System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting a behavior history and a retrieval history of a user, a means for collecting questionnaire data from the user, a means for analyzing the collected data and specifying a principle and thinking tendency of the user, a means for preparing a manifesto based on the principle and thinking tendency of the user, a means for collecting and analyzing a manifesto of a political candidate matching with the principle and thinking tendency of the user, and a means for proposing candidate information suitable for the user.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] In the traditional political system, it was difficult for ordinary citizens to gain a deep understanding of their own political principles and thoughts and select appropriate candidates based on that understanding. This has led to an increase in citizens with low interest in politics, declining voter turnout in elections, and the issue of choosing candidates based on insufficient information. It is also a significant burden for political candidates to prepare detailed manifestos covering a wide range of policies. The goal of this project is to solve these problems, make it easier for citizens to select candidates based on their policies, and reduce the burden of preparing manifestos for candidates. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. It includes a means for collecting a user's behavioral history and search history, and a means for collecting questionnaire data from the user, and a means for analyzing the collected data to identify the user's principles and tendencies of thought. It also includes a means for creating a draft manifesto based on the user's principles and tendencies of thought, displaying the draft manifesto to the user, and collecting feedback. It then provides a means for collecting and analyzing the manifestos of political candidates who match the user's principles and tendencies of thought, and using that information to suggest candidate information suitable for the user. It also includes a means for appropriately adjusting the generated draft manifesto and candidate suggestions based on the collected feedback. This allows citizens to gain a deeper understanding of their own political principles and tendencies, and helps them select appropriate candidates.

[0006] "Behavioral history" refers to a record of specific activities or actions that a user has taken on the Internet.

[0007] "Search History" refers to the history of search queries that a user has performed using a search engine.

[0008] "Survey data" refers to data including opinions and information entered by users in the form of a questionnaire.

[0009] "Ideas and thought patterns" refers to a user's political stance, beliefs, and opinion tendencies.

[0010] A "manifesto" is a detailed list of policies and promises that political candidates publish during elections.

[0011] A "collection means" is a method or device for obtaining data from a user.

[0012] "Analysis means" refers to methods or devices for analyzing collected data and extracting meaningful information.

[0013] The "suggestion means" refers to a method or device for presenting information suitable for the user based on the analyzed information.

[0014] "Feedback" refers to the reactions and evaluations received from users.

[0015] "Adjustment tools" are methods and devices that appropriately modify and optimize the system and the information it generates based on collected feedback. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention relates to a system that creates a draft manifesto based on the user's principles and tendencies, and proposes suitable political candidates. This system is implemented by the cooperation of a server, terminals, and users.

[0038] System configuration

[0039] Server: Mainly responsible for collecting and analyzing data, generating draft manifestos, and proposing candidates.

[0040] Terminal: Collects data from the user and communicates with the server to display the draft manifesto and candidate information.

[0041] Users: Provide data input and feedback to receive personalized manifesto proposals and candidate information.

[0042] Server Processing

[0043] 1. Collection and analysis of user data

[0044] Server: Collects user behavioral and search history from the Internet. Also collects data provided by users in questionnaires. Analyzes the collected data to identify users' beliefs and thinking patterns.

[0045] Example: The server collects the web pages frequently visited by users and the keywords they search for, and analyzes the level of interest in "environmental protection" and "economic policy." It also extracts opinions on "education reform" from survey responses.

[0046] 2. Generating a draft manifesto

[0047] Server: Based on the analysis results, a generative AI is used to create a draft manifesto. The draft manifesto is designed to fit the individual beliefs and thoughts of the user.

[0048] Example: The server generates a manifesto based on the user's interests, such as "policies focused on environmental protection," "specific measures to eliminate economic disparities," and "new proposals for educational reform."

[0049] 3. Collecting and analyzing candidate manifestos

[0050] Server: Collects and analyzes the published manifestos of multiple political candidates. Stores each candidate's policy content in a database.

[0051] Example: The server downloads manifestos from the official websites of political candidates and classifies and analyzes them into categories such as "environment," "economy," and "education."

[0052] 4. Proposing suitable candidates to users

[0053] Server: Identifies candidates whose policies match the user's principles and beliefs and proposes them to the user.

[0054] Example: If the user is highly interested in environmental policy, the server suggests, "Candidate A of the XX Party has environmental policies that are closest to your ideas."

[0055] Terminal handling

[0056] 1. Data Entry

[0057] Terminal: Collects survey data and behavioral and search history from users and sends them to the server.

[0058] Example: When a user answers a survey about economic policy on an app, the data is sent to a server.

[0059] 2. Manifest Display

[0060] Terminal: Shows the generated manifest draft received from the server to the user, collects feedback and sends it to the server.

[0061] Example: The app displays "policy proposals for environmental protection" and "measures to eliminate economic disparities," and users can rate the policy proposals by saying "I support / disagree."

[0062] 3. Viewing Candidate Information

[0063] Terminal: Displays information on suitable candidates sent from the server to the user.

[0064] Example: The app will say, "The candidate who most closely aligns with your views is candidate A of the X Party," and provide a link to detailed manifesto information.

[0065] User Action

[0066] 1. Data provision

[0067] User: Enters information about his / her beliefs and beliefs in a questionnaire format, and also provides his / her daily behavior and search history.

[0068] Example: A user answers a survey about "environmental protection," "economic policy," and "education reform."

[0069] 2. Confirmation of manifesto and candidates

[0070] Users: Review the provided manifesto proposals and provide feedback, then review the proposed candidate information and support or consider it.

[0071] Example: A user evaluates proposed manifestos and, based on that evaluation, decides, "This candidate's policies suit me."

[0072] In this way, the system of the present invention realizes the proposal of manifestos and political candidates based on the user's principles and thoughts through cooperation between the server, terminals, and users.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] Entering User Data

[0076] Users: Answer surveys provided by the app or web service and enter information about their beliefs and beliefs. In addition, their website browsing history and search history may be collected to the extent permitted by the user.

[0077] Step 2:

[0078] Data collection

[0079] Device: Survey data entered by users, as well as behavioral and search history, are collected. Search history and page browsing history are collected over a certain period of time.

[0080] Step 3:

[0081] Data transmission

[0082] Device: Collected survey data and behavioral and search history are sent to the server. Data is encrypted for privacy reasons.

[0083] Step 4:

[0084] Data analysis

[0085] Server: Analyzes the received data and identifies the user's beliefs and thinking patterns. Based on the survey responses and browsing and search history, natural language processing technology is used to extract important keywords and topics.

[0086] Step 5:

[0087] Generating a draft manifesto

[0088] Server: Using generative AI based on the analysis results, the server creates a draft manifesto that matches the user's principles and thinking, including specific policy proposals and pledges.

[0089] Step 6:

[0090] Submitting a manifest

[0091] Server: Sends the created manifesto to the user's device, formatting it in a way that is easy to read and relevant to the user's interests.

[0092] Step 7:

[0093] View the draft manifesto

[0094] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[0095] Step 8:

[0096] Collecting feedback

[0097] Terminal: Collects user feedback on the draft manifesto and sends it to the server. Feedback is provided in the form of quantitative evaluations (e.g., evaluation scores) and qualitative comments (e.g., free text).

[0098] Step 9:

[0099] Feedback Analysis

[0100] Server: Analyzes the received user feedback and uses it as data to improve the proposed manifesto and the system's suggestion functions.

[0101] Step 10:

[0102] Collecting candidate manifestos

[0103] Server: Collects published manifestos of multiple political candidates from the Internet, including official candidate websites and election catalogs.

[0104] Step 11:

[0105] Parsing the Manifest

[0106] Server: Analyzes the collected candidate manifestos and stores their main policy content in a database. Using natural language processing technology, categorizes each candidate's policies into categories.

[0107] Step 12:

[0108] Candidate proposals

[0109] Server: Identifies candidates from the database whose policies match the user's principles and thinking tendencies, and selects candidate information appropriate for the user.

[0110] Step 13:

[0111] Submit candidate information

[0112] Server: Sends information about selected candidates to the user's device in a format that is easy for the user to understand.

[0113] Step 14:

[0114] View candidate information

[0115] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[0116] Step 15:

[0117] Feedback and Adjustments

[0118] On the device: Collects the feedback provided by the user and sends it back to the server.

[0119] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[0120] In this way, by closely coordinating each step, a draft manifesto based on the user's principles and thoughts can be generated and appropriate candidates can be proposed.

[0121] Example 1

[0122] 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."

[0123] In modern society, it is difficult to identify appropriate political candidates based on a user's beliefs and beliefs. Conventional methods require users to collect, compare, and consider candidate information themselves, which is labor-intensive. Furthermore, there is a lack of a means to objectively evaluate how well a candidate's policies match the user's beliefs and beliefs. Therefore, there is a need for a method to improve the efficiency and accuracy of selecting appropriate candidates.

[0124] 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.

[0125] In this invention, the server includes means for collecting user behavioral history and search history, means for collecting questionnaire data from users, means for analyzing the collected data and identifying the user's principles and tendencies of thinking, means for creating a draft manifesto using a generative AI model based on the user's principles and tendencies of thinking, means for collecting published manifestos of political candidates that match the user's principles and tendencies of thinking, means for analyzing the collected candidate manifestos and storing them in a database, and means for suggesting candidate information suitable for the user based on the analysis results. This makes it possible to automatically identify appropriate political candidates and efficiently select candidates based on that information, without the user having to collect candidate information themselves.

[0126] "User behavioral history and search history" refers to a record of specific activities and search keywords that a user has performed on the Internet.

[0127] "Survey Data" refers to responses to questions collected directly from users.

[0128] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate new text or data.

[0129] A "draft manifesto" is a detailed proposal for specific policies and measures.

[0130] A "published manifesto" is a policy proposal or plan officially released by a political candidate or party.

[0131] A "database" is an information system for efficiently storing, managing, and retrieving structured data.

[0132] "Candidate Information" means detailed data and profile information about political candidates.

[0133] This invention relates to a system that creates a draft manifesto based on the user's principles and tendencies, and proposes suitable political candidates. This system is implemented by a combination of hardware and software, with a server, terminals, and users working together.

[0134] Server Processing

[0135] 1. Collection of User Data

[0136] Server: The server uses web crawlers and log analysis tools to collect user behavior and search history from the Internet. It also receives information provided by users in the form of questionnaires. The collected data is stored in a database.

[0137] Example: A server monitors a user's browser history and extracts keywords of interest such as "environmental protection" or "economic policy."

[0138] 2. User Data Analysis

[0139] Server: The server uses natural language processing (NLP) models and machine learning algorithms to analyze the collected data, specifically using Python's NLTK library and Sci-Kit Learn to identify the user's beliefs and tendencies.

[0140] Example: The server tokenizes text data including the user's search history and performs sentiment analysis to classify the user's areas of interest.

[0141] 3. Generating a draft manifesto

[0142] Server: The server uses a generative AI model (e.g., OpenAI® GPT) to create a manifesto based on the user's beliefs and tendencies. The generated manifesto will be tailored to the user's individual interests and beliefs.

[0143] Example: The prompt "Generate policy proposals based on the user's areas of interest" is input into a generative AI model, which generates a manifesto containing specific policy proposals.

[0144] 4. Collection and analysis of candidate manifestos

[0145] Server: Collects published manifestos from multiple political candidates. The collected manifestos are downloaded using a web scraping tool (e.g., the Scrapy framework) and converted into text data. They are then analyzed using an NLP model, and their policy content is organized and stored in a database.

[0146] Example: A server downloads manifesto PDFs from political candidates' official websites and classifies them into categories such as "environment," "economy," and "education."

[0147] 5. Proposing suitable candidates to users

[0148] Server: Based on the analysis results, it identifies candidates whose policies match the user's principles and thinking tendencies and makes recommendations. Suitable candidate information is extracted through a database query and notified to the user.

[0149] For example, the server selects the most suitable candidate for the user, for example, "proposing candidate A to users who are highly interested in environmental policy."

[0150] Terminal handling

[0151] 1. Data Entry

[0152] Terminal: The user enters survey data and behavioral and search history through the terminal. This data is sent to the server.

[0153] Example: A user answers a "survey on economic policy" on a smartphone app.

[0154] 2. Manifest Display

[0155] Terminal: The terminal displays the proposed manifest sent from the server to the user, and also collects user feedback and sends it back to the server.

[0156] Example: The app displays "policy proposals for environmental protection" or "measures to eliminate economic disparities," and users provide feedback such as "for" or "against."

[0157] 3. Viewing Candidate Information

[0158] Terminal: Display suitable candidate information to the user and provide a link to detailed manifesto information.

[0159] Example: The app might say, "The candidate who most closely aligns with your views is candidate A from the XX party," and provide a link to more information.

[0160] User Action

[0161] 1. Data provision

[0162] User: Users fill out a questionnaire about their beliefs and thoughts, and provide their daily behavior and search history.

[0163] Example: Answer a survey about "environmental protection," "economic policy," and "education reform."

[0164] 2. Confirmation of manifesto and candidates

[0165] Users: Review the proposed manifesto and provide feedback, then review relevant candidate information and consider supporting it.

[0166] Example: A user evaluates a proposed manifesto and decides, "This candidate's policies suit me."

[0167] As described above, the system of the present invention efficiently realizes the proposal of manifestos and political candidates based on the user's principles and thoughts through cooperation between the server, terminals, and users.

[0168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0169] Step 1: Collect user data

[0170] server:

[0171] Input: User behavioral and search history, survey data.

[0172] How it works: The server uses a web crawler to collect URLs and search keywords frequently visited by users, and also receives response data from survey forms. All of this data is stored in a database.

[0173] Output: User behavior history, search history, and survey data are saved in a database.

[0174] Step 2: Analyze user data

[0175] server:

[0176] Input: Behavioral history, search history, survey data.

[0177] How it works: The server uses natural language processing (NLP) and machine learning algorithms, such as Python's NLTK library and Sci-Kit Learn, to tokenize text data and perform sentiment analysis and interest classification. For example, it scores interest in "environmental protection" or "economic policy" based on search keywords.

[0178] Output: Analysis results showing the user's principles and thinking tendencies.

[0179] Step 3: Generate a draft manifesto

[0180] server:

[0181] Input: Analysis results of user's principles and thinking tendencies.

[0182] Specific operation: The server uses a generative AI model (e.g., OpenAI GPT) to generate a draft manifesto based on the user's analysis results. It inputs the prompt "Please generate policy proposals based on the user's areas of interest" into the AI ​​model and generates a manifesto containing specific policy proposals.

[0183] Output: A proposed manifesto personalized for the user.

[0184] Step 4: Collecting candidate manifestos

[0185] server:

[0186] Input: A political candidate's published manifesto.

[0187] Specific operation: The server uses a web scraping tool (e.g., the Scrapy framework) to download manifesto PDFs from each political candidate's official website or public documents and convert them into text data.

[0188] Output: Candidate manifesto text data is stored in the database.

[0189] Step 5: Parsing the candidate manifestos

[0190] server:

[0191] Input: Candidate manifesto text data.

[0192] How it works: The server uses an NLP model to tokenize the text data and classify each policy into a category, such as "environment," "economy," or "education." The classification results are then stored in a database.

[0193] Output: Candidate policy data organized by category.

[0194] Step 6: Propose candidates

[0195] server:

[0196] Input: Analysis of user principles and thought patterns, candidate policy data.

[0197] Specific operation: The server executes an SQL query and selects the candidate whose policies best match the user's areas of interest. For example, it generates results such as "suggest candidate A to users who are interested in environmental policies."

[0198] Output: Candidate suggestion data for the user.

[0199] Step 7: Displaying data and gathering user feedback

[0200] Device:

[0201] Input: Draft manifesto and candidate proposal data sent from the server.

[0202] What it does: The device displays the draft manifesto and candidate information to the user via an app or web interface, collects user feedback, and sends it to the server.

[0203] Output: User feedback data is sent to the server.

[0204] Through the above processing steps, the optimal manifesto proposal and political candidate proposals are realized for the user.

[0205] (Application example 1)

[0206] 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."

[0207] While existing technologies exist that suggest political candidates based on a user's beliefs and tendencies, they cannot be directly applied to suggesting products and services that take into account a user's purchasing behavior and preferences. As a result, personalized product suggestions cannot be made for users with specific interests and preferences, making it difficult to increase customer satisfaction.

[0208] 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.

[0209] In this invention, the server includes means for collecting user behavior history and search history, means for collecting questionnaire data from users, and means for analyzing the collected data to identify the user's principles and thinking tendencies, thereby enabling personalized suggestions of products or services that match the user's principles and thinking tendencies.

[0210] "User behavior history" refers to the history of operations and pages accessed by a user on the Internet or in an application.

[0211] "Search history" refers to the history of keywords or phrases that a user has entered and searched for on an Internet search engine.

[0212] "Survey data" refers to information about a user's interests, preferences, opinions, etc., provided in the form of a questionnaire.

[0213] "Analysis" refers to using collected data to analyze information and derive results for a specific purpose.

[0214] "Ideas and thinking tendencies" refer to the user's interests and tendencies in thinking regarding specific issues or fields.

[0215] A "manifesto proposal" is a draft policy proposal or action plan generated based on the user's principles and thinking.

[0216] "Goods or services" refers to products offered at a physical store or services provided to customers.

[0217] "Feedback" refers to the evaluation or opinion a user gives of the information or suggestions provided.

[0218] "Suggestion" refers to the act of recommending products or services that are determined to be optimal for the user based on the analysis results.

[0219] This invention is a system that collects and analyzes user behavior history, search history, and questionnaire data, and proposes personalized products and services based on the user's principles and thinking tendencies. This system is mainly composed of three entities: a server, a terminal, and a user.

[0220] Server Processing

[0221] The server has the following roles:

[0222] 1. Data Collection:

[0223] The server collects users' behavioral and search histories, including the web pages they visit, the keywords they search for, the content they view, etc. The server also collects survey data entered by users.

[0224] 2. Data Analysis:

[0225] The collected data is analyzed on the server to identify the user's principles and thought patterns. This process uses data mining and natural language processing techniques, specifically the Python libraries pandas and scikit-learn.

[0226] 3. Leveraging generative AI models:

[0227] Based on the analysis results, a generative AI model is used to suggest products or services suitable for the user. This AI model utilizes an advanced natural language processing model such as GPT-3 (registered trademark). The user's preferences and behavioral history are used as input for the prompt sentence.

[0228] Example prompt sentence:

[0229] User preferences: Likes red wine, price range: 2000-5000 yen, prioritizes food pairing

[0230] 4. Proposal generation:

[0231] The generative AI model generates suggestions and sends them to the device via a server, such as product recommendations for physical stores or suggestions for specific services.

[0232] Terminal handling

[0233] A terminal is a device that acts as an interface with a user. Typical terminals are smartphones, smart glasses, or head-mounted displays.

[0234] 1. Data Entry:

[0235] Through the device, users answer questionnaires and their behavioral and search history is collected, and this data is sent to the server in real time.

[0236] 2. View Proposals:

[0237] The suggestions received from the server are displayed to the user. If the user is using smart glasses or a head-mounted display, the suggestions can be displayed visually. For example, when the user stands in front of a particular wine shelf in a store, the display will say, "Here is the wine that is best suited for you."

[0238] 3. Gathering Feedback:

[0239] The device is equipped with a function that allows users to provide feedback on suggested products and services, which is sent to a server for further analysis.

[0240] User Action

[0241] Users are the primary data providers and beneficiaries of the system.

[0242] 1. Data provided by:

[0243] Users provide data through their daily activities and survey responses, which serves as the basis for identifying their beliefs and thought patterns.

[0244] 2. Confirmation of proposal:

[0245] Review the proposed products and services, and in some cases consider purchasing or using them. Also, contribute to improving the proposals through feedback.

[0246] In this way, the system of the present invention allows the server, terminal, and user to work together to propose personalized products and services based on the user's principles and thoughts.

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] Users use devices (smartphones, smart glasses, head-mounted displays, etc.) to answer a questionnaire. This questionnaire is intended to collect information about the user's preferences and interests. The device then transmits the survey data to a server in real time.

[0250] Input: User-entered survey responses

[0251] Output: Survey data sent to the server

[0252] Step 2:

[0253] The server collects users' behavioral and search histories from the Internet, and also receives survey data sent from their devices. The collected data is stored in a database.

[0254] Input: Behavioral history, search history, survey data

[0255] Output: User data stored in the database

[0256] Step 3:

[0257] The server analyzes the collected data and uses data mining and natural language processing techniques to identify the user's beliefs and tendencies. For example, it uses Python's "pandas" and "scikit-learn" to cleanse and analyze the data. This generates indicators that show the user's interests and preferences.

[0258] Input: User data retrieved from the database

[0259] Output: Indicators showing the user's principles and thinking tendencies

[0260] Step 4:

[0261] The server uses a generative AI model (e.g., GPT-3) to generate a prompt to suggest the best product or service to the user based on the analysis results.The prompt is then input into the AI ​​model to generate the suggestion.

[0262] Input: Indicators showing the user's beliefs and thinking tendencies

[0263] Output: Recommendations for the best product or service for the user

[0264] Step 5:

[0265] The server sends the generated suggestions to the device in real time. The suggestions are displayed to the user. If smart glasses or a head-mounted display are used, the suggestions are displayed visually.

[0266] Input: Proposal

[0267] Output: Proposal displayed on terminal

[0268] Step 6:

[0269] The user can check the proposed products or services through the terminal and enter their evaluation and opinions as feedback, which is also sent to the server in real time.

[0270] Input: User feedback

[0271] Output: Feedback data sent to the server

[0272] Step 7:

[0273] The server analyzes the collected feedback and uses it to improve the content of suggestions and the prompts of the AI ​​model. It continuously analyzes data and generates suggestions that better match the user's principles and thinking patterns.

[0274] Input: Feedback data

[0275] Output: Improved suggestions and prompts

[0276] 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.

[0277] This invention combines an emotion engine with a system that creates a draft manifesto based on the user's principles and tendencies and suggests suitable political candidates, allowing for more precise proposals. This system, which links the server, terminals, and users and includes an emotion engine, provides a draft manifesto and candidate information that also reflects the user's emotional state.

[0278] System configuration

[0279] Server: Collects and analyzes user data, generates manifesto proposals, identifies emotional states, and implements candidate proposals.

[0280] Terminal: Collects data from users, communicates with the server to display draft manifestos and candidate information, and also has an emotion engine.

[0281] Users: Answer surveys, provide behavioral and search history, and provide appropriate feedback.

[0282] Server Processing

[0283] 1. Collection and analysis of user data

[0284] Server: Collects user behavioral and search histories from the Internet, data provided in the form of questionnaires, and emotional data obtained by the emotion engine.

[0285] Example: If a user frequently reads articles about environmental protection and expresses a high interest in "education reform" in a survey, the data can be analyzed and the emotion engine can detect that the user feels strong "concern" about environmental issues.

[0286] 2. Data Analysis

[0287] Server: Analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. Extracts important keywords and topics based on beliefs and thought patterns.

[0288] Example: The server uses natural language processing technology to analyze the user's areas of interest and emotional state, determining things like "concerns about environmental protection" and "enthusiasm for economic policy."

[0289] 3. Generating a draft manifesto

[0290] Server: Based on the analysis results and emotional data, generative AI is used to create a draft manifesto that matches the user's beliefs, thought patterns, and emotional state.

[0291] Example: The server generates "policy proposals that reflect concerns about environmental protection" and "concrete measures to eliminate economic disparities."

[0292] 4. Submitting the manifest

[0293] Server: The created manifesto is sent to the user's device. Based on the emotional data, it is presented in a format that resonates most with the user.

[0294] 5. Display of the Manifesto Draft

[0295] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[0296] Example: An app displays "specific proposals for environmental protection" and "details of economic policies" in an easy-to-read format to users and collects their reactions.

[0297] 6. Gathering Feedback

[0298] Terminal: Collects user feedback on the proposed manifesto and sends it to the server.

[0299] Example: A user provides an evaluation such as "This policy is very good" or "This part needs improvement", which is then sent to the server.

[0300] 7. Feedback Analysis

[0301] Server: Analyzes the received feedback and uses it as data to improve the system's suggestion capabilities.

[0302] 8. Collecting candidate manifestos

[0303] Server: Collects and analyzes published manifestos of multiple political candidates from the Internet.

[0304] Example: A server downloads manifestos from political candidates' official websites and classifies and analyzes them into categories such as "environment," "economy," and "education."

[0305] 9. Candidate Proposals

[0306] Server: Identifies candidates whose policies match the user's principles, thought patterns, and emotional state, and proposes them to the user.

[0307] Example: If a user has a high interest in and strong feelings about environmental policy, the server might suggest, "Candidate A of the XX Party has environmental policies that are closest to your views."

[0308] 10. Submission of Candidate Information

[0309] Server: Sends information about selected candidates to the user's device.

[0310] 11. Display of Candidate Information

[0311] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[0312] Example: The app displays, "The candidate who most closely aligns with your views is candidate A of the XX Party," and provides detailed manifesto information.

[0313] 12. Feedback and Adjustments

[0314] On the device: Collects the feedback provided by the user and sends it back to the server.

[0315] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[0316] In this way, the system of the present invention works by linking the server, terminal, user, and emotion engine to propose manifestos and political candidates based on the user's principles, thoughts, and emotional state.

[0317] The processing flow will be explained below.

[0318] Step 1:

[0319] Entering User Data

[0320] Users: Answer surveys provided by the app or web service and enter information about their beliefs and beliefs. In addition, their website browsing history and search history may be collected to the extent permitted by the user.

[0321] Step 2:

[0322] Identifying emotional states

[0323] Device: When the user answers the questionnaire, the emotion engine uses the camera and microphone to identify the user's emotional state from their facial expressions and voice. The identified emotional state is recorded.

[0324] Example: While a user answers a questionnaire, the camera analyzes their facial expressions to identify emotions such as "happiness," "concern," and "excitement." The microphone also analyzes the tone of their voice to detect "excitement" and "anxiety."

[0325] Step 3:

[0326] Data collection

[0327] Terminal: Collects survey data entered by users, behavioral and search history, and identified emotional state data.

[0328] Step 4:

[0329] Data transmission

[0330] Device: Collected survey data, behavioral and search history, and emotional data are sent to the server. The data is encrypted for privacy reasons.

[0331] Step 5:

[0332] Data analysis

[0333] Server: Analyzes the received data and identifies the user's beliefs, thought patterns, and emotional state. Extracts important keywords and topics based on survey responses, browsing and search history, and emotional data.

[0334] Example: The server uses natural language processing technology to analyze the user's areas of interest and emotional state, determining things like "concerns about environmental protection" and "enthusiasm for economic policy."

[0335] Step 6:

[0336] Generating a draft manifesto

[0337] Server: Based on the analysis results and emotional data, generative AI is used to create a draft manifesto that matches the user's beliefs, thought patterns, and emotional state.

[0338] Example: The server generates "policy proposals that reflect concerns about environmental protection" and "concrete measures to eliminate economic disparities."

[0339] Step 7:

[0340] Submitting a manifest

[0341] Server: The created manifesto is sent to the user's device. Based on the emotional data, it is presented in a format that resonates most with the user.

[0342] Step 8:

[0343] View the draft manifesto

[0344] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[0345] Example: An app displays "specific proposals for environmental protection" and "details of economic policies" in an easy-to-read format to users and collects their reactions.

[0346] Step 9:

[0347] Collecting feedback

[0348] Terminal: Collects user feedback on the proposed manifesto and sends it to the server.

[0349] Example: A user provides an evaluation such as "This policy is very good" or "This part needs improvement", which is then sent to the server.

[0350] Step 10:

[0351] Feedback Analysis

[0352] Server: Analyzes the received user feedback and uses it as data to improve the system's suggestion functions.

[0353] Step 11:

[0354] Collecting candidate manifestos

[0355] Server: Collects and analyzes the published manifestos of multiple political candidates from the Internet, including official candidate websites and election catalogs.

[0356] Example: A server downloads manifestos from political candidates' official websites and classifies and analyzes them into categories such as "environment," "economy," and "education."

[0357] Step 12:

[0358] Candidate proposals

[0359] Server: Identifies candidates whose policies match the user's principles, thought patterns, and emotional state, and proposes them to the user.

[0360] Example: If a user has a high interest in and strong feelings about environmental policy, the server might suggest, "Candidate A of the XX Party has environmental policies that are closest to your views."

[0361] Step 13:

[0362] Submit candidate information

[0363] Server: Sends information about selected candidates to the user's device in a format that is easy for the user to understand.

[0364] Step 14:

[0365] View candidate information

[0366] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[0367] Example: The app displays, "The candidate who most closely aligns with your views is candidate A of the XX Party," and provides detailed manifesto information.

[0368] Step 15:

[0369] Feedback and Adjustments

[0370] On the device: Collects the feedback provided by the user and sends it back to the server.

[0371] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[0372] In this way, the system of the present invention works by linking the server, terminal, user, and emotion engine to propose manifestos and political candidates based on the user's principles, thoughts, and emotional state.

[0373] Example 2

[0374] 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."

[0375] Conventional political information systems collect and analyze users' behavioral history and survey data, and are able to provide manifesto proposals based on the user's principles and tendencies. However, these systems do not take into account the user's emotional state, which means that the proposed content does not fully take into account the user's feelings and emotions. Furthermore, there was an insufficient method for effectively analyzing feedback and reflecting it in future proposals.

[0376] 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.

[0377] In this invention, the server includes means for collecting a user's behavioral history and search history, means for collecting questionnaire data from the user, means for analyzing the collected data to identify the user's principles, thinking tendencies, and emotional state, means for creating a draft manifesto using a generative AI model based on the user's principles, thinking tendencies, and emotional state, means for collecting and analyzing manifestos of political candidates that match the user's principles, thinking tendencies, and emotional state, means for proposing candidate information suitable for the user, means for transmitting and displaying the proposed draft manifesto and candidate information to the user's terminal, means for collecting user feedback and transmitting it back to the server, and means for analyzing the collected feedback and adjusting the generation of subsequent draft manifestos and candidate proposals. This makes it possible to provide more sophisticated draft manifestos and candidate information that reflect the user's emotional state.

[0378] "User behavior history" refers to a record of a user's activities on the Internet, specifically website browsing history and click history.

[0379] "Search history" is a record of search queries and their results made by a user using a search engine.

[0380] "Survey Data" means information provided by a user in the form of a survey, including a user's opinions, interests, and other feedback.

[0381] "Ideas and tendencies" refers to the political and social beliefs and tendencies of thought held by the user.

[0382] "Emotional state" refers to the user's current emotion as analyzed by the emotion engine, including, for example, joy, sadness, worry, anger, etc.

[0383] "Generative AI model" refers to a program that uses artificial intelligence algorithms to generate a draft manifesto or other suggestions that are optimized for a user.

[0384] A "Manifesto" is a document of policy proposals created based on the user's principles, thought patterns, and emotional state.

[0385] "Political candidate" means a candidate running for election to public office.

[0386] A "manifesto" is a document of policies and principles published by a political candidate.

[0387] "Feedback" refers to ratings and opinions provided by users.

[0388] "Terminal" refers to electronic devices used by users, such as computers, smartphones, and tablets.

[0389] A "server" refers to a central computer that processes and stores data for a system to operate.

[0390] This invention is a system that provides individually optimized manifesto proposals and political candidate information based on the user's principles, thought patterns, and emotional state. This system operates in cooperation with a server, terminals, and users.

[0391] Specific server processing and technologies used

[0392] User Data Collection

[0393] The server collects users' behavioral and search histories from the Internet. For this purpose, it uses a web analysis tool (e.g., Google® Analytics). It also collects data provided by users in the form of questionnaires. In addition, it uses an emotion engine (e.g., Affectiva API) to obtain user emotion data.

[0394] Data analysis

[0395] The server analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. This analysis uses natural language processing techniques (e.g., Python's NLTK library or SpaCy). Techniques such as TF-IDF and Word2Vec are used to extract important keywords and topics.

[0396] Generating a draft manifesto

[0397] Based on the analysis results and emotional data, the server uses a generative AI model (e.g., OpenAI's GPT-3) to create a manifesto proposal tailored to the user's beliefs, thinking tendencies, and emotional state. A prompt sentence is generated and input into the generative AI model to generate a specific manifesto proposal. For example, the following prompt sentence can be used:

[0398] "The user is highly interested in environmental protection and is also enthusiastic about economic policy. Furthermore, the analysis results of the emotion engine indicate that the user feels strong concern about environmental issues. Please generate a draft manifesto for this user."

[0399] Submitting a draft manifesto

[0400] The generated manifest plan is sent from the server to the user's device. This communication is performed using the HTTP protocol, and the data is sent in JSON format.

[0401] Collecting candidate manifestos

[0402] The server collects, categorizes, and analyzes published manifestos from multiple political candidates from the Internet, using web scraping tools (e.g., Beautiful Soup, Scrapy).

[0403] Candidate proposals

[0404] The server recommends the best matched candidates based on the user's beliefs, tendencies, and emotional state, using a recommendation algorithm (e.g., cosine similarity calculation).

[0405] Specific terminal processing and technologies used

[0406] View the draft manifesto

[0407] The terminal displays the proposed manifest received from the server to the user, using front-end technology (e.g., React.js) to display data in an easy-to-read card or list format.

[0408] Collecting feedback

[0409] The terminal provides an input form for collecting feedback from the user and sending it back to the server via the HTTP protocol.

[0410] View candidate information

[0411] The terminal displays candidate information from the server to the user, again using front-end technology, in the form of cards containing candidate photos and detailed policy information.

[0412] Specific user operations

[0413] Data provision

[0414] Users answer questions in a questionnaire format and provide their behavioral history.

[0415] Providing Feedback

[0416] Users can provide their evaluations and opinions on the displayed manifesto proposals and candidate information, and this feedback will be reflected in future proposals.

[0417] In this way, the server, terminal, and user each play their own roles and work together to provide a system that provides more appropriate and precise manifesto proposals and candidate information based on the user's principles, thoughts, and emotional state.

[0418] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0419] Step 1:

[0420] User Data Collection

[0421] Input: User behavior history, search history, survey data, emotional data

[0422] Specific behavior:

[0423] 1. Collection of behavioral history and search history: The server automatically collects users' browsing history and search history from the Internet using web analysis tools (e.g., Google Analytics).

[0424] 2. Survey data collection: The server uses a web form to collect data entered by the user, such as opinions and interests, in the form of a survey.

[0425] 3. Acquiring emotion data: The server uses an emotion engine (e.g., Affectiva API) to detect emotions from the user's video and audio and collects the data.

[0426] Output: A set of collected user data (behavioral history, search history, survey data, emotional data)

[0427] Step 2:

[0428] Data analysis

[0429] Input: Complete set of collected user data

[0430] Specific behavior:

[0431] 1. Application of natural language processing technology: The server analyzes text data using natural language processing technologies such as Python's NLTK library and SpaCy.

[0432] 2. Identifying beliefs and thought patterns: The server uses techniques such as TF-IDF and Word2Vec to identify the user's beliefs and thought patterns, as well as important keywords and topics.

[0433] 3. Emotional state identification: Based on the output of the emotion engine, identify the user's current emotional state (e.g., joy, worry, anger, etc.).

[0434] Output: Analysis results including the user's beliefs, thinking tendencies, and emotional state

[0435] Step 3:

[0436] Generating a draft manifesto

[0437] Input: Analysis results (user's beliefs, thinking patterns, emotional state)

[0438] Specific behavior:

[0439] 1. Prompt generation: Based on the analysis results, the server creates a prompt to be input to the generative AI model.

[0440] Example: "The user is highly interested in environmental protection and is also enthusiastic about economic policy. Furthermore, the sentiment engine analysis indicates that they feel very concerned about environmental issues. Please generate a draft manifesto for this user."

[0441] 2. Applying a generative AI model: The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate a draft manifesto from the prompt.

[0442] 3. Adjusting the wording: Based on the emotion data, the server adjusts the wording of the generated manifesto proposal to make it most relatable to the user.

[0443] Output: The generated draft manifest

[0444] Step 4:

[0445] Submitting a draft manifesto

[0446] Input: Generated manifest proposal

[0447] Specific behavior:

[0448] 1. Data format conversion: The server converts the proposed manifest into JSON format for sending to the user's device.

[0449] 2. Data transmission: The server sends the proposed manifest to the user's device using the HTTP protocol.

[0450] Output: Draft manifest sent to user device

[0451] Step 5:

[0452] View the draft manifesto

[0453] Input: Manifest proposal sent to user device

[0454] Specific behavior:

[0455] 1. Data reception: The terminal receives the proposed manifest sent from the server.

[0456] 2. Displaying data: The device uses front-end technology (e.g., React.js) to display the proposed manifest in an easy-to-read card or list format.

[0457] Output: The proposed manifest displayed

[0458] Step 6:

[0459] Collecting feedback

[0460] Input: User feedback

[0461] Specific behavior:

[0462] 1. Displaying a feedback form: The terminal displays a user input form on the front end, allowing the user to enter feedback.

[0463] 2. Sending feedback: The terminal collects the feedback entered by the user and sends it to the server via the HTTP protocol.

[0464] Output: Feedback sent to the server

[0465] Step 7:

[0466] Feedback Analysis

[0467] Input: Feedback sent to the server

[0468] Specific behavior:

[0469] 1. Feedback collection: The server retrieves the feedback stored in the database.

[0470] 2. Data analysis: The server uses machine learning algorithms and statistical analysis to analyze users' rating trends.

[0471] Output: Feedback analysis results

[0472] Step 8:

[0473] Collecting candidate manifestos

[0474] Input: online political candidate manifestos

[0475] Specific behavior:

[0476] 1. Data extraction: The server extracts manifest data from the candidate's website using a web scraping tool (e.g., Beautiful Soup, Scrapy).

[0477] 2. Data classification and analysis: The server classifies the extracted data into categories such as "environment," "economy," and "education," and analyzes them.

[0478] Output: Categorized and parsed political candidate manifesto data

[0479] Step 9:

[0480] Candidate proposals

[0481] Input: Analysis results (user's ideology, thought patterns, emotional state), categorized and analyzed political candidate manifesto data

[0482] Specific behavior:

[0483] 1. Data Matching: The server matches the user profile data with the candidate manifest data.

[0484] 2. Recommendation: The server uses a recommendation algorithm (e.g., cosine similarity) to identify the best matching candidates.

[0485] Output: Recommended candidate information

[0486] Step 10:

[0487] Submit candidate information

[0488] Input: Recommended candidate information

[0489] Specific behavior:

[0490] 1. Data format conversion: The server converts the candidate information into JSON format for sending to the user's device.

[0491] 2. Data transmission: The server uses the HTTP protocol to transmit the candidate information to the user's terminal.

[0492] Output: Candidate information sent to user terminal

[0493] Step 11:

[0494] View candidate information

[0495] Input: Candidate information sent to the user's terminal

[0496] Specific behavior:

[0497] 1. Receiving data: The terminal receives the candidate information sent from the server.

[0498] 2. Display of data: The terminal uses front-end technology to display the candidate in the form of a card containing their photo and detailed policy information.

[0499] Output: Displayed candidate information

[0500] Step 12:

[0501] Re-feedback and adjustments

[0502] Input: User's re-feedback, previous feedback analysis results

[0503] Specific behavior:

[0504] 1. Feedback collection: The device collects the feedback provided by the user and sends it back to the server.

[0505] 2. Reflecting the feedback: The server will generate the next draft manifesto and adjust the candidate proposals based on the received feedback.

[0506] Output: Updated manifesto generation and candidate proposal algorithms

[0507] (Application example 2)

[0508] 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."

[0509] Conventional systems could identify principles and thought patterns based on a user's behavioral and search history, and then propose manifestos and candidates, but it was difficult to make precise proposals that reflected the user's emotional state.In addition, since security risk predictions and countermeasure proposals were not based on the user's emotions, it was a challenge to make accurate and empathetic security proposals to users.

[0510] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral history and search history, means for collecting questionnaire data from users, means for analyzing the collected data and identifying the user's principles and thought patterns, and means for performing emotion analysis. This enables the creation of a draft manifesto based on the user's principles and thought patterns, as well as the prediction and proposal of security risks that reflect the user's emotional state. The system also includes means for using a generative AI model to generate prompt sentences that are most relatable to the user, enabling more effective feedback collection and system improvement.

[0511] "User behavior history" refers to the history of activities and operations that a user has performed on the Internet.

[0512] "Search history" refers to the history of search queries a user has performed using a search engine.

[0513] "Survey data" refers to response data regarding opinions and thoughts collected from users.

[0514] "Ideas and thinking tendencies" refer to the user's beliefs, values, and interests.

[0515] A "manifesto" is a document of policy proposals created based on the user's principles and thinking.

[0516] A "political candidate" is a person running for public office.

[0517] "Emotion analysis" refers to identifying and analyzing a user's emotional state from input data.

[0518] "Risk prediction" is the estimation of possible future risks based on collected data.

[0519] "Suggestion" means presenting specific actions or options to the user.

[0520] A "generative AI model" is a model that uses artificial intelligence techniques to analyze data and perform a specified task.

[0521] A "prompt" is a question or instruction that is input to a generative AI model.

[0522] System Overview

[0523] This system collects users' behavioral and search histories and analyzes them together with survey data to identify their principles and thought patterns. It then uses a generative AI model to create a draft manifesto and performs emotion analysis to predict risks and make suggestions that reflect the user's emotional state.

[0524] Hardware and Software Configuration

[0525] The system has the following components:

[0526] Server: Collects and analyzes user data, generates a draft manifesto, identifies emotional states, predicts risks, and makes recommendations. The software used includes Python libraries with natural language processing techniques (e.g., transformers).

[0527] Terminal: Collects data from the user, communicates with the server, displays the manifesto and suggestion information, and also has an emotion engine.

[0528] Users: Answer surveys, provide behavioral and search history, and provide appropriate feedback.

[0529] Data collection and analysis

[0530] The server collects the user's behavioral history and search history, as well as data provided in the form of questionnaires. This data is analyzed to identify the user's beliefs, thought patterns, and emotional state.

[0531] Performing sentiment analysis

[0532] The server uses an emotion analysis model to analyze the emotional state of the data obtained from the user. For example, it uses natural language processing technology to identify emotions such as "concern" or "enthusiasm" from the collected text data.

[0533] Generate a draft manifest and risk forecast

[0534] Based on the analysis results and emotional data, the server uses a generative AI model to create a manifesto tailored to the user's beliefs, thought patterns, and emotional state, as well as generate risk predictions and countermeasures.

[0535] Submitting and Viewing Proposals

[0536] The server sends the created manifesto proposal and risk countermeasure proposal to the user's device, which displays these proposals to the user, who can review them and provide their evaluation and opinions for each item.

[0537] Collecting and analyzing feedback

[0538] The device collects user feedback on the suggestions and sends it to the server, which analyzes the feedback and uses it as data to improve the system's suggestion capabilities.

[0539] Examples and prompts

[0540] Specific examples

[0541] Example input: A user frequently searches for information related to "personal information leaks," "two-factor authentication," "security risks," etc.

[0542] Example prompt:

[0543] Recently, users have been concerned about personal information leaks and how to implement two-factor authentication. Sentiment analysis shows that users have strong concerns. What specific security measures would you suggest to alleviate negative sentiment?

[0544] The system's unique feature is that it analyzes user data and makes precise risk predictions and suggestions based on the user's emotional state.

[0545] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0546] Step 1:

[0547] The server collects the user's online behavior and search history. It receives a questionnaire from the user, converts it into a data format, and saves it. The input of this step is the user's behavior and questionnaire responses, and the output is a dataset.

[0548] Specifically, the server logs the user's behavior history in real time and periodically stores the collected data in a database.

[0549] Step 2:

[0550] The server analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. Natural language processing techniques are used to extract emotions from text data. The input is the collected dataset, and the output is a profile of beliefs, thought patterns, and emotional state.

[0551] Specifically, the server uses the Python library transformers to input text data into an emotion classification model and obtain analysis results.

[0552] Step 3:

[0553] Based on the generated profile, the server uses a generative AI model to create a proposed manifesto tailored to the user's beliefs, thought patterns, and emotional state. The input is the profile, and the output is a customized proposed manifesto.

[0554] Specifically, the server inputs the profile into the generative AI model and constructs the generated text as a draft manifesto.

[0555] Step 4:

[0556] The server generates risk predictions and countermeasures. Based on the results of emotion analysis, it creates specific security proposals and countermeasures tailored to the user. The input is emotional state and risk data, and the output is risk predictions and countermeasures.

[0557] Specifically, the server runs a risk assessment model based on the analysis results and generates specific suggested text using a generative AI model.

[0558] Step 5:

[0559] The server sends the created manifest proposal and risk countermeasure proposal to the user's terminal. The input is the generated proposal, and the output is the completion of transmission to the user's terminal.

[0560] Specifically, the server sends data to the user's terminal over the Internet and receives a confirmation response.

[0561] Step 6:

[0562] The terminal displays the received suggestions to the user, who then reviews them and provides ratings and opinions for each item. The input is the suggestions, and the output is user feedback.

[0563] Specifically, the device displays suggestions through an interface and collects user input in real time.

[0564] Step 7:

[0565] The device collects feedback and sends it to the server. The input is the user's evaluation and opinion, and the output is transmission to the server.

[0566] Specifically, the terminal converts the user's input into a data format and calls an API to send it to the server.

[0567] Step 8:

[0568] The server analyzes the received feedback and improves the system's suggestion function. The input is the feedback data, and the output is data for system improvement.

[0569] Specifically, the server stores the feedback in a database and uses an analytical algorithm to extract information to improve the accuracy of the next suggestion.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] [Second embodiment]

[0574] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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."

[0586] The present invention relates to a system that creates a draft manifesto based on the user's principles and tendencies, and proposes suitable political candidates. This system is implemented by the cooperation of a server, terminals, and users.

[0587] System configuration

[0588] Server: Mainly responsible for collecting and analyzing data, generating draft manifestos, and proposing candidates.

[0589] Terminal: Collects data from the user and communicates with the server to display the draft manifesto and candidate information.

[0590] Users: Provide data input and feedback to receive personalized manifesto proposals and candidate information.

[0591] Server Processing

[0592] 1. Collection and analysis of user data

[0593] Server: Collects user behavioral and search history from the Internet. Also collects data provided by users in questionnaires. Analyzes the collected data to identify users' beliefs and thinking patterns.

[0594] Example: The server collects the web pages frequently visited by users and the keywords they search for, and analyzes the level of interest in "environmental protection" and "economic policy." It also extracts opinions on "education reform" from survey responses.

[0595] 2. Generating a draft manifesto

[0596] Server: Based on the analysis results, a generative AI is used to create a draft manifesto. The draft manifesto is designed to fit the individual beliefs and thoughts of the user.

[0597] Example: The server generates a manifesto based on the user's interests, such as "policies focused on environmental protection," "specific measures to eliminate economic disparities," and "new proposals for educational reform."

[0598] 3. Collecting and analyzing candidate manifestos

[0599] Server: Collects and analyzes the published manifestos of multiple political candidates. Stores each candidate's policy content in a database.

[0600] Example: The server downloads manifestos from the official websites of political candidates and classifies and analyzes them into categories such as "environment," "economy," and "education."

[0601] 4. Proposing suitable candidates to users

[0602] Server: Identifies candidates whose policies match the user's principles and beliefs and proposes them to the user.

[0603] Example: If the user is highly interested in environmental policy, the server suggests, "Candidate A of the XX Party has environmental policies that are closest to your ideas."

[0604] Terminal handling

[0605] 1. Data Entry

[0606] Terminal: Collects survey data and behavioral and search history from users and sends them to the server.

[0607] Example: When a user answers a survey about economic policy on an app, the data is sent to a server.

[0608] 2. Manifest Display

[0609] Terminal: Shows the generated manifest draft received from the server to the user, collects feedback and sends it to the server.

[0610] Example: The app displays "policy proposals for environmental protection" and "measures to eliminate economic disparities," and users can rate the policy proposals by saying "I support / disagree."

[0611] 3. Viewing Candidate Information

[0612] Terminal: Displays information on suitable candidates sent from the server to the user.

[0613] Example: The app will say, "The candidate who most closely aligns with your views is candidate A of the X Party," and provide a link to detailed manifesto information.

[0614] User Action

[0615] 1. Data provision

[0616] User: Enters information about his / her beliefs and beliefs in a questionnaire format, and also provides his / her daily behavior and search history.

[0617] Example: A user answers a survey about "environmental protection," "economic policy," and "education reform."

[0618] 2. Confirmation of manifesto and candidates

[0619] Users: Review the provided manifesto proposals and provide feedback, then review the proposed candidate information and support or consider it.

[0620] Example: A user evaluates proposed manifestos and, based on that evaluation, decides, "This candidate's policies suit me."

[0621] In this way, the system of the present invention realizes the proposal of manifestos and political candidates based on the user's principles and thoughts through cooperation between the server, terminals, and users.

[0622] The processing flow will be explained below.

[0623] Step 1:

[0624] Entering User Data

[0625] Users: Answer surveys provided by the app or web service and enter information about their beliefs and beliefs. In addition, their website browsing history and search history may be collected to the extent permitted by the user.

[0626] Step 2:

[0627] Data collection

[0628] Device: Survey data entered by users, as well as behavioral and search history, are collected. Search history and page browsing history are collected over a certain period of time.

[0629] Step 3:

[0630] Data transmission

[0631] Device: Collected survey data and behavioral and search history are sent to the server. Data is encrypted for privacy reasons.

[0632] Step 4:

[0633] Data analysis

[0634] Server: Analyzes the received data and identifies the user's beliefs and thinking patterns. Based on the survey responses and browsing and search history, natural language processing technology is used to extract important keywords and topics.

[0635] Step 5:

[0636] Generating a draft manifesto

[0637] Server: Using generative AI based on the analysis results, the server creates a draft manifesto that matches the user's principles and thinking, including specific policy proposals and pledges.

[0638] Step 6:

[0639] Submitting a manifest

[0640] Server: Sends the created manifesto to the user's device, formatting it in a way that is easy to read and relevant to the user's interests.

[0641] Step 7:

[0642] View the draft manifesto

[0643] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[0644] Step 8:

[0645] Collecting feedback

[0646] Terminal: Collects user feedback on the draft manifesto and sends it to the server. Feedback is provided in the form of quantitative evaluations (e.g., evaluation scores) and qualitative comments (e.g., free text).

[0647] Step 9:

[0648] Feedback Analysis

[0649] Server: Analyzes the received user feedback and uses it as data to improve the proposed manifesto and the system's suggestion functions.

[0650] Step 10:

[0651] Collecting candidate manifestos

[0652] Server: Collects published manifestos of multiple political candidates from the Internet, including official candidate websites and election catalogs.

[0653] Step 11:

[0654] Parsing the Manifest

[0655] Server: Analyzes the collected candidate manifestos and stores their main policy content in a database. Using natural language processing technology, categorizes each candidate's policies into categories.

[0656] Step 12:

[0657] Candidate proposals

[0658] Server: Identifies candidates from the database whose policies match the user's principles and thinking tendencies, and selects candidate information appropriate for the user.

[0659] Step 13:

[0660] Submit candidate information

[0661] Server: Sends information about selected candidates to the user's device in a format that is easy for the user to understand.

[0662] Step 14:

[0663] View candidate information

[0664] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[0665] Step 15:

[0666] Feedback and Adjustments

[0667] On the device: Collects the feedback provided by the user and sends it back to the server.

[0668] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[0669] In this way, by closely coordinating each step, a draft manifesto based on the user's principles and thoughts can be generated and appropriate candidates can be proposed.

[0670] Example 1

[0671] 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."

[0672] In modern society, it is difficult to identify appropriate political candidates based on a user's beliefs and beliefs. Conventional methods require users to collect, compare, and consider candidate information themselves, which is labor-intensive. Furthermore, there is a lack of a means to objectively evaluate how well a candidate's policies match the user's beliefs and beliefs. Therefore, there is a need for a method to improve the efficiency and accuracy of selecting appropriate candidates.

[0673] 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.

[0674] In this invention, the server includes means for collecting user behavioral history and search history, means for collecting questionnaire data from users, means for analyzing the collected data and identifying the user's principles and tendencies of thinking, means for creating a draft manifesto using a generative AI model based on the user's principles and tendencies of thinking, means for collecting published manifestos of political candidates that match the user's principles and tendencies of thinking, means for analyzing the collected candidate manifestos and storing them in a database, and means for suggesting candidate information suitable for the user based on the analysis results. This makes it possible to automatically identify appropriate political candidates and efficiently select candidates based on that information, without the user having to collect candidate information themselves.

[0675] "User behavioral history and search history" refers to a record of specific activities and search keywords that a user has performed on the Internet.

[0676] "Survey Data" refers to responses to questions collected directly from users.

[0677] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate new text or data.

[0678] A "draft manifesto" is a detailed proposal for specific policies and measures.

[0679] A "published manifesto" is a policy proposal or plan officially released by a political candidate or party.

[0680] A "database" is an information system for efficiently storing, managing, and retrieving structured data.

[0681] "Candidate Information" means detailed data and profile information about political candidates.

[0682] This invention relates to a system that creates a draft manifesto based on the user's principles and tendencies, and proposes suitable political candidates. This system is implemented by a combination of hardware and software, with a server, terminals, and users working together.

[0683] Server Processing

[0684] 1. Collection of User Data

[0685] Server: The server uses web crawlers and log analysis tools to collect user behavior and search history from the Internet. It also receives information provided by users in the form of questionnaires. The collected data is stored in a database.

[0686] Example: A server monitors a user's browser history and extracts keywords of interest such as "environmental protection" or "economic policy."

[0687] 2. User Data Analysis

[0688] Server: The server uses natural language processing (NLP) models and machine learning algorithms to analyze the collected data, specifically using Python's NLTK library and Sci-Kit Learn to identify the user's beliefs and tendencies.

[0689] Example: The server tokenizes text data including the user's search history and performs sentiment analysis to classify the user's areas of interest.

[0690] 3. Generating a draft manifesto

[0691] Server: The server uses a generative AI model (e.g., OpenAI GPT) to create a manifesto based on the user's beliefs and tendencies. The generated manifesto will be tailored to the user's individual interests and beliefs.

[0692] Example: The prompt "Generate policy proposals based on the user's areas of interest" is input into a generative AI model, which generates a manifesto containing specific policy proposals.

[0693] 4. Collection and analysis of candidate manifestos

[0694] Server: Collects published manifestos from multiple political candidates. The collected manifestos are downloaded using a web scraping tool (e.g., the Scrapy framework) and converted into text data. They are then analyzed using an NLP model, and their policy content is organized and stored in a database.

[0695] Example: A server downloads manifesto PDFs from political candidates' official websites and classifies them into categories such as "environment," "economy," and "education."

[0696] 5. Proposing suitable candidates to users

[0697] Server: Based on the analysis results, it identifies candidates whose policies match the user's principles and thinking tendencies and makes recommendations. Suitable candidate information is extracted through a database query and notified to the user.

[0698] For example, the server selects the most suitable candidate for the user, for example, "proposing candidate A to users who are highly interested in environmental policy."

[0699] Terminal handling

[0700] 1. Data Entry

[0701] Terminal: The user enters survey data and behavioral and search history through the terminal. This data is sent to the server.

[0702] Example: A user answers a "survey on economic policy" on a smartphone app.

[0703] 2. Manifest Display

[0704] Terminal: The terminal displays the proposed manifest sent from the server to the user, and also collects user feedback and sends it back to the server.

[0705] Example: The app displays "policy proposals for environmental protection" or "measures to eliminate economic disparities," and users provide feedback such as "for" or "against."

[0706] 3. Viewing Candidate Information

[0707] Terminal: Display suitable candidate information to the user and provide a link to detailed manifesto information.

[0708] Example: The app might say, "The candidate who most closely aligns with your views is candidate A from the XX party," and provide a link to more information.

[0709] User Action

[0710] 1. Data provision

[0711] User: Users fill out a questionnaire about their beliefs and thoughts, and provide their daily behavior and search history.

[0712] Example: Answer a survey about "environmental protection," "economic policy," and "education reform."

[0713] 2. Confirmation of manifesto and candidates

[0714] Users: Review the proposed manifesto and provide feedback, then review relevant candidate information and consider supporting it.

[0715] Example: A user evaluates a proposed manifesto and decides, "This candidate's policies suit me."

[0716] As described above, the system of the present invention efficiently realizes the proposal of manifestos and political candidates based on the user's principles and thoughts through cooperation between the server, terminals, and users.

[0717] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0718] Step 1: Collect user data

[0719] server:

[0720] Input: User behavioral and search history, survey data.

[0721] How it works: The server uses a web crawler to collect URLs and search keywords frequently visited by users, and also receives response data from survey forms. All of this data is stored in a database.

[0722] Output: User behavior history, search history, and survey data are saved in a database.

[0723] Step 2: Analyze user data

[0724] server:

[0725] Input: Behavioral history, search history, survey data.

[0726] How it works: The server uses natural language processing (NLP) and machine learning algorithms, such as Python's NLTK library and Sci-Kit Learn, to tokenize text data and perform sentiment analysis and interest classification. For example, it scores interest in "environmental protection" or "economic policy" based on search keywords.

[0727] Output: Analysis results showing the user's principles and thinking tendencies.

[0728] Step 3: Generate a draft manifesto

[0729] server:

[0730] Input: Analysis results of user's principles and thinking tendencies.

[0731] Specific operation: The server uses a generative AI model (e.g., OpenAI GPT) to generate a draft manifesto based on the user's analysis results. It inputs the prompt "Please generate policy proposals based on the user's areas of interest" into the AI ​​model and generates a manifesto containing specific policy proposals.

[0732] Output: A proposed manifesto personalized for the user.

[0733] Step 4: Collecting candidate manifestos

[0734] server:

[0735] Input: A political candidate's published manifesto.

[0736] Specific operation: The server uses a web scraping tool (e.g., the Scrapy framework) to download manifesto PDFs from each political candidate's official website or public documents and convert them into text data.

[0737] Output: Candidate manifesto text data is stored in the database.

[0738] Step 5: Parsing the candidate manifestos

[0739] server:

[0740] Input: Candidate manifesto text data.

[0741] How it works: The server uses an NLP model to tokenize the text data and classify each policy into a category, such as "environment," "economy," or "education." The classification results are then stored in a database.

[0742] Output: Candidate policy data organized by category.

[0743] Step 6: Propose candidates

[0744] server:

[0745] Input: Analysis of user principles and thought patterns, candidate policy data.

[0746] Specific operation: The server executes an SQL query and selects the candidate whose policies best match the user's areas of interest. For example, it generates results such as "suggest candidate A to users who are interested in environmental policies."

[0747] Output: Candidate suggestion data for the user.

[0748] Step 7: Displaying data and gathering user feedback

[0749] Device:

[0750] Input: Draft manifesto and candidate proposal data sent from the server.

[0751] What it does: The device displays the draft manifesto and candidate information to the user via an app or web interface, collects user feedback, and sends it to the server.

[0752] Output: User feedback data is sent to the server.

[0753] Through the above processing steps, the optimal manifesto proposal and political candidate proposals are realized for the user.

[0754] (Application example 1)

[0755] 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."

[0756] While existing technologies exist that suggest political candidates based on a user's beliefs and tendencies, they cannot be directly applied to suggesting products and services that take into account a user's purchasing behavior and preferences. As a result, personalized product suggestions cannot be made for users with specific interests and preferences, making it difficult to increase customer satisfaction.

[0757] 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.

[0758] In this invention, the server includes means for collecting user behavior history and search history, means for collecting questionnaire data from users, and means for analyzing the collected data to identify the user's principles and thinking tendencies, thereby enabling personalized suggestions of products or services that match the user's principles and thinking tendencies.

[0759] "User behavior history" refers to the history of operations and pages accessed by a user on the Internet or in an application.

[0760] "Search history" refers to the history of keywords or phrases that a user has entered and searched for on an Internet search engine.

[0761] "Survey data" refers to information about a user's interests, preferences, opinions, etc., provided in the form of a questionnaire.

[0762] "Analysis" refers to using collected data to analyze information and derive results for a specific purpose.

[0763] "Ideas and thinking tendencies" refer to the user's interests and tendencies in thinking regarding specific issues or fields.

[0764] A "manifesto proposal" is a draft policy proposal or action plan generated based on the user's principles and thinking.

[0765] "Goods or services" refers to products offered at a physical store or services provided to customers.

[0766] "Feedback" refers to the evaluation or opinion a user gives of the information or suggestions provided.

[0767] "Suggestion" refers to the act of recommending products or services that are determined to be optimal for the user based on the analysis results.

[0768] This invention is a system that collects and analyzes user behavior history, search history, and questionnaire data, and proposes personalized products and services based on the user's principles and thinking tendencies. This system is mainly composed of three entities: a server, a terminal, and a user.

[0769] Server Processing

[0770] The server has the following roles:

[0771] 1. Data Collection:

[0772] The server collects users' behavioral and search histories, including the web pages they visit, the keywords they search for, the content they view, etc. The server also collects survey data entered by users.

[0773] 2. Data Analysis:

[0774] The collected data is analyzed on the server to identify the user's principles and thought patterns. This process uses data mining and natural language processing techniques, specifically the Python libraries pandas and scikit-learn.

[0775] 3. Leveraging generative AI models:

[0776] Based on the analysis results, a generative AI model is used to suggest suitable products or services to the user. This AI model utilizes an advanced natural language processing model such as GPT-3. The user's preferences and behavioral history are used as input for the prompt sentence.

[0777] Example prompt sentence:

[0778] User preferences: Likes red wine, price range: 2000-5000 yen, prioritizes food pairing

[0779] 4. Proposal generation:

[0780] The generative AI model generates suggestions and sends them to the device via a server, such as product recommendations for physical stores or suggestions for specific services.

[0781] Terminal handling

[0782] A terminal is a device that acts as an interface with a user. Typical terminals are smartphones, smart glasses, or head-mounted displays.

[0783] 1. Data Entry:

[0784] Through the device, users answer questionnaires and their behavioral and search history is collected, and this data is sent to the server in real time.

[0785] 2. View Proposals:

[0786] The suggestions received from the server are displayed to the user. If the user is using smart glasses or a head-mounted display, the suggestions can be displayed visually. For example, when the user stands in front of a particular wine shelf in a store, the display will say, "Here is the wine that is best suited for you."

[0787] 3. Gathering Feedback:

[0788] The device is equipped with a function that allows users to provide feedback on suggested products and services, which is sent to a server for further analysis.

[0789] User Action

[0790] Users are the primary data providers and beneficiaries of the system.

[0791] 1. Data provided by:

[0792] Users provide data through their daily activities and survey responses, which serves as the basis for identifying their beliefs and thought patterns.

[0793] 2. Confirmation of proposal:

[0794] Review the proposed products and services, and in some cases consider purchasing or using them. Also, contribute to improving the proposals through feedback.

[0795] In this way, the system of the present invention allows the server, terminal, and user to work together to propose personalized products and services based on the user's principles and thoughts.

[0796] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0797] Step 1:

[0798] Users use devices (smartphones, smart glasses, head-mounted displays, etc.) to answer a questionnaire. This questionnaire is intended to collect information about the user's preferences and interests. The device then transmits the survey data to a server in real time.

[0799] Input: User-entered survey responses

[0800] Output: Survey data sent to the server

[0801] Step 2:

[0802] The server collects users' behavioral and search histories from the Internet, and also receives survey data sent from their devices. The collected data is stored in a database.

[0803] Input: Behavioral history, search history, survey data

[0804] Output: User data stored in the database

[0805] Step 3:

[0806] The server analyzes the collected data and uses data mining and natural language processing techniques to identify the user's beliefs and tendencies. For example, it uses Python's "pandas" and "scikit-learn" to cleanse and analyze the data. This generates indicators that show the user's interests and preferences.

[0807] Input: User data retrieved from the database

[0808] Output: Indicators showing the user's principles and thinking tendencies

[0809] Step 4:

[0810] The server uses a generative AI model (e.g., GPT-3) to generate a prompt to suggest the best product or service to the user based on the analysis results.The prompt is then input into the AI ​​model to generate the suggestion.

[0811] Input: Indicators showing the user's beliefs and thinking tendencies

[0812] Output: Recommendations for the best product or service for the user

[0813] Step 5:

[0814] The server sends the generated suggestions to the device in real time. The suggestions are displayed to the user. If smart glasses or a head-mounted display are used, the suggestions are displayed visually.

[0815] Input: Proposal

[0816] Output: Proposal displayed on terminal

[0817] Step 6:

[0818] The user can check the proposed products or services through the terminal and enter their evaluation and opinions as feedback, which is also sent to the server in real time.

[0819] Input: User feedback

[0820] Output: Feedback data sent to the server

[0821] Step 7:

[0822] The server analyzes the collected feedback and uses it to improve the content of suggestions and the prompts of the AI ​​model. It continuously analyzes data and generates suggestions that better match the user's principles and thinking patterns.

[0823] Input: Feedback data

[0824] Output: Improved suggestions and prompts

[0825] 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.

[0826] This invention combines an emotion engine with a system that creates a draft manifesto based on the user's principles and tendencies and suggests suitable political candidates, allowing for more precise proposals. This system, which links the server, terminals, and users and includes an emotion engine, provides a draft manifesto and candidate information that also reflects the user's emotional state.

[0827] System configuration

[0828] Server: Collects and analyzes user data, generates manifesto proposals, identifies emotional states, and implements candidate proposals.

[0829] Terminal: Collects data from users, communicates with the server to display draft manifestos and candidate information, and also has an emotion engine.

[0830] Users: Answer surveys, provide behavioral and search history, and provide appropriate feedback.

[0831] Server Processing

[0832] 1. Collection and analysis of user data

[0833] Server: Collects user behavioral and search histories from the Internet, data provided in the form of questionnaires, and emotional data obtained by the emotion engine.

[0834] Example: If a user frequently reads articles about environmental protection and expresses a high interest in "education reform" in a survey, the data can be analyzed and the emotion engine can detect that the user feels strong "concern" about environmental issues.

[0835] 2. Data Analysis

[0836] Server: Analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. Extracts important keywords and topics based on beliefs and thought patterns.

[0837] Example: The server uses natural language processing technology to analyze the user's areas of interest and emotional state, determining things like "concerns about environmental protection" and "enthusiasm for economic policy."

[0838] 3. Generating a draft manifesto

[0839] Server: Based on the analysis results and emotional data, generative AI is used to create a draft manifesto that matches the user's beliefs, thought patterns, and emotional state.

[0840] Example: The server generates "policy proposals that reflect concerns about environmental protection" and "concrete measures to eliminate economic disparities."

[0841] 4. Submitting the manifest

[0842] Server: The created manifesto is sent to the user's device. Based on the emotional data, it is presented in a format that resonates most with the user.

[0843] 5. Display of the Manifesto Draft

[0844] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[0845] Example: An app displays "specific proposals for environmental protection" and "details of economic policies" in an easy-to-read format to users and collects their reactions.

[0846] 6. Gathering Feedback

[0847] Terminal: Collects user feedback on the proposed manifesto and sends it to the server.

[0848] Example: A user provides an evaluation such as "This policy is very good" or "This part needs improvement", which is then sent to the server.

[0849] 7. Feedback Analysis

[0850] Server: Analyzes the received feedback and uses it as data to improve the system's suggestion capabilities.

[0851] 8. Collecting candidate manifestos

[0852] Server: Collects and analyzes published manifestos of multiple political candidates from the Internet.

[0853] Example: A server downloads manifestos from political candidates' official websites and classifies and analyzes them into categories such as "environment," "economy," and "education."

[0854] 9. Candidate Proposals

[0855] Server: Identifies candidates whose policies match the user's principles, thought patterns, and emotional state, and proposes them to the user.

[0856] Example: If a user has a high interest in and strong feelings about environmental policy, the server might suggest, "Candidate A of the XX Party has environmental policies that are closest to your views."

[0857] 10. Submission of Candidate Information

[0858] Server: Sends information about selected candidates to the user's device.

[0859] 11. Display of Candidate Information

[0860] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[0861] Example: The app displays, "The candidate who most closely aligns with your views is candidate A of the XX Party," and provides detailed manifesto information.

[0862] 12. Feedback and Adjustments

[0863] On the device: Collects the feedback provided by the user and sends it back to the server.

[0864] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[0865] In this way, the system of the present invention works by linking the server, terminal, user, and emotion engine to propose manifestos and political candidates based on the user's principles, thoughts, and emotional state.

[0866] The processing flow will be explained below.

[0867] Step 1:

[0868] Entering User Data

[0869] Users: Answer surveys provided by the app or web service and enter information about their beliefs and beliefs. In addition, their website browsing history and search history may be collected to the extent permitted by the user.

[0870] Step 2:

[0871] Identifying emotional states

[0872] Device: When the user answers the questionnaire, the emotion engine uses the camera and microphone to identify the user's emotional state from their facial expressions and voice. The identified emotional state is recorded.

[0873] Example: While a user answers a questionnaire, the camera analyzes their facial expressions to identify emotions such as "happiness," "concern," and "excitement." The microphone also analyzes the tone of their voice to detect "excitement" and "anxiety."

[0874] Step 3:

[0875] Data collection

[0876] Terminal: Collects survey data entered by users, behavioral and search history, and identified emotional state data.

[0877] Step 4:

[0878] Data transmission

[0879] Device: Collected survey data, behavioral and search history, and emotional data are sent to the server. The data is encrypted for privacy reasons.

[0880] Step 5:

[0881] Data analysis

[0882] Server: Analyzes the received data and identifies the user's beliefs, thought patterns, and emotional state. Extracts important keywords and topics based on survey responses, browsing and search history, and emotional data.

[0883] Example: The server uses natural language processing technology to analyze the user's areas of interest and emotional state, determining things like "concerns about environmental protection" and "enthusiasm for economic policy."

[0884] Step 6:

[0885] Generating a draft manifesto

[0886] Server: Based on the analysis results and emotional data, generative AI is used to create a draft manifesto that matches the user's beliefs, thought patterns, and emotional state.

[0887] Example: The server generates "policy proposals that reflect concerns about environmental protection" and "concrete measures to eliminate economic disparities."

[0888] Step 7:

[0889] Submitting a manifest

[0890] Server: The created manifesto is sent to the user's device. Based on the emotional data, it is presented in a format that resonates most with the user.

[0891] Step 8:

[0892] View the draft manifesto

[0893] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[0894] Example: An app displays "specific proposals for environmental protection" and "details of economic policies" in an easy-to-read format to users and collects their reactions.

[0895] Step 9:

[0896] Collecting feedback

[0897] Terminal: Collects user feedback on the proposed manifesto and sends it to the server.

[0898] Example: A user provides an evaluation such as "This policy is very good" or "This part needs improvement", which is then sent to the server.

[0899] Step 10:

[0900] Feedback Analysis

[0901] Server: Analyzes the received user feedback and uses it as data to improve the system's suggestion functions.

[0902] Step 11:

[0903] Collecting candidate manifestos

[0904] Server: Collects and analyzes the published manifestos of multiple political candidates from the Internet, including official candidate websites and election catalogs.

[0905] Example: A server downloads manifestos from political candidates' official websites and classifies and analyzes them into categories such as "environment," "economy," and "education."

[0906] Step 12:

[0907] Candidate proposals

[0908] Server: Identifies candidates whose policies match the user's principles, thought patterns, and emotional state, and proposes them to the user.

[0909] Example: If a user has a high interest in and strong feelings about environmental policy, the server might suggest, "Candidate A of the XX Party has environmental policies that are closest to your views."

[0910] Step 13:

[0911] Submit candidate information

[0912] Server: Sends information about selected candidates to the user's device in a format that is easy for the user to understand.

[0913] Step 14:

[0914] View candidate information

[0915] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[0916] Example: The app displays, "The candidate who most closely aligns with your views is candidate A of the XX Party," and provides detailed manifesto information.

[0917] Step 15:

[0918] Feedback and Adjustments

[0919] On the device: Collects the feedback provided by the user and sends it back to the server.

[0920] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[0921] In this way, the system of the present invention works by linking the server, terminal, user, and emotion engine to propose manifestos and political candidates based on the user's principles, thoughts, and emotional state.

[0922] Example 2

[0923] 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."

[0924] Conventional political information systems collect and analyze users' behavioral history and survey data, and are able to provide manifesto proposals based on the user's principles and tendencies. However, these systems do not take into account the user's emotional state, which means that the proposed content does not fully take into account the user's feelings and emotions. Furthermore, there was an insufficient method for effectively analyzing feedback and reflecting it in future proposals.

[0925] 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.

[0926] In this invention, the server includes means for collecting a user's behavioral history and search history, means for collecting questionnaire data from the user, means for analyzing the collected data to identify the user's principles, thinking tendencies, and emotional state, means for creating a draft manifesto using a generative AI model based on the user's principles, thinking tendencies, and emotional state, means for collecting and analyzing manifestos of political candidates that match the user's principles, thinking tendencies, and emotional state, means for proposing candidate information suitable for the user, means for transmitting and displaying the proposed draft manifesto and candidate information to the user's terminal, means for collecting user feedback and transmitting it back to the server, and means for analyzing the collected feedback and adjusting the generation of subsequent draft manifestos and candidate proposals. This makes it possible to provide more sophisticated draft manifestos and candidate information that reflect the user's emotional state.

[0927] "User behavior history" refers to a record of a user's activities on the Internet, specifically website browsing history and click history.

[0928] "Search history" is a record of search queries and their results made by a user using a search engine.

[0929] "Survey Data" means information provided by a user in the form of a survey, including a user's opinions, interests, and other feedback.

[0930] "Ideas and tendencies" refers to the political and social beliefs and tendencies of thought held by the user.

[0931] "Emotional state" refers to the user's current emotion as analyzed by the emotion engine, including, for example, joy, sadness, worry, anger, etc.

[0932] "Generative AI model" refers to a program that uses artificial intelligence algorithms to generate a draft manifesto or other suggestions that are optimized for a user.

[0933] A "Manifesto" is a document of policy proposals created based on the user's principles, thought patterns, and emotional state.

[0934] "Political candidate" means a candidate running for election to public office.

[0935] A "manifesto" is a document of policies and principles published by a political candidate.

[0936] "Feedback" refers to ratings and opinions provided by users.

[0937] "Terminal" refers to electronic devices used by users, such as computers, smartphones, and tablets.

[0938] A "server" refers to a central computer that processes and stores data for a system to operate.

[0939] This invention is a system that provides individually optimized manifesto proposals and political candidate information based on the user's principles, thought patterns, and emotional state. This system operates in cooperation with a server, terminals, and users.

[0940] Specific server processing and technologies used

[0941] User Data Collection

[0942] The server collects users' behavioral and search histories from the Internet. For this purpose, it uses web analysis tools (e.g., Google Analytics). It also collects data provided by users in the form of questionnaires. In addition, it uses an emotion engine (e.g., Affectiva API) to obtain user emotion data.

[0943] Data analysis

[0944] The server analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. This analysis uses natural language processing techniques (e.g., Python's NLTK library or SpaCy). Techniques such as TF-IDF and Word2Vec are used to extract important keywords and topics.

[0945] Generating a draft manifesto

[0946] Based on the analysis results and emotional data, the server uses a generative AI model (e.g., OpenAI's GPT-3) to create a manifesto proposal tailored to the user's beliefs, thinking tendencies, and emotional state. A prompt sentence is generated and input into the generative AI model to generate a specific manifesto proposal. For example, the following prompt sentence can be used:

[0947] "The user is highly interested in environmental protection and is also enthusiastic about economic policy. Furthermore, the analysis results of the emotion engine indicate that the user feels strong concern about environmental issues. Please generate a draft manifesto for this user."

[0948] Submitting a draft manifesto

[0949] The generated manifest plan is sent from the server to the user's device. This communication is performed using the HTTP protocol, and the data is sent in JSON format.

[0950] Collecting candidate manifestos

[0951] The server collects, categorizes, and analyzes published manifestos from multiple political candidates from the Internet, using web scraping tools (e.g., Beautiful Soup, Scrapy).

[0952] Candidate proposals

[0953] The server recommends the best matched candidates based on the user's beliefs, tendencies, and emotional state, using a recommendation algorithm (e.g., cosine similarity calculation).

[0954] Specific terminal processing and technologies used

[0955] View the draft manifesto

[0956] The terminal displays the proposed manifest received from the server to the user, using front-end technology (e.g., React.js) to display data in an easy-to-read card or list format.

[0957] Collecting feedback

[0958] The terminal provides an input form for collecting feedback from the user and sending it back to the server via the HTTP protocol.

[0959] View candidate information

[0960] The terminal displays candidate information from the server to the user, again using front-end technology, in the form of cards containing candidate photos and detailed policy information.

[0961] Specific user operations

[0962] Data provision

[0963] Users answer questions in a questionnaire format and provide their behavioral history.

[0964] Providing Feedback

[0965] Users can provide their evaluations and opinions on the displayed manifesto proposals and candidate information, and this feedback will be reflected in future proposals.

[0966] In this way, the server, terminal, and user each play their own roles and work together to provide a system that provides more appropriate and precise manifesto proposals and candidate information based on the user's principles, thoughts, and emotional state.

[0967] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0968] Step 1:

[0969] User Data Collection

[0970] Input: User behavior history, search history, survey data, emotional data

[0971] Specific behavior:

[0972] 1. Collection of behavioral history and search history: The server automatically collects users' browsing history and search history from the Internet using web analysis tools (e.g., Google Analytics).

[0973] 2. Survey data collection: The server uses a web form to collect data entered by the user, such as opinions and interests, in the form of a survey.

[0974] 3. Acquiring emotion data: The server uses an emotion engine (e.g., Affectiva API) to detect emotions from the user's video and audio and collects the data.

[0975] Output: A set of collected user data (behavioral history, search history, survey data, emotional data)

[0976] Step 2:

[0977] Data analysis

[0978] Input: Complete set of collected user data

[0979] Specific behavior:

[0980] 1. Application of natural language processing technology: The server analyzes text data using natural language processing technologies such as Python's NLTK library and SpaCy.

[0981] 2. Identifying beliefs and thought patterns: The server uses techniques such as TF-IDF and Word2Vec to identify the user's beliefs and thought patterns, as well as important keywords and topics.

[0982] 3. Emotional state identification: Based on the output of the emotion engine, identify the user's current emotional state (e.g., joy, worry, anger, etc.).

[0983] Output: Analysis results including the user's beliefs, thinking tendencies, and emotional state

[0984] Step 3:

[0985] Generating a draft manifesto

[0986] Input: Analysis results (user's beliefs, thinking patterns, emotional state)

[0987] Specific behavior:

[0988] 1. Prompt generation: Based on the analysis results, the server creates a prompt to be input to the generative AI model.

[0989] Example: "The user is highly interested in environmental protection and is also enthusiastic about economic policy. Furthermore, the sentiment engine analysis indicates that they feel very concerned about environmental issues. Please generate a draft manifesto for this user."

[0990] 2. Applying a generative AI model: The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate a draft manifesto from the prompt.

[0991] 3. Adjusting the wording: Based on the emotion data, the server adjusts the wording of the generated manifesto proposal to make it most relatable to the user.

[0992] Output: The generated draft manifest

[0993] Step 4:

[0994] Submitting a draft manifesto

[0995] Input: Generated manifest proposal

[0996] Specific behavior:

[0997] 1. Data format conversion: The server converts the proposed manifest into JSON format for sending to the user's device.

[0998] 2. Data transmission: The server sends the proposed manifest to the user's device using the HTTP protocol.

[0999] Output: Draft manifest sent to user device

[1000] Step 5:

[1001] View the draft manifesto

[1002] Input: Manifest proposal sent to user device

[1003] Specific behavior:

[1004] 1. Data reception: The terminal receives the proposed manifest sent from the server.

[1005] 2. Displaying data: The device uses front-end technology (e.g., React.js) to display the proposed manifest in an easy-to-read card or list format.

[1006] Output: The proposed manifest displayed

[1007] Step 6:

[1008] Collecting feedback

[1009] Input: User feedback

[1010] Specific behavior:

[1011] 1. Displaying a feedback form: The terminal displays a user input form on the front end, allowing the user to enter feedback.

[1012] 2. Sending feedback: The terminal collects the feedback entered by the user and sends it to the server via the HTTP protocol.

[1013] Output: Feedback sent to the server

[1014] Step 7:

[1015] Feedback Analysis

[1016] Input: Feedback sent to the server

[1017] Specific behavior:

[1018] 1. Feedback collection: The server retrieves the feedback stored in the database.

[1019] 2. Data analysis: The server uses machine learning algorithms and statistical analysis to analyze users' rating trends.

[1020] Output: Feedback analysis results

[1021] Step 8:

[1022] Collecting candidate manifestos

[1023] Input: online political candidate manifestos

[1024] Specific behavior:

[1025] 1. Data extraction: The server extracts manifest data from the candidate's website using a web scraping tool (e.g., Beautiful Soup, Scrapy).

[1026] 2. Data classification and analysis: The server classifies the extracted data into categories such as "environment," "economy," and "education," and analyzes them.

[1027] Output: Categorized and parsed political candidate manifesto data

[1028] Step 9:

[1029] Candidate proposals

[1030] Input: Analysis results (user's ideology, thought patterns, emotional state), categorized and analyzed political candidate manifesto data

[1031] Specific behavior:

[1032] 1. Data Matching: The server matches the user profile data with the candidate manifest data.

[1033] 2. Recommendation: The server uses a recommendation algorithm (e.g., cosine similarity) to identify the best matching candidates.

[1034] Output: Recommended candidate information

[1035] Step 10:

[1036] Submit candidate information

[1037] Input: Recommended candidate information

[1038] Specific behavior:

[1039] 1. Data format conversion: The server converts the candidate information into JSON format for sending to the user's device.

[1040] 2. Data transmission: The server uses the HTTP protocol to transmit the candidate information to the user's terminal.

[1041] Output: Candidate information sent to user terminal

[1042] Step 11:

[1043] View candidate information

[1044] Input: Candidate information sent to the user's terminal

[1045] Specific behavior:

[1046] 1. Receiving data: The terminal receives the candidate information sent from the server.

[1047] 2. Display of data: The terminal uses front-end technology to display the candidate in the form of a card containing their photo and detailed policy information.

[1048] Output: Displayed candidate information

[1049] Step 12:

[1050] Re-feedback and adjustments

[1051] Input: User's re-feedback, previous feedback analysis results

[1052] Specific behavior:

[1053] 1. Feedback collection: The device collects the feedback provided by the user and sends it back to the server.

[1054] 2. Reflecting the feedback: The server will generate the next draft manifesto and adjust the candidate proposals based on the received feedback.

[1055] Output: Updated manifesto generation and candidate proposal algorithms

[1056] (Application example 2)

[1057] 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."

[1058] Conventional systems could identify principles and thought patterns based on a user's behavioral and search history, and then propose manifestos and candidates, but it was difficult to make precise proposals that reflected the user's emotional state.In addition, since security risk predictions and countermeasure proposals were not based on the user's emotions, it was a challenge to make accurate and empathetic security proposals to users.

[1059] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral history and search history, means for collecting questionnaire data from users, means for analyzing the collected data and identifying the user's principles and thought patterns, and means for performing emotion analysis. This enables the creation of a draft manifesto based on the user's principles and thought patterns, as well as the prediction and proposal of security risks that reflect the user's emotional state. The system also includes means for using a generative AI model to generate prompt sentences that are most relatable to the user, enabling more effective feedback collection and system improvement.

[1060] "User behavior history" refers to the history of activities and operations that a user has performed on the Internet.

[1061] "Search history" refers to the history of search queries a user has performed using a search engine.

[1062] "Survey data" refers to response data regarding opinions and thoughts collected from users.

[1063] "Ideas and thinking tendencies" refer to the user's beliefs, values, and interests.

[1064] A "manifesto" is a document of policy proposals created based on the user's principles and thinking.

[1065] A "political candidate" is a person running for public office.

[1066] "Emotion analysis" refers to identifying and analyzing a user's emotional state from input data.

[1067] "Risk prediction" is the estimation of possible future risks based on collected data.

[1068] "Suggestion" means presenting specific actions or options to the user.

[1069] A "generative AI model" is a model that uses artificial intelligence techniques to analyze data and perform a specified task.

[1070] A "prompt" is a question or instruction that is input to a generative AI model.

[1071] System Overview

[1072] This system collects users' behavioral and search histories and analyzes them together with survey data to identify their principles and thought patterns. It then uses a generative AI model to create a draft manifesto and performs emotion analysis to predict risks and make suggestions that reflect the user's emotional state.

[1073] Hardware and Software Configuration

[1074] The system has the following components:

[1075] Server: Collects and analyzes user data, generates a draft manifesto, identifies emotional states, predicts risks, and makes recommendations. The software used includes Python libraries with natural language processing techniques (e.g., transformers).

[1076] Terminal: Collects data from the user, communicates with the server, displays the manifesto and suggestion information, and also has an emotion engine.

[1077] Users: Answer surveys, provide behavioral and search history, and provide appropriate feedback.

[1078] Data collection and analysis

[1079] The server collects the user's behavioral history and search history, as well as data provided in the form of questionnaires. This data is analyzed to identify the user's beliefs, thought patterns, and emotional state.

[1080] Performing sentiment analysis

[1081] The server uses an emotion analysis model to analyze the emotional state of the data obtained from the user. For example, it uses natural language processing technology to identify emotions such as "concern" or "enthusiasm" from the collected text data.

[1082] Generate a draft manifest and risk forecast

[1083] Based on the analysis results and emotional data, the server uses a generative AI model to create a manifesto tailored to the user's beliefs, thought patterns, and emotional state, as well as generate risk predictions and countermeasures.

[1084] Submitting and Viewing Proposals

[1085] The server sends the created manifesto proposal and risk countermeasure proposal to the user's device, which displays these proposals to the user, who can review them and provide their evaluation and opinions for each item.

[1086] Collecting and analyzing feedback

[1087] The device collects user feedback on the suggestions and sends it to the server, which analyzes the feedback and uses it as data to improve the system's suggestion capabilities.

[1088] Examples and prompts

[1089] Specific examples

[1090] Example input: A user frequently searches for information related to "personal information leaks," "two-factor authentication," "security risks," etc.

[1091] Example prompt:

[1092] Recently, users have been concerned about personal information leaks and how to implement two-factor authentication. Sentiment analysis shows that users have strong concerns. What specific security measures would you suggest to alleviate negative sentiment?

[1093] The system's unique feature is that it analyzes user data and makes precise risk predictions and suggestions based on the user's emotional state.

[1094] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1095] Step 1:

[1096] The server collects the user's online behavior and search history. It receives a questionnaire from the user, converts it into a data format, and saves it. The input of this step is the user's behavior and questionnaire responses, and the output is a dataset.

[1097] Specifically, the server logs the user's behavior history in real time and periodically stores the collected data in a database.

[1098] Step 2:

[1099] The server analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. Natural language processing techniques are used to extract emotions from text data. The input is the collected dataset, and the output is a profile of beliefs, thought patterns, and emotional state.

[1100] Specifically, the server uses the Python library transformers to input text data into an emotion classification model and obtain analysis results.

[1101] Step 3:

[1102] Based on the generated profile, the server uses a generative AI model to create a proposed manifesto tailored to the user's beliefs, thought patterns, and emotional state. The input is the profile, and the output is a customized proposed manifesto.

[1103] Specifically, the server inputs the profile into the generative AI model and constructs the generated text as a draft manifesto.

[1104] Step 4:

[1105] The server generates risk predictions and countermeasures. Based on the results of emotion analysis, it creates specific security proposals and countermeasures tailored to the user. The input is emotional state and risk data, and the output is risk predictions and countermeasures.

[1106] Specifically, the server runs a risk assessment model based on the analysis results and generates specific suggested text using a generative AI model.

[1107] Step 5:

[1108] The server sends the created manifest proposal and risk countermeasure proposal to the user's terminal. The input is the generated proposal, and the output is the completion of transmission to the user's terminal.

[1109] Specifically, the server sends data to the user's terminal over the Internet and receives a confirmation response.

[1110] Step 6:

[1111] The terminal displays the received suggestions to the user, who then reviews them and provides ratings and opinions for each item. The input is the suggestions, and the output is user feedback.

[1112] Specifically, the device displays suggestions through an interface and collects user input in real time.

[1113] Step 7:

[1114] The device collects feedback and sends it to the server. The input is the user's evaluation and opinion, and the output is transmission to the server.

[1115] Specifically, the terminal converts the user's input into a data format and calls an API to send it to the server.

[1116] Step 8:

[1117] The server analyzes the received feedback and improves the system's suggestion function. The input is the feedback data, and the output is data for system improvement.

[1118] Specifically, the server stores the feedback in a database and uses an analytical algorithm to extract information to improve the accuracy of the next suggestion.

[1119] 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.

[1120] 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.

[1121] 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.

[1122] [Third embodiment]

[1123] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1124] 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.

[1125] 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).

[1126] 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.

[1127] 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.

[1128] 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).

[1129] 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.

[1130] 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.

[1131] 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.

[1132] 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.

[1133] 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.

[1134] 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."

[1135] The present invention relates to a system that creates a draft manifesto based on the user's principles and tendencies, and proposes suitable political candidates. This system is implemented by the cooperation of a server, terminals, and users.

[1136] System configuration

[1137] Server: Mainly responsible for collecting and analyzing data, generating draft manifestos, and proposing candidates.

[1138] Terminal: Collects data from the user and communicates with the server to display the draft manifesto and candidate information.

[1139] Users: Provide data input and feedback to receive personalized manifesto proposals and candidate information.

[1140] Server Processing

[1141] 1. Collection and analysis of user data

[1142] Server: Collects user behavioral and search history from the Internet. Also collects data provided by users in questionnaires. Analyzes the collected data to identify users' beliefs and thinking patterns.

[1143] Example: The server collects the web pages frequently visited by users and the keywords they search for, and analyzes the level of interest in "environmental protection" and "economic policy." It also extracts opinions on "education reform" from survey responses.

[1144] 2. Generating a draft manifesto

[1145] Server: Based on the analysis results, a generative AI is used to create a draft manifesto. The draft manifesto is designed to fit the individual beliefs and thoughts of the user.

[1146] Example: The server generates a manifesto based on the user's interests, such as "policies focused on environmental protection," "specific measures to eliminate economic disparities," and "new proposals for educational reform."

[1147] 3. Collecting and analyzing candidate manifestos

[1148] Server: Collects and analyzes the published manifestos of multiple political candidates. Stores each candidate's policy content in a database.

[1149] Example: The server downloads manifestos from the official websites of political candidates and classifies and analyzes them into categories such as "environment," "economy," and "education."

[1150] 4. Proposing suitable candidates to users

[1151] Server: Identifies candidates whose policies match the user's principles and beliefs and proposes them to the user.

[1152] Example: If the user is highly interested in environmental policy, the server suggests, "Candidate A of the XX Party has environmental policies that are closest to your ideas."

[1153] Terminal handling

[1154] 1. Data Entry

[1155] Terminal: Collects survey data and behavioral and search history from users and sends them to the server.

[1156] Example: When a user answers a survey about economic policy on an app, the data is sent to a server.

[1157] 2. Manifest Display

[1158] Terminal: Shows the generated manifest draft received from the server to the user, collects feedback and sends it to the server.

[1159] Example: The app displays "policy proposals for environmental protection" and "measures to eliminate economic disparities," and users can rate the policy proposals by saying "I support / disagree."

[1160] 3. Viewing Candidate Information

[1161] Terminal: Displays information on suitable candidates sent from the server to the user.

[1162] Example: The app will say, "The candidate who most closely aligns with your views is candidate A of the X Party," and provide a link to detailed manifesto information.

[1163] User Action

[1164] 1. Data provision

[1165] User: Enters information about his / her beliefs and beliefs in a questionnaire format, and also provides his / her daily behavior and search history.

[1166] Example: A user answers a survey about "environmental protection," "economic policy," and "education reform."

[1167] 2. Confirmation of manifesto and candidates

[1168] Users: Review the provided manifesto proposals and provide feedback, then review the proposed candidate information and support or consider it.

[1169] Example: A user evaluates proposed manifestos and, based on that evaluation, decides, "This candidate's policies suit me."

[1170] In this way, the system of the present invention realizes the proposal of manifestos and political candidates based on the user's principles and thoughts through cooperation between the server, terminals, and users.

[1171] The processing flow will be explained below.

[1172] Step 1:

[1173] Entering User Data

[1174] Users: Answer surveys provided by the app or web service and enter information about their beliefs and beliefs. In addition, their website browsing history and search history may be collected to the extent permitted by the user.

[1175] Step 2:

[1176] Data collection

[1177] Device: Survey data entered by users, as well as behavioral and search history, are collected. Search history and page browsing history are collected over a certain period of time.

[1178] Step 3:

[1179] Data transmission

[1180] Device: Collected survey data and behavioral and search history are sent to the server. Data is encrypted for privacy reasons.

[1181] Step 4:

[1182] Data analysis

[1183] Server: Analyzes the received data and identifies the user's beliefs and thinking patterns. Based on the survey responses and browsing and search history, natural language processing technology is used to extract important keywords and topics.

[1184] Step 5:

[1185] Generating a draft manifesto

[1186] Server: Using generative AI based on the analysis results, the server creates a draft manifesto that matches the user's principles and thinking, including specific policy proposals and pledges.

[1187] Step 6:

[1188] Submitting a manifest

[1189] Server: Sends the created manifesto to the user's device, formatting it in a way that is easy to read and relevant to the user's interests.

[1190] Step 7:

[1191] View the draft manifesto

[1192] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[1193] Step 8:

[1194] Collecting feedback

[1195] Terminal: Collects user feedback on the draft manifesto and sends it to the server. Feedback is provided in the form of quantitative evaluations (e.g., evaluation scores) and qualitative comments (e.g., free text).

[1196] Step 9:

[1197] Feedback Analysis

[1198] Server: Analyzes the received user feedback and uses it as data to improve the proposed manifesto and the system's suggestion functions.

[1199] Step 10:

[1200] Collecting candidate manifestos

[1201] Server: Collects published manifestos of multiple political candidates from the Internet, including official candidate websites and election catalogs.

[1202] Step 11:

[1203] Parsing the Manifest

[1204] Server: Analyzes the collected candidate manifestos and stores their main policy content in a database. Using natural language processing technology, categorizes each candidate's policies into categories.

[1205] Step 12:

[1206] Candidate proposals

[1207] Server: Identifies candidates from the database whose policies match the user's principles and thinking tendencies, and selects candidate information appropriate for the user.

[1208] Step 13:

[1209] Submit candidate information

[1210] Server: Sends information about selected candidates to the user's device in a format that is easy for the user to understand.

[1211] Step 14:

[1212] View candidate information

[1213] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[1214] Step 15:

[1215] Feedback and Adjustments

[1216] On the device: Collects the feedback provided by the user and sends it back to the server.

[1217] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[1218] In this way, by closely coordinating each step, a draft manifesto based on the user's principles and thoughts can be generated and appropriate candidates can be proposed.

[1219] Example 1

[1220] 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."

[1221] In modern society, it is difficult to identify appropriate political candidates based on a user's beliefs and beliefs. Conventional methods require users to collect, compare, and consider candidate information themselves, which is labor-intensive. Furthermore, there is a lack of a means to objectively evaluate how well a candidate's policies match the user's beliefs and beliefs. Therefore, there is a need for a method to improve the efficiency and accuracy of selecting appropriate candidates.

[1222] 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.

[1223] In this invention, the server includes means for collecting user behavioral history and search history, means for collecting questionnaire data from users, means for analyzing the collected data and identifying the user's principles and tendencies of thinking, means for creating a draft manifesto using a generative AI model based on the user's principles and tendencies of thinking, means for collecting published manifestos of political candidates that match the user's principles and tendencies of thinking, means for analyzing the collected candidate manifestos and storing them in a database, and means for suggesting candidate information suitable for the user based on the analysis results. This makes it possible to automatically identify appropriate political candidates and efficiently select candidates based on that information, without the user having to collect candidate information themselves.

[1224] "User behavioral history and search history" refers to a record of specific activities and search keywords that a user has performed on the Internet.

[1225] "Survey Data" refers to responses to questions collected directly from users.

[1226] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate new text or data.

[1227] A "draft manifesto" is a detailed proposal for specific policies and measures.

[1228] A "published manifesto" is a policy proposal or plan officially released by a political candidate or party.

[1229] A "database" is an information system for efficiently storing, managing, and retrieving structured data.

[1230] "Candidate Information" means detailed data and profile information about political candidates.

[1231] This invention relates to a system that creates a draft manifesto based on the user's principles and tendencies, and proposes suitable political candidates. This system is implemented by a combination of hardware and software, with a server, terminals, and users working together.

[1232] Server Processing

[1233] 1. Collection of User Data

[1234] Server: The server uses web crawlers and log analysis tools to collect user behavior and search history from the Internet. It also receives information provided by users in the form of questionnaires. The collected data is stored in a database.

[1235] Example: A server monitors a user's browser history and extracts keywords of interest such as "environmental protection" or "economic policy."

[1236] 2. User Data Analysis

[1237] Server: The server uses natural language processing (NLP) models and machine learning algorithms to analyze the collected data, specifically using Python's NLTK library and Sci-Kit Learn to identify the user's beliefs and tendencies.

[1238] Example: The server tokenizes text data including the user's search history and performs sentiment analysis to classify the user's areas of interest.

[1239] 3. Generating a draft manifesto

[1240] Server: The server uses a generative AI model (e.g., OpenAI GPT) to create a manifesto based on the user's beliefs and tendencies. The generated manifesto will be tailored to the user's individual interests and beliefs.

[1241] Example: The prompt "Generate policy proposals based on the user's areas of interest" is input into a generative AI model, which generates a manifesto containing specific policy proposals.

[1242] 4. Collection and analysis of candidate manifestos

[1243] Server: Collects published manifestos from multiple political candidates. The collected manifestos are downloaded using a web scraping tool (e.g., the Scrapy framework) and converted into text data. They are then analyzed using an NLP model, and their policy content is organized and stored in a database.

[1244] Example: A server downloads manifesto PDFs from political candidates' official websites and classifies them into categories such as "environment," "economy," and "education."

[1245] 5. Proposing suitable candidates to users

[1246] Server: Based on the analysis results, it identifies candidates whose policies match the user's principles and thinking tendencies and makes recommendations. Suitable candidate information is extracted through a database query and notified to the user.

[1247] For example, the server selects the most suitable candidate for the user, for example, "proposing candidate A to users who are highly interested in environmental policy."

[1248] Terminal handling

[1249] 1. Data Entry

[1250] Terminal: The user enters survey data and behavioral and search history through the terminal. This data is sent to the server.

[1251] Example: A user answers a "survey on economic policy" on a smartphone app.

[1252] 2. Manifest Display

[1253] Terminal: The terminal displays the proposed manifest sent from the server to the user, and also collects user feedback and sends it back to the server.

[1254] Example: The app displays "policy proposals for environmental protection" or "measures to eliminate economic disparities," and users provide feedback such as "for" or "against."

[1255] 3. Viewing Candidate Information

[1256] Terminal: Display suitable candidate information to the user and provide a link to detailed manifesto information.

[1257] Example: The app might say, "The candidate who most closely aligns with your views is candidate A from the XX party," and provide a link to more information.

[1258] User Action

[1259] 1. Data provision

[1260] User: Users fill out a questionnaire about their beliefs and thoughts, and provide their daily behavior and search history.

[1261] Example: Answer a survey about "environmental protection," "economic policy," and "education reform."

[1262] 2. Confirmation of manifesto and candidates

[1263] Users: Review the proposed manifesto and provide feedback, then review relevant candidate information and consider supporting it.

[1264] Example: A user evaluates a proposed manifesto and decides, "This candidate's policies suit me."

[1265] As described above, the system of the present invention efficiently realizes the proposal of manifestos and political candidates based on the user's principles and thoughts through cooperation between the server, terminals, and users.

[1266] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1267] Step 1: Collect user data

[1268] server:

[1269] Input: User behavioral and search history, survey data.

[1270] How it works: The server uses a web crawler to collect URLs and search keywords frequently visited by users, and also receives response data from survey forms. All of this data is stored in a database.

[1271] Output: User behavior history, search history, and survey data are saved in a database.

[1272] Step 2: Analyze user data

[1273] server:

[1274] Input: Behavioral history, search history, survey data.

[1275] How it works: The server uses natural language processing (NLP) and machine learning algorithms, such as Python's NLTK library and Sci-Kit Learn, to tokenize text data and perform sentiment analysis and interest classification. For example, it scores interest in "environmental protection" or "economic policy" based on search keywords.

[1276] Output: Analysis results showing the user's principles and thinking tendencies.

[1277] Step 3: Generate a draft manifesto

[1278] server:

[1279] Input: Analysis results of user's principles and thinking tendencies.

[1280] Specific operation: The server uses a generative AI model (e.g., OpenAI GPT) to generate a draft manifesto based on the user's analysis results. It inputs the prompt "Please generate policy proposals based on the user's areas of interest" into the AI ​​model and generates a manifesto containing specific policy proposals.

[1281] Output: A proposed manifesto personalized for the user.

[1282] Step 4: Collecting candidate manifestos

[1283] server:

[1284] Input: A political candidate's published manifesto.

[1285] Specific operation: The server uses a web scraping tool (e.g., the Scrapy framework) to download manifesto PDFs from each political candidate's official website or public documents and convert them into text data.

[1286] Output: Candidate manifesto text data is stored in the database.

[1287] Step 5: Parsing the candidate manifestos

[1288] server:

[1289] Input: Candidate manifesto text data.

[1290] How it works: The server uses an NLP model to tokenize the text data and classify each policy into a category, such as "environment," "economy," or "education." The classification results are then stored in a database.

[1291] Output: Candidate policy data organized by category.

[1292] Step 6: Propose candidates

[1293] server:

[1294] Input: Analysis of user principles and thought patterns, candidate policy data.

[1295] Specific operation: The server executes an SQL query and selects the candidate whose policies best match the user's areas of interest. For example, it generates results such as "suggest candidate A to users who are interested in environmental policies."

[1296] Output: Candidate suggestion data for the user.

[1297] Step 7: Displaying data and gathering user feedback

[1298] Device:

[1299] Input: Draft manifesto and candidate proposal data sent from the server.

[1300] What it does: The device displays the draft manifesto and candidate information to the user via an app or web interface, collects user feedback, and sends it to the server.

[1301] Output: User feedback data is sent to the server.

[1302] Through the above processing steps, the optimal manifesto proposal and political candidate proposals are realized for the user.

[1303] (Application example 1)

[1304] 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."

[1305] While existing technologies exist that suggest political candidates based on a user's beliefs and tendencies, they cannot be directly applied to suggesting products and services that take into account a user's purchasing behavior and preferences. As a result, personalized product suggestions cannot be made for users with specific interests and preferences, making it difficult to increase customer satisfaction.

[1306] 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.

[1307] In this invention, the server includes means for collecting user behavior history and search history, means for collecting questionnaire data from users, and means for analyzing the collected data to identify the user's principles and thinking tendencies, thereby enabling personalized suggestions of products or services that match the user's principles and thinking tendencies.

[1308] "User behavior history" refers to the history of operations and pages accessed by a user on the Internet or in an application.

[1309] "Search history" refers to the history of keywords or phrases that a user has entered and searched for on an Internet search engine.

[1310] "Survey data" refers to information about a user's interests, preferences, opinions, etc., provided in the form of a questionnaire.

[1311] "Analysis" refers to using collected data to analyze information and derive results for a specific purpose.

[1312] "Ideas and thinking tendencies" refer to the user's interests and tendencies in thinking regarding specific issues or fields.

[1313] A "manifesto proposal" is a draft policy proposal or action plan generated based on the user's principles and thinking.

[1314] "Goods or services" refers to products offered at a physical store or services provided to customers.

[1315] "Feedback" refers to the evaluation or opinion a user gives of the information or suggestions provided.

[1316] "Suggestion" refers to the act of recommending products or services that are determined to be optimal for the user based on the analysis results.

[1317] This invention is a system that collects and analyzes user behavior history, search history, and questionnaire data, and proposes personalized products and services based on the user's principles and thinking tendencies. This system is mainly composed of three entities: a server, a terminal, and a user.

[1318] Server Processing

[1319] The server has the following roles:

[1320] 1. Data Collection:

[1321] The server collects users' behavioral and search histories, including the web pages they visit, the keywords they search for, the content they view, etc. The server also collects survey data entered by users.

[1322] 2. Data Analysis:

[1323] The collected data is analyzed on the server to identify the user's principles and thought patterns. This process uses data mining and natural language processing techniques, specifically the Python libraries pandas and scikit-learn.

[1324] 3. Leveraging generative AI models:

[1325] Based on the analysis results, a generative AI model is used to suggest suitable products or services to the user. This AI model utilizes an advanced natural language processing model such as GPT-3. The user's preferences and behavioral history are used as input for the prompt sentence.

[1326] Example prompt sentence:

[1327] User preferences: Likes red wine, price range: 2000-5000 yen, prioritizes food pairing

[1328] 4. Proposal generation:

[1329] The generative AI model generates suggestions and sends them to the device via a server, such as product recommendations for physical stores or suggestions for specific services.

[1330] Terminal handling

[1331] A terminal is a device that acts as an interface with a user. Typical terminals are smartphones, smart glasses, or head-mounted displays.

[1332] 1. Data Entry:

[1333] Through the device, users answer questionnaires and their behavioral and search history is collected, and this data is sent to the server in real time.

[1334] 2. View Proposals:

[1335] The suggestions received from the server are displayed to the user. If the user is using smart glasses or a head-mounted display, the suggestions can be displayed visually. For example, when the user stands in front of a particular wine shelf in a store, the display will say, "Here is the wine that is best suited for you."

[1336] 3. Gathering Feedback:

[1337] The device is equipped with a function that allows users to provide feedback on suggested products and services, which is sent to a server for further analysis.

[1338] User Action

[1339] Users are the primary data providers and beneficiaries of the system.

[1340] 1. Data provided by:

[1341] Users provide data through their daily activities and survey responses, which serves as the basis for identifying their beliefs and thought patterns.

[1342] 2. Confirmation of proposal:

[1343] Review the proposed products and services, and in some cases consider purchasing or using them. Also, contribute to improving the proposals through feedback.

[1344] In this way, the system of the present invention allows the server, terminal, and user to work together to propose personalized products and services based on the user's principles and thoughts.

[1345] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1346] Step 1:

[1347] Users use devices (smartphones, smart glasses, head-mounted displays, etc.) to answer a questionnaire. This questionnaire is intended to collect information about the user's preferences and interests. The device then transmits the survey data to a server in real time.

[1348] Input: User-entered survey responses

[1349] Output: Survey data sent to the server

[1350] Step 2:

[1351] The server collects users' behavioral and search histories from the Internet, and also receives survey data sent from their devices. The collected data is stored in a database.

[1352] Input: Behavioral history, search history, survey data

[1353] Output: User data stored in the database

[1354] Step 3:

[1355] The server analyzes the collected data and uses data mining and natural language processing techniques to identify the user's beliefs and tendencies. For example, it uses Python's "pandas" and "scikit-learn" to cleanse and analyze the data. This generates indicators that show the user's interests and preferences.

[1356] Input: User data retrieved from the database

[1357] Output: Indicators showing the user's principles and thinking tendencies

[1358] Step 4:

[1359] The server uses a generative AI model (e.g., GPT-3) to generate a prompt to suggest the best product or service to the user based on the analysis results.The prompt is then input into the AI ​​model to generate the suggestion.

[1360] Input: Indicators showing the user's beliefs and thinking tendencies

[1361] Output: Recommendations for the best product or service for the user

[1362] Step 5:

[1363] The server sends the generated suggestions to the device in real time. The suggestions are displayed to the user. If smart glasses or a head-mounted display are used, the suggestions are displayed visually.

[1364] Input: Proposal

[1365] Output: Proposal displayed on terminal

[1366] Step 6:

[1367] The user can check the proposed products or services through the terminal and enter their evaluation and opinions as feedback, which is also sent to the server in real time.

[1368] Input: User feedback

[1369] Output: Feedback data sent to the server

[1370] Step 7:

[1371] The server analyzes the collected feedback and uses it to improve the content of suggestions and the prompts of the AI ​​model. It continuously analyzes data and generates suggestions that better match the user's principles and thinking patterns.

[1372] Input: Feedback data

[1373] Output: Improved suggestions and prompts

[1374] 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.

[1375] This invention combines an emotion engine with a system that creates a draft manifesto based on the user's principles and tendencies and suggests suitable political candidates, allowing for more precise proposals. This system, which links the server, terminals, and users and includes an emotion engine, provides a draft manifesto and candidate information that also reflects the user's emotional state.

[1376] System configuration

[1377] Server: Collects and analyzes user data, generates manifesto proposals, identifies emotional states, and implements candidate proposals.

[1378] Terminal: Collects data from users, communicates with the server to display draft manifestos and candidate information, and also has an emotion engine.

[1379] Users: Answer surveys, provide behavioral and search history, and provide appropriate feedback.

[1380] Server Processing

[1381] 1. Collection and analysis of user data

[1382] Server: Collects user behavioral and search histories from the Internet, data provided in the form of questionnaires, and emotional data obtained by the emotion engine.

[1383] Example: If a user frequently reads articles about environmental protection and expresses a high interest in "education reform" in a survey, the data can be analyzed and the emotion engine can detect that the user feels strong "concern" about environmental issues.

[1384] 2. Data Analysis

[1385] Server: Analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. Extracts important keywords and topics based on beliefs and thought patterns.

[1386] Example: The server uses natural language processing technology to analyze the user's areas of interest and emotional state, determining things like "concerns about environmental protection" and "enthusiasm for economic policy."

[1387] 3. Generating a draft manifesto

[1388] Server: Based on the analysis results and emotional data, generative AI is used to create a draft manifesto that matches the user's beliefs, thought patterns, and emotional state.

[1389] Example: The server generates "policy proposals that reflect concerns about environmental protection" and "concrete measures to eliminate economic disparities."

[1390] 4. Submitting the manifest

[1391] Server: The created manifesto is sent to the user's device. Based on the emotional data, it is presented in a format that resonates most with the user.

[1392] 5. Display of the Manifesto Draft

[1393] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[1394] Example: An app displays "specific proposals for environmental protection" and "details of economic policies" in an easy-to-read format to users and collects their reactions.

[1395] 6. Gathering Feedback

[1396] Terminal: Collects user feedback on the proposed manifesto and sends it to the server.

[1397] Example: A user provides an evaluation such as "This policy is very good" or "This part needs improvement", which is then sent to the server.

[1398] 7. Feedback Analysis

[1399] Server: Analyzes the received feedback and uses it as data to improve the system's suggestion capabilities.

[1400] 8. Collecting candidate manifestos

[1401] Server: Collects and analyzes published manifestos of multiple political candidates from the Internet.

[1402] Example: A server downloads manifestos from political candidates' official websites and classifies and analyzes them into categories such as "environment," "economy," and "education."

[1403] 9. Candidate Proposals

[1404] Server: Identifies candidates whose policies match the user's principles, thought patterns, and emotional state, and proposes them to the user.

[1405] Example: If a user has a high interest in and strong feelings about environmental policy, the server might suggest, "Candidate A of the XX Party has environmental policies that are closest to your views."

[1406] 10. Submission of Candidate Information

[1407] Server: Sends information about selected candidates to the user's device.

[1408] 11. Display of Candidate Information

[1409] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[1410] Example: The app displays, "The candidate who most closely aligns with your views is candidate A of the XX Party," and provides detailed manifesto information.

[1411] 12. Feedback and Adjustments

[1412] On the device: Collects the feedback provided by the user and sends it back to the server.

[1413] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[1414] In this way, the system of the present invention works by linking the server, terminal, user, and emotion engine to propose manifestos and political candidates based on the user's principles, thoughts, and emotional state.

[1415] The processing flow will be explained below.

[1416] Step 1:

[1417] Entering User Data

[1418] Users: Answer surveys provided by the app or web service and enter information about their beliefs and beliefs. In addition, their website browsing history and search history may be collected to the extent permitted by the user.

[1419] Step 2:

[1420] Identifying emotional states

[1421] Device: When the user answers the questionnaire, the emotion engine uses the camera and microphone to identify the user's emotional state from their facial expressions and voice. The identified emotional state is recorded.

[1422] Example: While a user answers a questionnaire, the camera analyzes their facial expressions to identify emotions such as "happiness," "concern," and "excitement." The microphone also analyzes the tone of their voice to detect "excitement" and "anxiety."

[1423] Step 3:

[1424] Data collection

[1425] Terminal: Collects survey data entered by users, behavioral and search history, and identified emotional state data.

[1426] Step 4:

[1427] Data transmission

[1428] Device: Collected survey data, behavioral and search history, and emotional data are sent to the server. The data is encrypted for privacy reasons.

[1429] Step 5:

[1430] Data analysis

[1431] Server: Analyzes the received data and identifies the user's beliefs, thought patterns, and emotional state. Extracts important keywords and topics based on survey responses, browsing and search history, and emotional data.

[1432] Example: The server uses natural language processing technology to analyze the user's areas of interest and emotional state, determining things like "concerns about environmental protection" and "enthusiasm for economic policy."

[1433] Step 6:

[1434] Generating a draft manifesto

[1435] Server: Based on the analysis results and emotional data, generative AI is used to create a draft manifesto that matches the user's beliefs, thought patterns, and emotional state.

[1436] Example: The server generates "policy proposals that reflect concerns about environmental protection" and "concrete measures to eliminate economic disparities."

[1437] Step 7:

[1438] Submitting a manifest

[1439] Server: The created manifesto is sent to the user's device. Based on the emotional data, it is presented in a format that resonates most with the user.

[1440] Step 8:

[1441] View the draft manifesto

[1442] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[1443] Example: An app displays "specific proposals for environmental protection" and "details of economic policies" in an easy-to-read format to users and collects their reactions.

[1444] Step 9:

[1445] Collecting feedback

[1446] Terminal: Collects user feedback on the proposed manifesto and sends it to the server.

[1447] Example: A user provides an evaluation such as "This policy is very good" or "This part needs improvement", which is then sent to the server.

[1448] Step 10:

[1449] Feedback Analysis

[1450] Server: Analyzes the received user feedback and uses it as data to improve the system's suggestion functions.

[1451] Step 11:

[1452] Collecting candidate manifestos

[1453] Server: Collects and analyzes the published manifestos of multiple political candidates from the Internet, including official candidate websites and election catalogs.

[1454] Example: A server downloads manifestos from political candidates' official websites and classifies and analyzes them into categories such as "environment," "economy," and "education."

[1455] Step 12:

[1456] Candidate proposals

[1457] Server: Identifies candidates whose policies match the user's principles, thought patterns, and emotional state, and proposes them to the user.

[1458] Example: If a user has a high interest in and strong feelings about environmental policy, the server might suggest, "Candidate A of the XX Party has environmental policies that are closest to your views."

[1459] Step 13:

[1460] Submit candidate information

[1461] Server: Sends information about selected candidates to the user's device in a format that is easy for the user to understand.

[1462] Step 14:

[1463] View candidate information

[1464] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[1465] Example: The app displays, "The candidate who most closely aligns with your views is candidate A of the XX Party," and provides detailed manifesto information.

[1466] Step 15:

[1467] Feedback and Adjustments

[1468] On the device: Collects the feedback provided by the user and sends it back to the server.

[1469] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[1470] In this way, the system of the present invention works by linking the server, terminal, user, and emotion engine to propose manifestos and political candidates based on the user's principles, thoughts, and emotional state.

[1471] Example 2

[1472] 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."

[1473] Conventional political information systems collect and analyze users' behavioral history and survey data, and are able to provide manifesto proposals based on the user's principles and tendencies. However, these systems do not take into account the user's emotional state, which means that the proposed content does not fully take into account the user's feelings and emotions. Furthermore, there was an insufficient method for effectively analyzing feedback and reflecting it in future proposals.

[1474] 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.

[1475] In this invention, the server includes means for collecting a user's behavioral history and search history, means for collecting questionnaire data from the user, means for analyzing the collected data to identify the user's principles, thinking tendencies, and emotional state, means for creating a draft manifesto using a generative AI model based on the user's principles, thinking tendencies, and emotional state, means for collecting and analyzing manifestos of political candidates that match the user's principles, thinking tendencies, and emotional state, means for proposing candidate information suitable for the user, means for transmitting and displaying the proposed draft manifesto and candidate information to the user's terminal, means for collecting user feedback and transmitting it back to the server, and means for analyzing the collected feedback and adjusting the generation of subsequent draft manifestos and candidate proposals. This makes it possible to provide more sophisticated draft manifestos and candidate information that reflect the user's emotional state.

[1476] "User behavior history" refers to a record of a user's activities on the Internet, specifically website browsing history and click history.

[1477] "Search history" is a record of search queries and their results made by a user using a search engine.

[1478] "Survey Data" means information provided by a user in the form of a survey, including a user's opinions, interests, and other feedback.

[1479] "Ideas and tendencies" refers to the political and social beliefs and tendencies of thought held by the user.

[1480] "Emotional state" refers to the user's current emotion as analyzed by the emotion engine, including, for example, joy, sadness, worry, anger, etc.

[1481] "Generative AI model" refers to a program that uses artificial intelligence algorithms to generate a draft manifesto or other suggestions that are optimized for a user.

[1482] A "Manifesto" is a document of policy proposals created based on the user's principles, thought patterns, and emotional state.

[1483] "Political candidate" means a candidate running for election to public office.

[1484] A "manifesto" is a document of policies and principles published by a political candidate.

[1485] "Feedback" refers to ratings and opinions provided by users.

[1486] "Terminal" refers to electronic devices used by users, such as computers, smartphones, and tablets.

[1487] A "server" refers to a central computer that processes and stores data for a system to operate.

[1488] This invention is a system that provides individually optimized manifesto proposals and political candidate information based on the user's principles, thought patterns, and emotional state. This system operates in cooperation with a server, terminals, and users.

[1489] Specific server processing and technologies used

[1490] User Data Collection

[1491] The server collects users' behavioral and search histories from the Internet. For this purpose, it uses web analysis tools (e.g., Google Analytics). It also collects data provided by users in the form of questionnaires. In addition, it uses an emotion engine (e.g., Affectiva API) to obtain user emotion data.

[1492] Data analysis

[1493] The server analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. This analysis uses natural language processing techniques (e.g., Python's NLTK library or SpaCy). Techniques such as TF-IDF and Word2Vec are used to extract important keywords and topics.

[1494] Generating a draft manifesto

[1495] Based on the analysis results and emotional data, the server uses a generative AI model (e.g., OpenAI's GPT-3) to create a manifesto proposal tailored to the user's beliefs, thinking tendencies, and emotional state. A prompt sentence is generated and input into the generative AI model to generate a specific manifesto proposal. For example, the following prompt sentence can be used:

[1496] "The user is highly interested in environmental protection and is also enthusiastic about economic policy. Furthermore, the analysis results of the emotion engine indicate that the user feels strong concern about environmental issues. Please generate a draft manifesto for this user."

[1497] Submitting a draft manifesto

[1498] The generated manifest plan is sent from the server to the user's device. This communication is performed using the HTTP protocol, and the data is sent in JSON format.

[1499] Collecting candidate manifestos

[1500] The server collects, categorizes, and analyzes published manifestos from multiple political candidates from the Internet, using web scraping tools (e.g., Beautiful Soup, Scrapy).

[1501] Candidate proposals

[1502] The server recommends the best matched candidates based on the user's beliefs, tendencies, and emotional state, using a recommendation algorithm (e.g., cosine similarity calculation).

[1503] Specific terminal processing and technologies used

[1504] View the draft manifesto

[1505] The terminal displays the proposed manifest received from the server to the user, using front-end technology (e.g., React.js) to display data in an easy-to-read card or list format.

[1506] Collecting feedback

[1507] The terminal provides an input form for collecting feedback from the user and sending it back to the server via the HTTP protocol.

[1508] View candidate information

[1509] The terminal displays candidate information from the server to the user, again using front-end technology, in the form of cards containing candidate photos and detailed policy information.

[1510] Specific user operations

[1511] Data provision

[1512] Users answer questions in a questionnaire format and provide their behavioral history.

[1513] Providing Feedback

[1514] Users can provide their evaluations and opinions on the displayed manifesto proposals and candidate information, and this feedback will be reflected in future proposals.

[1515] In this way, the server, terminal, and user each play their own roles and work together to provide a system that provides more appropriate and precise manifesto proposals and candidate information based on the user's principles, thoughts, and emotional state.

[1516] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1517] Step 1:

[1518] User Data Collection

[1519] Input: User behavior history, search history, survey data, emotional data

[1520] Specific behavior:

[1521] 1. Collection of behavioral history and search history: The server automatically collects users' browsing history and search history from the Internet using web analysis tools (e.g., Google Analytics).

[1522] 2. Survey data collection: The server uses a web form to collect data entered by the user, such as opinions and interests, in the form of a survey.

[1523] 3. Acquiring emotion data: The server uses an emotion engine (e.g., Affectiva API) to detect emotions from the user's video and audio and collects the data.

[1524] Output: A set of collected user data (behavioral history, search history, survey data, emotional data)

[1525] Step 2:

[1526] Data analysis

[1527] Input: Complete set of collected user data

[1528] Specific behavior:

[1529] 1. Application of natural language processing technology: The server analyzes text data using natural language processing technologies such as Python's NLTK library and SpaCy.

[1530] 2. Identifying beliefs and thought patterns: The server uses techniques such as TF-IDF and Word2Vec to identify the user's beliefs and thought patterns, as well as important keywords and topics.

[1531] 3. Emotional state identification: Based on the output of the emotion engine, identify the user's current emotional state (e.g., joy, worry, anger, etc.).

[1532] Output: Analysis results including the user's beliefs, thinking tendencies, and emotional state

[1533] Step 3:

[1534] Generating a draft manifesto

[1535] Input: Analysis results (user's beliefs, thinking patterns, emotional state)

[1536] Specific behavior:

[1537] 1. Prompt generation: Based on the analysis results, the server creates a prompt to be input to the generative AI model.

[1538] Example: "The user is highly interested in environmental protection and is also enthusiastic about economic policy. Furthermore, the sentiment engine analysis indicates that they feel very concerned about environmental issues. Please generate a draft manifesto for this user."

[1539] 2. Applying a generative AI model: The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate a draft manifesto from the prompt.

[1540] 3. Adjusting the wording: Based on the emotion data, the server adjusts the wording of the generated manifesto proposal to make it most relatable to the user.

[1541] Output: The generated draft manifest

[1542] Step 4:

[1543] Submitting a draft manifesto

[1544] Input: Generated manifest proposal

[1545] Specific behavior:

[1546] 1. Data format conversion: The server converts the proposed manifest into JSON format for sending to the user's device.

[1547] 2. Data transmission: The server sends the proposed manifest to the user's device using the HTTP protocol.

[1548] Output: Draft manifest sent to user device

[1549] Step 5:

[1550] View the draft manifesto

[1551] Input: Manifest proposal sent to user device

[1552] Specific behavior:

[1553] 1. Data reception: The terminal receives the proposed manifest sent from the server.

[1554] 2. Displaying data: The device uses front-end technology (e.g., React.js) to display the proposed manifest in an easy-to-read card or list format.

[1555] Output: The proposed manifest displayed

[1556] Step 6:

[1557] Collecting feedback

[1558] Input: User feedback

[1559] Specific behavior:

[1560] 1. Displaying a feedback form: The terminal displays a user input form on the front end, allowing the user to enter feedback.

[1561] 2. Sending feedback: The terminal collects the feedback entered by the user and sends it to the server via the HTTP protocol.

[1562] Output: Feedback sent to the server

[1563] Step 7:

[1564] Feedback Analysis

[1565] Input: Feedback sent to the server

[1566] Specific behavior:

[1567] 1. Feedback collection: The server retrieves the feedback stored in the database.

[1568] 2. Data analysis: The server uses machine learning algorithms and statistical analysis to analyze users' rating trends.

[1569] Output: Feedback analysis results

[1570] Step 8:

[1571] Collecting candidate manifestos

[1572] Input: online political candidate manifestos

[1573] Specific behavior:

[1574] 1. Data extraction: The server extracts manifest data from the candidate's website using a web scraping tool (e.g., Beautiful Soup, Scrapy).

[1575] 2. Data classification and analysis: The server classifies the extracted data into categories such as "environment," "economy," and "education," and analyzes them.

[1576] Output: Categorized and parsed political candidate manifesto data

[1577] Step 9:

[1578] Candidate proposals

[1579] Input: Analysis results (user's ideology, thought patterns, emotional state), categorized and analyzed political candidate manifesto data

[1580] Specific behavior:

[1581] 1. Data Matching: The server matches the user profile data with the candidate manifest data.

[1582] 2. Recommendation: The server uses a recommendation algorithm (e.g., cosine similarity) to identify the best matching candidates.

[1583] Output: Recommended candidate information

[1584] Step 10:

[1585] Submit candidate information

[1586] Input: Recommended candidate information

[1587] Specific behavior:

[1588] 1. Data format conversion: The server converts the candidate information into JSON format for sending to the user's device.

[1589] 2. Data transmission: The server uses the HTTP protocol to transmit the candidate information to the user's terminal.

[1590] Output: Candidate information sent to user terminal

[1591] Step 11:

[1592] View candidate information

[1593] Input: Candidate information sent to the user's terminal

[1594] Specific behavior:

[1595] 1. Receiving data: The terminal receives the candidate information sent from the server.

[1596] 2. Display of data: The terminal uses front-end technology to display the candidate in the form of a card containing their photo and detailed policy information.

[1597] Output: Displayed candidate information

[1598] Step 12:

[1599] Re-feedback and adjustments

[1600] Input: User's re-feedback, previous feedback analysis results

[1601] Specific behavior:

[1602] 1. Feedback collection: The device collects the feedback provided by the user and sends it back to the server.

[1603] 2. Reflecting the feedback: The server will generate the next draft manifesto and adjust the candidate proposals based on the received feedback.

[1604] Output: Updated manifesto generation and candidate proposal algorithms

[1605] (Application example 2)

[1606] 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."

[1607] Conventional systems could identify principles and thought patterns based on a user's behavioral and search history, and then propose manifestos and candidates, but it was difficult to make precise proposals that reflected the user's emotional state.In addition, since security risk predictions and countermeasure proposals were not based on the user's emotions, it was a challenge to make accurate and empathetic security proposals to users.

[1608] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral history and search history, means for collecting questionnaire data from users, means for analyzing the collected data and identifying the user's principles and thought patterns, and means for performing emotion analysis. This enables the creation of a draft manifesto based on the user's principles and thought patterns, as well as the prediction and proposal of security risks that reflect the user's emotional state. The system also includes means for using a generative AI model to generate prompt sentences that are most relatable to the user, enabling more effective feedback collection and system improvement.

[1609] "User behavior history" refers to the history of activities and operations that a user has performed on the Internet.

[1610] "Search history" refers to the history of search queries a user has performed using a search engine.

[1611] "Survey data" refers to response data regarding opinions and thoughts collected from users.

[1612] "Ideas and thinking tendencies" refer to the user's beliefs, values, and interests.

[1613] A "manifesto" is a document of policy proposals created based on the user's principles and thinking.

[1614] A "political candidate" is a person running for public office.

[1615] "Emotion analysis" refers to identifying and analyzing a user's emotional state from input data.

[1616] "Risk prediction" is the estimation of possible future risks based on collected data.

[1617] "Suggestion" means presenting specific actions or options to the user.

[1618] A "generative AI model" is a model that uses artificial intelligence techniques to analyze data and perform a specified task.

[1619] A "prompt" is a question or instruction that is input to a generative AI model.

[1620] System Overview

[1621] This system collects users' behavioral and search histories and analyzes them together with survey data to identify their principles and thought patterns. It then uses a generative AI model to create a draft manifesto and performs emotion analysis to predict risks and make suggestions that reflect the user's emotional state.

[1622] Hardware and Software Configuration

[1623] The system has the following components:

[1624] Server: Collects and analyzes user data, generates a draft manifesto, identifies emotional states, predicts risks, and makes recommendations. The software used includes Python libraries with natural language processing techniques (e.g., transformers).

[1625] Terminal: Collects data from the user, communicates with the server, displays the manifesto and suggestion information, and also has an emotion engine.

[1626] Users: Answer surveys, provide behavioral and search history, and provide appropriate feedback.

[1627] Data collection and analysis

[1628] The server collects the user's behavioral history and search history, as well as data provided in the form of questionnaires. This data is analyzed to identify the user's beliefs, thought patterns, and emotional state.

[1629] Performing sentiment analysis

[1630] The server uses an emotion analysis model to analyze the emotional state of the data obtained from the user. For example, it uses natural language processing technology to identify emotions such as "concern" or "enthusiasm" from the collected text data.

[1631] Generate a draft manifest and risk forecast

[1632] Based on the analysis results and emotional data, the server uses a generative AI model to create a manifesto tailored to the user's beliefs, thought patterns, and emotional state, as well as generate risk predictions and countermeasures.

[1633] Submitting and Viewing Proposals

[1634] The server sends the created manifesto proposal and risk countermeasure proposal to the user's device, which displays these proposals to the user, who can review them and provide their evaluation and opinions for each item.

[1635] Collecting and analyzing feedback

[1636] The device collects user feedback on the suggestions and sends it to the server, which analyzes the feedback and uses it as data to improve the system's suggestion capabilities.

[1637] Examples and prompts

[1638] Specific examples

[1639] Example input: A user frequently searches for information related to "personal information leaks," "two-factor authentication," "security risks," etc.

[1640] Example prompt:

[1641] Recently, users have been concerned about personal information leaks and how to implement two-factor authentication. Sentiment analysis shows that users have strong concerns. What specific security measures would you suggest to alleviate negative sentiment?

[1642] The system's unique feature is that it analyzes user data and makes precise risk predictions and suggestions based on the user's emotional state.

[1643] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1644] Step 1:

[1645] The server collects the user's online behavior and search history. It receives a questionnaire from the user, converts it into a data format, and saves it. The input of this step is the user's behavior and questionnaire responses, and the output is a dataset.

[1646] Specifically, the server logs the user's behavior history in real time and periodically stores the collected data in a database.

[1647] Step 2:

[1648] The server analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. Natural language processing techniques are used to extract emotions from text data. The input is the collected dataset, and the output is a profile of beliefs, thought patterns, and emotional state.

[1649] Specifically, the server uses the Python library transformers to input text data into an emotion classification model and obtain analysis results.

[1650] Step 3:

[1651] Based on the generated profile, the server uses a generative AI model to create a proposed manifesto tailored to the user's beliefs, thought patterns, and emotional state. The input is the profile, and the output is a customized proposed manifesto.

[1652] Specifically, the server inputs the profile into the generative AI model and constructs the generated text as a draft manifesto.

[1653] Step 4:

[1654] The server generates risk predictions and countermeasures. Based on the results of emotion analysis, it creates specific security proposals and countermeasures tailored to the user. The input is emotional state and risk data, and the output is risk predictions and countermeasures.

[1655] Specifically, the server runs a risk assessment model based on the analysis results and generates specific suggested text using a generative AI model.

[1656] Step 5:

[1657] The server sends the created manifest proposal and risk countermeasure proposal to the user's terminal. The input is the generated proposal, and the output is the completion of transmission to the user's terminal.

[1658] Specifically, the server sends data to the user's terminal over the Internet and receives a confirmation response.

[1659] Step 6:

[1660] The terminal displays the received suggestions to the user, who then reviews them and provides ratings and opinions for each item. The input is the suggestions, and the output is user feedback.

[1661] Specifically, the device displays suggestions through an interface and collects user input in real time.

[1662] Step 7:

[1663] The device collects feedback and sends it to the server. The input is the user's evaluation and opinion, and the output is transmission to the server.

[1664] Specifically, the terminal converts the user's input into a data format and calls an API to send it to the server.

[1665] Step 8:

[1666] The server analyzes the received feedback and improves the system's suggestion function. The input is the feedback data, and the output is data for system improvement.

[1667] Specifically, the server stores the feedback in a database and uses an analytical algorithm to extract information to improve the accuracy of the next suggestion.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] [Fourth embodiment]

[1672] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1673] 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.

[1674] 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).

[1675] 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.

[1676] 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.

[1677] 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).

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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."

[1685] The present invention relates to a system that creates a draft manifesto based on the user's principles and tendencies, and proposes suitable political candidates. This system is implemented by the cooperation of a server, terminals, and users.

[1686] System configuration

[1687] Server: Mainly responsible for collecting and analyzing data, generating draft manifestos, and proposing candidates.

[1688] Terminal: Collects data from the user and communicates with the server to display the draft manifesto and candidate information.

[1689] Users: Provide data input and feedback to receive personalized manifesto proposals and candidate information.

[1690] Server Processing

[1691] 1. Collection and analysis of user data

[1692] Server: Collects user behavioral and search history from the Internet. Also collects data provided by users in questionnaires. Analyzes the collected data to identify users' beliefs and thinking patterns.

[1693] Example: The server collects the web pages frequently visited by users and the keywords they search for, and analyzes the level of interest in "environmental protection" and "economic policy." It also extracts opinions on "education reform" from survey responses.

[1694] 2. Generating a draft manifesto

[1695] Server: Based on the analysis results, a generative AI is used to create a draft manifesto. The draft manifesto is designed to fit the individual beliefs and thoughts of the user.

[1696] Example: The server generates a manifesto based on the user's interests, such as "policies focused on environmental protection," "specific measures to eliminate economic disparities," and "new proposals for educational reform."

[1697] 3. Collecting and analyzing candidate manifestos

[1698] Server: Collects and analyzes the published manifestos of multiple political candidates. Stores each candidate's policy content in a database.

[1699] Example: The server downloads manifestos from the official websites of political candidates and classifies and analyzes them into categories such as "environment," "economy," and "education."

[1700] 4. Proposing suitable candidates to users

[1701] Server: Identifies candidates whose policies match the user's principles and beliefs and proposes them to the user.

[1702] Example: If the user is highly interested in environmental policy, the server suggests, "Candidate A of the XX Party has environmental policies that are closest to your ideas."

[1703] Terminal handling

[1704] 1. Data Entry

[1705] Terminal: Collects survey data and behavioral and search history from users and sends them to the server.

[1706] Example: When a user answers a survey about economic policy on an app, the data is sent to a server.

[1707] 2. Manifest Display

[1708] Terminal: Shows the generated manifest draft received from the server to the user, collects feedback and sends it to the server.

[1709] Example: The app displays "policy proposals for environmental protection" and "measures to eliminate economic disparities," and users can rate the policy proposals by saying "I support / disagree."

[1710] 3. Viewing Candidate Information

[1711] Terminal: Displays information on suitable candidates sent from the server to the user.

[1712] Example: The app will say, "The candidate who most closely aligns with your views is candidate A of the X Party," and provide a link to detailed manifesto information.

[1713] User Action

[1714] 1. Data provision

[1715] User: Enters information about his / her beliefs and beliefs in a questionnaire format, and also provides his / her daily behavior and search history.

[1716] Example: A user answers a survey about "environmental protection," "economic policy," and "education reform."

[1717] 2. Confirmation of manifesto and candidates

[1718] Users: Review the provided manifesto proposals and provide feedback, then review the proposed candidate information and support or consider it.

[1719] Example: A user evaluates proposed manifestos and, based on that evaluation, decides, "This candidate's policies suit me."

[1720] In this way, the system of the present invention realizes the proposal of manifestos and political candidates based on the user's principles and thoughts through cooperation between the server, terminals, and users.

[1721] The processing flow will be explained below.

[1722] Step 1:

[1723] Entering User Data

[1724] Users: Answer surveys provided by the app or web service and enter information about their beliefs and beliefs. In addition, their website browsing history and search history may be collected to the extent permitted by the user.

[1725] Step 2:

[1726] Data collection

[1727] Device: Survey data entered by users, as well as behavioral and search history, are collected. Search history and page browsing history are collected over a certain period of time.

[1728] Step 3:

[1729] Data transmission

[1730] Device: Collected survey data and behavioral and search history are sent to the server. Data is encrypted for privacy reasons.

[1731] Step 4:

[1732] Data analysis

[1733] Server: Analyzes the received data and identifies the user's beliefs and thinking patterns. Based on the survey responses and browsing and search history, natural language processing technology is used to extract important keywords and topics.

[1734] Step 5:

[1735] Generating a draft manifesto

[1736] Server: Using generative AI based on the analysis results, the server creates a draft manifesto that matches the user's principles and thinking, including specific policy proposals and pledges.

[1737] Step 6:

[1738] Submitting a manifest

[1739] Server: Sends the created manifesto to the user's device, formatting it in a way that is easy to read and relevant to the user's interests.

[1740] Step 7:

[1741] View the draft manifesto

[1742] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[1743] Step 8:

[1744] Collecting feedback

[1745] Terminal: Collects user feedback on the draft manifesto and sends it to the server. Feedback is provided in the form of quantitative evaluations (e.g., evaluation scores) and qualitative comments (e.g., free text).

[1746] Step 9:

[1747] Feedback Analysis

[1748] Server: Analyzes the received user feedback and uses it as data to improve the proposed manifesto and the system's suggestion functions.

[1749] Step 10:

[1750] Collecting candidate manifestos

[1751] Server: Collects published manifestos of multiple political candidates from the Internet, including official candidate websites and election catalogs.

[1752] Step 11:

[1753] Parsing the Manifest

[1754] Server: Analyzes the collected candidate manifestos and stores their main policy content in a database. Using natural language processing technology, categorizes each candidate's policies into categories.

[1755] Step 12:

[1756] Candidate proposals

[1757] Server: Identifies candidates from the database whose policies match the user's principles and thinking tendencies, and selects candidate information appropriate for the user.

[1758] Step 13:

[1759] Submit candidate information

[1760] Server: Sends information about selected candidates to the user's device in a format that is easy for the user to understand.

[1761] Step 14:

[1762] View candidate information

[1763] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[1764] Step 15:

[1765] Feedback and Adjustments

[1766] On the device: Collects the feedback provided by the user and sends it back to the server.

[1767] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[1768] In this way, by closely coordinating each step, a draft manifesto based on the user's principles and thoughts can be generated and appropriate candidates can be proposed.

[1769] Example 1

[1770] 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."

[1771] In modern society, it is difficult to identify appropriate political candidates based on a user's beliefs and beliefs. Conventional methods require users to collect, compare, and consider candidate information themselves, which is labor-intensive. Furthermore, there is a lack of a means to objectively evaluate how well a candidate's policies match the user's beliefs and beliefs. Therefore, there is a need for a method to improve the efficiency and accuracy of selecting appropriate candidates.

[1772] 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.

[1773] In this invention, the server includes means for collecting user behavioral history and search history, means for collecting questionnaire data from users, means for analyzing the collected data and identifying the user's principles and tendencies of thinking, means for creating a draft manifesto using a generative AI model based on the user's principles and tendencies of thinking, means for collecting published manifestos of political candidates that match the user's principles and tendencies of thinking, means for analyzing the collected candidate manifestos and storing them in a database, and means for suggesting candidate information suitable for the user based on the analysis results. This makes it possible to automatically identify appropriate political candidates and efficiently select candidates based on that information, without the user having to collect candidate information themselves.

[1774] "User behavioral history and search history" refers to a record of specific activities and search keywords that a user has performed on the Internet.

[1775] "Survey Data" refers to responses to questions collected directly from users.

[1776] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to generate new text or data.

[1777] A "draft manifesto" is a detailed proposal for specific policies and measures.

[1778] A "published manifesto" is a policy proposal or plan officially released by a political candidate or party.

[1779] A "database" is an information system for efficiently storing, managing, and retrieving structured data.

[1780] "Candidate Information" means detailed data and profile information about political candidates.

[1781] This invention relates to a system that creates a draft manifesto based on the user's principles and tendencies, and proposes suitable political candidates. This system is implemented by a combination of hardware and software, with a server, terminals, and users working together.

[1782] Server Processing

[1783] 1. Collection of User Data

[1784] Server: The server uses web crawlers and log analysis tools to collect user behavior and search history from the Internet. It also receives information provided by users in the form of questionnaires. The collected data is stored in a database.

[1785] Example: A server monitors a user's browser history and extracts keywords of interest such as "environmental protection" or "economic policy."

[1786] 2. User Data Analysis

[1787] Server: The server uses natural language processing (NLP) models and machine learning algorithms to analyze the collected data, specifically using Python's NLTK library and Sci-Kit Learn to identify the user's beliefs and tendencies.

[1788] Example: The server tokenizes text data including the user's search history and performs sentiment analysis to classify the user's areas of interest.

[1789] 3. Generating a draft manifesto

[1790] Server: The server uses a generative AI model (e.g., OpenAI GPT) to create a manifesto based on the user's beliefs and tendencies. The generated manifesto will be tailored to the user's individual interests and beliefs.

[1791] Example: The prompt "Generate policy proposals based on the user's areas of interest" is input into a generative AI model, which generates a manifesto containing specific policy proposals.

[1792] 4. Collection and analysis of candidate manifestos

[1793] Server: Collects published manifestos from multiple political candidates. The collected manifestos are downloaded using a web scraping tool (e.g., the Scrapy framework) and converted into text data. They are then analyzed using an NLP model, and their policy content is organized and stored in a database.

[1794] Example: A server downloads manifesto PDFs from political candidates' official websites and classifies them into categories such as "environment," "economy," and "education."

[1795] 5. Proposing suitable candidates to users

[1796] Server: Based on the analysis results, it identifies candidates whose policies match the user's principles and thinking tendencies and makes recommendations. Suitable candidate information is extracted through a database query and notified to the user.

[1797] For example, the server selects the most suitable candidate for the user, for example, "proposing candidate A to users who are highly interested in environmental policy."

[1798] Terminal handling

[1799] 1. Data Entry

[1800] Terminal: The user enters survey data and behavioral and search history through the terminal. This data is sent to the server.

[1801] Example: A user answers a "survey on economic policy" on a smartphone app.

[1802] 2. Manifest Display

[1803] Terminal: The terminal displays the proposed manifest sent from the server to the user, and also collects user feedback and sends it back to the server.

[1804] Example: The app displays "policy proposals for environmental protection" or "measures to eliminate economic disparities," and users provide feedback such as "for" or "against."

[1805] 3. Viewing Candidate Information

[1806] Terminal: Display suitable candidate information to the user and provide a link to detailed manifesto information.

[1807] Example: The app might say, "The candidate who most closely aligns with your views is candidate A from the XX party," and provide a link to more information.

[1808] User Action

[1809] 1. Data provision

[1810] User: Users fill out a questionnaire about their beliefs and thoughts, and provide their daily behavior and search history.

[1811] Example: Answer a survey about "environmental protection," "economic policy," and "education reform."

[1812] 2. Confirmation of manifesto and candidates

[1813] Users: Review the proposed manifesto and provide feedback, then review relevant candidate information and consider supporting it.

[1814] Example: A user evaluates a proposed manifesto and decides, "This candidate's policies suit me."

[1815] As described above, the system of the present invention efficiently realizes the proposal of manifestos and political candidates based on the user's principles and thoughts through cooperation between the server, terminals, and users.

[1816] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1817] Step 1: Collect user data

[1818] server:

[1819] Input: User behavioral and search history, survey data.

[1820] How it works: The server uses a web crawler to collect URLs and search keywords frequently visited by users, and also receives response data from survey forms. All of this data is stored in a database.

[1821] Output: User behavior history, search history, and survey data are saved in a database.

[1822] Step 2: Analyze user data

[1823] server:

[1824] Input: Behavioral history, search history, survey data.

[1825] How it works: The server uses natural language processing (NLP) and machine learning algorithms, such as Python's NLTK library and Sci-Kit Learn, to tokenize text data and perform sentiment analysis and interest classification. For example, it scores interest in "environmental protection" or "economic policy" based on search keywords.

[1826] Output: Analysis results showing the user's principles and thinking tendencies.

[1827] Step 3: Generate a draft manifesto

[1828] server:

[1829] Input: Analysis results of user's principles and thinking tendencies.

[1830] Specific operation: The server uses a generative AI model (e.g., OpenAI GPT) to generate a draft manifesto based on the user's analysis results. It inputs the prompt "Please generate policy proposals based on the user's areas of interest" into the AI ​​model and generates a manifesto containing specific policy proposals.

[1831] Output: A proposed manifesto personalized for the user.

[1832] Step 4: Collecting candidate manifestos

[1833] server:

[1834] Input: A political candidate's published manifesto.

[1835] Specific operation: The server uses a web scraping tool (e.g., the Scrapy framework) to download manifesto PDFs from each political candidate's official website or public documents and convert them into text data.

[1836] Output: Candidate manifesto text data is stored in the database.

[1837] Step 5: Parsing the candidate manifestos

[1838] server:

[1839] Input: Candidate manifesto text data.

[1840] How it works: The server uses an NLP model to tokenize the text data and classify each policy into a category, such as "environment," "economy," or "education." The classification results are then stored in a database.

[1841] Output: Candidate policy data organized by category.

[1842] Step 6: Propose candidates

[1843] server:

[1844] Input: Analysis of user principles and thought patterns, candidate policy data.

[1845] Specific operation: The server executes an SQL query and selects the candidate whose policies best match the user's areas of interest. For example, it generates results such as "suggest candidate A to users who are interested in environmental policies."

[1846] Output: Candidate suggestion data for the user.

[1847] Step 7: Displaying data and gathering user feedback

[1848] Device:

[1849] Input: Draft manifesto and candidate proposal data sent from the server.

[1850] What it does: The device displays the draft manifesto and candidate information to the user via an app or web interface, collects user feedback, and sends it to the server.

[1851] Output: User feedback data is sent to the server.

[1852] Through the above processing steps, the optimal manifesto proposal and political candidate proposals are realized for the user.

[1853] (Application example 1)

[1854] 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."

[1855] While existing technologies exist that suggest political candidates based on a user's beliefs and tendencies, they cannot be directly applied to suggesting products and services that take into account a user's purchasing behavior and preferences. As a result, personalized product suggestions cannot be made for users with specific interests and preferences, making it difficult to increase customer satisfaction.

[1856] 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.

[1857] In this invention, the server includes means for collecting user behavior history and search history, means for collecting questionnaire data from users, and means for analyzing the collected data to identify the user's principles and thinking tendencies, thereby enabling personalized suggestions of products or services that match the user's principles and thinking tendencies.

[1858] "User behavior history" refers to the history of operations and pages accessed by a user on the Internet or in an application.

[1859] "Search history" refers to the history of keywords or phrases that a user has entered and searched for on an Internet search engine.

[1860] "Survey data" refers to information about a user's interests, preferences, opinions, etc., provided in the form of a questionnaire.

[1861] "Analysis" refers to using collected data to analyze information and derive results for a specific purpose.

[1862] "Ideas and thinking tendencies" refer to the user's interests and tendencies in thinking regarding specific issues or fields.

[1863] A "manifesto proposal" is a draft policy proposal or action plan generated based on the user's principles and thinking.

[1864] "Goods or services" refers to products offered at a physical store or services provided to customers.

[1865] "Feedback" refers to the evaluation or opinion a user gives of the information or suggestions provided.

[1866] "Suggestion" refers to the act of recommending products or services that are determined to be optimal for the user based on the analysis results.

[1867] This invention is a system that collects and analyzes user behavior history, search history, and questionnaire data, and proposes personalized products and services based on the user's principles and thinking tendencies. This system is mainly composed of three entities: a server, a terminal, and a user.

[1868] Server Processing

[1869] The server has the following roles:

[1870] 1. Data Collection:

[1871] The server collects users' behavioral and search histories, including the web pages they visit, the keywords they search for, the content they view, etc. The server also collects survey data entered by users.

[1872] 2. Data Analysis:

[1873] The collected data is analyzed on the server to identify the user's principles and thought patterns. This process uses data mining and natural language processing techniques, specifically the Python libraries pandas and scikit-learn.

[1874] 3. Leveraging generative AI models:

[1875] Based on the analysis results, a generative AI model is used to suggest suitable products or services to the user. This AI model utilizes an advanced natural language processing model such as GPT-3. The user's preferences and behavioral history are used as input for the prompt sentence.

[1876] Example prompt sentence:

[1877] User preferences: Likes red wine, price range: 2000-5000 yen, prioritizes food pairing

[1878] 4. Proposal generation:

[1879] The generative AI model generates suggestions and sends them to the device via a server, such as product recommendations for physical stores or suggestions for specific services.

[1880] Terminal handling

[1881] A terminal is a device that acts as an interface with a user. Typical terminals are smartphones, smart glasses, or head-mounted displays.

[1882] 1. Data Entry:

[1883] Through the device, users answer questionnaires and their behavioral and search history is collected, and this data is sent to the server in real time.

[1884] 2. View Proposals:

[1885] The suggestions received from the server are displayed to the user. If the user is using smart glasses or a head-mounted display, the suggestions can be displayed visually. For example, when the user stands in front of a particular wine shelf in a store, the display will say, "Here is the wine that is best suited for you."

[1886] 3. Gathering Feedback:

[1887] The device is equipped with a function that allows users to provide feedback on suggested products and services, which is sent to a server for further analysis.

[1888] User Action

[1889] Users are the primary data providers and beneficiaries of the system.

[1890] 1. Data provided by:

[1891] Users provide data through their daily activities and survey responses, which serves as the basis for identifying their beliefs and thought patterns.

[1892] 2. Confirmation of proposal:

[1893] Review the proposed products and services, and in some cases consider purchasing or using them. Also, contribute to improving the proposals through feedback.

[1894] In this way, the system of the present invention allows the server, terminal, and user to work together to propose personalized products and services based on the user's principles and thoughts.

[1895] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1896] Step 1:

[1897] Users use devices (smartphones, smart glasses, head-mounted displays, etc.) to answer a questionnaire. This questionnaire is intended to collect information about the user's preferences and interests. The device then transmits the survey data to a server in real time.

[1898] Input: User-entered survey responses

[1899] Output: Survey data sent to the server

[1900] Step 2:

[1901] The server collects users' behavioral and search histories from the Internet, and also receives survey data sent from their devices. The collected data is stored in a database.

[1902] Input: Behavioral history, search history, survey data

[1903] Output: User data stored in the database

[1904] Step 3:

[1905] The server analyzes the collected data and uses data mining and natural language processing techniques to identify the user's beliefs and tendencies. For example, it uses Python's "pandas" and "scikit-learn" to cleanse and analyze the data. This generates indicators that show the user's interests and preferences.

[1906] Input: User data retrieved from the database

[1907] Output: Indicators showing the user's principles and thinking tendencies

[1908] Step 4:

[1909] The server uses a generative AI model (e.g., GPT-3) to generate a prompt to suggest the best product or service to the user based on the analysis results.The prompt is then input into the AI ​​model to generate the suggestion.

[1910] Input: Indicators showing the user's beliefs and thinking tendencies

[1911] Output: Recommendations for the best product or service for the user

[1912] Step 5:

[1913] The server sends the generated suggestions to the device in real time. The suggestions are displayed to the user. If smart glasses or a head-mounted display are used, the suggestions are displayed visually.

[1914] Input: Proposal

[1915] Output: Proposal displayed on terminal

[1916] Step 6:

[1917] The user can check the proposed products or services through the terminal and enter their evaluation and opinions as feedback, which is also sent to the server in real time.

[1918] Input: User feedback

[1919] Output: Feedback data sent to the server

[1920] Step 7:

[1921] The server analyzes the collected feedback and uses it to improve the content of suggestions and the prompts of the AI ​​model. It continuously analyzes data and generates suggestions that better match the user's principles and thinking patterns.

[1922] Input: Feedback data

[1923] Output: Improved suggestions and prompts

[1924] 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.

[1925] This invention combines an emotion engine with a system that creates a draft manifesto based on the user's principles and tendencies and suggests suitable political candidates, allowing for more precise proposals. This system, which links the server, terminals, and users and includes an emotion engine, provides a draft manifesto and candidate information that also reflects the user's emotional state.

[1926] System configuration

[1927] Server: Collects and analyzes user data, generates manifesto proposals, identifies emotional states, and implements candidate proposals.

[1928] Terminal: Collects data from users, communicates with the server to display draft manifestos and candidate information, and also has an emotion engine.

[1929] Users: Answer surveys, provide behavioral and search history, and provide appropriate feedback.

[1930] Server Processing

[1931] 1. Collection and analysis of user data

[1932] Server: Collects user behavioral and search histories from the Internet, data provided in the form of questionnaires, and emotional data obtained by the emotion engine.

[1933] Example: If a user frequently reads articles about environmental protection and expresses a high interest in "education reform" in a survey, the data can be analyzed and the emotion engine can detect that the user feels strong "concern" about environmental issues.

[1934] 2. Data Analysis

[1935] Server: Analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. Extracts important keywords and topics based on beliefs and thought patterns.

[1936] Example: The server uses natural language processing technology to analyze the user's areas of interest and emotional state, determining things like "concerns about environmental protection" and "enthusiasm for economic policy."

[1937] 3. Generating a draft manifesto

[1938] Server: Based on the analysis results and emotional data, generative AI is used to create a draft manifesto that matches the user's beliefs, thought patterns, and emotional state.

[1939] Example: The server generates "policy proposals that reflect concerns about environmental protection" and "concrete measures to eliminate economic disparities."

[1940] 4. Submitting the manifest

[1941] Server: The created manifesto is sent to the user's device. Based on the emotional data, it is presented in a format that resonates most with the user.

[1942] 5. Display of the Manifesto Draft

[1943] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[1944] Example: An app displays "specific proposals for environmental protection" and "details of economic policies" in an easy-to-read format to users and collects their reactions.

[1945] 6. Gathering Feedback

[1946] Terminal: Collects user feedback on the proposed manifesto and sends it to the server.

[1947] Example: A user provides an evaluation such as "This policy is very good" or "This part needs improvement", which is then sent to the server.

[1948] 7. Feedback Analysis

[1949] Server: Analyzes the received feedback and uses it as data to improve the system's suggestion capabilities.

[1950] 8. Collecting candidate manifestos

[1951] Server: Collects and analyzes published manifestos of multiple political candidates from the Internet.

[1952] Example: A server downloads manifestos from political candidates' official websites and classifies and analyzes them into categories such as "environment," "economy," and "education."

[1953] 9. Candidate Proposals

[1954] Server: Identifies candidates whose policies match the user's principles, thought patterns, and emotional state, and proposes them to the user.

[1955] Example: If a user has a high interest in and strong feelings about environmental policy, the server might suggest, "Candidate A of the XX Party has environmental policies that are closest to your views."

[1956] 10. Submission of Candidate Information

[1957] Server: Sends information about selected candidates to the user's device.

[1958] 11. Display of Candidate Information

[1959] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[1960] Example: The app displays, "The candidate who most closely aligns with your views is candidate A of the XX Party," and provides detailed manifesto information.

[1961] 12. Feedback and Adjustments

[1962] On the device: Collects the feedback provided by the user and sends it back to the server.

[1963] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[1964] In this way, the system of the present invention works by linking the server, terminal, user, and emotion engine to propose manifestos and political candidates based on the user's principles, thoughts, and emotional state.

[1965] The processing flow will be explained below.

[1966] Step 1:

[1967] Entering User Data

[1968] Users: Answer surveys provided by the app or web service and enter information about their beliefs and beliefs. In addition, their website browsing history and search history may be collected to the extent permitted by the user.

[1969] Step 2:

[1970] Identifying emotional states

[1971] Device: When the user answers the questionnaire, the emotion engine uses the camera and microphone to identify the user's emotional state from their facial expressions and voice. The identified emotional state is recorded.

[1972] Example: While a user answers a questionnaire, the camera analyzes their facial expressions to identify emotions such as "happiness," "concern," and "excitement." The microphone also analyzes the tone of their voice to detect "excitement" and "anxiety."

[1973] Step 3:

[1974] Data collection

[1975] Terminal: Collects survey data entered by users, behavioral and search history, and identified emotional state data.

[1976] Step 4:

[1977] Data transmission

[1978] Device: Collected survey data, behavioral and search history, and emotional data are sent to the server. The data is encrypted for privacy reasons.

[1979] Step 5:

[1980] Data analysis

[1981] Server: Analyzes the received data and identifies the user's beliefs, thought patterns, and emotional state. Extracts important keywords and topics based on survey responses, browsing and search history, and emotional data.

[1982] Example: The server uses natural language processing technology to analyze the user's areas of interest and emotional state, determining things like "concerns about environmental protection" and "enthusiasm for economic policy."

[1983] Step 6:

[1984] Generating a draft manifesto

[1985] Server: Based on the analysis results and emotional data, generative AI is used to create a draft manifesto that matches the user's beliefs, thought patterns, and emotional state.

[1986] Example: The server generates "policy proposals that reflect concerns about environmental protection" and "concrete measures to eliminate economic disparities."

[1987] Step 7:

[1988] Submitting a manifest

[1989] Server: The created manifesto is sent to the user's device. Based on the emotional data, it is presented in a format that resonates most with the user.

[1990] Step 8:

[1991] View the draft manifesto

[1992] Terminal: The terminal displays the draft manifest received from the server to the user, who can review it and provide ratings and opinions for each item.

[1993] Example: An app displays "specific proposals for environmental protection" and "details of economic policies" in an easy-to-read format to users and collects their reactions.

[1994] Step 9:

[1995] Collecting feedback

[1996] Terminal: Collects user feedback on the proposed manifesto and sends it to the server.

[1997] Example: A user provides an evaluation such as "This policy is very good" or "This part needs improvement", which is then sent to the server.

[1998] Step 10:

[1999] Feedback Analysis

[2000] Server: Analyzes the received user feedback and uses it as data to improve the system's suggestion functions.

[2001] Step 11:

[2002] Collecting candidate manifestos

[2003] Server: Collects and analyzes the published manifestos of multiple political candidates from the Internet, including official candidate websites and election catalogs.

[2004] Example: A server downloads manifestos from political candidates' official websites and classifies and analyzes them into categories such as "environment," "economy," and "education."

[2005] Step 12:

[2006] Candidate proposals

[2007] Server: Identifies candidates whose policies match the user's principles, thought patterns, and emotional state, and proposes them to the user.

[2008] Example: If a user has a high interest in and strong feelings about environmental policy, the server might suggest, "Candidate A of the XX Party has environmental policies that are closest to your views."

[2009] Step 13:

[2010] Submit candidate information

[2011] Server: Sends information about selected candidates to the user's device in a format that is easy for the user to understand.

[2012] Step 14:

[2013] View candidate information

[2014] Terminal: The terminal displays the candidate information sent from the server to the user, allowing the user to review and evaluate the policies of the proposed candidates.

[2015] Example: The app displays, "The candidate who most closely aligns with your views is candidate A of the XX Party," and provides detailed manifesto information.

[2016] Step 15:

[2017] Feedback and Adjustments

[2018] On the device: Collects the feedback provided by the user and sends it back to the server.

[2019] Server: Based on the feedback received, the server will generate future manifesto proposals and adjust candidate proposals.

[2020] In this way, the system of the present invention works by linking the server, terminal, user, and emotion engine to propose manifestos and political candidates based on the user's principles, thoughts, and emotional state.

[2021] Example 2

[2022] 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."

[2023] Conventional political information systems collect and analyze users' behavioral history and survey data, and are able to provide manifesto proposals based on the user's principles and tendencies. However, these systems do not take into account the user's emotional state, which means that the proposed content does not fully take into account the user's feelings and emotions. Furthermore, there was an insufficient method for effectively analyzing feedback and reflecting it in future proposals.

[2024] 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.

[2025] In this invention, the server includes means for collecting a user's behavioral history and search history, means for collecting questionnaire data from the user, means for analyzing the collected data to identify the user's principles, thinking tendencies, and emotional state, means for creating a draft manifesto using a generative AI model based on the user's principles, thinking tendencies, and emotional state, means for collecting and analyzing manifestos of political candidates that match the user's principles, thinking tendencies, and emotional state, means for proposing candidate information suitable for the user, means for transmitting and displaying the proposed draft manifesto and candidate information to the user's terminal, means for collecting user feedback and transmitting it back to the server, and means for analyzing the collected feedback and adjusting the generation of subsequent draft manifestos and candidate proposals. This makes it possible to provide more sophisticated draft manifestos and candidate information that reflect the user's emotional state.

[2026] "User behavior history" refers to a record of a user's activities on the Internet, specifically website browsing history and click history.

[2027] "Search history" is a record of search queries and their results made by a user using a search engine.

[2028] "Survey Data" means information provided by a user in the form of a survey, including a user's opinions, interests, and other feedback.

[2029] "Ideas and tendencies" refers to the political and social beliefs and tendencies of thought held by the user.

[2030] "Emotional state" refers to the user's current emotion as analyzed by the emotion engine, including, for example, joy, sadness, worry, anger, etc.

[2031] "Generative AI model" refers to a program that uses artificial intelligence algorithms to generate a draft manifesto or other suggestions that are optimized for a user.

[2032] A "Manifesto" is a document of policy proposals created based on the user's principles, thought patterns, and emotional state.

[2033] "Political candidate" means a candidate running for election to public office.

[2034] A "manifesto" is a document of policies and principles published by a political candidate.

[2035] "Feedback" refers to ratings and opinions provided by users.

[2036] "Terminal" refers to electronic devices used by users, such as computers, smartphones, and tablets.

[2037] A "server" refers to a central computer that processes and stores data for a system to operate.

[2038] This invention is a system that provides individually optimized manifesto proposals and political candidate information based on the user's principles, thought patterns, and emotional state. This system operates in cooperation with a server, terminals, and users.

[2039] Specific server processing and technologies used

[2040] User Data Collection

[2041] The server collects users' behavioral and search histories from the Internet. For this purpose, it uses web analysis tools (e.g., Google Analytics). It also collects data provided by users in the form of questionnaires. In addition, it uses an emotion engine (e.g., Affectiva API) to obtain user emotion data.

[2042] Data analysis

[2043] The server analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. This analysis uses natural language processing techniques (e.g., Python's NLTK library or SpaCy). Techniques such as TF-IDF and Word2Vec are used to extract important keywords and topics.

[2044] Generating a draft manifesto

[2045] Based on the analysis results and emotional data, the server uses a generative AI model (e.g., OpenAI's GPT-3) to create a manifesto proposal tailored to the user's beliefs, thinking tendencies, and emotional state. A prompt sentence is generated and input into the generative AI model to generate a specific manifesto proposal. For example, the following prompt sentence can be used:

[2046] "The user is highly interested in environmental protection and is also enthusiastic about economic policy. Furthermore, the analysis results of the emotion engine indicate that the user feels strong concern about environmental issues. Please generate a draft manifesto for this user."

[2047] Submitting a draft manifesto

[2048] The generated manifest plan is sent from the server to the user's device. This communication is performed using the HTTP protocol, and the data is sent in JSON format.

[2049] Collecting candidate manifestos

[2050] The server collects, categorizes, and analyzes published manifestos from multiple political candidates from the Internet, using web scraping tools (e.g., Beautiful Soup, Scrapy).

[2051] Candidate proposals

[2052] The server recommends the best matched candidates based on the user's beliefs, tendencies, and emotional state, using a recommendation algorithm (e.g., cosine similarity calculation).

[2053] Specific terminal processing and technologies used

[2054] View the draft manifesto

[2055] The terminal displays the proposed manifest received from the server to the user, using front-end technology (e.g., React.js) to display data in an easy-to-read card or list format.

[2056] Collecting feedback

[2057] The terminal provides an input form for collecting feedback from the user and sending it back to the server via the HTTP protocol.

[2058] View candidate information

[2059] The terminal displays candidate information from the server to the user, again using front-end technology, in the form of cards containing candidate photos and detailed policy information.

[2060] Specific user operations

[2061] Data provision

[2062] Users answer questions in a questionnaire format and provide their behavioral history.

[2063] Providing Feedback

[2064] Users can provide their evaluations and opinions on the displayed manifesto proposals and candidate information, and this feedback will be reflected in future proposals.

[2065] In this way, the server, terminal, and user each play their own roles and work together to provide a system that provides more appropriate and precise manifesto proposals and candidate information based on the user's principles, thoughts, and emotional state.

[2066] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2067] Step 1:

[2068] User Data Collection

[2069] Input: User behavior history, search history, survey data, emotional data

[2070] Specific behavior:

[2071] 1. Collection of behavioral history and search history: The server automatically collects users' browsing history and search history from the Internet using web analysis tools (e.g., Google Analytics).

[2072] 2. Survey data collection: The server uses a web form to collect data entered by the user, such as opinions and interests, in the form of a survey.

[2073] 3. Acquiring emotion data: The server uses an emotion engine (e.g., Affectiva API) to detect emotions from the user's video and audio and collects the data.

[2074] Output: A set of collected user data (behavioral history, search history, survey data, emotional data)

[2075] Step 2:

[2076] Data analysis

[2077] Input: Complete set of collected user data

[2078] Specific behavior:

[2079] 1. Application of natural language processing technology: The server analyzes text data using natural language processing technologies such as Python's NLTK library and SpaCy.

[2080] 2. Identifying beliefs and thought patterns: The server uses techniques such as TF-IDF and Word2Vec to identify the user's beliefs and thought patterns, as well as important keywords and topics.

[2081] 3. Emotional state identification: Based on the output of the emotion engine, identify the user's current emotional state (e.g., joy, worry, anger, etc.).

[2082] Output: Analysis results including the user's beliefs, thinking tendencies, and emotional state

[2083] Step 3:

[2084] Generating a draft manifesto

[2085] Input: Analysis results (user's beliefs, thinking patterns, emotional state)

[2086] Specific behavior:

[2087] 1. Prompt generation: Based on the analysis results, the server creates a prompt to be input to the generative AI model.

[2088] Example: "The user is highly interested in environmental protection and is also enthusiastic about economic policy. Furthermore, the sentiment engine analysis indicates that they feel very concerned about environmental issues. Please generate a draft manifesto for this user."

[2089] 2. Applying a generative AI model: The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate a draft manifesto from the prompt.

[2090] 3. Adjusting the wording: Based on the emotion data, the server adjusts the wording of the generated manifesto proposal to make it most relatable to the user.

[2091] Output: The generated draft manifest

[2092] Step 4:

[2093] Submitting a draft manifesto

[2094] Input: Generated manifest proposal

[2095] Specific behavior:

[2096] 1. Data format conversion: The server converts the proposed manifest into JSON format for sending to the user's device.

[2097] 2. Data transmission: The server sends the proposed manifest to the user's device using the HTTP protocol.

[2098] Output: Draft manifest sent to user device

[2099] Step 5:

[2100] View the draft manifesto

[2101] Input: Manifest proposal sent to user device

[2102] Specific behavior:

[2103] 1. Data reception: The terminal receives the proposed manifest sent from the server.

[2104] 2. Displaying data: The device uses front-end technology (e.g., React.js) to display the proposed manifest in an easy-to-read card or list format.

[2105] Output: The proposed manifest displayed

[2106] Step 6:

[2107] Collecting feedback

[2108] Input: User feedback

[2109] Specific behavior:

[2110] 1. Displaying a feedback form: The terminal displays a user input form on the front end, allowing the user to enter feedback.

[2111] 2. Sending feedback: The terminal collects the feedback entered by the user and sends it to the server via the HTTP protocol.

[2112] Output: Feedback sent to the server

[2113] Step 7:

[2114] Feedback Analysis

[2115] Input: Feedback sent to the server

[2116] Specific behavior:

[2117] 1. Feedback collection: The server retrieves the feedback stored in the database.

[2118] 2. Data analysis: The server uses machine learning algorithms and statistical analysis to analyze users' rating trends.

[2119] Output: Feedback analysis results

[2120] Step 8:

[2121] Collecting candidate manifestos

[2122] Input: online political candidate manifestos

[2123] Specific behavior:

[2124] 1. Data extraction: The server extracts manifest data from the candidate's website using a web scraping tool (e.g., Beautiful Soup, Scrapy).

[2125] 2. Data classification and analysis: The server classifies the extracted data into categories such as "environment," "economy," and "education," and analyzes them.

[2126] Output: Categorized and parsed political candidate manifesto data

[2127] Step 9:

[2128] Candidate proposals

[2129] Input: Analysis results (user's ideology, thought patterns, emotional state), categorized and analyzed political candidate manifesto data

[2130] Specific behavior:

[2131] 1. Data Matching: The server matches the user profile data with the candidate manifest data.

[2132] 2. Recommendation: The server uses a recommendation algorithm (e.g., cosine similarity) to identify the best matching candidates.

[2133] Output: Recommended candidate information

[2134] Step 10:

[2135] Submit candidate information

[2136] Input: Recommended candidate information

[2137] Specific behavior:

[2138] 1. Data format conversion: The server converts the candidate information into JSON format for sending to the user's device.

[2139] 2. Data transmission: The server uses the HTTP protocol to transmit the candidate information to the user's terminal.

[2140] Output: Candidate information sent to user terminal

[2141] Step 11:

[2142] View candidate information

[2143] Input: Candidate information sent to the user's terminal

[2144] Specific behavior:

[2145] 1. Receiving data: The terminal receives the candidate information sent from the server.

[2146] 2. Display of data: The terminal uses front-end technology to display the candidate in the form of a card containing their photo and detailed policy information.

[2147] Output: Displayed candidate information

[2148] Step 12:

[2149] Re-feedback and adjustments

[2150] Input: User's re-feedback, previous feedback analysis results

[2151] Specific behavior:

[2152] 1. Feedback collection: The device collects the feedback provided by the user and sends it back to the server.

[2153] 2. Reflecting the feedback: The server will generate the next draft manifesto and adjust the candidate proposals based on the received feedback.

[2154] Output: Updated manifesto generation and candidate proposal algorithms

[2155] (Application example 2)

[2156] 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."

[2157] Conventional systems could identify principles and thought patterns based on a user's behavioral and search history, and then propose manifestos and candidates, but it was difficult to make precise proposals that reflected the user's emotional state.In addition, since security risk predictions and countermeasure proposals were not based on the user's emotions, it was a challenge to make accurate and empathetic security proposals to users.

[2158] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral history and search history, means for collecting questionnaire data from users, means for analyzing the collected data and identifying the user's principles and thought patterns, and means for performing emotion analysis. This enables the creation of a draft manifesto based on the user's principles and thought patterns, as well as the prediction and proposal of security risks that reflect the user's emotional state. The system also includes means for using a generative AI model to generate prompt sentences that are most relatable to the user, enabling more effective feedback collection and system improvement.

[2159] "User behavior history" refers to the history of activities and operations that a user has performed on the Internet.

[2160] "Search history" refers to the history of search queries a user has performed using a search engine.

[2161] "Survey data" refers to response data regarding opinions and thoughts collected from users.

[2162] "Ideas and thinking tendencies" refer to the user's beliefs, values, and interests.

[2163] A "manifesto" is a document of policy proposals created based on the user's principles and thinking.

[2164] A "political candidate" is a person running for public office.

[2165] "Emotion analysis" refers to identifying and analyzing a user's emotional state from input data.

[2166] "Risk prediction" is the estimation of possible future risks based on collected data.

[2167] "Suggestion" means presenting specific actions or options to the user.

[2168] A "generative AI model" is a model that uses artificial intelligence techniques to analyze data and perform a specified task.

[2169] A "prompt" is a question or instruction that is input to a generative AI model.

[2170] System Overview

[2171] This system collects users' behavioral and search histories and analyzes them together with survey data to identify their principles and thought patterns. It then uses a generative AI model to create a draft manifesto and performs emotion analysis to predict risks and make suggestions that reflect the user's emotional state.

[2172] Hardware and Software Configuration

[2173] The system has the following components:

[2174] Server: Collects and analyzes user data, generates a draft manifesto, identifies emotional states, predicts risks, and makes recommendations. The software used includes Python libraries with natural language processing techniques (e.g., transformers).

[2175] Terminal: Collects data from the user, communicates with the server, displays the manifesto and suggestion information, and also has an emotion engine.

[2176] Users: Answer surveys, provide behavioral and search history, and provide appropriate feedback.

[2177] Data collection and analysis

[2178] The server collects the user's behavioral history and search history, as well as data provided in the form of questionnaires. This data is analyzed to identify the user's beliefs, thought patterns, and emotional state.

[2179] Performing sentiment analysis

[2180] The server uses an emotion analysis model to analyze the emotional state of the data obtained from the user. For example, it uses natural language processing technology to identify emotions such as "concern" or "enthusiasm" from the collected text data.

[2181] Generate a draft manifest and risk forecast

[2182] Based on the analysis results and emotional data, the server uses a generative AI model to create a manifesto tailored to the user's beliefs, thought patterns, and emotional state, as well as generate risk predictions and countermeasures.

[2183] Submitting and Viewing Proposals

[2184] The server sends the created manifesto proposal and risk countermeasure proposal to the user's device, which displays these proposals to the user, who can review them and provide their evaluation and opinions for each item.

[2185] Collecting and analyzing feedback

[2186] The device collects user feedback on the suggestions and sends it to the server, which analyzes the feedback and uses it as data to improve the system's suggestion capabilities.

[2187] Examples and prompts

[2188] Specific examples

[2189] Example input: A user frequently searches for information related to "personal information leaks," "two-factor authentication," "security risks," etc.

[2190] Example prompt:

[2191] Recently, users have been concerned about personal information leaks and how to implement two-factor authentication. Sentiment analysis shows that users have strong concerns. What specific security measures would you suggest to alleviate negative sentiment?

[2192] The system's unique feature is that it analyzes user data and makes precise risk predictions and suggestions based on the user's emotional state.

[2193] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2194] Step 1:

[2195] The server collects the user's online behavior and search history. It receives a questionnaire from the user, converts it into a data format, and saves it. The input of this step is the user's behavior and questionnaire responses, and the output is a dataset.

[2196] Specifically, the server logs the user's behavior history in real time and periodically stores the collected data in a database.

[2197] Step 2:

[2198] The server analyzes the collected data to identify the user's beliefs, thought patterns, and emotional state. Natural language processing techniques are used to extract emotions from text data. The input is the collected dataset, and the output is a profile of beliefs, thought patterns, and emotional state.

[2199] Specifically, the server uses the Python library transformers to input text data into an emotion classification model and obtain analysis results.

[2200] Step 3:

[2201] Based on the generated profile, the server uses a generative AI model to create a proposed manifesto tailored to the user's beliefs, thought patterns, and emotional state. The input is the profile, and the output is a customized proposed manifesto.

[2202] Specifically, the server inputs the profile into the generative AI model and constructs the generated text as a draft manifesto.

[2203] Step 4:

[2204] The server generates risk predictions and countermeasures. Based on the results of emotion analysis, it creates specific security proposals and countermeasures tailored to the user. The input is emotional state and risk data, and the output is risk predictions and countermeasures.

[2205] Specifically, the server runs a risk assessment model based on the analysis results and generates specific suggested text using a generative AI model.

[2206] Step 5:

[2207] The server sends the created manifest proposal and risk countermeasure proposal to the user's terminal. The input is the generated proposal, and the output is the completion of transmission to the user's terminal.

[2208] Specifically, the server sends data to the user's terminal over the Internet and receives a confirmation response.

[2209] Step 6:

[2210] The terminal displays the received suggestions to the user, who then reviews them and provides ratings and opinions for each item. The input is the suggestions, and the output is user feedback.

[2211] Specifically, the device displays suggestions through an interface and collects user input in real time.

[2212] Step 7:

[2213] The device collects feedback and sends it to the server. The input is the user's evaluation and opinion, and the output is transmission to the server.

[2214] Specifically, the terminal converts the user's input into a data format and calls an API to send it to the server.

[2215] Step 8:

[2216] The server analyzes the received feedback and improves the system's suggestion function. The input is the feedback data, and the output is data for system improvement.

[2217] Specifically, the server stores the feedback in a database and uses an analytical algorithm to extract information to improve the accuracy of the next suggestion.

[2218] 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.

[2219] 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.

[2220] 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.

[2221] 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.

[2222] 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.

[2223] 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.

[2224] 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).

[2225] 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.

[2226] 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."

[2227] 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.

[2228] 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).

[2229] 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.

[2230] 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.

[2231] 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.

[2232] 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.

[2233] 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.

[2234] 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.

[2235] 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.

[2236] 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.

[2237] 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.

[2238] 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.

[2239] The following is further disclosed regarding the above embodiment.

[2240] (Claim 1)

[2241] A means for collecting user behavior history and search history;

[2242] means for collecting survey data from users;

[2243] A means for analyzing the collected data and identifying the user's principles and thought patterns;

[2244] A means of creating a manifesto based on the user's principles and tendencies;

[2245] A means for collecting and analyzing manifestos of political candidates that match the user's principles and thought patterns;

[2246] A system including a means for suggesting suitable candidate information to a user.

[2247] (Claim 2)

[2248] 10. The system of claim 1, further comprising means for displaying the generated draft manifest to a user and collecting feedback.

[2249] (Claim 3)

[2250] 10. The system of claim 1, further comprising means for analyzing feedback and adjusting the draft manifesto and candidate proposals.

[2251] "Example 1"

[2252] (Claim 1)

[2253] A means for collecting user behavior history and search history;

[2254] means for collecting survey data from users;

[2255] A means for analyzing the collected data and identifying the user's principles and thought patterns;

[2256] A means for creating a draft manifesto using a generative AI model based on the user's principles and thinking tendencies;

[2257] A means of collecting published manifestos of political candidates that match the user's ideological and thought tendencies;

[2258] A means for analyzing the collected candidate manifestos and storing them in a database;

[2259] A system including a means for suggesting suitable candidate information to a user based on the analysis results.

[2260] (Claim 2)

[2261] 10. The system of claim 1, further comprising means for displaying the generated draft manifest to a user and collecting feedback.

[2262] (Claim 3)

[2263] 10. The system of claim 1, further comprising means for analyzing feedback and adjusting the draft manifesto and candidate proposals.

[2264] "Application Example 1"

[2265] (Claim 1)

[2266] A means for collecting user behavior history and search history;

[2267] means for collecting survey data from users;

[2268] A means for analyzing the collected data and identifying the user's principles and thought patterns;

[2269] A means of creating a manifesto based on the user's principles and tendencies;

[2270] A means for collecting and analyzing manifestos of political candidates that match the user's principles and thought patterns;

[2271] A means for suggesting suitable candidate information to a user;

[2272] A means for suggesting the most suitable products or services to individual users based on the collected data;

[2273] A system including means for collecting user feedback on proposed products or services.

[2274] (Claim 2)

[2275] 10. The system of claim 1, further comprising means for displaying the generated draft manifesto and proposed products or services to a user and collecting feedback.

[2276] (Claim 3)

[2277] 10. The system of claim 1, further comprising means for analyzing the feedback and adjusting the draft manifesto and candidate proposals, and product or service proposals.

[2278] "Example 2: Combining Emotion Engines" ...

Claims

1. A means for collecting user behavior history and search history; means for collecting survey data from users; A means for analyzing the collected data and identifying the user's principles and thought patterns; A means for creating a draft manifesto based on the user's principles and tendencies of thought; A means of collecting and analyzing manifestos of political candidates that match the user's principles and thought patterns; A system including a means for suggesting suitable candidate information to a user.

2. 10. The system of claim 1, further comprising means for displaying the generated draft manifest to a user and collecting feedback.

3. 10. The system of claim 1, further comprising means for analyzing feedback and adjusting the draft manifesto and candidate proposals.

Images