746] The server then determines the order in which ads are displayed based on the user's past behavior and areas of interest. Once a properly personalized ad list is generated, it is sent to the user's device.

[0747] The device displays this personalized ad list to the user. The user views the ads and provides feedback on their content. The device collects this feedback data and sends it to the server. The server uses this feedback data to continuously improve the accuracy of the generative AI model.

[0748] The specific hardware and software used are Python and Flask for the server, MySQL for database management, GPT-4 API for the generative AI model, and React Native for the front-end application.

[0749] Specific examples

[0750] Below are some specific examples of ad personalization:

[0751] Original ad copy

[0752] "The latest smartphone is now available. Its AI camera and 5G compatibility will change your everyday life."

[0753] Personalized ad text

[0754] "New smartphone released with AI camera and high-speed internet"

[0755] Prompt Sentence Examples

[0756] "If the user has an intermediate level of vocabulary, personalize the original ad copy, 'The latest smartphone is here. It changes your daily life with its AI camera and 5G connectivity,' in simpler terms."

[0757] In this way, the present invention realizes maximizing the effectiveness of advertising by appropriately personalizing advertising information in accordance with the user's vocabulary and writing ability.

[0758] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0759] Program processing steps

[0760] Step 1:

[0761] The user launches the application on their device and creates an account.

[0762] Input: User information from your device (username, password, email address, etc.)

[0763] Output: User information sent to the server

[0764] Specific operation: The terminal sends the entered user information to the server.

[0765] Step 2:

[0766] The server creates an account based on the received user information and stores it in the database.

[0767] Input: User information sent to the server

[0768] Output: Account information stored in the database

[0769] Specific operation: The server receives the user information and saves it as a new record in the database (MySQL).

[0770] Step 3:

[0771] As an initial setting, the server generates questionnaires and quizzes to measure vocabulary and writing ability, and sends them to the user's device.

[0772] Input: New user registration information

[0773] Output: Initial setup quiz and survey

[0774] Specific operation: The server generates quizzes and surveys and sends them to the user's device.

[0775] Step 4:

[0776] Users complete default quizzes and surveys.

[0777] Input: Quizzes and surveys

[0778] Output: User's answer

[0779] Specific operation: The user answers the displayed quiz or survey and sends it to their device.

[0780] Step 5:

[0781] The terminal sends the user's response data to the server.

[0782] Input: User response data

[0783] Output: Response data sent to the server

[0784] Specific operation: The user device sends the response data to the server.

[0785] Step 6:

[0786] The server receives the response data, analyzes the user's vocabulary and writing ability using a generative AI model, and stores the results in a database.

[0787] Input: User response data

[0788] Output: Vocabulary and writing ability measurements

[0789] Specific operation: The server analyzes the response data, measures vocabulary and writing ability, and stores the results in a database.

[0790] Step 7:

[0791] The server retrieves the latest ad information from an external ad API.

[0792] Input: External Ads API

[0793] Output: Retrieved advertising information

[0794] Specific operation: The server periodically calls the external advertising API to obtain the latest advertising information.

[0795] Step 8:

[0796] The server uses a generative AI model to personalize advertising information based on the user's vocabulary and writing ability.

[0797] Input: Vocabulary and writing ability test results, advertising information

[0798] Output: Personalized advertising information

[0799] Specific operation: The server uses a generative AI model (GPT-4) to rewrite advertising information into expressions that are easy for users to understand.

[0800] Step 9:

[0801] The server determines the order in which ads are displayed based on the user's past behavioral data and areas of interest.

[0802] Input: User behavior data, interests, personalized advertising

[0803] Output: Prioritized ad list

[0804] Specific operation: The server determines the ad ranking based on the user's behavioral data and areas of interest, and generates an ad list.

[0805] Step 10:

[0806] The server transmits the personalized advertisement list to the user terminal.

[0807] Input: Prioritized Ad List

[0808] Output: Ad list sent to the user's device

[0809] Specific operation: The server sends a personalized ad list to the user's device.

[0810] Step 11:

[0811] The device displays a personalized list of ads to the user.

[0812] Input: Personalized Ad List

[0813] Output: The ad shown to the user

[0814] Specific operation: The user device receives the advertisement list and displays it to the user.

[0815] Step 12:

[0816] Users view ads and provide feedback.

[0817] Enter: Advertisement

[0818] Output: User feedback

[0819] What happens: Users view the ad and provide feedback on comprehension and interest.

[0820] Step 13:

[0821] The user terminal collects feedback data and transmits it to the server.

[0822] Input: User feedback

[0823] Output: Feedback data sent to the server

[0824] Specific operation: The user device collects feedback data and sends it to the server.

[0825] Step 14:

[0826] The server uses the feedback data to improve the accuracy of the generative AI model.

[0827] Input: Feedback data

[0828] Output: An updated generative AI model

[0829] What it does: The server analyzes the feedback data to improve the accuracy of the generative AI model (GPT-4) and help with future ad personalization.

[0830] 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.

[0831] The present invention provides a system that personalizes news articles based on a user's vocabulary and kanji ability, and further recognizes the user's emotions to provide appropriate content. The following describes an embodiment of the system in detail.

[0832] Program processing explanation

[0833] 1. User registration and initial settings

[0834] User: Opens the application for the first time and enters the required information on the account creation page.

[0835] Terminal: Sends the entered information to the server.

[0836] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[0837] On the device: Present the initial setup survey to the user and collect their responses.

[0838] 2. Vocabulary and Kanji ability assessment

[0839] User: View the quizzes and sentences provided in the application and enter their answers.

[0840] Device: Sends quiz answer data to the server.

[0841] Server: Receives the response data and uses an AI model to analyze and measure the user's vocabulary and kanji ability. The results are stored in a database.

[0842] 3. Emotion recognition using the emotion engine

[0843] User: Enter text or voice input.

[0844] Terminal: Sends user input data (text or voice) to the server.

[0845] Server: The emotion engine analyzes the input data and recognizes the user's emotions. For example, if the user is sad or happy, it records the emotion label.

[0846] Server: Stores the analysis results in a database.

[0847] 4. Personalize your news articles

[0848] Server: Gets the latest news articles from an external news API.

[0849] Server: Using each user's vocabulary data, the server uses generative AI to rewrite news articles into expressions that are easier for the user to understand. For example, it converts "GDP" to "gross domestic product" and "growth" to "risen."

[0850] Server: Determines the order in which news articles are displayed based on user interest data and emotion recognition results.

[0851] For example, if a user is feeling sad, encouraging content or positive news articles will be prioritized.

[0852] Server: Generates a personalized list of news articles and sends it to the device.

[0853] 5. View articles and gather feedback

[0854] On your device: Show personalized news articles to your users.

[0855] User: Read the article and provide feedback on comprehension and satisfaction.

[0856] Terminal: Display a feedback form and collect user input.

[0857] Device: Sends feedback data to the server.

[0858] Server: Stores the feedback in a database and uses this data to improve the generative AI model.

[0859] Specific examples

[0860] Example of article personalization based on sentiment

[0861] 3.1. Server: Receives the text "I'm not feeling very well today" entered by User A on his smartphone.

[0862] 3.2. Server: The emotion engine analyzes this input data and recognizes that User A has the emotion "sad."

[0863] 4.1. Server: Get the latest news article "Economic Growth Outlook".

[0864] 4.2. Server: User A's vocabulary data is intermediate, so convert "economic growth" to "economic rise."

[0865] 4.3. Server: Since User A is feeling "sad," it is decided to prioritize displaying articles with encouraging and positive content.

[0866] 4.4. Server: Generates a personalized article list and sends it to the device.

[0867] 5.1. Device: Show personalized article with positive content to User A.

[0868] 5.2. User: Reads the article and gives feedback saying that it "inspired me."

[0869] 5.3. Terminal: Collects feedback and sends it to the server.

[0870] 5.4. Server: Stores the feedback and uses it to improve the generative AI model.

[0871] As described above, the present invention aims to eliminate the digital divide and improve the user experience by providing information that takes into account the user's vocabulary, kanji ability, and even emotional state, and by providing content that is easy for users to understand and appropriate.

[0872] The processing flow will be explained below.

[0873] Program processing details

[0874] Step 1:

[0875] User: Opens the application for the first time and enters the required information on the account creation page.

[0876] Step 2:

[0877] Terminal: Sends the entered information to the server.

[0878] Step 3:

[0879] Server: Creates an account based on the provided user information and saves it in the database.

[0880] Server: Generates the initial setup survey and sends it to the device.

[0881] Step 4:

[0882] On the device: Present the initial setup survey to the user and collect their responses.

[0883] Step 5:

[0884] User: Complete the initial setup survey and send the answers to your device.

[0885] Step 6:

[0886] Terminal: Sends the response data to the server.

[0887] Step 7:

[0888] Server: Receives the response data and uses the AI ​​model to perform an initial vocabulary assessment and initial interest settings for the user.

[0889] Step 8:

[0890] User: View the quizzes and sentences provided in the application and enter answers to them.

[0891] Step 9:

[0892] Terminal: Collects user quiz answer data and sends it to the server.

[0893] Step 10:

[0894] Server: Receives the response data and uses an AI model to measure the user's vocabulary and kanji ability.

[0895] Server: Stores the measurement results in a database.

[0896] Step 11:

[0897] User: Express their feelings through text input or voice input.

[0898] Step 12:

[0899] Device: Sends the user's text and voice data to the server.

[0900] Step 13:

[0901] Server: The emotion engine analyzes the input data and recognizes the user's emotions.

[0902] For example, the text "I'm tired today" is analyzed and the emotion label "tired" is assigned.

[0903] Step 14:

[0904] Server: Stores the sentiment analysis results in a database.

[0905] Step 15:

[0906] Server: Gets the latest news articles from an external news API.

[0907] Step 16:

[0908] Server: Based on each user's vocabulary data, generative AI is used to rewrite news articles into expressions that are easy for the user to understand.

[0909] For example, convert "GDP" to "gross domestic product" and "growth" to "increased."

[0910] Step 17:

[0911] Server: Determines the order in which news articles are displayed based on user interest data and emotion recognition results.

[0912] For example, if a user is feeling "tired," positive articles related to energy recovery will be displayed first.

[0913] Step 18:

[0914] Server: Generates a personalized list of news articles and sends it to the device.

[0915] Step 19:

[0916] On your device: Show personalized news articles to your users.

[0917] Step 20:

[0918] User: Read the article and provide feedback on comprehension and satisfaction.

[0919] Step 21:

[0920] Terminal: Display a feedback form and collect user input.

[0921] Step 22:

[0922] Device: Sends feedback data to the server.

[0923] Step 23:

[0924] Server: Receives feedback and stores it in a database.

[0925] Server: Improves the generative AI model based on feedback data.

[0926] Through the above steps, the system of the present invention aims to provide personalized information based on the user's vocabulary, kanji ability, and emotional state, and to provide appropriate content that is easy for the user to understand.

[0927] Example 2

[0928] 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."

[0929] Conventional news article delivery systems lacked personalization based on the user's vocabulary and kanji ability, making it difficult to understand articles. Furthermore, because they provided information without taking the user's emotional state into consideration, content that did not match the user's emotions was sometimes displayed, resulting in a lack of improvement in the user experience. This could widen the information gap.

[0930] 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.

[0931] In this invention, the server includes means for collecting input information from the user's terminal, means for measuring the user's vocabulary and kanji ability by an AI, means for rewriting publicly available news articles by a generation AI based on the measurement results, means for preferentially displaying articles based on the user's frequently used words and areas of interest, means for recognizing the user's emotional state, and means for preferentially displaying appropriate news articles based on the recognized emotions. This makes it possible to personalize news articles according to the user's vocabulary and kanji ability and provide content that takes emotions into consideration.

[0932] "User's device" refers to a device used by a user to input and view information, including smartphones, tablets, and personal computers.

[0933] "Input information" refers to data provided by a user through a terminal, and includes various types of information such as text, audio, and images.

[0934] "Vocabulary" refers to the range and depth of words a user can understand and use.

[0935] "Kanji ability" refers to the range of kanji that a user can understand and use, and the level of understanding of those kanji.

[0936] "AI" or "artificial intelligence" refers to algorithms and technologies used to measure and analyze a user's vocabulary and kanji skills.

[0937] "Public news stories" refers to current public news content obtained from external news services.

[0938] "Generative AI" refers to a generative model that uses natural language processing to rewrite news articles in a way that is easy for users to understand.

[0939] "User's frequently used words" refers to words and phrases that the user frequently uses in their past operation history and input content.

[0940] "Areas of interest" refers to the topics or categories that a user is interested in, and are determined based on past browsing history and survey responses.

[0941] "Emotional state" refers to the emotion the user is currently feeling, and is expressed by different emotion labels such as joy, sadness, anger, etc.

[0942] "Means for preferentially displaying appropriate news articles based on emotions" refers to the process of selecting the news articles that best fit the emotional state of the user as recognized by the emotion engine and adjusting the display order.

[0943] The present invention is a system that personalizes news articles based on a user's vocabulary and kanji ability, and further recognizes the user's emotions to provide appropriate content. Specific embodiments of the system are described below.

[0944] Program processing and the hardware and software used

[0945] 1. User registration and initial settings

[0946] When using the application for the first time, the user opens the application and enters the required information on the account creation page. The device sends that information to the server. The server creates an account based on the provided information and stores it in a database. It also generates an initial setup questionnaire and sends it to the device. The user answers the questionnaire, and the device collects the answers and sends them to the server.

[0947] The hardware and software used include devices such as smartphones and PCs, cloud-based database servers, and survey generation software.

[0948] 2. Vocabulary and Kanji ability assessment

[0949] Users view quizzes and sentences provided on the application and enter their answers. The device then sends the answer data to the server. The server then analyzes the answer data using an AI model (e.g., OpenAI GPT-3) to measure the user's vocabulary and kanji ability. The measurement results are stored in a database.

[0950] 3. Emotion recognition using the emotion engine

[0951] Users input their understanding and satisfaction with news articles using text or voice. The device sends the input data to a server, which then uses an emotion engine (e.g., Google Cloud Natural Language) to analyze the input data and recognize the user's emotional state. The results are then stored in a database.

[0952] 4. Personalize your news articles

[0953] The server retrieves the latest news articles from an external news API (e.g., NewsAPI). Based on each user's vocabulary data, it uses generative AI (e.g., OpenAI GPT-3) to rewrite the news articles into a format that is easy for the user to understand. Furthermore, it determines the display order based on the user's interest data and emotion recognition results. For example, if the user is sad, it prioritizes positive news. A personalized list of news articles is generated and sent to the device.

[0954] 5. View articles and gather feedback

[0955] The device displays personalized news articles to the user, who then reads the article and provides feedback on their understanding and satisfaction. The device collects the feedback and sends it to a server, which stores it in a database and uses it to improve the generative AI model.

[0956] Specific examples

[0957] For example, consider a scenario where a user is using an app for the first time.

[0958] 1. Users create an account and answer a short survey for initial setup, including questions like, "What is your favorite news genre?"

[0959] 2. To measure vocabulary and kanji ability, users take a quiz to answer the meaning of "economic growth," and the results are sent to the server. The AI ​​model determines the user's level of understanding as "intermediate."

[0960] 3. Emotion recognition: if a user types "I'm a little sad today," the emotion engine will analyze this and record it as "sad."

[0961] 4. In news article personalization, the server converts the retrieved news article "economic growth outlook" into "economic upturn" based on the user's vocabulary, and further prioritizes positive content for "sad" users.

[0962] 5. When displaying articles, a personalized article titled "Economic Growth" is displayed to the user, and if the user provides feedback such as "This article cheered me up," that information is sent to the server and reflected in future content provision.

[0963] The system can provide optimal news articles based on the user's vocabulary, kanji ability, and even emotional state, and the model is continually improved based on feedback, further enhancing the user experience.

[0964] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0965] Step 1: User registration and initial setup

[0966] Input: A user launches an application for the first time and enters information such as their name, email address, and password.

[0967] Operation and data processing: The device sends the user's input information to the server. The server receives the input information and stores it in a database as account information. The server then generates an initial setup questionnaire and sends it to the device.

[0968] Output: The server generates a new account ID and survey data and sends them to the device. The device displays the survey to the user, who answers it. The user's answers are then sent from the device to the server again and saved.

[0969] Step 2: Vocabulary and Kanji Ability Assessment

[0970] Input: The user answers a quiz or statement.

[0971] Operation and data processing: The device sends the user's answers to the server. The server analyzes the answers using an AI model (e.g., OpenAI GPT-3). The analysis results are used to measure the user's vocabulary and kanji ability. The server then stores the results in a database.

[0972] Output: The server generates evaluation data on the user's vocabulary and Kanji ability and records it in a database.

[0973] Step 3: Emotion recognition by the emotion engine

[0974] Input: Users input their understanding and satisfaction with a news article using text or voice.

[0975] Operation and data processing: The device sends this input data to the server. The server analyzes the input data using an emotion engine (e.g., Google Cloud Natural Language). As a result, the server recognizes the user's emotional state. The server stores the recognized emotion results in a database.

[0976] Output: The server generates an emotion label for the user (e.g., happy, sad) and records it in a database.

[0977] Step 4: Personalize your news articles

[0978] Input: Latest news articles retrieved by the server from an external news API (e.g., NewsAPI), along with the user's vocabulary, kanji ability, emotional state, and interest data.

[0979] Operation and data processing: Based on each user's vocabulary data, the server uses generative AI (e.g., OpenAI GPT-3) to rewrite the retrieved news articles into a format that is easy for the user to understand. In addition, the server takes into account the user's emotional state and interest data to determine the display order of news articles.

[0980] Output: The server generates a personalized list of news articles and sends it to the device.

[0981] Step 5: View the article and gather feedback

[0982] Input: A personalized list of news articles sent by the server.

[0983] Operation and Data Processing: The device displays personalized news articles to the user. The user reads the articles and provides feedback on their understanding and satisfaction. The device then sends this feedback to the server.

[0984] Output: The server stores the received feedback in a database and uses it to improve the generative AI model in the future.

[0985] (Application example 2)

[0986] 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."

[0987] Conventional news delivery systems provide news articles without considering the user's vocabulary, kanji ability, or emotional state, which often results in content that is not suited to the user's understanding or interests. Furthermore, content that is uninteresting to the user or information that does not match their emotional state is displayed, resulting in a lower level of satisfaction in the reader experience. This can cause users to lose interest in the news, and for news providers, it can also cause a problem of reduced effectiveness in conveying information.

[0988] 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.

[0989] In this invention, the server includes means for collecting input information from the user's terminal and using AI to measure the user's vocabulary and kanji ability, means for the generation AI to rewrite publicly available news articles based on the results of the measurement, means for preferentially displaying articles based on the user's frequently used words and areas of interest, and means for recognizing the user's emotions and adjusting the content of the news articles according to their emotional state. This makes it possible to provide news content that is easy for the user to understand and takes into consideration their emotions.

[0990] "User device" refers to the electronic device used by the user to view news articles, including smartphones and tablets.

[0991] "Input information" refers to data provided by the user via the device, and includes text, audio, quiz answers, etc.

[0992] "AI" is an abbreviation for Artificial Intelligence, and refers to technology for analyzing and measuring users' vocabulary and kanji ability.

[0993] "Generative AI" refers to artificial intelligence technology that automatically converts news articles and text data into a form that is easy for users to understand.

[0994] "Emotional state" refers to the user's current mood or emotion, and includes states such as "sad," "happy," and "anxious."

[0995] "Emotion recognition" refers to the process of analyzing a user's text or voice input data to identify their emotions.

[0996] "News Article" means a piece of information obtained from an external news source, including domestic and international events and social issues.

[0997] "Rewriting" refers to using generative AI to convert the content of a news article to suit the user's vocabulary and kanji ability.

[0998] "Terminology" refers to difficult words and expressions used in a specific field that are difficult for general users to understand.

[0999] "Areas" refer to words that users frequently use or areas of interest, such as "economy," "sports," and "entertainment."

[1000] "Adjusting" refers to changing the content and display order of news articles depending on the user's emotional state.

[1001] MODE FOR CARRYING OUT THE INVENTION

[1002] System Overview

[1003] This invention is a system that provides news articles that are easy for users to understand and that respond to their emotions. The system collects input information from the user's device, measures their vocabulary and kanji ability based on that information using AI, and then rewrites the news article using a generation AI. It also recognizes the user's emotions and adjusts the content of the article according to their emotional state.

[1004] Hardware and software used

[1005] The system includes the following hardware and software:

[1006] Hardware

[1007] User's device (smartphone, tablet, etc.)

[1008] Server (processes and stores data)

[1009] software

[1010] Analysis Library (TextBlob)

[1011] Emotion Recognition Model (Hugging Face's transformers)

[1012] Web API (retrieving news articles)

[1013] Process Overview

[1014] 1. User registration and initial settings

[1015] User: Opens the application, enters the required information, and creates an account.

[1016] Device: The entered information is sent to the server, which stores the user information, then generates an initial setup questionnaire and sends it to the device.

[1017] User: Answers the survey and the device sends the answers to the server.

[1018] 2. Vocabulary and Kanji ability assessment

[1019] User: Views quizzes and statements and enters answers to them.

[1020] Device: Sends quiz answer data to the server, which analyzes the data and measures the user's vocabulary and kanji ability.

[1021] 3. Emotional Recognition

[1022] User: Enter text or voice input.

[1023] Device: Sends input data to the server, which uses an emotion engine to recognize the user's emotions.

[1024] 4. Personalize your news articles

[1025] Server: The latest news articles obtained from an external news API are rewritten using a generative AI based on the user's vocabulary data. In addition, the display order of articles is determined based on the user's emotion recognition results.

[1026] Server: Generates a personalized list of news articles and sends it to the device.

[1027] Specific examples

[1028] 1. The server receives the text "I'm not feeling very well today" entered by user A on his smartphone.

[1029] 2. The server uses the emotion engine to recognize that User A has the emotion "sad."

[1030] 3. The server retrieves the "economic growth outlook" from an external news API.

[1031] 4. The server converts "economic growth" to "economic rise" based on User A's vocabulary data (intermediate level).

[1032] 5. The server sees that User A is feeling sad, so it displays a news article with an encouraging message added. For example, it displays "The economy may continue to improve. Cheer up!"

[1033] 6. User A reads the article and gives feedback saying, "It's encouraging."

[1034] Prompt Sentence Examples

[1035] The user types, "I'm not feeling very good today." The emotion recognition engine analyzes this text and identifies the user's emotion as "sad." Please rewrite the following news article, "Economic Growth Outlook," to fit the user's vocabulary level (intermediate level), and add an encouraging message.

[1036] This will enable news articles to be provided that meet the individual needs of users, improving the user experience.

[1037] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1038] Step 1:

[1039] User registration and initial settings

[1040] User: Opens the application and enters the required information (name, email address, password, etc.) on the account creation page.

[1041] Terminal: Sends the entered information to the server.

[1042] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[1043] User: Answers an initial setup questionnaire, and the device sends the answers to the server, which collects basic information and initial data about the user.

[1044] Step 2:

[1045] Vocabulary and Kanji ability assessment

[1046] User: View the quizzes and sentences presented in the application and enter the answers.

[1047] Device: Sends quiz answer data to the server.

[1048] Server: Analyzes the received response data and uses an AI model to measure the user's vocabulary and kanji ability. The results are stored in a database. For example, it determines how well the user understands words such as "GDP" and "growth."

[1049] Step 3:

[1050] Emotion recognition

[1051] User: Enter text or voice input.

[1052] Terminal: Sends input data (text or voice) to the server.

[1053] Server: The emotion engine analyzes the input data and recognizes the user's emotions. For example, it analyzes the input text "I'm not feeling very good today" and assigns an emotion label such as "sad." The analysis results are stored in a database.

[1054] Step 4:

[1055] Personalized news articles

[1056] Server: Gets the latest news articles from an external news API.

[1057] Server: Using each user's vocabulary data, the server uses generative AI to rewrite news articles into expressions that are easier for the user to understand. For example, it converts "economic growth" into "economic growth" and "GDP" into "gross domestic product."

[1058] Server: Adjust the order and content of articles based on the user's emotion recognition results. For example, if the user is expressing sadness, encouragement and positive articles will be displayed first.

[1059] Server: Generates a tailored list of news articles and sends it to the device.

[1060] Step 5:

[1061] View articles and gather feedback

[1062] Device: Display personalized news articles to users. For example, present an article to User A saying, "The economy may continue to improve. Stay strong!"

[1063] User: Read the article and provide feedback on comprehension and satisfaction.

[1064] Terminal: Displays a feedback form and collects input from the user, then sends this input data to the server.

[1065] Server: The feedback data is stored in a database and used to improve the generative AI model and emotion engine. For example, if a user gives feedback that they felt energized, this information is stored and used for future personalization.

[1066] 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.

[1067] 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.

[1068] 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.

[1069] [Third embodiment]

[1070] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1071] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1072] 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).

[1073] 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.

[1074] 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.

[1075] 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).

[1076] 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.

[1077] 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.

[1078] 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.

[1079] 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.

[1080] 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.

[1081] 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."

[1082] The present invention is a system for providing personalized news articles based on a user's vocabulary and kanji ability. An embodiment of the system will be described in detail below.

[1083] Program processing explanation

[1084] 1. User registration and initial settings

[1085] User: Opens the application for the first time and enters the required information on the account creation page.

[1086] Terminal: Sends the entered information to the server.

[1087] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[1088] On the device: Present the initial setup survey to the user and collect their responses.

[1089] 2. Vocabulary and Kanji ability assessment

[1090] User: View the quizzes and sentences provided in the application and enter their answers.

[1091] Device: Sends quiz answer data to the server.

[1092] Server: Receives the response data and uses an AI model to analyze and measure the user's vocabulary and kanji ability. The results are stored in a database.

[1093] 3. Personalize your news articles

[1094] Server: Gets the latest news articles from an external news API.

[1095] Server: Reflecting each user's vocabulary and kanji ability, the server uses generative AI to rewrite news articles into expressions that are easy for the user to understand. For example, it converts the term "fiscal policy" into "how the government spends money."

[1096] Server: Determines the ranking of news articles based on user interest data.

[1097] Server: Generates a personalized list of news articles and sends it to the user's device.

[1098] 4. View articles and gather feedback

[1099] On your device: Show personalized news articles to your users.

[1100] User: Read the article and provide feedback on comprehension and satisfaction.

[1101] Device: Collects user feedback and sends it to the server.

[1102] Server: Stores the feedback in a database and uses this data to improve the generative AI model.

[1103] Specific examples

[1104] Article personalization examples

[1105] 3.1. Server: Get the economy-related news article "Japan's GDP grows."

[1106] 3.2. Server: User A's vocabulary and kanji ability are intermediate, so convert "GDP" to "gross domestic product" and "growth" to "got up."

[1107] 3.3. Server: Since user A is interested in economics, place this article at the top of the priority display list.

[1108] 3.4. Server: Generates a personalized article list and sends it to the device.

[1109] 4.1. Device: Display the personalized article "Japan's GDP has increased" to User A.

[1110] 4.2. User: Reads the article and gives feedback that it was easy to understand.

[1111] 4.3. Terminal: Display the feedback form and collect User A's input.

[1112] 4.5. Server: Analyzes the feedback data and uses it to improve the generative AI model.

[1113] As described above, the present invention aims to eliminate the information gap by providing news articles that correspond to the user's vocabulary and kanji ability, thereby improving the user's ease of accepting information and level of interest.

[1114] The processing flow will be explained below.

[1115] Program processing details

[1116] Step 1:

[1117] User: Opens the application for the first time and enters the required information on the account creation page.

[1118] Step 2:

[1119] Terminal: Sends the entered information to the server.

[1120] Step 3:

[1121] Server: Creates an account based on the provided user information and saves it in the database.

[1122] Server: Generates the initial survey and sends it to the device.

[1123] Step 4:

[1124] On the device: Present the initial setup survey to the user and collect their responses.

[1125] Step 5:

[1126] User: Complete the initial setup survey and send the answers to your device.

[1127] Step 6:

[1128] Terminal: Sends the response data to the server.

[1129] Step 7:

[1130] Server: Receives the response data and uses the AI ​​model to perform an initial vocabulary assessment and initial interest settings for the user.

[1131] Step 8:

[1132] User: View the quizzes and sentences provided in the application and enter answers to them.

[1133] Step 9:

[1134] Terminal: Collects user quiz answer data and sends it to the server.

[1135] Step 10:

[1136] Server: Receives the response data and uses an AI model to measure the user's vocabulary and kanji ability.

[1137] Server: Stores the measurement results in a database.

[1138] Step 11:

[1139] Server: Gets the latest news articles from an external news API.

[1140] Step 12:

[1141] Server: Based on each user's vocabulary data, generative AI is used to rewrite news articles into expressions that are easy for the user to understand.

[1142] For example, convert "GDP" to "gross domestic product" and "growth" to "increased."

[1143] Step 13:

[1144] Server: Determines the ranking of news articles based on user interest data.

[1145] Step 14:

[1146] Server: Generates a personalized list of news articles and sends it to the device.

[1147] Step 15:

[1148] On your device: Show personalized news articles to your users.

[1149] Step 16:

[1150] User: Read the article and provide feedback on comprehension and satisfaction.

[1151] Step 17:

[1152] Terminal: Display a feedback form and collect user input.

[1153] Step 18:

[1154] Device: Sends feedback data to the server.

[1155] Step 19:

[1156] Server: Receives feedback and stores it in a database.

[1157] Server: Analyzes the feedback data and uses it to improve the generative AI model.

[1158] Through the above steps, the system of the present invention aims to provide personalized information tailored to the user's vocabulary and kanji ability, thereby eliminating the information gap.

[1159] Example 1

[1160] 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."

[1161] In modern society, a huge amount of news articles are updated daily, but not all of this information is easy to understand for all users. In particular, users with different vocabulary and kanji abilities have difficulty understanding news articles that contain technical terms and difficult vocabulary, resulting in differences in how easily they accept information. As a result, certain user groups may not be able to obtain sufficient information, and the information gap may widen. In addition, articles are not sufficiently personalized based on users' interests, making it difficult for users to find articles that will interest them. Therefore, there is a need for a system that provides easy-to-understand news articles that suit each user's vocabulary and kanji ability.

[1162] 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.

[1163] In this invention, the server includes means for collecting input information from the user's device, means for an AI to measure the user's vocabulary and kanji ability, means for the generation AI to rewrite publicly available news articles based on the results of the measurement, means for preferentially displaying articles based on the user's frequently used words and areas of interest, and means for collecting feedback from the user and improving the AI ​​model. This enables users to receive easy-to-understand news articles that are suited to their vocabulary and kanji ability, making it easier for them to access information that is of high interest to them.

[1164] "User's device" refers to a device such as a computer, smartphone, or tablet used by a user.

[1165] "Input information" refers to information that a user enters through a device, including name, email address, password, survey responses, quiz and assignment responses, etc.

[1166] "Vocabulary" refers to the range and number of words a user can understand and use.

[1167] "Kanji ability" refers to the range and number of kanji characters that a user can understand and use.

[1168] "AI" is an abbreviation for artificial intelligence, and specifically refers to computer programs and algorithms that analyze user input and evaluate vocabulary and kanji ability.

[1169] "Public news articles" refers to the latest economic, social, political, and other news articles obtained from external news providers.

[1170] "Generative AI" refers to a generative artificial intelligence model that rewrites news articles into expressions that suit the user's vocabulary and kanji ability.

[1171] "Frequent words" refer to words or phrases that a user has frequently used or searched for in the past.

[1172] "Areas of interest" refers to news categories and topics that users have expressed a high level of interest in through surveys, usage history, etc.

[1173] "Feedback" refers to opinions and ratings regarding understanding and satisfaction that users provide after reading a news article.

[1174] "AI model improvement" refers to the process of utilizing collected feedback data to improve the performance of a generative AI model.

[1175] The present invention is a system that provides personalized news articles based on the user's vocabulary and kanji ability. To implement this system, the following specific steps are taken.

[1176] User registration and initial settings

[1177] First, the user installs the application and, when using it for the first time, enters required information such as name, email address, and password on the account creation page. The device sends the entered information to the server. The server receives this information and creates a new account. Specifically, it stores this information in a database (e.g., MySQL). Next, the server generates an initial setup questionnaire for the user and sends it to the device. The device displays the questionnaire to the user and prompts the user to enter their answers. The answered information is sent from the device to the server and stored in the database.

[1178] Vocabulary and Kanji ability assessment

[1179] The user views quizzes and sentences provided within the application and enters their answers. The device sends this answer data to the server. The server uses a generative AI model (e.g., GPT-3) to analyze the received answer data. At this time, the server sends the following prompt to the generative AI model: "Based on the user's answers, please evaluate this user's vocabulary and kanji ability." The generative AI model evaluates the user's vocabulary and kanji ability based on this prompt. The evaluation results are stored in a database by the server.

[1180] Personalized news articles

[1181] Next, the server retrieves the latest news articles using an external news API (e.g., newsAPI.org). These retrieved news articles are rewritten to suit each user's vocabulary and kanji ability. The server then uses a generative AI model to send a prompt such as, "The user's vocabulary is intermediate. Please simplify the wording of this article." This prompt converts the wording of the article. For example, it converts "fiscal policy" to "how the government uses money." In this way, articles that have been rewritten to be easier for the user to understand are ranked based on the user's interest data. Finally, a personalized list of news articles is generated and sent to the device.

[1182] View articles and gather feedback

[1183] The device displays a personalized list of news articles sent from the server to the user. The user reads the articles and provides feedback on their understanding and satisfaction. The feedback is sent to the server via the device. The server stores the received feedback in a database and uses it to improve the generative AI model.

[1184] Specific examples

[1185] In personalizing a news article, the server retrieves an economic news article titled "Japan's GDP grows."

[1186] If User A's vocabulary and kanji ability are intermediate, the generation AI will convert "GDP" to "gross domestic product" and "growth" to "risen."

[1187] User A is very interested in economics, so this article will be placed high on the priority display list.

[1188] This personalized article list is sent to the device and displayed to User A.

[1189] This invention provides news articles tailored to the individual abilities of users, facilitating their understanding and acceptance of information, thereby realizing a system that alleviates the digital divide and efficiently provides information tailored to the user's interests.

[1190] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1191] Step 1:

[1192] User registration and initial settings

[1193] User: The user opens the application and enters the required information on the account creation page, such as name, email address, and password.

[1194] Input: User information such as name, email address, and password.

[1195] Terminal: The terminal sends the entered information to the server.

[1196] Output: User information sent to the server.

[1197] Server: The server creates a new account based on the received user information, stores this information in a database, and generates an initial setup survey and sends it to the device.

[1198] Input: User information sent from the device.

[1199] Output: Saved user account, generated survey.

[1200] Terminal: The terminal displays the survey to the user and prompts the user to enter their answers.

[1201] Input: Initial Setup Survey.

[1202] User: The user completes an initial setup questionnaire and enters the answers into the device.

[1203] Output: User's survey responses.

[1204] Terminal: The terminal sends the survey responses to the server.

[1205] Input: User's survey response information.

[1206] Output: Survey responses sent to the server.

[1207] Step 2:

[1208] Vocabulary and Kanji ability assessment

[1209] User: The user enters answers to quizzes and assignments.

[1210] Input: Quiz or assignment, user's answer.

[1211] Terminal: The terminal sends the user's response data to the server.

[1212] Output: The response data sent to the server.

[1213] Server: Based on the received answer data, the server generates prompt sentences to evaluate the user's vocabulary and kanji ability, and sends these prompt sentences to the generative AI model.

[1214] Input: User response data.

[1215] Output: A prompt to the generative AI model.

[1216] Generative AI model: The generative AI model evaluates the user's vocabulary and kanji ability based on the prompt sentence.

[1217] Output: The evaluation result.

[1218] Server: The server stores the evaluation results received from the generative AI model in a database.

[1219] Input: Evaluation results from a generative AI model.

[1220] Output: Evaluation results stored in a database.

[1221] Step 3:

[1222] Personalized news articles

[1223] Server: The server retrieves the latest news articles from an external news API.

[1224] Input: News API request.

[1225] Output: News article data.

[1226] Server: The server uses a generative AI model to generate prompts for rewriting the retrieved news articles based on the user's vocabulary and kanji ability. Example prompt: "The user has intermediate vocabulary, so please simplify the wording of this article."

[1227] Input: News article, user vocabulary and kanji proficiency data.

[1228] Output: A prompt to the generative AI model.

[1229] Generative AI model: Based on a prompt, the generative AI model rewrites news articles in a way that is easy for users to understand.

[1230] Output: A rewritten news article.

[1231] Server: The server determines the order in which news articles to display based on the user's interest data, generates a personalized news article list, and sends it to the device.

[1232] Input: rewritten news articles, user interest data.

[1233] Output: A personalized list of news articles.

[1234] Step 4:

[1235] View articles and gather feedback

[1236] Device: The device displays a personalized list of news articles to the user.

[1237] Input: A list of news articles sent by the server.

[1238] User: The user reads the article and provides feedback on their understanding and satisfaction.

[1239] Output: User feedback data.

[1240] Device: The device sends the user's feedback to the server.

[1241] Input: User feedback data.

[1242] Server: The server stores the received feedback data in a database and uses it to improve the generative AI model.

[1243] Output: Feedback data stored in a database.

[1244] (Application example 1)

[1245] 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."

[1246] Conventional advertising systems do not personalize ads based on the user's vocabulary or writing ability, which results in ads that are difficult for users to understand. This reduces the effectiveness of ads and makes it difficult for them to attract users' interest.

[1247] 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.

[1248] In this invention, the server includes means for collecting input information from the user's terminal and measuring the user's vocabulary and writing ability with an AI, means for the generation AI to rewrite advertising information based on the measurement results, and means for preferentially displaying advertisements based on the user's frequently used words and areas of interest. This allows advertisements to be optimized according to the user's level of understanding, making it possible to more effectively attract the user's interest and attention.

[1249] Key Word Definitions

[1250] "User" refers to any individual or group of people who use the System.

[1251] "User terminal" refers to an electronic device that allows a user to access and operate the system.

[1252] "Input information" refers to data entered by a user into a terminal and transmitted to a server.

[1253] "Vocabulary" refers to the range of words a user can understand and use.

[1254] "Character ability" refers to the range of characters, kana notations, and kanji characters that a user can understand and use.

[1255] "AI" refers to an information processing system that uses artificial intelligence technology.

[1256] "Advertising information" refers to information intended to promote the sale of products or advertise services.

[1257] "Generative AI" refers to a technology that uses artificial intelligence to generate sentences based on the user's vocabulary and writing ability.

[1258] A "word" refers to the smallest unit of expression in language.

[1259] A "field" refers to an area related to a particular subject or theme.

[1260] "Means" refers to the process or method for achieving a goal.

[1261] A "system" refers to a set of mechanisms in which multiple components work together to achieve a specific function.

[1262] patent specification

[1263] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below.

[1264] First, the user launches the application on their device and creates an account. When creating an account, they enter the necessary information. The device then sends the entered information to the server.

[1265] The server creates an account based on the received user information, stores this data in a database, and then generates questionnaires and quizzes to measure the user's vocabulary and writing ability as an initial setting, and sends them to the user's device.

[1266] Users answer these initial setup quizzes and surveys. The device sends the user's response data to the server. The server then analyzes the response data using an AI model to measure the user's vocabulary and writing ability. The analysis results are stored in a database.

[1267] The server then retrieves the latest ad information from an external ad API. Based on the user's vocabulary and writing ability measurements, the server uses a generative AI model to rewrite the ad information into language that is easier for the user to understand. For example, it converts technical jargon and difficult vocabulary into simpler language. This rewriting process uses advanced natural language processing models such as GPT-4.

[1268] The server then determines the order in which ads are displayed based on the user's past behavior and areas of interest. Once a properly personalized ad list is generated, it is sent to the user's device.

[1269] The device displays this personalized ad list to the user. The user views the ads and provides feedback on their content. The device collects this feedback data and sends it to the server. The server uses this feedback data to continuously improve the accuracy of the generative AI model.

[1270] The specific hardware and software used are Python and Flask for the server, MySQL for database management, GPT-4 API for the generative AI model, and React Native for the front-end application.

[1271] Specific examples

[1272] Below are some specific examples of ad personalization:

[1273] Original ad copy

[1274] "The latest smartphone is now available. Its AI camera and 5G compatibility will change your everyday life."

[1275] Personalized ad text

[1276] "New smartphone released with AI camera and high-speed internet"

[1277] Prompt Sentence Examples

[1278] "If the user has an intermediate level of vocabulary, personalize the original ad copy, 'The latest smartphone is here. It changes your daily life with its AI camera and 5G connectivity,' in simpler terms."

[1279] In this way, the present invention realizes maximizing the effectiveness of advertising by appropriately personalizing advertising information in accordance with the user's vocabulary and writing ability.

[1280] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1281] Program processing steps

[1282] Step 1:

[1283] The user launches the application on their device and creates an account.

[1284] Input: User information from your device (username, password, email address, etc.)

[1285] Output: User information sent to the server

[1286] Specific operation: The terminal sends the entered user information to the server.

[1287] Step 2:

[1288] The server creates an account based on the received user information and stores it in the database.

[1289] Input: User information sent to the server

[1290] Output: Account information stored in the database

[1291] Specific operation: The server receives the user information and saves it as a new record in the database (MySQL).

[1292] Step 3:

[1293] As an initial setting, the server generates questionnaires and quizzes to measure vocabulary and writing ability, and sends them to the user's device.

[1294] Input: New user registration information

[1295] Output: Initial setup quiz and survey

[1296] Specific operation: The server generates quizzes and surveys and sends them to the user's device.

[1297] Step 4:

[1298] Users complete default quizzes and surveys.

[1299] Input: Quizzes and surveys

[1300] Output: User's answer

[1301] Specific operation: The user answers the displayed quiz or survey and sends it to their device.

[1302] Step 5:

[1303] The terminal sends the user's response data to the server.

[1304] Input: User response data

[1305] Output: Response data sent to the server

[1306] Specific operation: The user device sends the response data to the server.

[1307] Step 6:

[1308] The server receives the response data, analyzes the user's vocabulary and writing ability using a generative AI model, and stores the results in a database.

[1309] Input: User response data

[1310] Output: Vocabulary and writing ability measurements

[1311] Specific operation: The server analyzes the response data, measures vocabulary and writing ability, and stores the results in a database.

[1312] Step 7:

[1313] The server retrieves the latest ad information from an external ad API.

[1314] Input: External Ads API

[1315] Output: Retrieved advertising information

[1316] Specific operation: The server periodically calls the external advertising API to obtain the latest advertising information.

[1317] Step 8:

[1318] The server uses a generative AI model to personalize advertising information based on the user's vocabulary and writing ability.

[1319] Input: Vocabulary and writing ability test results, advertising information

[1320] Output: Personalized advertising information

[1321] Specific operation: The server uses a generative AI model (GPT-4) to rewrite advertising information into expressions that are easy for users to understand.

[1322] Step 9:

[1323] The server determines the order in which ads are displayed based on the user's past behavioral data and areas of interest.

[1324] Input: User behavior data, interests, personalized advertising

[1325] Output: Prioritized ad list

[1326] Specific operation: The server determines the ad ranking based on the user's behavioral data and areas of interest, and generates an ad list.

[1327] Step 10:

[1328] The server transmits the personalized advertisement list to the user terminal.

[1329] Input: Prioritized Ad List

[1330] Output: Ad list sent to the user's device

[1331] Specific operation: The server sends a personalized ad list to the user's device.

[1332] Step 11:

[1333] The device displays a personalized list of ads to the user.

[1334] Input: Personalized Ad List

[1335] Output: The ad shown to the user

[1336] Specific operation: The user device receives the advertisement list and displays it to the user.

[1337] Step 12:

[1338] Users view ads and provide feedback.

[1339] Enter: Advertisement

[1340] Output: User feedback

[1341] What happens: Users view the ad and provide feedback on comprehension and interest.

[1342] Step 13:

[1343] The user terminal collects feedback data and transmits it to the server.

[1344] Input: User feedback

[1345] Output: Feedback data sent to the server

[1346] Specific operation: The user device collects feedback data and sends it to the server.

[1347] Step 14:

[1348] The server uses the feedback data to improve the accuracy of the generative AI model.

[1349] Input: Feedback data

[1350] Output: An updated generative AI model

[1351] What it does: The server analyzes the feedback data to improve the accuracy of the generative AI model (GPT-4) and help with future ad personalization.

[1352] 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.

[1353] The present invention provides a system that personalizes news articles based on a user's vocabulary and kanji ability, and further recognizes the user's emotions to provide appropriate content. The following describes an embodiment of the system in detail.

[1354] Program processing explanation

[1355] 1. User registration and initial settings

[1356] User: Opens the application for the first time and enters the required information on the account creation page.

[1357] Terminal: Sends the entered information to the server.

[1358] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[1359] On the device: Present the initial setup survey to the user and collect their responses.

[1360] 2. Vocabulary and Kanji ability assessment

[1361] User: View the quizzes and sentences provided in the application and enter their answers.

[1362] Device: Sends quiz answer data to the server.

[1363] Server: Receives the response data and uses an AI model to analyze and measure the user's vocabulary and kanji ability. The results are stored in a database.

[1364] 3. Emotion recognition using the emotion engine

[1365] User: Enter text or voice input.

[1366] Terminal: Sends user input data (text or voice) to the server.

[1367] Server: The emotion engine analyzes the input data and recognizes the user's emotions. For example, if the user is sad or happy, it records the emotion label.

[1368] Server: Stores the analysis results in a database.

[1369] 4. Personalize your news articles

[1370] Server: Gets the latest news articles from an external news API.

[1371] Server: Using each user's vocabulary data, the server uses generative AI to rewrite news articles into expressions that are easier for the user to understand. For example, it converts "GDP" to "gross domestic product" and "growth" to "risen."

[1372] Server: Determines the order in which news articles are displayed based on user interest data and emotion recognition results.

[1373] For example, if a user is feeling sad, encouraging content or positive news articles will be prioritized.

[1374] Server: Generates a personalized list of news articles and sends it to the device.

[1375] 5. View articles and gather feedback

[1376] On your device: Show personalized news articles to your users.

[1377] User: Read the article and provide feedback on comprehension and satisfaction.

[1378] Terminal: Display a feedback form and collect user input.

[1379] Device: Sends feedback data to the server.

[1380] Server: Stores the feedback in a database and uses this data to improve the generative AI model.

[1381] Specific examples

[1382] Example of article personalization based on sentiment

[1383] 3.1. Server: Receives the text "I'm not feeling very well today" entered by User A on his smartphone.

[1384] 3.2. Server: The emotion engine analyzes this input data and recognizes that User A has the emotion "sad."

[1385] 4.1. Server: Get the latest news article "Economic Growth Outlook".

[1386] 4.2. Server: User A's vocabulary data is intermediate, so convert "economic growth" to "economic rise."

[1387] 4.3. Server: Since User A is feeling "sad," it is decided to prioritize displaying articles with encouraging and positive content.

[1388] 4.4. Server: Generates a personalized article list and sends it to the device.

[1389] 5.1. Device: Show personalized article with positive content to User A.

[1390] 5.2. User: Reads the article and gives feedback saying that it "inspired me."

[1391] 5.3. Terminal: Collects feedback and sends it to the server.

[1392] 5.4. Server: Stores the feedback and uses it to improve the generative AI model.

[1393] As described above, the present invention aims to eliminate the digital divide and improve the user experience by providing information that takes into account the user's vocabulary, kanji ability, and even emotional state, and by providing content that is easy for users to understand and appropriate.

[1394] The processing flow will be explained below.

[1395] Program processing details

[1396] Step 1:

[1397] User: Opens the application for the first time and enters the required information on the account creation page.

[1398] Step 2:

[1399] Terminal: Sends the entered information to the server.

[1400] Step 3:

[1401] Server: Creates an account based on the provided user information and saves it in the database.

[1402] Server: Generates the initial setup survey and sends it to the device.

[1403] Step 4:

[1404] On the device: Present the initial setup survey to the user and collect their responses.

[1405] Step 5:

[1406] User: Complete the initial setup survey and send the answers to your device.

[1407] Step 6:

[1408] Terminal: Sends the response data to the server.

[1409] Step 7:

[1410] Server: Receives the response data and uses the AI ​​model to perform an initial vocabulary assessment and initial interest settings for the user.

[1411] Step 8:

[1412] User: View the quizzes and sentences provided in the application and enter answers to them.

[1413] Step 9:

[1414] Terminal: Collects user quiz answer data and sends it to the server.

[1415] Step 10:

[1416] Server: Receives the response data and uses an AI model to measure the user's vocabulary and kanji ability.

[1417] Server: Stores the measurement results in a database.

[1418] Step 11:

[1419] User: Express their feelings through text input or voice input.

[1420] Step 12:

[1421] Device: Sends the user's text and voice data to the server.

[1422] Step 13:

[1423] Server: The emotion engine analyzes the input data and recognizes the user's emotions.

[1424] For example, the text "I'm tired today" is analyzed and the emotion label "tired" is assigned.

[1425] Step 14:

[1426] Server: Stores the sentiment analysis results in a database.

[1427] Step 15:

[1428] Server: Gets the latest news articles from an external news API.

[1429] Step 16:

[1430] Server: Based on each user's vocabulary data, generative AI is used to rewrite news articles into expressions that are easy for the user to understand.

[1431] For example, convert "GDP" to "gross domestic product" and "growth" to "increased."

[1432] Step 17:

[1433] Server: Determines the order in which news articles are displayed based on user interest data and emotion recognition results.

[1434] For example, if a user is feeling "tired," positive articles related to energy recovery will be displayed first.

[1435] Step 18:

[1436] Server: Generates a personalized list of news articles and sends it to the device.

[1437] Step 19:

[1438] On your device: Show personalized news articles to your users.

[1439] Step 20:

[1440] User: Read the article and provide feedback on comprehension and satisfaction.

[1441] Step 21:

[1442] Terminal: Display a feedback form and collect user input.

[1443] Step 22:

[1444] Device: Sends feedback data to the server.

[1445] Step 23:

[1446] Server: Receives feedback and stores it in a database.

[1447] Server: Improves the generative AI model based on feedback data.

[1448] Through the above steps, the system of the present invention aims to provide personalized information based on the user's vocabulary, kanji ability, and emotional state, and to provide appropriate content that is easy for the user to understand.

[1449] Example 2

[1450] 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."

[1451] Conventional news article delivery systems lacked personalization based on the user's vocabulary and kanji ability, making it difficult to understand articles. Furthermore, because they provided information without taking the user's emotional state into consideration, content that did not match the user's emotions was sometimes displayed, resulting in a lack of improvement in the user experience. This could widen the information gap.

[1452] 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.

[1453] In this invention, the server includes means for collecting input information from the user's terminal, means for measuring the user's vocabulary and kanji ability by an AI, means for rewriting publicly available news articles by a generation AI based on the measurement results, means for preferentially displaying articles based on the user's frequently used words and areas of interest, means for recognizing the user's emotional state, and means for preferentially displaying appropriate news articles based on the recognized emotions. This makes it possible to personalize news articles according to the user's vocabulary and kanji ability and provide content that takes emotions into consideration.

[1454] "User's device" refers to a device used by a user to input and view information, including smartphones, tablets, and personal computers.

[1455] "Input information" refers to data provided by a user through a terminal, and includes various types of information such as text, audio, and images.

[1456] "Vocabulary" refers to the range and depth of words a user can understand and use.

[1457] "Kanji ability" refers to the range of kanji that a user can understand and use, and the level of understanding of those kanji.

[1458] "AI" or "artificial intelligence" refers to algorithms and technologies used to measure and analyze a user's vocabulary and kanji skills.

[1459] "Public news stories" refers to current public news content obtained from external news services.

[1460] "Generative AI" refers to a generative model that uses natural language processing to rewrite news articles in a way that is easy for users to understand.

[1461] "User's frequently used words" refers to words and phrases that the user frequently uses in their past operation history and input content.

[1462] "Areas of interest" refers to the topics or categories that a user is interested in, and are determined based on past browsing history and survey responses.

[1463] "Emotional state" refers to the emotion the user is currently feeling, and is expressed by different emotion labels such as joy, sadness, anger, etc.

[1464] "Means for preferentially displaying appropriate news articles based on emotions" refers to the process of selecting the news articles that best fit the emotional state of the user as recognized by the emotion engine and adjusting the display order.

[1465] The present invention is a system that personalizes news articles based on a user's vocabulary and kanji ability, and further recognizes the user's emotions to provide appropriate content. Specific embodiments of the system are described below.

[1466] Program processing and the hardware and software used

[1467] 1. User registration and initial settings

[1468] When using the application for the first time, the user opens the application and enters the required information on the account creation page. The device sends that information to the server. The server creates an account based on the provided information and stores it in a database. It also generates an initial setup questionnaire and sends it to the device. The user answers the questionnaire, and the device collects the answers and sends them to the server.

[1469] The hardware and software used include devices such as smartphones and PCs, cloud-based database servers, and survey generation software.

[1470] 2. Vocabulary and Kanji ability assessment

[1471] Users view quizzes and sentences provided on the application and enter their answers. The device then sends the answer data to the server. The server then analyzes the answer data using an AI model (e.g., OpenAI GPT-3) to measure the user's vocabulary and kanji ability. The measurement results are stored in a database.

[1472] 3. Emotion recognition using the emotion engine

[1473] Users input their understanding and satisfaction with news articles using text or voice. The device sends the input data to a server, which then uses an emotion engine (e.g., Google Cloud Natural Language) to analyze the input data and recognize the user's emotional state. The results are then stored in a database.

[1474] 4. Personalize your news articles

[1475] The server retrieves the latest news articles from an external news API (e.g., NewsAPI). Based on each user's vocabulary data, it uses generative AI (e.g., OpenAI GPT-3) to rewrite the news articles into a format that is easy for the user to understand. Furthermore, it determines the display order based on the user's interest data and emotion recognition results. For example, if the user is sad, it prioritizes positive news. A personalized list of news articles is generated and sent to the device.

[1476] 5. View articles and gather feedback

[1477] The device displays personalized news articles to the user, who then reads the article and provides feedback on their understanding and satisfaction. The device collects the feedback and sends it to a server, which stores it in a database and uses it to improve the generative AI model.

[1478] Specific examples

[1479] For example, consider a scenario where a user is using an app for the first time.

[1480] 1. Users create an account and answer a short survey for initial setup, including questions like, "What is your favorite news genre?"

[1481] 2. To measure vocabulary and kanji ability, users take a quiz to answer the meaning of "economic growth," and the results are sent to the server. The AI ​​model determines the user's level of understanding as "intermediate."

[1482] 3. Emotion recognition: if a user types "I'm a little sad today," the emotion engine will analyze this and record it as "sad."

[1483] 4. In news article personalization, the server converts the retrieved news article "economic growth outlook" into "economic upturn" based on the user's vocabulary, and further prioritizes positive content for "sad" users.

[1484] 5. When displaying articles, a personalized article titled "Economic Growth" is displayed to the user, and if the user provides feedback such as "This article cheered me up," that information is sent to the server and reflected in future content provision.

[1485] The system can provide optimal news articles based on the user's vocabulary, kanji ability, and even emotional state, and the model is continually improved based on feedback, further enhancing the user experience.

[1486] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1487] Step 1: User registration and initial setup

[1488] Input: A user launches an application for the first time and enters information such as their name, email address, and password.

[1489] Operation and data processing: The device sends the user's input information to the server. The server receives the input information and stores it in a database as account information. The server then generates an initial setup questionnaire and sends it to the device.

[1490] Output: The server generates a new account ID and survey data and sends them to the device. The device displays the survey to the user, who answers it. The user's answers are then sent from the device to the server again and saved.

[1491] Step 2: Vocabulary and Kanji Ability Assessment

[1492] Input: The user answers a quiz or statement.

[1493] Operation and data processing: The device sends the user's answers to the server. The server analyzes the answers using an AI model (e.g., OpenAI GPT-3). The analysis results are used to measure the user's vocabulary and kanji ability. The server then stores the results in a database.

[1494] Output: The server generates evaluation data on the user's vocabulary and Kanji ability and records it in a database.

[1495] Step 3: Emotion recognition by the emotion engine

[1496] Input: Users input their understanding and satisfaction with a news article using text or voice.

[1497] Operation and data processing: The device sends this input data to the server. The server analyzes the input data using an emotion engine (e.g., Google Cloud Natural Language). As a result, the server recognizes the user's emotional state. The server stores the recognized emotion results in a database.

[1498] Output: The server generates an emotion label for the user (e.g., happy, sad) and records it in a database.

[1499] Step 4: Personalize your news articles

[1500] Input: Latest news articles retrieved by the server from an external news API (e.g., NewsAPI), along with the user's vocabulary, kanji ability, emotional state, and interest data.

[1501] Operation and data processing: Based on each user's vocabulary data, the server uses generative AI (e.g., OpenAI GPT-3) to rewrite the retrieved news articles into a format that is easy for the user to understand. In addition, the server takes into account the user's emotional state and interest data to determine the display order of news articles.

[1502] Output: The server generates a personalized list of news articles and sends it to the device.

[1503] Step 5: View the article and gather feedback

[1504] Input: A personalized list of news articles sent by the server.

[1505] Operation and Data Processing: The device displays personalized news articles to the user. The user reads the articles and provides feedback on their understanding and satisfaction. The device then sends this feedback to the server.

[1506] Output: The server stores the received feedback in a database and uses it to improve the generative AI model in the future.

[1507] (Application example 2)

[1508] 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."

[1509] Conventional news delivery systems provide news articles without considering the user's vocabulary, kanji ability, or emotional state, which often results in content that is not suited to the user's understanding or interests. Furthermore, content that is uninteresting to the user or information that does not match their emotional state is displayed, resulting in a lower level of satisfaction in the reader experience. This can cause users to lose interest in the news, and for news providers, it can also cause a problem of reduced effectiveness in conveying information.

[1510] 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.

[1511] In this invention, the server includes means for collecting input information from the user's terminal and using AI to measure the user's vocabulary and kanji ability, means for the generation AI to rewrite publicly available news articles based on the results of the measurement, means for preferentially displaying articles based on the user's frequently used words and areas of interest, and means for recognizing the user's emotions and adjusting the content of the news articles according to their emotional state. This makes it possible to provide news content that is easy for the user to understand and takes into consideration their emotions.

[1512] "User device" refers to the electronic device used by the user to view news articles, including smartphones and tablets.

[1513] "Input information" refers to data provided by the user via the device, and includes text, audio, quiz answers, etc.

[1514] "AI" is an abbreviation for Artificial Intelligence, and refers to technology for analyzing and measuring users' vocabulary and kanji ability.

[1515] "Generative AI" refers to artificial intelligence technology that automatically converts news articles and text data into a form that is easy for users to understand.

[1516] "Emotional state" refers to the user's current mood or emotion, and includes states such as "sad," "happy," and "anxious."

[1517] "Emotion recognition" refers to the process of analyzing a user's text or voice input data to identify their emotions.

[1518] "News Article" means a piece of information obtained from an external news source, including domestic and international events and social issues.

[1519] "Rewriting" refers to using generative AI to convert the content of a news article to suit the user's vocabulary and kanji ability.

[1520] "Terminology" refers to difficult words and expressions used in a specific field that are difficult for general users to understand.

[1521] "Areas" refer to words that users frequently use or areas of interest, such as "economy," "sports," and "entertainment."

[1522] "Adjusting" refers to changing the content and display order of news articles depending on the user's emotional state.

[1523] MODE FOR CARRYING OUT THE INVENTION

[1524] System Overview

[1525] This invention is a system that provides news articles that are easy for users to understand and that respond to their emotions. The system collects input information from the user's device, measures their vocabulary and kanji ability based on that information using AI, and then rewrites the news article using a generation AI. It also recognizes the user's emotions and adjusts the content of the article according to their emotional state.

[1526] Hardware and software used

[1527] The system includes the following hardware and software:

[1528] Hardware

[1529] User's device (smartphone, tablet, etc.)

[1530] Server (processes and stores data)

[1531] software

[1532] Analysis Library (TextBlob)

[1533] Emotion Recognition Model (Hugging Face's transformers)

[1534] Web API (retrieving news articles)

[1535] Process Overview

[1536] 1. User registration and initial settings

[1537] User: Opens the application, enters the required information, and creates an account.

[1538] Device: The entered information is sent to the server, which stores the user information, then generates an initial setup questionnaire and sends it to the device.

[1539] User: Answers the survey and the device sends the answers to the server.

[1540] 2. Vocabulary and Kanji ability assessment

[1541] User: Views quizzes and statements and enters answers to them.

[1542] Device: Sends quiz answer data to the server, which analyzes the data and measures the user's vocabulary and kanji ability.

[1543] 3. Emotional Recognition

[1544] User: Enter text or voice input.

[1545] Device: Sends input data to the server, which uses an emotion engine to recognize the user's emotions.

[1546] 4. Personalize your news articles

[1547] Server: The latest news articles obtained from an external news API are rewritten using a generative AI based on the user's vocabulary data. In addition, the display order of articles is determined based on the user's emotion recognition results.

[1548] Server: Generates a personalized list of news articles and sends it to the device.

[1549] Specific examples

[1550] 1. The server receives the text "I'm not feeling very well today" entered by user A on his smartphone.

[1551] 2. The server uses the emotion engine to recognize that User A has the emotion "sad."

[1552] 3. The server retrieves the "economic growth outlook" from an external news API.

[1553] 4. The server converts "economic growth" to "economic rise" based on User A's vocabulary data (intermediate level).

[1554] 5. The server sees that User A is feeling sad, so it displays a news article with an encouraging message added. For example, it displays "The economy may continue to improve. Cheer up!"

[1555] 6. User A reads the article and gives feedback saying, "It's encouraging."

[1556] Prompt Sentence Examples

[1557] The user types, "I'm not feeling very good today." The emotion recognition engine analyzes this text and identifies the user's emotion as "sad." Please rewrite the following news article, "Economic Growth Outlook," to fit the user's vocabulary level (intermediate level), and add an encouraging message.

[1558] This will enable news articles to be provided that meet the individual needs of users, improving the user experience.

[1559] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1560] Step 1:

[1561] User registration and initial settings

[1562] User: Opens the application and enters the required information (name, email address, password, etc.) on the account creation page.

[1563] Terminal: Sends the entered information to the server.

[1564] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[1565] User: Answers an initial setup questionnaire, and the device sends the answers to the server, which collects basic information and initial data about the user.

[1566] Step 2:

[1567] Vocabulary and Kanji ability assessment

[1568] User: View the quizzes and sentences presented in the application and enter the answers.

[1569] Device: Sends quiz answer data to the server.

[1570] Server: Analyzes the received response data and uses an AI model to measure the user's vocabulary and kanji ability. The results are stored in a database. For example, it determines how well the user understands words such as "GDP" and "growth."

[1571] Step 3:

[1572] Emotion recognition

[1573] User: Enter text or voice input.

[1574] Terminal: Sends input data (text or voice) to the server.

[1575] Server: The emotion engine analyzes the input data and recognizes the user's emotions. For example, it analyzes the input text "I'm not feeling very good today" and assigns an emotion label such as "sad." The analysis results are stored in a database.

[1576] Step 4:

[1577] Personalized news articles

[1578] Server: Gets the latest news articles from an external news API.

[1579] Server: Using each user's vocabulary data, the server uses generative AI to rewrite news articles into expressions that are easier for the user to understand. For example, it converts "economic growth" into "economic growth" and "GDP" into "gross domestic product."

[1580] Server: Adjust the order and content of articles based on the user's emotion recognition results. For example, if the user is expressing sadness, encouragement and positive articles will be displayed first.

[1581] Server: Generates a tailored list of news articles and sends it to the device.

[1582] Step 5:

[1583] View articles and gather feedback

[1584] Device: Display personalized news articles to users. For example, present an article to User A saying, "The economy may continue to improve. Stay strong!"

[1585] User: Read the article and provide feedback on comprehension and satisfaction.

[1586] Terminal: Displays a feedback form and collects input from the user, then sends this input data to the server.

[1587] Server: The feedback data is stored in a database and used to improve the generative AI model and emotion engine. For example, if a user gives feedback that they felt energized, this information is stored and used for future personalization.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] [Fourth embodiment]

[1592] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1593] 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.

[1594] 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).

[1595] 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.

[1596] 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.

[1597] 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).

[1598] 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.

[1599] 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.

[1600] 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.

[1601] 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.

[1602] 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.

[1603] 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.

[1604] 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."

[1605] The present invention is a system for providing personalized news articles based on a user's vocabulary and kanji ability. An embodiment of the system will be described in detail below.

[1606] Program processing explanation

[1607] 1. User registration and initial settings

[1608] User: Opens the application for the first time and enters the required information on the account creation page.

[1609] Terminal: Sends the entered information to the server.

[1610] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[1611] On the device: Present the initial setup survey to the user and collect their responses.

[1612] 2. Vocabulary and Kanji ability assessment

[1613] User: View the quizzes and sentences provided in the application and enter their answers.

[1614] Device: Sends quiz answer data to the server.

[1615] Server: Receives the response data and uses an AI model to analyze and measure the user's vocabulary and kanji ability. The results are stored in a database.

[1616] 3. Personalize your news articles

[1617] Server: Gets the latest news articles from an external news API.

[1618] Server: Reflecting each user's vocabulary and kanji ability, the server uses generative AI to rewrite news articles into expressions that are easy for the user to understand. For example, it converts the term "fiscal policy" into "how the government spends money."

[1619] Server: Determines the ranking of news articles based on user interest data.

[1620] Server: Generates a personalized list of news articles and sends it to the user's device.

[1621] 4. View articles and gather feedback

[1622] On your device: Show personalized news articles to your users.

[1623] User: Read the article and provide feedback on comprehension and satisfaction.

[1624] Device: Collects user feedback and sends it to the server.

[1625] Server: Stores the feedback in a database and uses this data to improve the generative AI model.

[1626] Specific examples

[1627] Article personalization examples

[1628] 3.1. Server: Get the economy-related news article "Japan's GDP grows."

[1629] 3.2. Server: User A's vocabulary and kanji ability are intermediate, so convert "GDP" to "gross domestic product" and "growth" to "got up."

[1630] 3.3. Server: Since user A is interested in economics, place this article at the top of the priority display list.

[1631] 3.4. Server: Generates a personalized article list and sends it to the device.

[1632] 4.1. Device: Display the personalized article "Japan's GDP has increased" to User A.

[1633] 4.2. User: Reads the article and gives feedback that it was easy to understand.

[1634] 4.3. Terminal: Display the feedback form and collect User A's input.

[1635] 4.5. Server: Analyzes the feedback data and uses it to improve the generative AI model.

[1636] As described above, the present invention aims to eliminate the information gap by providing news articles that correspond to the user's vocabulary and kanji ability, thereby improving the user's ease of accepting information and level of interest.

[1637] The processing flow will be explained below.

[1638] Program processing details

[1639] Step 1:

[1640] User: Opens the application for the first time and enters the required information on the account creation page.

[1641] Step 2:

[1642] Terminal: Sends the entered information to the server.

[1643] Step 3:

[1644] Server: Creates an account based on the provided user information and saves it in the database.

[1645] Server: Generates the initial survey and sends it to the device.

[1646] Step 4:

[1647] On the device: Present the initial setup survey to the user and collect their responses.

[1648] Step 5:

[1649] User: Complete the initial setup survey and send the answers to your device.

[1650] Step 6:

[1651] Terminal: Sends the response data to the server.

[1652] Step 7:

[1653] Server: Receives the response data and uses the AI ​​model to perform an initial vocabulary assessment and initial interest settings for the user.

[1654] Step 8:

[1655] User: View the quizzes and sentences provided in the application and enter answers to them.

[1656] Step 9:

[1657] Terminal: Collects user quiz answer data and sends it to the server.

[1658] Step 10:

[1659] Server: Receives the response data and uses an AI model to measure the user's vocabulary and kanji ability.

[1660] Server: Stores the measurement results in a database.

[1661] Step 11:

[1662] Server: Gets the latest news articles from an external news API.

[1663] Step 12:

[1664] Server: Based on each user's vocabulary data, generative AI is used to rewrite news articles into expressions that are easy for the user to understand.

[1665] For example, convert "GDP" to "gross domestic product" and "growth" to "increased."

[1666] Step 13:

[1667] Server: Determines the ranking of news articles based on user interest data.

[1668] Step 14:

[1669] Server: Generates a personalized list of news articles and sends it to the device.

[1670] Step 15:

[1671] On your device: Show personalized news articles to your users.

[1672] Step 16:

[1673] User: Read the article and provide feedback on comprehension and satisfaction.

[1674] Step 17:

[1675] Terminal: Display a feedback form and collect user input.

[1676] Step 18:

[1677] Device: Sends feedback data to the server.

[1678] Step 19:

[1679] Server: Receives feedback and stores it in a database.

[1680] Server: Analyzes the feedback data and uses it to improve the generative AI model.

[1681] Through the above steps, the system of the present invention aims to provide personalized information tailored to the user's vocabulary and kanji ability, thereby eliminating the information gap.

[1682] Example 1

[1683] 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."

[1684] In modern society, a huge amount of news articles are updated daily, but not all of this information is easy to understand for all users. In particular, users with different vocabulary and kanji abilities have difficulty understanding news articles that contain technical terms and difficult vocabulary, resulting in differences in how easily they accept information. As a result, certain user groups may not be able to obtain sufficient information, and the information gap may widen. In addition, articles are not sufficiently personalized based on users' interests, making it difficult for users to find articles that will interest them. Therefore, there is a need for a system that provides easy-to-understand news articles that suit each user's vocabulary and kanji ability.

[1685] 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.

[1686] In this invention, the server includes means for collecting input information from the user's device, means for an AI to measure the user's vocabulary and kanji ability, means for the generation AI to rewrite publicly available news articles based on the results of the measurement, means for preferentially displaying articles based on the user's frequently used words and areas of interest, and means for collecting feedback from the user and improving the AI ​​model. This enables users to receive easy-to-understand news articles that are suited to their vocabulary and kanji ability, making it easier for them to access information that is of high interest to them.

[1687] "User's device" refers to a device such as a computer, smartphone, or tablet used by a user.

[1688] "Input information" refers to information that a user enters through a device, including name, email address, password, survey responses, quiz and assignment responses, etc.

[1689] "Vocabulary" refers to the range and number of words a user can understand and use.

[1690] "Kanji ability" refers to the range and number of kanji characters that a user can understand and use.

[1691] "AI" is an abbreviation for artificial intelligence, and specifically refers to computer programs and algorithms that analyze user input and evaluate vocabulary and kanji ability.

[1692] "Public news articles" refers to the latest economic, social, political, and other news articles obtained from external news providers.

[1693] "Generative AI" refers to a generative artificial intelligence model that rewrites news articles into expressions that suit the user's vocabulary and kanji ability.

[1694] "Frequent words" refer to words or phrases that a user has frequently used or searched for in the past.

[1695] "Areas of interest" refers to news categories and topics that users have expressed a high level of interest in through surveys, usage history, etc.

[1696] "Feedback" refers to opinions and ratings regarding understanding and satisfaction that users provide after reading a news article.

[1697] "AI model improvement" refers to the process of utilizing collected feedback data to improve the performance of a generative AI model.

[1698] The present invention is a system that provides personalized news articles based on the user's vocabulary and kanji ability. To implement this system, the following specific steps are taken.

[1699] User registration and initial settings

[1700] First, the user installs the application and, when using it for the first time, enters required information such as name, email address, and password on the account creation page. The device sends the entered information to the server. The server receives this information and creates a new account. Specifically, it stores this information in a database (e.g., MySQL). Next, the server generates an initial setup questionnaire for the user and sends it to the device. The device displays the questionnaire to the user and prompts the user to enter their answers. The answered information is sent from the device to the server and stored in the database.

[1701] Vocabulary and Kanji ability assessment

[1702] The user views quizzes and sentences provided within the application and enters their answers. The device sends this answer data to the server. The server uses a generative AI model (e.g., GPT-3) to analyze the received answer data. At this time, the server sends the following prompt to the generative AI model: "Based on the user's answers, please evaluate this user's vocabulary and kanji ability." The generative AI model evaluates the user's vocabulary and kanji ability based on this prompt. The evaluation results are stored in a database by the server.

[1703] Personalized news articles

[1704] Next, the server retrieves the latest news articles using an external news API (e.g., newsAPI.org). These retrieved news articles are rewritten to suit each user's vocabulary and kanji ability. The server then uses a generative AI model to send a prompt such as, "The user's vocabulary is intermediate. Please simplify the wording of this article." This prompt converts the wording of the article. For example, it converts "fiscal policy" to "how the government uses money." In this way, articles that have been rewritten to be easier for the user to understand are ranked based on the user's interest data. Finally, a personalized list of news articles is generated and sent to the device.

[1705] View articles and gather feedback

[1706] The device displays a personalized list of news articles sent from the server to the user. The user reads the articles and provides feedback on their understanding and satisfaction. The feedback is sent to the server via the device. The server stores the received feedback in a database and uses it to improve the generative AI model.

[1707] Specific examples

[1708] In personalizing a news article, the server retrieves an economic news article titled "Japan's GDP grows."

[1709] If User A's vocabulary and kanji ability are intermediate, the generation AI will convert "GDP" to "gross domestic product" and "growth" to "risen."

[1710] User A is very interested in economics, so this article will be placed high on the priority display list.

[1711] This personalized article list is sent to the device and displayed to User A.

[1712] This invention provides news articles tailored to the individual abilities of users, facilitating their understanding and acceptance of information, thereby realizing a system that alleviates the digital divide and efficiently provides information tailored to the user's interests.

[1713] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1714] Step 1:

[1715] User registration and initial settings

[1716] User: The user opens the application and enters the required information on the account creation page, such as name, email address, and password.

[1717] Input: User information such as name, email address, and password.

[1718] Terminal: The terminal sends the entered information to the server.

[1719] Output: User information sent to the server.

[1720] Server: The server creates a new account based on the received user information, stores this information in a database, and generates an initial setup survey and sends it to the device.

[1721] Input: User information sent from the device.

[1722] Output: Saved user account, generated survey.

[1723] Terminal: The terminal displays the survey to the user and prompts the user to enter their answers.

[1724] Input: Initial Setup Survey.

[1725] User: The user completes an initial setup questionnaire and enters the answers into the device.

[1726] Output: User's survey responses.

[1727] Terminal: The terminal sends the survey responses to the server.

[1728] Input: User's survey response information.

[1729] Output: Survey responses sent to the server.

[1730] Step 2:

[1731] Vocabulary and Kanji ability assessment

[1732] User: The user enters answers to quizzes and assignments.

[1733] Input: Quiz or assignment, user's answer.

[1734] Terminal: The terminal sends the user's response data to the server.

[1735] Output: The response data sent to the server.

[1736] Server: Based on the received answer data, the server generates prompt sentences to evaluate the user's vocabulary and kanji ability, and sends these prompt sentences to the generative AI model.

[1737] Input: User response data.

[1738] Output: A prompt to the generative AI model.

[1739] Generative AI model: The generative AI model evaluates the user's vocabulary and kanji ability based on the prompt sentence.

[1740] Output: The evaluation result.

[1741] Server: The server stores the evaluation results received from the generative AI model in a database.

[1742] Input: Evaluation results from a generative AI model.

[1743] Output: Evaluation results stored in a database.

[1744] Step 3:

[1745] Personalized news articles

[1746] Server: The server retrieves the latest news articles from an external news API.

[1747] Input: News API request.

[1748] Output: News article data.

[1749] Server: The server uses a generative AI model to generate prompts for rewriting the retrieved news articles based on the user's vocabulary and kanji ability. Example prompt: "The user has intermediate vocabulary, so please simplify the wording of this article."

[1750] Input: News article, user vocabulary and kanji proficiency data.

[1751] Output: A prompt to the generative AI model.

[1752] Generative AI model: Based on a prompt, the generative AI model rewrites news articles in a way that is easy for users to understand.

[1753] Output: A rewritten news article.

[1754] Server: The server determines the order in which news articles to display based on the user's interest data, generates a personalized news article list, and sends it to the device.

[1755] Input: rewritten news articles, user interest data.

[1756] Output: A personalized list of news articles.

[1757] Step 4:

[1758] View articles and gather feedback

[1759] Device: The device displays a personalized list of news articles to the user.

[1760] Input: A list of news articles sent by the server.

[1761] User: The user reads the article and provides feedback on their understanding and satisfaction.

[1762] Output: User feedback data.

[1763] Device: The device sends the user's feedback to the server.

[1764] Input: User feedback data.

[1765] Server: The server stores the received feedback data in a database and uses it to improve the generative AI model.

[1766] Output: Feedback data stored in a database.

[1767] (Application example 1)

[1768] 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."

[1769] Conventional advertising systems do not personalize ads based on the user's vocabulary or writing ability, which results in ads that are difficult for users to understand. This reduces the effectiveness of ads and makes it difficult for them to attract users' interest.

[1770] 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.

[1771] In this invention, the server includes means for collecting input information from the user's terminal and measuring the user's vocabulary and writing ability with an AI, means for the generation AI to rewrite advertising information based on the measurement results, and means for preferentially displaying advertisements based on the user's frequently used words and areas of interest. This allows advertisements to be optimized according to the user's level of understanding, making it possible to more effectively attract the user's interest and attention.

[1772] Key Word Definitions

[1773] "User" refers to any individual or group of people who use the System.

[1774] "User terminal" refers to an electronic device that allows a user to access and operate the system.

[1775] "Input information" refers to data entered by a user into a terminal and transmitted to a server.

[1776] "Vocabulary" refers to the range of words a user can understand and use.

[1777] "Character ability" refers to the range of characters, kana notations, and kanji characters that a user can understand and use.

[1778] "AI" refers to an information processing system that uses artificial intelligence technology.

[1779] "Advertising information" refers to information intended to promote the sale of products or advertise services.

[1780] "Generative AI" refers to a technology that uses artificial intelligence to generate sentences based on the user's vocabulary and writing ability.

[1781] A "word" refers to the smallest unit of expression in language.

[1782] A "field" refers to an area related to a particular subject or theme.

[1783] "Means" refers to the process or method for achieving a goal.

[1784] A "system" refers to a set of mechanisms in which multiple components work together to achieve a specific function.

[1785] patent specification

[1786] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below.

[1787] First, the user launches the application on their device and creates an account. When creating an account, they enter the necessary information. The device then sends the entered information to the server.

[1788] The server creates an account based on the received user information, stores this data in a database, and then generates questionnaires and quizzes to measure the user's vocabulary and writing ability as an initial setting, and sends them to the user's device.

[1789] Users answer these initial setup quizzes and surveys. The device sends the user's response data to the server. The server then analyzes the response data using an AI model to measure the user's vocabulary and writing ability. The analysis results are stored in a database.

[1790] The server then retrieves the latest ad information from an external ad API. Based on the user's vocabulary and writing ability measurements, the server uses a generative AI model to rewrite the ad information into language that is easier for the user to understand. For example, it converts technical jargon and difficult vocabulary into simpler language. This rewriting process uses advanced natural language processing models such as GPT-4.

[1791] The server then determines the order in which ads are displayed based on the user's past behavior and areas of interest. Once a properly personalized ad list is generated, it is sent to the user's device.

[1792] The device displays this personalized ad list to the user. The user views the ads and provides feedback on their content. The device collects this feedback data and sends it to the server. The server uses this feedback data to continuously improve the accuracy of the generative AI model.

[1793] The specific hardware and software used are Python and Flask for the server, MySQL for database management, GPT-4 API for the generative AI model, and React Native for the front-end application.

[1794] Specific examples

[1795] Below are some specific examples of ad personalization:

[1796] Original ad copy

[1797] "The latest smartphone is now available. Its AI camera and 5G compatibility will change your everyday life."

[1798] Personalized ad text

[1799] "New smartphone released with AI camera and high-speed internet"

[1800] Prompt Sentence Examples

[1801] "If the user has an intermediate level of vocabulary, personalize the original ad copy, 'The latest smartphone is here. It changes your daily life with its AI camera and 5G connectivity,' in simpler terms."

[1802] In this way, the present invention realizes maximizing the effectiveness of advertising by appropriately personalizing advertising information in accordance with the user's vocabulary and writing ability.

[1803] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1804] Program processing steps

[1805] Step 1:

[1806] The user launches the application on their device and creates an account.

[1807] Input: User information from your device (username, password, email address, etc.)

[1808] Output: User information sent to the server

[1809] Specific operation: The terminal sends the entered user information to the server.

[1810] Step 2:

[1811] The server creates an account based on the received user information and stores it in the database.

[1812] Input: User information sent to the server

[1813] Output: Account information stored in the database

[1814] Specific operation: The server receives the user information and saves it as a new record in the database (MySQL).

[1815] Step 3:

[1816] As an initial setting, the server generates questionnaires and quizzes to measure vocabulary and writing ability, and sends them to the user's device.

[1817] Input: New user registration information

[1818] Output: Initial setup quiz and survey

[1819] Specific operation: The server generates quizzes and surveys and sends them to the user's device.

[1820] Step 4:

[1821] Users complete default quizzes and surveys.

[1822] Input: Quizzes and surveys

[1823] Output: User's answer

[1824] Specific operation: The user answers the displayed quiz or survey and sends it to their device.

[1825] Step 5:

[1826] The terminal sends the user's response data to the server.

[1827] Input: User response data

[1828] Output: Response data sent to the server

[1829] Specific operation: The user device sends the response data to the server.

[1830] Step 6:

[1831] The server receives the response data, analyzes the user's vocabulary and writing ability using a generative AI model, and stores the results in a database.

[1832] Input: User response data

[1833] Output: Vocabulary and writing ability measurements

[1834] Specific operation: The server analyzes the response data, measures vocabulary and writing ability, and stores the results in a database.

[1835] Step 7:

[1836] The server retrieves the latest ad information from an external ad API.

[1837] Input: External Ads API

[1838] Output: Retrieved advertising information

[1839] Specific operation: The server periodically calls the external advertising API to obtain the latest advertising information.

[1840] Step 8:

[1841] The server uses a generative AI model to personalize advertising information based on the user's vocabulary and writing ability.

[1842] Input: Vocabulary and writing ability test results, advertising information

[1843] Output: Personalized advertising information

[1844] Specific operation: The server uses a generative AI model (GPT-4) to rewrite advertising information into expressions that are easy for users to understand.

[1845] Step 9:

[1846] The server determines the order in which ads are displayed based on the user's past behavioral data and areas of interest.

[1847] Input: User behavior data, interests, personalized advertising

[1848] Output: Prioritized ad list

[1849] Specific operation: The server determines the ad ranking based on the user's behavioral data and areas of interest, and generates an ad list.

[1850] Step 10:

[1851] The server transmits the personalized advertisement list to the user terminal.

[1852] Input: Prioritized Ad List

[1853] Output: Ad list sent to the user's device

[1854] Specific operation: The server sends a personalized ad list to the user's device.

[1855] Step 11:

[1856] The device displays a personalized list of ads to the user.

[1857] Input: Personalized Ad List

[1858] Output: The ad shown to the user

[1859] Specific operation: The user device receives the advertisement list and displays it to the user.

[1860] Step 12:

[1861] Users view ads and provide feedback.

[1862] Enter: Advertisement

[1863] Output: User feedback

[1864] What happens: Users view the ad and provide feedback on comprehension and interest.

[1865] Step 13:

[1866] The user terminal collects feedback data and transmits it to the server.

[1867] Input: User feedback

[1868] Output: Feedback data sent to the server

[1869] Specific operation: The user device collects feedback data and sends it to the server.

[1870] Step 14:

[1871] The server uses the feedback data to improve the accuracy of the generative AI model.

[1872] Input: Feedback data

[1873] Output: An updated generative AI model

[1874] What it does: The server analyzes the feedback data to improve the accuracy of the generative AI model (GPT-4) and help with future ad personalization.

[1875] 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.

[1876] The present invention provides a system that personalizes news articles based on a user's vocabulary and kanji ability, and further recognizes the user's emotions to provide appropriate content. The following describes an embodiment of the system in detail.

[1877] Program processing explanation

[1878] 1. User registration and initial settings

[1879] User: Opens the application for the first time and enters the required information on the account creation page.

[1880] Terminal: Sends the entered information to the server.

[1881] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[1882] On the device: Present the initial setup survey to the user and collect their responses.

[1883] 2. Vocabulary and Kanji ability assessment

[1884] User: View the quizzes and sentences provided in the application and enter their answers.

[1885] Device: Sends quiz answer data to the server.

[1886] Server: Receives the response data and uses an AI model to analyze and measure the user's vocabulary and kanji ability. The results are stored in a database.

[1887] 3. Emotion recognition using the emotion engine

[1888] User: Enter text or voice input.

[1889] Terminal: Sends user input data (text or voice) to the server.

[1890] Server: The emotion engine analyzes the input data and recognizes the user's emotions. For example, if the user is sad or happy, it records the emotion label.

[1891] Server: Stores the analysis results in a database.

[1892] 4. Personalize your news articles

[1893] Server: Gets the latest news articles from an external news API.

[1894] Server: Using each user's vocabulary data, the server uses generative AI to rewrite news articles into expressions that are easier for the user to understand. For example, it converts "GDP" to "gross domestic product" and "growth" to "risen."

[1895] Server: Determines the order in which news articles are displayed based on user interest data and emotion recognition results.

[1896] For example, if a user is feeling sad, encouraging content or positive news articles will be prioritized.

[1897] Server: Generates a personalized list of news articles and sends it to the device.

[1898] 5. View articles and gather feedback

[1899] On your device: Show personalized news articles to your users.

[1900] User: Read the article and provide feedback on comprehension and satisfaction.

[1901] Terminal: Display a feedback form and collect user input.

[1902] Device: Sends feedback data to the server.

[1903] Server: Stores the feedback in a database and uses this data to improve the generative AI model.

[1904] Specific examples

[1905] Example of article personalization based on sentiment

[1906] 3.1. Server: Receives the text "I'm not feeling very well today" entered by User A on his smartphone.

[1907] 3.2. Server: The emotion engine analyzes this input data and recognizes that User A has the emotion "sad."

[1908] 4.1. Server: Get the latest news article "Economic Growth Outlook".

[1909] 4.2. Server: User A's vocabulary data is intermediate, so convert "economic growth" to "economic rise."

[1910] 4.3. Server: Since User A is feeling "sad," it is decided to prioritize displaying articles with encouraging and positive content.

[1911] 4.4. Server: Generates a personalized article list and sends it to the device.

[1912] 5.1. Device: Show personalized article with positive content to User A.

[1913] 5.2. User: Reads the article and gives feedback saying that it "inspired me."

[1914] 5.3. Terminal: Collects feedback and sends it to the server.

[1915] 5.4. Server: Stores the feedback and uses it to improve the generative AI model.

[1916] As described above, the present invention aims to eliminate the digital divide and improve the user experience by providing information that takes into account the user's vocabulary, kanji ability, and even emotional state, and by providing content that is easy for users to understand and appropriate.

[1917] The processing flow will be explained below.

[1918] Program processing details

[1919] Step 1:

[1920] User: Opens the application for the first time and enters the required information on the account creation page.

[1921] Step 2:

[1922] Terminal: Sends the entered information to the server.

[1923] Step 3:

[1924] Server: Creates an account based on the provided user information and saves it in the database.

[1925] Server: Generates the initial setup survey and sends it to the device.

[1926] Step 4:

[1927] On the device: Present the initial setup survey to the user and collect their responses.

[1928] Step 5:

[1929] User: Complete the initial setup survey and send the answers to your device.

[1930] Step 6:

[1931] Terminal: Sends the response data to the server.

[1932] Step 7:

[1933] Server: Receives the response data and uses the AI ​​model to perform an initial vocabulary assessment and initial interest settings for the user.

[1934] Step 8:

[1935] User: View the quizzes and sentences provided in the application and enter answers to them.

[1936] Step 9:

[1937] Terminal: Collects user quiz answer data and sends it to the server.

[1938] Step 10:

[1939] Server: Receives the response data and uses an AI model to measure the user's vocabulary and kanji ability.

[1940] Server: Stores the measurement results in a database.

[1941] Step 11:

[1942] User: Express their feelings through text input or voice input.

[1943] Step 12:

[1944] Device: Sends the user's text and voice data to the server.

[1945] Step 13:

[1946] Server: The emotion engine analyzes the input data and recognizes the user's emotions.

[1947] For example, the text "I'm tired today" is analyzed and the emotion label "tired" is assigned.

[1948] Step 14:

[1949] Server: Stores the sentiment analysis results in a database.

[1950] Step 15:

[1951] Server: Gets the latest news articles from an external news API.

[1952] Step 16:

[1953] Server: Based on each user's vocabulary data, generative AI is used to rewrite news articles into expressions that are easy for the user to understand.

[1954] For example, convert "GDP" to "gross domestic product" and "growth" to "increased."

[1955] Step 17:

[1956] Server: Determines the order in which news articles are displayed based on user interest data and emotion recognition results.

[1957] For example, if a user is feeling "tired," positive articles related to energy recovery will be displayed first.

[1958] Step 18:

[1959] Server: Generates a personalized list of news articles and sends it to the device.

[1960] Step 19:

[1961] On your device: Show personalized news articles to your users.

[1962] Step 20:

[1963] User: Read the article and provide feedback on comprehension and satisfaction.

[1964] Step 21:

[1965] Terminal: Display a feedback form and collect user input.

[1966] Step 22:

[1967] Device: Sends feedback data to the server.

[1968] Step 23:

[1969] Server: Receives feedback and stores it in a database.

[1970] Server: Improves the generative AI model based on feedback data.

[1971] Through the above steps, the system of the present invention aims to provide personalized information based on the user's vocabulary, kanji ability, and emotional state, and to provide appropriate content that is easy for the user to understand.

[1972] Example 2

[1973] 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."

[1974] Conventional news article delivery systems lacked personalization based on the user's vocabulary and kanji ability, making it difficult to understand articles. Furthermore, because they provided information without taking the user's emotional state into consideration, content that did not match the user's emotions was sometimes displayed, resulting in a lack of improvement in the user experience. This could widen the information gap.

[1975] 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.

[1976] In this invention, the server includes means for collecting input information from the user's terminal, means for measuring the user's vocabulary and kanji ability by an AI, means for rewriting publicly available news articles by a generation AI based on the measurement results, means for preferentially displaying articles based on the user's frequently used words and areas of interest, means for recognizing the user's emotional state, and means for preferentially displaying appropriate news articles based on the recognized emotions. This makes it possible to personalize news articles according to the user's vocabulary and kanji ability and provide content that takes emotions into consideration.

[1977] "User's device" refers to a device used by a user to input and view information, including smartphones, tablets, and personal computers.

[1978] "Input information" refers to data provided by a user through a terminal, and includes various types of information such as text, audio, and images.

[1979] "Vocabulary" refers to the range and depth of words a user can understand and use.

[1980] "Kanji ability" refers to the range of kanji that a user can understand and use, and the level of understanding of those kanji.

[1981] "AI" or "artificial intelligence" refers to algorithms and technologies used to measure and analyze a user's vocabulary and kanji skills.

[1982] "Public news stories" refers to current public news content obtained from external news services.

[1983] "Generative AI" refers to a generative model that uses natural language processing to rewrite news articles in a way that is easy for users to understand.

[1984] "User's frequently used words" refers to words and phrases that the user frequently uses in their past operation history and input content.

[1985] "Areas of interest" refers to the topics or categories that a user is interested in, and are determined based on past browsing history and survey responses.

[1986] "Emotional state" refers to the emotion the user is currently feeling, and is expressed by different emotion labels such as joy, sadness, anger, etc.

[1987] "Means for preferentially displaying appropriate news articles based on emotions" refers to the process of selecting the news articles that best fit the emotional state of the user as recognized by the emotion engine and adjusting the display order.

[1988] The present invention is a system that personalizes news articles based on a user's vocabulary and kanji ability, and further recognizes the user's emotions to provide appropriate content. Specific embodiments of the system are described below.

[1989] Program processing and the hardware and software used

[1990] 1. User registration and initial settings

[1991] When using the application for the first time, the user opens the application and enters the required information on the account creation page. The device sends that information to the server. The server creates an account based on the provided information and stores it in a database. It also generates an initial setup questionnaire and sends it to the device. The user answers the questionnaire, and the device collects the answers and sends them to the server.

[1992] The hardware and software used include devices such as smartphones and PCs, cloud-based database servers, and survey generation software.

[1993] 2. Vocabulary and Kanji ability assessment

[1994] Users view quizzes and sentences provided on the application and enter their answers. The device then sends the answer data to the server. The server then analyzes the answer data using an AI model (e.g., OpenAI GPT-3) to measure the user's vocabulary and kanji ability. The measurement results are stored in a database.

[1995] 3. Emotion recognition using the emotion engine

[1996] Users input their understanding and satisfaction with news articles using text or voice. The device sends the input data to a server, which then uses an emotion engine (e.g., Google Cloud Natural Language) to analyze the input data and recognize the user's emotional state. The results are then stored in a database.

[1997] 4. Personalize your news articles

[1998] The server retrieves the latest news articles from an external news API (e.g., NewsAPI). Based on each user's vocabulary data, it uses generative AI (e.g., OpenAI GPT-3) to rewrite the news articles into a format that is easy for the user to understand. Furthermore, it determines the display order based on the user's interest data and emotion recognition results. For example, if the user is sad, it prioritizes positive news. A personalized list of news articles is generated and sent to the device.

[1999] 5. View articles and gather feedback

[2000] The device displays personalized news articles to the user, who then reads the article and provides feedback on their understanding and satisfaction. The device collects the feedback and sends it to a server, which stores it in a database and uses it to improve the generative AI model.

[2001] Specific examples

[2002] For example, consider a scenario where a user is using an app for the first time.

[2003] 1. Users create an account and answer a short survey for initial setup, including questions like, "What is your favorite news genre?"

[2004] 2. To measure vocabulary and kanji ability, users take a quiz to answer the meaning of "economic growth," and the results are sent to the server. The AI ​​model determines the user's level of understanding as "intermediate."

[2005] 3. Emotion recognition: if a user types "I'm a little sad today," the emotion engine will analyze this and record it as "sad."

[2006] 4. In news article personalization, the server converts the retrieved news article "economic growth outlook" into "economic upturn" based on the user's vocabulary, and further prioritizes positive content for "sad" users.

[2007] 5. When displaying articles, a personalized article titled "Economic Growth" is displayed to the user, and if the user provides feedback such as "This article cheered me up," that information is sent to the server and reflected in future content provision.

[2008] The system can provide optimal news articles based on the user's vocabulary, kanji ability, and even emotional state, and the model is continually improved based on feedback, further enhancing the user experience.

[2009] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2010] Step 1: User registration and initial setup

[2011] Input: A user launches an application for the first time and enters information such as their name, email address, and password.

[2012] Operation and data processing: The device sends the user's input information to the server. The server receives the input information and stores it in a database as account information. The server then generates an initial setup questionnaire and sends it to the device.

[2013] Output: The server generates a new account ID and survey data and sends them to the device. The device displays the survey to the user, who answers it. The user's answers are then sent from the device to the server again and saved.

[2014] Step 2: Vocabulary and Kanji Ability Assessment

[2015] Input: The user answers a quiz or statement.

[2016] Operation and data processing: The device sends the user's answers to the server. The server analyzes the answers using an AI model (e.g., OpenAI GPT-3). The analysis results are used to measure the user's vocabulary and kanji ability. The server then stores the results in a database.

[2017] Output: The server generates evaluation data on the user's vocabulary and Kanji ability and records it in a database.

[2018] Step 3: Emotion recognition by the emotion engine

[2019] Input: Users input their understanding and satisfaction with a news article using text or voice.

[2020] Operation and data processing: The device sends this input data to the server. The server analyzes the input data using an emotion engine (e.g., Google Cloud Natural Language). As a result, the server recognizes the user's emotional state. The server stores the recognized emotion results in a database.

[2021] Output: The server generates an emotion label for the user (e.g., happy, sad) and records it in a database.

[2022] Step 4: Personalize your news articles

[2023] Input: Latest news articles retrieved by the server from an external news API (e.g., NewsAPI), along with the user's vocabulary, kanji ability, emotional state, and interest data.

[2024] Operation and data processing: Based on each user's vocabulary data, the server uses generative AI (e.g., OpenAI GPT-3) to rewrite the retrieved news articles into a format that is easy for the user to understand. In addition, the server takes into account the user's emotional state and interest data to determine the display order of news articles.

[2025] Output: The server generates a personalized list of news articles and sends it to the device.

[2026] Step 5: View the article and gather feedback

[2027] Input: A personalized list of news articles sent by the server.

[2028] Operation and Data Processing: The device displays personalized news articles to the user. The user reads the articles and provides feedback on their understanding and satisfaction. The device then sends this feedback to the server.

[2029] Output: The server stores the received feedback in a database and uses it to improve the generative AI model in the future.

[2030] (Application example 2)

[2031] 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."

[2032] Conventional news delivery systems provide news articles without considering the user's vocabulary, kanji ability, or emotional state, which often results in content that is not suited to the user's understanding or interests. Furthermore, content that is uninteresting to the user or information that does not match their emotional state is displayed, resulting in a lower level of satisfaction in the reader experience. This can cause users to lose interest in the news, and for news providers, it can also cause a problem of reduced effectiveness in conveying information.

[2033] 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.

[2034] In this invention, the server includes means for collecting input information from the user's terminal and using AI to measure the user's vocabulary and kanji ability, means for the generation AI to rewrite publicly available news articles based on the results of the measurement, means for preferentially displaying articles based on the user's frequently used words and areas of interest, and means for recognizing the user's emotions and adjusting the content of the news articles according to their emotional state. This makes it possible to provide news content that is easy for the user to understand and takes into consideration their emotions.

[2035] "User device" refers to the electronic device used by the user to view news articles, including smartphones and tablets.

[2036] "Input information" refers to data provided by the user via the device, and includes text, audio, quiz answers, etc.

[2037] "AI" is an abbreviation for Artificial Intelligence, and refers to technology for analyzing and measuring users' vocabulary and kanji ability.

[2038] "Generative AI" refers to artificial intelligence technology that automatically converts news articles and text data into a form that is easy for users to understand.

[2039] "Emotional state" refers to the user's current mood or emotion, and includes states such as "sad," "happy," and "anxious."

[2040] "Emotion recognition" refers to the process of analyzing a user's text or voice input data to identify their emotions.

[2041] "News Article" means a piece of information obtained from an external news source, including domestic and international events and social issues.

[2042] "Rewriting" refers to using generative AI to convert the content of a news article to suit the user's vocabulary and kanji ability.

[2043] "Terminology" refers to difficult words and expressions used in a specific field that are difficult for general users to understand.

[2044] "Areas" refer to words that users frequently use or areas of interest, such as "economy," "sports," and "entertainment."

[2045] "Adjusting" refers to changing the content and display order of news articles depending on the user's emotional state.

[2046] MODE FOR CARRYING OUT THE INVENTION

[2047] System Overview

[2048] This invention is a system that provides news articles that are easy for users to understand and that respond to their emotions. The system collects input information from the user's device, measures their vocabulary and kanji ability based on that information using AI, and then rewrites the news article using a generation AI. It also recognizes the user's emotions and adjusts the content of the article according to their emotional state.

[2049] Hardware and software used

[2050] The system includes the following hardware and software:

[2051] Hardware

[2052] User's device (smartphone, tablet, etc.)

[2053] Server (processes and stores data)

[2054] software

[2055] Analysis Library (TextBlob)

[2056] Emotion Recognition Model (Hugging Face's transformers)

[2057] Web API (retrieving news articles)

[2058] Process Overview

[2059] 1. User registration and initial settings

[2060] User: Opens the application, enters the required information, and creates an account.

[2061] Device: The entered information is sent to the server, which stores the user information, then generates an initial setup questionnaire and sends it to the device.

[2062] User: Answers the survey and the device sends the answers to the server.

[2063] 2. Vocabulary and Kanji ability assessment

[2064] User: Views quizzes and statements and enters answers to them.

[2065] Device: Sends quiz answer data to the server, which analyzes the data and measures the user's vocabulary and kanji ability.

[2066] 3. Emotional Recognition

[2067] User: Enter text or voice input.

[2068] Device: Sends input data to the server, which uses an emotion engine to recognize the user's emotions.

[2069] 4. Personalize your news articles

[2070] Server: The latest news articles obtained from an external news API are rewritten using a generative AI based on the user's vocabulary data. In addition, the display order of articles is determined based on the user's emotion recognition results.

[2071] Server: Generates a personalized list of news articles and sends it to the device.

[2072] Specific examples

[2073] 1. The server receives the text "I'm not feeling very well today" entered by user A on his smartphone.

[2074] 2. The server uses the emotion engine to recognize that User A has the emotion "sad."

[2075] 3. The server retrieves the "economic growth outlook" from an external news API.

[2076] 4. The server converts "economic growth" to "economic rise" based on User A's vocabulary data (intermediate level).

[2077] 5. The server sees that User A is feeling sad, so it displays a news article with an encouraging message added. For example, it displays "The economy may continue to improve. Cheer up!"

[2078] 6. User A reads the article and gives feedback saying, "It's encouraging."

[2079] Prompt Sentence Examples

[2080] The user types, "I'm not feeling very good today." The emotion recognition engine analyzes this text and identifies the user's emotion as "sad." Please rewrite the following news article, "Economic Growth Outlook," to fit the user's vocabulary level (intermediate level), and add an encouraging message.

[2081] This will enable news articles to be provided that meet the individual needs of users, improving the user experience.

[2082] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2083] Step 1:

[2084] User registration and initial settings

[2085] User: Opens the application and enters the required information (name, email address, password, etc.) on the account creation page.

[2086] Terminal: Sends the entered information to the server.

[2087] Server: Creates an account based on the provided user information and stores it in the database. Then generates an initial setup questionnaire and sends it to the device.

[2088] User: Answers an initial setup questionnaire, and the device sends the answers to the server, which collects basic information and initial data about the user.

[2089] Step 2:

[2090] Vocabulary and Kanji ability assessment

[2091] User: View the quizzes and sentences presented in the application and enter the answers.

[2092] Device: Sends quiz answer data to the server.

[2093] Server: Analyzes the received response data and uses an AI model to measure the user's vocabulary and kanji ability. The results are stored in a database. For example, it determines how well the user understands words such as "GDP" and "growth."

[2094] Step 3:

[2095] Emotion recognition

[2096] User: Enter text or voice input.

[2097] Terminal: Sends input data (text or voice) to the server.

[2098] Server: The emotion engine analyzes the input data and recognizes the user's emotions. For example, it analyzes the input text "I'm not feeling very good today" and assigns an emotion label such as "sad." The analysis results are stored in a database.

[2099] Step 4:

[2100] Personalized news articles

[2101] Server: Gets the latest news articles from an external news API.

[2102] Server: Using each user's vocabulary data, the server uses generative AI to rewrite news articles into expressions that are easier for the user to understand. For example, it converts "economic growth" into "economic growth" and "GDP" into "gross domestic product."

[2103] Server: Adjust the order and content of articles based on the user's emotion recognition results. For example, if the user is expressing sadness, encouragement and positive articles will be displayed first.

[2104] Server: Generates a tailored list of news articles and sends it to the device.

[2105] Step 5:

[2106] View articles and gather feedback

[2107] Device: Display personalized news articles to users. For example, present an article to User A saying, "The economy may continue to improve. Stay strong!"

[2108] User: Read the article and provide feedback on comprehension and satisfaction.

[2109] Terminal: Displays a feedback form and collects input from the user, then sends this input data to the server.

[2110] Server: The feedback data is stored in a database and used to improve the generative AI model and emotion engine. For example, if a user gives feedback that they felt energized, this information is stored and used for future personalization.

[2111] 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.

[2112] 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.

[2113] 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 robot 414.

[2114] 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.

[2115] FIG. 9 is a diagram illustrating 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 actions 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.

[2116] 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.

[2117] 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).

[2118] 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.

[2119] 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."

[2120] 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.

[2121] 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).

[2122] 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.

[2123] 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.

[2124] 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.

[2125] 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.

[2126] 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.

[2127] 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.

[2128] 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.

[2129] 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.

[2130] 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.

[2131] 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.

[2132] The following is further disclosed regarding the above embodiment.

[2133] (Claim 1)

[2134] Collecting input information from the user's device,

[2135] A means for AI to measure users' vocabulary and kanji ability,

[2136] A means for generating AI to rewrite publicly available news articles based on the above measurement results,

[2137] A way to prioritize articles based on users' frequently used words and areas of interest,

[2138] A system including:

[2139] (Claim 2)

[2140] 2. The system of claim 1, wherein the means for measuring the user's vocabulary and kanji ability includes means for analyzing answers to quizzes and sentences.

[2141] (Claim 3)

[2142] The system of claim 1, wherein the means for rewriting news articles using generative AI includes a means for converting technical terms and difficult vocabulary into expressions that are easy for users to understand.

[2143] "Example 1"

[2144] (Claim 1)

[2145] A means for collecting input information from the user's device;

[2146] A means for AI to measure users' vocabulary and kanji ability,

[2147] A means for generating AI to rewrite publicly available news articles based on the above measurement results,

[2148] A way to prioritize articles based on users' frequently used words and areas of interest, and

[2149] A means to collect user feedback and improve the AI ​​model;

[2150] A system including:

[2151] (Claim 2)

[2152] 2. The system of claim 1, wherein the means for measuring the user's vocabulary and kanji ability includes means for analyzing answers to quizzes and sentences.

[2153] (Claim 3)

[2154] The system of claim 1, wherein the means for rewriting news articles using generative AI includes a means for converting technical terms and difficult vocabulary into expressions that are easy for users to understand.

[2155] "Application Example 1"

[2156] Claims

[2157] (Claim 1)

[2158] Collecting input information from the user's device,

[2159] A means for AI to measure users' vocabulary and writing ability,

[2160] A means for the generation AI to rewrite advertising information based on the measurement results;

[2161] A way to prioritize ads based on users' frequently used words and areas of interest;

[2162] A system including:

[2163] (Claim 2)

[2164] 10. The system of claim 1, wherein the means for measuring the user's vocabulary and writing ability includes means for analyzing responses to quizzes and passages.

[2165] (Claim 3)

[2166] The system of claim 1, wherein the means for rewriting advertising information using generative AI includes a means for converting technical terms and difficult vocabulary into expressions that are easy for users to understand.

[2167] "Example 2: Combining Emotion Engines"

[2168] (Claim 1)

[2169] A means for collecting input information from the user's device;

[2170] A means for AI to measure users' vocabulary and kanji ability,

[2171] A means for generating AI to rewrite publicly available news articles based on the above measurement results,

[2172] A way to prioritize articles based on users' frequently used words and areas of interest,

[2173] a means for recognizing the emotional state of a user;

[2174] a means for prioritizing the display of relevant news articles based on the recognized sentiment;

[2175] A system including:

[2176] (Claim 2)

[2177] 2. The system of claim 1, wherein the means for measuring the user's vocabulary and kanji ability includes means for analyzing answers to quizzes and sentences.

[2178] (Claim 3)

[2179] The system of claim 1, wherein the means for rewriting news articles using generative AI includes a means for converting technical terms and difficult vocabulary into expressions that are easy for users to understand.

[2180] "Application example 2 when combining emotion engines"

[2181] (Claim 1)

[2182] Collecting input information from the user's device,

[2183] A means for AI to measure users' vocabulary and kanji ability,

[2184] A means for generating AI to rewrite publicly available news articles based on the above measurement results,

[2185] A way to prioritize articles based on users' frequently used words and areas of interest,

[2186] a means for recognizing a user's emotions and adjusting the content of news articles according to the user's emotional state;

[2187] A system including:

[2188] (Claim 2)

[2189] 2. The system of claim 1, wherein the means for measuring the user's vocabulary and kanji ability includes means for analyzing answers to quizzes and sentences.

[2190] (Claim 3)

[2191] The system of claim 1, wherein the means for rewriting news articles using generative AI includes a means for converting technical terms and difficult vocabulary into expressions that are easy for users to understand. [Explanation of symbols]

[2192] 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. Collecting input information from the user's device, A means for AI to measure users' vocabulary and kanji ability, A means for generating AI to rewrite publicly available news articles based on the above measurement results, A way to prioritize articles based on users' frequently used words and areas of interest, A system including:

2. 2. The system according to claim 1, wherein the means for measuring the user's vocabulary and kanji ability includes means for analyzing answers to quizzes and sentences.

3. The system of claim 1, wherein the means for rewriting news articles using generative AI includes a means for converting technical terms and difficult vocabulary into expressions that are easy for users to understand.

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