system

Abstract

We provide the system. [Solution] A means of displaying a survey form on the user's terminal and collecting data on the user's interests and values, A means of sending the collected data to a server and storing it in a database, A means of collecting information on candidates and political parties from external data sources, analyzing it using natural language processing technology, and matching the collected data with user interests, Based on the analysis results, a means of recommending suitable candidates and political parties to users, A means of displaying the recommended results on the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern information society, it is difficult for a voter to effectively grasp a vast amount of information related to politics and select an appropriate candidate to vote for. In particular, for people with busy lives or those with little interest in politics, there is a lack of time and effort to compare and evaluate the policies of each candidate and political party. This may lead to a decline in the voting rate and hinder the improvement of democracy. Therefore, there is a need for a system that supports rational voting destination selection based on the values and interests of individual voters.

Means for Solving the Problems

[0005] This invention provides a means for displaying a questionnaire on a user terminal and collecting data on the user's interests and values. The collected data is transmitted to a server and stored in a database. The server also collects information on candidates and political parties from external data sources and analyzes it using natural language processing technology. This calculates the degree of agreement between the collected user data and the candidate information, and recommends candidates and political parties that are suitable for the user. The recommendation results are displayed on the user terminal, providing an environment in which users can quickly and easily select their voting destination. The server also collects feedback from users and uses it to improve the accuracy of the recommendation algorithm. This enables users to select the optimal voting destination based on their own values, thereby contributing to the development of democracy.

[0006] A "user terminal" is a device used by a user to input or view information, and includes computers, smartphones, tablets, and other similar devices.

[0007] A "survey form" is an electronic document or screen containing questions designed to gather information about a user's values ​​and interests.

[0008] A "server" is a central computer system that manages, processes, and stores data on a network.

[0009] A "database" is an information management system for systematically organizing, storing, and retrieving data.

[0010] "Natural language processing technology" is an artificial intelligence technology that enables computers to understand and analyze human language.

[0011] "Information on candidates and political parties" refers to data such as pledges, speeches, achievements, and evaluations of individuals and organizations running for election.

[0012] A "recommendation algorithm" refers to a series of logical procedures for suggesting suitable options to users based on data analysis results.

[0013] "Feedback" refers to users' evaluations and opinions on a service or system, and is used for improvement. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that recommends voting options based on the user's values ​​and interests, and primarily consists of communication between the user's terminal and the server. The operation of this system is carried out according to the following procedure.

[0036] First, a survey form is displayed on the user's device, allowing them to express their values ​​and interests in detail. By answering this form, the user communicates their political interests and important themes to the system. This information is sent from the device to a server, where it is stored in a database.

[0037] The server collects the latest information on candidates and political parties through external data sources. This information includes candidates' pledges, past achievements, speech content, and social media activity. This data is meticulously analyzed using natural language processing techniques. As a result of the analysis, the candidate and political party information is categorized by topic, and the degree to which it matches the user's response data is determined.

[0038] Based on the analysis results, the server generates a list of recommended candidates and political parties. The list is designed to prioritize candidates that align with the user's values, making it easy for the user to choose the candidate that best suits their values. The recommendations are sent to the user's device and presented visually on it.

[0039] For example, if a user considers environmental issues important, a survey response reflecting this interest is sent to the server. Through analysis, the server prioritizes listing candidates who focus on environmental policies and presents this candidate information to the user. The user can then use this information to inform their actual voting decisions.

[0040] In this way, the present invention is a system that enables flexible voting suggestions that respond to the diverse values ​​of users, thereby contributing to the development of democracy.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device displays a login screen to the user. The user enters the appropriate authentication information and logs into their account. After logging in, the device displays a survey form. The user answers questions about policy themes and values ​​they are interested in in the survey. Once the user has completed the survey, the device sends this data to the server.

[0044] Step 2:

[0045] The server processes the user survey data it receives and stores it in a database. The server then utilizes external data sources to collect information about candidates and political parties related to the election. This includes data such as campaign promises, past performance, speech content, and social media ratings.

[0046] Step 3:

[0047] The server analyzes the candidate information it collects using natural language processing techniques. This analysis categorizes the information by policy and evaluates how well each candidate's policies match the areas of interest indicated in the user's responses. Based on this evaluation, a score is calculated for each candidate.

[0048] Step 4:

[0049] Based on the calculated score, the server generates a list of recommended candidates and political parties deemed most suitable for the user. This list is sorted in descending order of recommendation. The recommendation list includes each candidate's main pledges and brief background information.

[0050] Step 5:

[0051] The server generates a list of recommendations and sends it to the user's device in real time. The device retrieves this information and presents it to the user in an easy-to-understand visual format. The user can review the recommendations and view detailed information. This enables the user to make informed voting decisions.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] In the modern electoral system, there is a challenge in that it is difficult for individual voters to easily find candidates or organizations that align with their own values. Therefore, there is a need for methods to support voters with diverse values ​​in making the best choices.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for collecting information about the user's values ​​and interests, means for gathering and analyzing details of candidates and organizations from external sources, and means for recommending candidates and organizations that match the user's values. This makes it possible for users to easily identify candidates and organizations that are suitable for their own values.

[0057] A "user terminal" is a computer device that displays survey forms and collects information from users regarding their values ​​and interests.

[0058] A "server" is a computer system that receives information sent by a user and stores it in a memory device.

[0059] A "survey form" is a collection of questions designed to specifically investigate a user's values ​​and interests.

[0060] "External information sources" are external data providers that offer detailed information about candidates or organizations.

[0061] "Information processing technology" refers to the techniques used to analyze natural language and structured data.

[0062] "Recommended methods" refer to the methods used to present candidates or organizations that are a good match for the user based on the analysis results.

[0063] A "memory device" is a data storage device used to store received information.

[0064] "Values" refer to a collection of a user's beliefs and important principles.

[0065] "Candidates or organizations" refer to individuals or organizations that represent options in a political election.

[0066] "Social evaluation" refers to the overall assessment and opinion of a candidate or organization by society.

[0067] This invention consists of a system that recommends voting options that align with the user's values. The system operates primarily through communication between the user's terminal and the server.

[0068] Users input their values ​​and interests specifically through a survey form. The survey form is displayed on the user's device interface using technologies such as JavaScript® and React. When a user enters information into the form and presses the submit button, that information is sent from the device to the server via an HTTP request.

[0069] The server processes the received information using Python frameworks such as Flask and Django, and stores it in a database. PostgreSQL and MySQL (registered trademarks) are used as the database. The server validates user information and returns an error message to the user if invalid input is detected.

[0070] Furthermore, the server collects data about candidates and organizations from external sources. This operation is performed using web scraping tools such as Python's BeautifulSoup and Scrapy, or through provided APIs. The collected data includes candidates' policies, past achievements, speech content, and social media activity.

[0071] The server uses natural language processing techniques to analyze the collected data. Specifically, it uses Python's NLTK and spaCy to classify text data into topics. This process reveals how much emphasis each candidate places on specific policy areas.

[0072] The analysis results are matched with the user's values ​​on the server, and a recommendation list is generated that includes candidates and organizations with a high degree of match. This list generation algorithm is implemented using Python's Pandas and NumPy libraries. The generated list is sent to the user's device and displayed visually using a visualization library (e.g., Chart.js or D3.js).

[0073] As a concrete example, if a user is interested in environmental issues, they send a survey response based on that value to the server. The server analyzes the collected candidate information and prioritizes listing candidates who are focused on environmental policies. Based on this information, the user can choose the best candidate to vote for based on their own values.

[0074] An example of a prompt to a generative AI model is, "Based on this information, please generate a list of candidates that are best suited to users who are most interested in environmental issues."

[0075] Thus, this invention flexibly recommends voting options according to the diverse values ​​of users, thereby contributing to the realization of democracy.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users receive a survey form on their device that allows them to express their values ​​and interests in detail. The survey form is displayed via a web interface, and users answer questions about political themes and issues that are important to them. The input for this step consists of human selection options, and the output is structured response data.

[0079] Step 2:

[0080] The terminal formats the user's response data and sends it to the server as an HTTP request. The input is the user's survey responses, and the output is information formatted into a data format for delivery to the server. This step also includes data format checks and validation.

[0081] Step 3:

[0082] The server stores the received data in a database. The input here is the user's response data sent from the terminal, and the output is the information recorded in the database. The server checks the data format and returns an error message to the terminal if necessary.

[0083] Step 4:

[0084] The server accesses external data sources to collect information about candidates and organizations. The input for this step is the address or API endpoint of the external information source, and the output is candidate data that is retrieved and stored as text information. The server uses web scraping tools or APIs.

[0085] Step 5:

[0086] The server analyzes the collected data using natural language processing techniques. The input is raw text data obtained from external sources, and the output is the analysis results classified by topic. Python's NLTK and spaCy are used to identify each candidate's policies and activities.

[0087] Step 6:

[0088] Based on the analysis results, the server generates a list of recommended candidates and organizations that match the user's values. The input consists of the user's survey data and the analysis results of candidate information; the output is a list of candidates suitable for the user. An algorithm that calculates the degree of data matching is used to generate the list.

[0089] Step 7:

[0090] The server sends the generated recommendation list to the user's terminal. The input for this step is candidate recommendation data, and the output is information visualized on the user's screen. The terminal uses a visualization library to present the information to the user in an easy-to-understand manner.

[0091] By coordinating each step, the system makes it easier for users to find candidates who match their values.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In modern metropolitan areas, many residents lack adequate information regarding political participation, making it difficult for them to make informed decisions. In particular, information tailored to the individual values ​​and interests of residents is often insufficient in fostering public awareness within local communities and in selecting candidates during elections. This raises concerns that it will hinder residents' political expression and impede the development of democratic processes within their communities.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for displaying a questionnaire form on a user terminal and collecting data on the user's interests and values, means for transmitting the collected data to an information processing device and storing it in a data storage device, and means for providing information related to raising public awareness in the community and supporting election candidates. This makes it possible for residents to easily support candidates based on their own values ​​and make informed decisions.

[0097] A "user terminal" is an electronic device used by a user to input and retrieve information, and includes devices such as smartphones and computers.

[0098] A "survey form" is an electronic input screen with questions arranged on it, provided to collect data about users' interests and values.

[0099] An "information processing device" is a device consisting of a computer system or server that receives, analyzes, and stores collected data.

[0100] "Data storage devices" refer to devices, including storage devices and cloud storage, used to store collected data for the long term.

[0101] "External information sources" refer to sources of information, such as the internet and public databases, that provide information about candidates and organizations.

[0102] "Natural language processing technology" is a technology that uses computers to analyze the language that humans use on a daily basis, and is applied to the understanding and classification of text data.

[0103] "Individual targets or organizations" refers to groups or organizations with specific objectives, such as individual candidates or political parties involved in elections.

[0104] "Raising public awareness" is a concept that refers to activities and information provision aimed at promoting a sense of community and contribution among residents of a local area.

[0105] A "prompt" is input text used to give instructions to a generative AI model and generate responses or outputs.

[0106] The system for realizing this invention mainly consists of a user terminal and an information processing device (server). A questionnaire form for obtaining information about specific individuals or organizations related to the election is displayed on the user terminal. Data about the user's values ​​and interests is collected through this form. This data is transmitted to the information processing device via information communication means and stored in a data storage device.

[0107] The server retrieves information about individual subjects and organizations from external sources and analyzes that information using natural language processing technology. This analysis utilizes generative AI models specializing in understanding and classifying text data. The analyzed information is then tailored to align with the user's values ​​and interests. Based on the analysis results, the information processing device provides the user with information related to raising public awareness in the local community. This makes it easier for users to obtain information about candidates and organizations that align with their own values, thereby promoting informed decision-making.

[0108] For example, if a user has a strong interest in environmental protection, the information processing device will identify specific entities promoting environmental policies and provide that information to the user. This allows the user to receive information that aligns with their own values.

[0109] An example of a prompt is, "Please prioritize listing candidates related to the city's transportation problems. Provide information in a priority order that aligns with the user's values." By utilizing prompts in this way, the generative AI model can accurately provide the information the user is looking for.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user accesses a survey form on their device and enters information about their interests and values. The entered data is sent from the device to the server and stored. The input here is the user's values ​​data, and the output is the user information stored on the server.

[0113] Step 2:

[0114] The server collects data about individual subjects and organizations from external sources. This data includes election candidates' policies and past performance. The input is data from external sources, and the output is new information stored on the server.

[0115] Step 3:

[0116] The server analyzes the collected data using a generating AI model and natural language processing. Here, all data stored on the server is analyzed as input, and its relationships are evaluated. The output is the analyzed information. Through this process, the data is classified by semantic units and filtered based on the user's values.

[0117] Step 4:

[0118] Based on the analysis results, the server generates a list of recommended individuals and organizations that align with the user's values. The input to the generation process is analysis data processed using natural language processing, and the output is recommended information that matches those values. This recommended information is prioritized.

[0119] Step 5:

[0120] The server sends the generated recommendation information to the user's terminal. The user's terminal receives this information and displays it visually. Here, the input is the recommendation information, and the output is what is displayed on the user's screen.

[0121] Step 6:

[0122] Users can review the displayed recommendations and send more detailed information or additional feedback to the server. This feedback is used to improve the accuracy of future recommendations. The input is user feedback, and the output is data used to improve the server's algorithms.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention is a system that improves recommendation accuracy by analyzing the user's emotional state, in addition to recommending voting options based on the user's values ​​and interests. This system mainly consists of a user terminal, a server, and an emotion engine.

[0125] First, the user's device presents them with a survey form. Here, the user answers questions about their values ​​and interests. The emotion engine analyzes the user's facial expressions and tone of voice while they answer the survey, and evaluates their emotional state. This evaluated emotional information is sent to the server along with the survey response data.

[0126] The server collects information on candidates and political parties using external data sources. This includes candidates' pledges, past achievements, and social media posts. The server analyzes this information using natural language processing techniques and organizes candidate information into categories based on user interests. At the same time, it takes into account the user's emotional state, evaluates the user's emotional response to each candidate, and incorporates this into the recommendation algorithm.

[0127] Based on the analysis, the server generates a list of recommended candidates and political parties suitable for the user. This list includes information on candidates deemed most suitable based on the user's interests and sentiment ratings. The recommendation list is quickly sent to the user's device and displayed to them. This allows the user to receive highly accurate recommendations, including those based on emotional satisfaction.

[0128] For example, suppose a user expresses interest in economic policy, and the emotion engine determines that the user's emotions are calm during the survey. In this case, the server prioritizes including candidates who prioritize economic policy and predicts they will have the most positive impact on the user's emotions in the recommendation list.

[0129] In this way, the present invention provides highly accurate voting recommendations that take into account both the user's interests and emotions, thereby supporting the decision-making process in voting.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device displays a login screen to the user. The user enters their username and password to log in to their account. If successful, the device displays a survey form to the user. The user fills out the survey, selects their interests and values, and submits it.

[0133] Step 2:

[0134] The device transmits the user's facial expressions and voice, as they answer the survey, to an emotion engine via its camera and microphone. The emotion engine analyzes the data in real time and identifies the user's emotional state from their facial expressions and tone of voice. This information, along with an emotion label, is sent to the server along with the survey response.

[0135] Step 3:

[0136] The server stores survey data and sentiment information received from users in a database. During this process, the data is anonymized to protect user privacy. The server then collects the latest information on candidates and political parties from external data sources. This information includes campaign promises, social media posts, and speeches.

[0137] Step 4:

[0138] The server uses natural language processing technology to analyze candidate information. The goal of the analysis is to calculate a score indicating how each candidate's and party's policies relate to the user's interests. Furthermore, it incorporates user sentiment data to evaluate which candidates best meet the user's emotional needs.

[0139] Step 5:

[0140] Based on the analysis results, the server uses a recommendation algorithm to generate a list of suitable candidates for the user. Candidates are ranked in order of their level of interest and emotional affinity. The generated recommendation list is tailored to the user's individual needs.

[0141] Step 6:

[0142] The server sends a list of recommendations to the user's device. The device displays the list of recommended candidates to the user and allows them to view detailed information. The user can then use this information to decide on their voting behavior. The system collects feedback on the user's satisfaction with the recommendations and uses it to improve future algorithms.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] In today's information society, rationally and emotionally selecting who to vote for in an election is a challenging task. In particular, the sheer volume of information available about elections makes it difficult to gain a detailed understanding of individual candidates and political parties. Furthermore, there are few systems that can recommend voting options that take into account an individual's emotional state, resulting in low satisfaction with the chosen option.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes means for collecting information about an individual's values ​​and interests, means for evaluating emotional information, and means for analyzing the collected information and making recommendations. This enables highly accurate recommendations for voting targets that take into account an individual's values ​​and emotional state.

[0148] A "user terminal" is an information processing device used by an individual, and is a device that displays questionnaires and checks recommendation results through an interface.

[0149] A "survey form" is a question-based input screen used to collect information about an individual's values ​​and interests.

[0150] "Information about an individual's values ​​and interests" refers to data that shows an individual's thoughts, beliefs, preferences, and areas of interest.

[0151] "Emotional analysis means" refers to a technology or device for detecting and evaluating an individual's emotional state from their facial expressions and tone of voice.

[0152] A "server" is an information processing device that analyzes received information and provides the processing results to the individual.

[0153] "Emotional information" refers to data about an individual's emotional state, and is the evaluation result obtained through emotion analysis methods.

[0154] "External information sources" refer to external databases and websites that provide information about candidates and political organizations.

[0155] "Natural language processing technology" is a technology that analyzes human language and extracts and organizes information from it.

[0156] A "recommendation algorithm" is a computational method for presenting the optimal choice based on an individual's values ​​and emotions.

[0157] "Recommendation results" refer to a list of information on candidates and political organizations presented to an individual based on analytical and computational methods.

[0158] "Feedback" refers to information about users' experiences and opinions, which is used to improve the recommendation system.

[0159] In order to implement this invention, the user terminal, the server, and the emotion analysis means must work together in coordination.

[0160] The user terminal presents each individual with a questionnaire form. This questionnaire investigates the individual's values ​​and interests and consists of multiple-choice and open-ended questions. The terminal also uses input devices such as a camera and microphone to record the individual's facial expressions and voice as they answer the questionnaire. This enables sentiment analysis.

[0161] The emotion analysis system works in conjunction with a server to detect an individual's emotional state from their facial expressions and voice. Specifically, it uses emotion analysis software (such as a common facial recognition API or voice analysis tool) to analyze an individual's emotions in real time as they answer a questionnaire. This evaluated emotion data is sent to the server along with the questionnaire data.

[0162] The server collects information about candidates and political organizations from external sources. It utilizes web scraping techniques and API connections for information gathering. The server analyzes the collected information using natural language processing techniques (e.g., common natural language processing libraries) and organizes it into categories based on individual values. Furthermore, the server uses a recommendation algorithm to generate recommendations for the most suitable candidates based on sentiment and interest information. This recommendation algorithm is built as a generative AI model and operates using prompts provided from outside the model.

[0163] As a concrete example, suppose a user has a strong interest in economic policy and is rated as "calm" in the sentiment analysis during the survey. In this case, the server will prioritize including in the recommendation list candidates who are predicted to have the most positive impact on the individual's emotional state among the candidates focused on economic policy.

[0164] Examples of prompt messages include the following:

[0165] User interests: Economic policy

[0166] User's emotional state: Calm

[0167] Target of recommendation: Candidate / Political Party

[0168] Objective: To recommend the best candidate based on their emotions and interests.

[0169] This invention enables highly accurate candidate recommendations that take into account individual emotions and values, thereby supporting better decision-making.

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

[0171] Step 1:

[0172] The device displays a survey and provides individuals with a form containing questions about their values ​​and interests.

[0173] Input: Question items

[0174] Output: User response data

[0175] Individuals answer questionnaire forms on their devices, providing information about their values ​​and interests. This process generates data about their values ​​and interests.

[0176] Step 2:

[0177] The device records the individual's facial expressions and voice and transmits them to an emotion analysis system.

[0178] Input: Individual facial expression and voice data

[0179] Output: Emotional information

[0180] The device uses a camera and microphone to record an individual's facial expressions and voice in real time. This data is then passed to emotion analysis software and used as foundational data for evaluating the individual's emotional state.

[0181] Step 3:

[0182] The emotion analysis system processes facial expression and voice data to identify the emotional state.

[0183] Input: Facial expression and voice data

[0184] Output: Evaluated sentiment information

[0185] The emotion analysis method uses specific algorithms (e.g., voice analysis tools or facial recognition APIs) to analyze and evaluate the emotional state from the input facial expressions and voice. As a result, the individual's emotional state is generated as data.

[0186] Step 4:

[0187] The device integrates survey data and sentiment information and sends it to the server.

[0188] Input: Survey response data, evaluated sentiment information

[0189] Output: Integrated personal information

[0190] The device integrates information about an individual's values, interests, and emotions into a single dataset and sends it to the server.

[0191] Step 5:

[0192] The server collects information about candidates and political organizations from external sources.

[0193] Input: External data source

[0194] Output: Information on candidates and political organizations

[0195] The server uses web scraping and API connections to collect information about candidates and political organizations. This information includes candidates' pledges, past achievements, and social standing.

[0196] Step 6:

[0197] The server uses natural language processing technology to analyze and organize the collected information.

[0198] Input: Information about candidates and political organizations

[0199] Output: Information organized by category

[0200] The server uses a common natural language processing library to analyze the collected information and organize it into categories based on the user's interests.

[0201] Step 7:

[0202] The server executes a recommendation algorithm based on the evaluated sentiment information and organized information.

[0203] Input: Integrated personal information, information organized by category

[0204] Output: Recommendation List

[0205] The server uses a generative AI model to select the most suitable candidates from the input personal information and organized data, and generates a recommendation list.

[0206] Step 8:

[0207] The device receives a list of recommendations and displays them to the individual.

[0208] Input: Recommendation list

[0209] Output: Displayed recommendation list

[0210] The device displays a list of recommendations received from the server to the individual, prompting them to consider them. This allows individuals to make more informed voting decisions.

[0211] (Application Example 2)

[0212] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0213] In today's consumer society, individual consumers often spend a great deal of time and effort selecting the most suitable products and services from a vast amount of information. Furthermore, traditional recommendation systems often fail to consider the consumer's emotional state, relying solely on past purchase and search history. This makes them incapable of suggesting truly valuable products and services to users.

[0214] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0215] In this invention, the server includes means for presenting an evaluation form to the user terminal and acquiring information on interests, values, and purchase history; means for transferring the acquired information to a communication device and storing it in a storage device; and means for acquiring information on products and services from external sources, analyzing it using natural language processing technology, and matching the acquired information with the user's interests and emotional information. This enables highly personalized recommendations that take emotional states into account.

[0216] A "user terminal" is an information processing device used by users to input or receive information. This device has functions for presenting evaluation forms and displaying recommendation results.

[0217] A "rating form" refers to a screen or interface used to obtain information related to a user's values ​​and purchase history. Users enter the necessary information here.

[0218] A "communication device" is a device used to transfer information acquired from a user terminal to a server. It typically includes network communication capabilities.

[0219] A "storage device" is a device that stores transferred data and keeps it in a state where it can be accessed as needed.

[0220] "Product and service information" refers to detailed information about a specific product or service. This information is obtained from external sources and is subject to analysis.

[0221] "External information sources" refer to external databases or information providers that offer data about products or services.

[0222] "Natural language processing technology" is a technology that enables machines to understand and process human language. This makes it possible to analyze acquired information and match it with the user's interests and emotional information.

[0223] "Emotional information" refers to data that indicates a user's emotional state. It is typically obtained from the user's facial expressions, tone of voice, and other similar data.

[0224] "Personalized recommendations" refer to suggestions for products and services optimized according to the individual user's characteristics. They are customized based on the user's purchase history and sentiment information.

[0225] The system for implementing this invention consists of a user terminal, a communication device, a storage device, and a server. The user terminal functions as an interface with the user and obtains information related to the user's interests, values, and purchase history through evaluation forms. This information is then transferred to the server via the communication device and stored in the storage device.

[0226] The server retrieves information about products and services from external sources based on information received from the user. This information is analyzed using natural language processing technology and matched with the user's interests and emotional information. In this process, the server utilizes emotional information such as the user's facial expressions and tone of voice to provide highly accurate, personalized recommendations.

[0227] As a concrete example, if a user is interested in relaxation products, the server analyzes related information using natural language processing technology and recommends appropriate products. In this process, the user's purchase history from when they were feeling stressed is taken into consideration, and new products best suited to their current emotional state can be suggested.

[0228] A concrete example of a prompt used when determining recommendations to present to a user using a generative AI model is, "Based on past data, please recommend products that users tend to purchase during stressful periods." Based on this prompt, the system can identify the most useful products and services for the user and present them as recommendations.

[0229] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0230] Step 1:

[0231] The user operates the device and enters information into an evaluation form. This includes the user's interests, values, and purchase history. This input data forms the basis for designing a personalized user experience. The device collects this data and prepares to send it to the server via a communication device.

[0232] Step 2:

[0233] The server receives information acquired from the terminal and stores it in storage. A database is used here to efficiently manage the input evaluation data. The input is data related to the user's interests and emotions, and the output is data in an organized, stored state.

[0234] Step 3:

[0235] The server collects information about products and services from external sources. It analyzes this information using natural language processing techniques and compares the retrieved information with user data. In this process, the server analyzes vast amounts of data, determining the category and importance of the information. The input is raw data from external sources, and the output is the analyzed data.

[0236] Step 4:

[0237] The server uses a generative AI model to create prompt messages that recommend the most suitable products and services to the user. Based on the user's input data and analyzed information, the model uses these prompts to formulate recommendations tailored to the user's needs. The input consists of user information and analyzed product data, and the output is a personalized recommendation list.

[0238] Step 5:

[0239] The server transfers a generated list of recommendations to the terminal, which then displays this information to the user. The user can then review the recommended products and services and proceed with a purchase or another action. The input is the list of recommendations, and the output is the displayed data presented to the user.

[0240] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0241] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0242] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0243] [Second Embodiment]

[0244] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0245] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0246] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0247] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0248] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0249] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0250] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0251] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0252] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0253] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0254] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0255] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0256] This invention is a system that recommends voting options based on the user's values ​​and interests, and primarily consists of communication between the user's terminal and the server. The operation of this system is carried out according to the following procedure.

[0257] First, a survey form is displayed on the user's device, allowing them to express their values ​​and interests in detail. By answering this form, the user communicates their political interests and important themes to the system. This information is sent from the device to a server, where it is stored in a database.

[0258] The server collects the latest information on candidates and political parties through external data sources. This information includes candidates' pledges, past achievements, speech content, and social media activity. This data is meticulously analyzed using natural language processing techniques. As a result of the analysis, the candidate and political party information is categorized by topic, and the degree to which it matches the user's response data is determined.

[0259] Based on the analysis results, the server generates a list of recommended candidates and political parties. The list is designed to prioritize candidates that align with the user's values, making it easy for the user to choose the candidate that best suits their values. The recommendations are sent to the user's device and presented visually on it.

[0260] For example, if a user considers environmental issues important, a survey response reflecting this interest is sent to the server. Through analysis, the server prioritizes listing candidates who focus on environmental policies and presents this candidate information to the user. The user can then use this information to inform their actual voting decisions.

[0261] In this way, the present invention is a system that enables flexible voting suggestions that respond to the diverse values ​​of users, thereby contributing to the development of democracy.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] The device displays a login screen to the user. The user enters the appropriate authentication information and logs into their account. After logging in, the device displays a survey form. The user answers questions about policy themes and values ​​they are interested in in the survey. Once the user has completed the survey, the device sends this data to the server.

[0265] Step 2:

[0266] The server processes the user survey data it receives and stores it in a database. The server then utilizes external data sources to collect information about candidates and political parties related to the election. This includes data such as campaign promises, past performance, speech content, and social media ratings.

[0267] Step 3:

[0268] The server analyzes the candidate information it collects using natural language processing techniques. This analysis categorizes the information by policy and evaluates how well each candidate's policies match the areas of interest indicated in the user's responses. Based on this evaluation, a score is calculated for each candidate.

[0269] Step 4:

[0270] Based on the calculated score, the server generates a list of recommended candidates and political parties deemed most suitable for the user. This list is sorted in descending order of recommendation. The recommendation list includes each candidate's main pledges and brief background information.

[0271] Step 5:

[0272] The server generates a list of recommendations and sends it to the user's device in real time. The device retrieves this information and presents it to the user in an easy-to-understand visual format. The user can review the recommendations and view detailed information. This enables the user to make informed voting decisions.

[0273] (Example 1)

[0274] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] In modern election systems, there is a problem that it is difficult for individual voters to easily find candidates or groups that match their values. Therefore, there is a need for a method to assist voters with diverse values in making optimal choices.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0277] In this invention, the server includes means for collecting information on the values and interests of users, means for collecting and analyzing details of candidates and groups from external information sources, and means for recommending candidates and groups that match the values of users. As a result, it becomes possible for users to easily identify candidates and groups suitable for their values.

[0278] A "user terminal" is a computer device that displays a questionnaire form and collects information on values and interests from users.

[0279] A "server" is a computer system that receives information transmitted from users and stores it in a storage device.

[0280] A "questionnaire form" is a set of questions for specifically investigating the values and interests of users.

[0281] An "external information source" is an external data provider that provides detailed information on candidates and groups.

[0282] "Information processing technology" is a technology used for analyzing natural language and structured data.

[0283] "Means for recommending" is a method for presenting candidates and groups that match users based on the analysis results.

[0284] A "storage device" is a data storage device for storing received information.

[0285] "Values" refer to the set of a user's beliefs and ideas that are considered important.

[0286] "Candidates or groups" refer to the individuals or organizations that are options in political elections.

[0287] "Social evaluation" refers to the overall evaluation and opinions of society towards candidates or groups.

[0288] The present invention is composed of a system that recommends a voting destination that matches the values of users. The system mainly operates through communication between a user terminal and a server.

[0289] The user specifically inputs their own values and interests through a questionnaire form. The questionnaire form is displayed on the interface of the user terminal using technologies such as JavaScript and React. When the user enters information into the form and presses the send button, the information is sent from the terminal to the server via an HTTP request.

[0290] The server processes the received information using frameworks such as Flask or Django in Python and stores it in a database. PostgreSQL or MySQL is used for the database. The server validates the user information and returns an error message to the user if it detects incorrect input.

[0291] Furthermore, the server collects data on candidates or groups from external information sources. This operation is executed by web scraping tools such as BeautifulSoup or Scrapy in Python, or by provided APIs. The data collected includes the policies of candidates, past achievements, speech content, and activity status on SNS, etc.

[0292] The server uses natural language processing techniques to analyze the collected data. Specifically, it uses Python's NLTK and spaCy to classify text data into topics. This process reveals how much emphasis each candidate places on specific policy areas.

[0293] The analysis results are matched with the user's values ​​on the server, and a recommendation list is generated that includes candidates and organizations with a high degree of match. This list generation algorithm is implemented using Python's Pandas and NumPy libraries. The generated list is sent to the user's device and displayed visually using a visualization library (e.g., Chart.js or D3.js).

[0294] As a concrete example, if a user is interested in environmental issues, they send a survey response based on that value to the server. The server analyzes the collected candidate information and prioritizes listing candidates who are focused on environmental policies. Based on this information, the user can choose the best candidate to vote for based on their own values.

[0295] An example of a prompt to a generative AI model is, "Based on this information, please generate a list of candidates that are best suited to users who are most interested in environmental issues."

[0296] Thus, this invention flexibly recommends voting options according to the diverse values ​​of users, thereby contributing to the realization of democracy.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] The user receives a questionnaire form on the terminal to specifically express their values and interests. The questionnaire form is displayed via a web interface, and the user answers questions about the political themes and issues they consider important. The input for this step is the user's selected items, and the output is structured response data.

[0300] Step 2:

[0301] The terminal formats the user's response data and sends it to the server as an HTTP request. The input is the user's questionnaire response, and the output is information formatted in a data type for passing to the server. In this step, data format checks and validations are also performed.

[0302] Step 3:

[0303] The server saves the received data in a database. The input here is the user response data sent from the terminal, and the output is the information recorded in the database. The server checks the data format and returns an error message to the terminal if necessary.

[0304] Step 4:

[0305] The server accesses external data sources and collects information about candidates and organizations. The input for this step is the address of the external information source or the API endpoint, and the output is candidate data that is obtained and saved as text information. The server uses web scraping tools or APIs.

[0306] Step 5:

[0307] The server analyzes the collected data using natural language processing techniques. The input is the raw text data obtained externally, and the output is the analysis results classified by topic. Python's NLTK and spaCy are used to identify the policies and activities of each candidate.

[0308] Step 6:

[0309] Based on the analysis results, the server generates a list of recommended candidates and organizations that match the user's values. The input consists of the user's survey data and the analysis results of candidate information; the output is a list of candidates suitable for the user. An algorithm that calculates the degree of data matching is used to generate the list.

[0310] Step 7:

[0311] The server sends the generated recommendation list to the user's terminal. The input for this step is candidate recommendation data, and the output is information visualized on the user's screen. The terminal uses a visualization library to present the information to the user in an easy-to-understand manner.

[0312] By coordinating each step, the system makes it easier for users to find candidates who match their values.

[0313] (Application Example 1)

[0314] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0315] In modern metropolitan areas, many residents lack adequate information regarding political participation, making it difficult for them to make informed decisions. In particular, information tailored to the individual values ​​and interests of residents is often insufficient in fostering public awareness within local communities and in selecting candidates during elections. This raises concerns that it will hinder residents' political expression and impede the development of democratic processes within their communities.

[0316] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0317] In this invention, the server includes means for displaying a questionnaire form on a user terminal and collecting data on the user's interests and values, means for transmitting the collected data to an information processing device and storing it in a data storage device, and means for providing information related to raising public awareness in the community and supporting election candidates. This makes it possible for residents to easily support candidates based on their own values ​​and make informed decisions.

[0318] A "user terminal" is an electronic device used by a user to input and retrieve information, and includes devices such as smartphones and computers.

[0319] A "survey form" is an electronic input screen with questions arranged on it, provided to collect data about users' interests and values.

[0320] An "information processing device" is a device consisting of a computer system or server that receives, analyzes, and stores collected data.

[0321] "Data storage devices" refer to devices, including storage devices and cloud storage, used to store collected data for the long term.

[0322] "External information sources" refer to sources of information, such as the internet and public databases, that provide information about candidates and organizations.

[0323] "Natural language processing technology" is a technology that uses computers to analyze the language that humans use on a daily basis, and is applied to the understanding and classification of text data.

[0324] "Individual targets or organizations" refers to groups or organizations with specific objectives, such as individual candidates or political parties involved in elections.

[0325] "Raising public awareness" is a concept that refers to activities and information provision aimed at promoting a sense of community and contribution among residents of a local area.

[0326] A "prompt" is input text used to give instructions to a generative AI model and generate responses or outputs.

[0327] The system for realizing this invention mainly consists of a user terminal and an information processing device (server). A questionnaire form for obtaining information about specific individuals or organizations related to the election is displayed on the user terminal. Data about the user's values ​​and interests is collected through this form. This data is transmitted to the information processing device via information communication means and stored in a data storage device.

[0328] The server retrieves information about individual subjects and organizations from external sources and analyzes that information using natural language processing technology. This analysis utilizes generative AI models specializing in understanding and classifying text data. The analyzed information is then tailored to align with the user's values ​​and interests. Based on the analysis results, the information processing device provides the user with information related to raising public awareness in the local community. This makes it easier for users to obtain information about candidates and organizations that align with their own values, thereby promoting informed decision-making.

[0329] For example, if a user has a strong interest in environmental protection, the information processing device will identify specific entities promoting environmental policies and provide that information to the user. This allows the user to receive information that aligns with their own values.

[0330] An example of a prompt is, "Please prioritize listing candidates related to the city's transportation problems. Provide information in a priority order that aligns with the user's values." By utilizing prompts in this way, the generative AI model can accurately provide the information the user is looking for.

[0331] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0332] Step 1:

[0333] The user accesses a survey form on their device and enters information about their interests and values. The entered data is sent from the device to the server and stored. The input here is the user's values ​​data, and the output is the user information stored on the server.

[0334] Step 2:

[0335] The server collects data about individual subjects and organizations from external sources. This data includes election candidates' policies and past performance. The input is data from external sources, and the output is new information stored on the server.

[0336] Step 3:

[0337] The server analyzes the collected data using a generating AI model and natural language processing. Here, all data stored on the server is analyzed as input, and its relationships are evaluated. The output is the analyzed information. Through this process, the data is classified by semantic units and filtered based on the user's values.

[0338] Step 4:

[0339] Based on the analysis results, the server generates a list of recommended individuals and organizations that align with the user's values. The input to the generation process is analysis data processed using natural language processing, and the output is recommended information that matches those values. This recommended information is prioritized.

[0340] Step 5:

[0341] The server sends the generated recommendation information to the user's terminal. The user's terminal receives this information and displays it visually. Here, the input is the recommendation information, and the output is what is displayed on the user's screen.

[0342] Step 6:

[0343] Users can review the displayed recommendations and send more detailed information or additional feedback to the server. This feedback is used to improve the accuracy of future recommendations. The input is user feedback, and the output is data used to improve the server's algorithms.

[0344] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0345] This invention is a system that improves recommendation accuracy by analyzing the user's emotional state, in addition to recommending voting options based on the user's values ​​and interests. This system mainly consists of a user terminal, a server, and an emotion engine.

[0346] First, the user's device presents them with a survey form. Here, the user answers questions about their values ​​and interests. The emotion engine analyzes the user's facial expressions and tone of voice while they answer the survey, and evaluates their emotional state. This evaluated emotional information is sent to the server along with the survey response data.

[0347] The server collects information on candidates and political parties using external data sources. This includes candidates' pledges, past achievements, and social media posts. The server analyzes this information using natural language processing techniques and organizes candidate information into categories based on user interests. At the same time, it takes into account the user's emotional state, evaluates the user's emotional response to each candidate, and incorporates this into the recommendation algorithm.

[0348] Based on the analysis, the server generates a list of recommended candidates and political parties suitable for the user. This list includes information on candidates deemed most suitable based on the user's interests and sentiment ratings. The recommendation list is quickly sent to the user's device and displayed to them. This allows the user to receive highly accurate recommendations, including those based on emotional satisfaction.

[0349] For example, suppose a user expresses interest in economic policy, and the emotion engine determines that the user's emotions are calm during the survey. In this case, the server prioritizes including candidates who prioritize economic policy and predicts they will have the most positive impact on the user's emotions in the recommendation list.

[0350] In this way, the present invention provides highly accurate voting recommendations that take into account both the user's interests and emotions, thereby supporting the decision-making process in voting.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The device displays a login screen to the user. The user enters their username and password to log in to their account. If successful, the device displays a survey form to the user. The user fills out the survey, selects their interests and values, and submits it.

[0354] Step 2:

[0355] The device transmits the user's facial expressions and voice, as they answer the survey, to an emotion engine via its camera and microphone. The emotion engine analyzes the data in real time and identifies the user's emotional state from their facial expressions and tone of voice. This information, along with an emotion label, is sent to the server along with the survey response.

[0356] Step 3:

[0357] The server stores survey data and sentiment information received from users in a database. During this process, the data is anonymized to protect user privacy. The server then collects the latest information on candidates and political parties from external data sources. This information includes campaign promises, social media posts, and speeches.

[0358] Step 4:

[0359] The server uses natural language processing technology to analyze candidate information. The goal of the analysis is to calculate a score indicating how each candidate's and party's policies relate to the user's interests. Furthermore, it incorporates user sentiment data to evaluate which candidates best meet the user's emotional needs.

[0360] Step 5:

[0361] Based on the analysis results, the server uses a recommendation algorithm to generate a list of suitable candidates for the user. Candidates are ranked in order of their level of interest and emotional affinity. The generated recommendation list is tailored to the user's individual needs.

[0362] Step 6:

[0363] The server sends a list of recommendations to the user's device. The device displays the list of recommended candidates to the user and allows them to view detailed information. The user can then use this information to decide on their voting behavior. The system collects feedback on the user's satisfaction with the recommendations and uses it to improve future algorithms.

[0364] (Example 2)

[0365] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0366] In today's information society, rationally and emotionally selecting who to vote for in an election is a challenging task. In particular, the sheer volume of information available about elections makes it difficult to gain a detailed understanding of individual candidates and political parties. Furthermore, there are few systems that can recommend voting options that take into account an individual's emotional state, resulting in low satisfaction with the chosen option.

[0367] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0368] In this invention, the server includes means for collecting information about an individual's values ​​and interests, means for evaluating emotional information, and means for analyzing the collected information and making recommendations. This enables highly accurate recommendations for voting targets that take into account an individual's values ​​and emotional state.

[0369] A "user terminal" is an information processing device used by an individual, and is a device that displays questionnaires and checks recommendation results through an interface.

[0370] A "survey form" is a question-based input screen used to collect information about an individual's values ​​and interests.

[0371] "Information about an individual's values ​​and interests" refers to data that shows an individual's thoughts, beliefs, preferences, and areas of interest.

[0372] "Emotional analysis means" refers to a technology or device for detecting and evaluating an individual's emotional state from their facial expressions and tone of voice.

[0373] A "server" is an information processing device that analyzes received information and provides the processing results to the individual.

[0374] "Emotional information" refers to data about an individual's emotional state, and is the evaluation result obtained through emotion analysis methods.

[0375] "External information sources" refer to external databases and websites that provide information about candidates and political organizations.

[0376] "Natural language processing technology" is a technology that analyzes human language and extracts and organizes information from it.

[0377] A "recommendation algorithm" is a computational method for presenting the optimal choice based on an individual's values ​​and emotions.

[0378] "Recommendation results" refer to a list of information on candidates and political organizations presented to an individual based on analytical and computational methods.

[0379] "Feedback" refers to information about users' experiences and opinions, which is used to improve the recommendation system.

[0380] In order to implement this invention, the user terminal, the server, and the emotion analysis means must work together in coordination.

[0381] The user terminal presents each individual with a questionnaire form. This questionnaire investigates the individual's values ​​and interests and consists of multiple-choice and open-ended questions. The terminal also uses input devices such as a camera and microphone to record the individual's facial expressions and voice as they answer the questionnaire. This enables sentiment analysis.

[0382] The emotion analysis system works in conjunction with a server to detect an individual's emotional state from their facial expressions and voice. Specifically, it uses emotion analysis software (such as a common facial recognition API or voice analysis tool) to analyze an individual's emotions in real time as they answer a questionnaire. This evaluated emotion data is sent to the server along with the questionnaire data.

[0383] The server collects information about candidates and political organizations from external sources. It utilizes web scraping techniques and API connections for information gathering. The server analyzes the collected information using natural language processing techniques (e.g., common natural language processing libraries) and organizes it into categories based on individual values. Furthermore, the server uses a recommendation algorithm to generate recommendations for the most suitable candidates based on sentiment and interest information. This recommendation algorithm is built as a generative AI model and operates using prompts provided from outside the model.

[0384] As a concrete example, suppose a user has a strong interest in economic policy and is rated as "calm" in the sentiment analysis during the survey. In this case, the server will prioritize including in the recommendation list candidates who are predicted to have the most positive impact on the individual's emotional state among the candidates focused on economic policy.

[0385] Examples of prompt messages include the following:

[0386] User interests: Economic policy

[0387] User's emotional state: Calm

[0388] Target of recommendation: Candidate / Political Party

[0389] Objective: To recommend the best candidate based on their emotions and interests.

[0390] This invention enables highly accurate candidate recommendations that take into account individual emotions and values, thereby supporting better decision-making.

[0391] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0392] Step 1:

[0393] The device displays a survey and provides individuals with a form containing questions about their values ​​and interests.

[0394] Input: Question items

[0395] Output: User response data

[0396] Individuals answer questionnaire forms on their devices, providing information about their values ​​and interests. This process generates data about their values ​​and interests.

[0397] Step 2:

[0398] The device records the individual's facial expressions and voice and transmits them to an emotion analysis system.

[0399] Input: Individual facial expression and voice data

[0400] Output: Emotional information

[0401] The device uses a camera and microphone to record an individual's facial expressions and voice in real time. This data is then passed to emotion analysis software and used as foundational data for evaluating the individual's emotional state.

[0402] Step 3:

[0403] The emotion analysis system processes facial expression and voice data to identify the emotional state.

[0404] Input: Facial expression and voice data

[0405] Output: Evaluated sentiment information

[0406] The emotion analysis method uses specific algorithms (e.g., voice analysis tools or facial recognition APIs) to analyze and evaluate the emotional state from the input facial expressions and voice. As a result, the individual's emotional state is generated as data.

[0407] Step 4:

[0408] The device integrates survey data and sentiment information and sends it to the server.

[0409] Input: Survey response data, evaluated sentiment information

[0410] Output: Integrated personal information

[0411] The device integrates information about an individual's values, interests, and emotions into a single dataset and sends it to the server.

[0412] Step 5:

[0413] The server collects information about candidates and political organizations from external sources.

[0414] Input: External data source

[0415] Output: Information on candidates and political organizations

[0416] The server uses web scraping and API connections to collect information about candidates and political organizations. This information includes candidates' pledges, past achievements, and social standing.

[0417] Step 6:

[0418] The server uses natural language processing technology to analyze and organize the collected information.

[0419] Input: Information about candidates and political organizations

[0420] Output: Information organized by category

[0421] The server uses a common natural language processing library to analyze the collected information and organize it into categories based on the user's interests.

[0422] Step 7:

[0423] The server executes a recommendation algorithm based on the evaluated sentiment information and organized information.

[0424] Input: Integrated personal information, information organized by category

[0425] Output: Recommendation List

[0426] The server uses a generative AI model to select the most suitable candidates from the input personal information and organized data, and generates a recommendation list.

[0427] Step 8:

[0428] The device receives a list of recommendations and displays them to the individual.

[0429] Input: Recommendation list

[0430] Output: Displayed recommendation list

[0431] The device displays a list of recommendations received from the server to the individual, prompting them to consider them. This allows individuals to make more informed voting decisions.

[0432] (Application Example 2)

[0433] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0434] In today's consumer society, individual consumers often spend a great deal of time and effort selecting the most suitable products and services from a vast amount of information. Furthermore, traditional recommendation systems often fail to consider the consumer's emotional state, relying solely on past purchase and search history. This makes them incapable of suggesting truly valuable products and services to users.

[0435] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0436] In this invention, the server includes means for presenting an evaluation form to the user terminal and acquiring information on interests, values, and purchase history; means for transferring the acquired information to a communication device and storing it in a storage device; and means for acquiring information on products and services from external sources, analyzing it using natural language processing technology, and matching the acquired information with the user's interests and emotional information. This enables highly personalized recommendations that take emotional states into account.

[0437] A "user terminal" is an information processing device used by users to input or receive information. This device has functions for presenting evaluation forms and displaying recommendation results.

[0438] A "rating form" refers to a screen or interface used to obtain information related to a user's values ​​and purchase history. Users enter the necessary information here.

[0439] A "communication device" is a device used to transfer information acquired from a user terminal to a server. It typically includes network communication capabilities.

[0440] A "storage device" is a device that stores transferred data and keeps it in a state where it can be accessed as needed.

[0441] "Product and service information" refers to detailed information about a specific product or service. This information is obtained from external sources and is subject to analysis.

[0442] "External information sources" refer to external databases or information providers that offer data about products or services.

[0443] "Natural language processing technology" is a technology that enables machines to understand and process human language. This makes it possible to analyze acquired information and match it with the user's interests and emotional information.

[0444] "Emotional information" refers to data that indicates a user's emotional state. It is typically obtained from the user's facial expressions, tone of voice, and other similar data.

[0445] "Personalized recommendations" refer to suggestions for products and services optimized according to the individual user's characteristics. They are customized based on the user's purchase history and sentiment information.

[0446] The system for implementing this invention consists of a user terminal, a communication device, a storage device, and a server. The user terminal functions as an interface with the user and obtains information related to the user's interests, values, and purchase history through evaluation forms. This information is then transferred to the server via the communication device and stored in the storage device.

[0447] The server retrieves information about products and services from external sources based on information received from the user. This information is analyzed using natural language processing technology and matched with the user's interests and emotional information. In this process, the server utilizes emotional information such as the user's facial expressions and tone of voice to provide highly accurate, personalized recommendations.

[0448] As a concrete example, if a user is interested in relaxation products, the server analyzes related information using natural language processing technology and recommends appropriate products. In this process, the user's purchase history from when they were feeling stressed is taken into consideration, and new products best suited to their current emotional state can be suggested.

[0449] A concrete example of a prompt used when determining recommendations to present to a user using a generative AI model is, "Based on past data, please recommend products that users tend to purchase during stressful periods." Based on this prompt, the system can identify the most useful products and services for the user and present them as recommendations.

[0450] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0451] Step 1:

[0452] The user operates the device and enters information into an evaluation form. This includes the user's interests, values, and purchase history. This input data forms the basis for designing a personalized user experience. The device collects this data and prepares to send it to the server via a communication device.

[0453] Step 2:

[0454] The server receives information acquired from the terminal and stores it in storage. A database is used here to efficiently manage the input evaluation data. The input is data related to the user's interests and emotions, and the output is data in an organized, stored state.

[0455] Step 3:

[0456] The server collects information about products and services from external sources. It analyzes this information using natural language processing techniques and compares the retrieved information with user data. In this process, the server analyzes vast amounts of data, determining the category and importance of the information. The input is raw data from external sources, and the output is the analyzed data.

[0457] Step 4:

[0458] The server uses a generative AI model to create prompt messages that recommend the most suitable products and services to the user. Based on the user's input data and analyzed information, the model uses these prompts to formulate recommendations tailored to the user's needs. The input consists of user information and analyzed product data, and the output is a personalized recommendation list.

[0459] Step 5:

[0460] The server transfers a generated list of recommendations to the terminal, which then displays this information to the user. The user can then review the recommended products and services and proceed with a purchase or another action. The input is the list of recommendations, and the output is the displayed data presented to the user.

[0461] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0462] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0463] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0464] [Third Embodiment]

[0465] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0466] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0467] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0468] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0469] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0470] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0471] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0472] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0473] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0474] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0475] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0476] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0477] This invention is a system that recommends voting options based on the user's values ​​and interests, and primarily consists of communication between the user's terminal and the server. The operation of this system is carried out according to the following procedure.

[0478] First, a survey form is displayed on the user's device, allowing them to express their values ​​and interests in detail. By answering this form, the user communicates their political interests and important themes to the system. This information is sent from the device to a server, where it is stored in a database.

[0479] The server collects the latest information on candidates and political parties through external data sources. This information includes candidates' pledges, past achievements, speech content, and social media activity. This data is meticulously analyzed using natural language processing techniques. As a result of the analysis, the candidate and political party information is categorized by topic, and the degree to which it matches the user's response data is determined.

[0480] Based on the analysis results, the server generates a list of recommended candidates and political parties. The list is designed to prioritize candidates that align with the user's values, making it easy for the user to choose the candidate that best suits their values. The recommendations are sent to the user's device and presented visually on it.

[0481] For example, if a user considers environmental issues important, a survey response reflecting this interest is sent to the server. Through analysis, the server prioritizes listing candidates who focus on environmental policies and presents this candidate information to the user. The user can then use this information to inform their actual voting decisions.

[0482] In this way, the present invention is a system that enables flexible voting suggestions that respond to the diverse values ​​of users, thereby contributing to the development of democracy.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] The device displays a login screen to the user. The user enters the appropriate authentication information and logs into their account. After logging in, the device displays a survey form. The user answers questions about policy themes and values ​​they are interested in in the survey. Once the user has completed the survey, the device sends this data to the server.

[0486] Step 2:

[0487] The server processes the user survey data it receives and stores it in a database. The server then utilizes external data sources to collect information about candidates and political parties related to the election. This includes data such as campaign promises, past performance, speech content, and social media ratings.

[0488] Step 3:

[0489] The server analyzes the candidate information it collects using natural language processing techniques. This analysis categorizes the information by policy and evaluates how well each candidate's policies match the areas of interest indicated in the user's responses. Based on this evaluation, a score is calculated for each candidate.

[0490] Step 4:

[0491] Based on the calculated score, the server generates a list of recommended candidates and political parties deemed most suitable for the user. This list is sorted in descending order of recommendation. The recommendation list includes each candidate's main pledges and brief background information.

[0492] Step 5:

[0493] The server generates a list of recommendations and sends it to the user's device in real time. The device retrieves this information and presents it to the user in an easy-to-understand visual format. The user can review the recommendations and view detailed information. This enables the user to make informed voting decisions.

[0494] (Example 1)

[0495] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0496] In the modern electoral system, there is a challenge in that it is difficult for individual voters to easily find candidates or organizations that align with their own values. Therefore, there is a need for methods to support voters with diverse values ​​in making the best choices.

[0497] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0498] In this invention, the server includes means for collecting information about the user's values ​​and interests, means for gathering and analyzing details of candidates and organizations from external sources, and means for recommending candidates and organizations that match the user's values. This makes it possible for users to easily identify candidates and organizations that are suitable for their own values.

[0499] A "user terminal" is a computer device that displays survey forms and collects information from users regarding their values ​​and interests.

[0500] A "server" is a computer system that receives information sent by a user and stores it in a memory device.

[0501] A "survey form" is a collection of questions designed to specifically investigate a user's values ​​and interests.

[0502] "External information sources" are external data providers that offer detailed information about candidates or organizations.

[0503] "Information processing technology" refers to the techniques used to analyze natural language and structured data.

[0504] "Recommended methods" refer to the methods used to present candidates or organizations that are a good match for the user based on the analysis results.

[0505] A "memory device" is a data storage device used to store received information.

[0506] "Values" refer to a collection of a user's beliefs and important principles.

[0507] "Candidates or organizations" refer to individuals or organizations that represent options in a political election.

[0508] "Social evaluation" refers to the overall assessment and opinion of a candidate or organization by society.

[0509] This invention consists of a system that recommends voting options that align with the user's values. The system operates primarily through communication between the user's terminal and the server.

[0510] Users input their values ​​and interests specifically through a survey form. The survey form is displayed on the user's device interface using technologies such as JavaScript and React. When a user enters information into the form and presses the submit button, that information is sent from the device to the server via an HTTP request.

[0511] The server processes the received information using Python frameworks such as Flask or Django and stores it in a database. PostgreSQL or MySQL are used for the database. The server validates user information and returns an error message to the user if invalid input is detected.

[0512] Furthermore, the server collects data about candidates and organizations from external sources. This operation is performed using web scraping tools such as Python's BeautifulSoup and Scrapy, or through provided APIs. The collected data includes candidates' policies, past achievements, speech content, and social media activity.

[0513] The server uses natural language processing techniques to analyze the collected data. Specifically, it uses Python's NLTK and spaCy to classify text data into topics. This process reveals how much emphasis each candidate places on specific policy areas.

[0514] The analysis results are matched with the user's values ​​on the server, and a recommendation list is generated that includes candidates and organizations with a high degree of match. This list generation algorithm is implemented using Python's Pandas and NumPy libraries. The generated list is sent to the user's device and displayed visually using a visualization library (e.g., Chart.js or D3.js).

[0515] As a concrete example, if a user is interested in environmental issues, they send a survey response based on that value to the server. The server analyzes the collected candidate information and prioritizes listing candidates who are focused on environmental policies. Based on this information, the user can choose the best candidate to vote for based on their own values.

[0516] An example of a prompt to a generative AI model is, "Based on this information, please generate a list of candidates that are best suited to users who are most interested in environmental issues."

[0517] Thus, this invention flexibly recommends voting options according to the diverse values ​​of users, thereby contributing to the realization of democracy.

[0518] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0519] Step 1:

[0520] Users receive a survey form on their device that allows them to express their values ​​and interests in detail. The survey form is displayed via a web interface, and users answer questions about political themes and issues that are important to them. The input for this step consists of human selection options, and the output is structured response data.

[0521] Step 2:

[0522] The terminal formats the user's response data and sends it to the server as an HTTP request. The input is the user's survey responses, and the output is information formatted into a data format for delivery to the server. This step also includes data format checks and validation.

[0523] Step 3:

[0524] The server stores the received data in a database. The input here is the user's response data sent from the terminal, and the output is the information recorded in the database. The server checks the data format and returns an error message to the terminal if necessary.

[0525] Step 4:

[0526] The server accesses external data sources to collect information about candidates and organizations. The input for this step is the address or API endpoint of the external information source, and the output is candidate data that is retrieved and stored as text information. The server uses web scraping tools or APIs.

[0527] Step 5:

[0528] The server analyzes the collected data using natural language processing techniques. The input is raw text data obtained from external sources, and the output is the analysis results classified by topic. Python's NLTK and spaCy are used to identify each candidate's policies and activities.

[0529] Step 6:

[0530] Based on the analysis results, the server generates a list of recommended candidates and organizations that match the user's values. The input consists of the user's survey data and the analysis results of candidate information; the output is a list of candidates suitable for the user. An algorithm that calculates the degree of data matching is used to generate the list.

[0531] Step 7:

[0532] The server sends the generated recommendation list to the user's terminal. The input for this step is candidate recommendation data, and the output is information visualized on the user's screen. The terminal uses a visualization library to present the information to the user in an easy-to-understand manner.

[0533] By coordinating each step, the system makes it easier for users to find candidates who match their values.

[0534] (Application Example 1)

[0535] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0536] In modern metropolitan areas, many residents lack adequate information regarding political participation, making it difficult for them to make informed decisions. In particular, information tailored to the individual values ​​and interests of residents is often insufficient in fostering public awareness within local communities and in selecting candidates during elections. This raises concerns that it will hinder residents' political expression and impede the development of democratic processes within their communities.

[0537] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0538] In this invention, the server includes means for displaying a questionnaire form on a user terminal and collecting data on the user's interests and values, means for transmitting the collected data to an information processing device and storing it in a data storage device, and means for providing information related to raising public awareness in the community and supporting election candidates. This makes it possible for residents to easily support candidates based on their own values ​​and make informed decisions.

[0539] A "user terminal" is an electronic device used by a user to input and retrieve information, and includes devices such as smartphones and computers.

[0540] A "survey form" is an electronic input screen with questions arranged on it, provided to collect data about users' interests and values.

[0541] An "information processing device" is a device consisting of a computer system or server that receives, analyzes, and stores collected data.

[0542] "Data storage devices" refer to devices, including storage devices and cloud storage, used to store collected data for the long term.

[0543] "External information sources" refer to sources of information, such as the internet and public databases, that provide information about candidates and organizations.

[0544] "Natural language processing technology" is a technology that uses computers to analyze the language that humans use on a daily basis, and is applied to the understanding and classification of text data.

[0545] "Individual targets or organizations" refers to groups or organizations with specific objectives, such as individual candidates or political parties involved in elections.

[0546] "Raising public awareness" is a concept that refers to activities and information provision aimed at promoting a sense of community and contribution among residents of a local area.

[0547] A "prompt" is input text used to give instructions to a generative AI model and generate responses or outputs.

[0548] The system for realizing this invention mainly consists of a user terminal and an information processing device (server). A questionnaire form for obtaining information about specific individuals or organizations related to the election is displayed on the user terminal. Data about the user's values ​​and interests is collected through this form. This data is transmitted to the information processing device via information communication means and stored in a data storage device.

[0549] The server retrieves information about individual subjects and organizations from external sources and analyzes that information using natural language processing technology. This analysis utilizes generative AI models specializing in understanding and classifying text data. The analyzed information is then tailored to align with the user's values ​​and interests. Based on the analysis results, the information processing device provides the user with information related to raising public awareness in the local community. This makes it easier for users to obtain information about candidates and organizations that align with their own values, thereby promoting informed decision-making.

[0550] For example, if a user has a strong interest in environmental protection, the information processing device will identify specific entities promoting environmental policies and provide that information to the user. This allows the user to receive information that aligns with their own values.

[0551] An example of a prompt is, "Please prioritize listing candidates related to the city's transportation problems. Provide information in a priority order that aligns with the user's values." By utilizing prompts in this way, the generative AI model can accurately provide the information the user is looking for.

[0552] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0553] Step 1:

[0554] The user accesses a survey form on their device and enters information about their interests and values. The entered data is sent from the device to the server and stored. The input here is the user's values ​​data, and the output is the user information stored on the server.

[0555] Step 2:

[0556] The server collects data about individual subjects and organizations from external sources. This data includes election candidates' policies and past performance. The input is data from external sources, and the output is new information stored on the server.

[0557] Step 3:

[0558] The server analyzes the collected data using a generating AI model and natural language processing. Here, all data stored on the server is analyzed as input, and its relationships are evaluated. The output is the analyzed information. Through this process, the data is classified by semantic units and filtered based on the user's values.

[0559] Step 4:

[0560] Based on the analysis results, the server generates a list of recommended individuals and organizations that align with the user's values. The input to the generation process is analysis data processed using natural language processing, and the output is recommended information that matches those values. This recommended information is prioritized.

[0561] Step 5:

[0562] The server sends the generated recommendation information to the user's terminal. The user's terminal receives this information and displays it visually. Here, the input is the recommendation information, and the output is what is displayed on the user's screen.

[0563] Step 6:

[0564] Users can review the displayed recommendations and send more detailed information or additional feedback to the server. This feedback is used to improve the accuracy of future recommendations. The input is user feedback, and the output is data used to improve the server's algorithms.

[0565] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0566] This invention is a system that improves recommendation accuracy by analyzing the user's emotional state, in addition to recommending voting options based on the user's values ​​and interests. This system mainly consists of a user terminal, a server, and an emotion engine.

[0567] First, the user's device presents them with a survey form. Here, the user answers questions about their values ​​and interests. The emotion engine analyzes the user's facial expressions and tone of voice while they answer the survey, and evaluates their emotional state. This evaluated emotional information is sent to the server along with the survey response data.

[0568] The server collects information on candidates and political parties using external data sources. This includes candidates' pledges, past achievements, and social media posts. The server analyzes this information using natural language processing techniques and organizes candidate information into categories based on user interests. At the same time, it takes into account the user's emotional state, evaluates the user's emotional response to each candidate, and incorporates this into the recommendation algorithm.

[0569] Based on the analysis, the server generates a list of recommended candidates and political parties suitable for the user. This list includes information on candidates deemed most suitable based on the user's interests and sentiment ratings. The recommendation list is quickly sent to the user's device and displayed to them. This allows the user to receive highly accurate recommendations, including those based on emotional satisfaction.

[0570] For example, suppose a user expresses interest in economic policy, and the emotion engine determines that the user's emotions are calm during the survey. In this case, the server prioritizes including candidates who prioritize economic policy and predicts they will have the most positive impact on the user's emotions in the recommendation list.

[0571] In this way, the present invention provides highly accurate voting recommendations that take into account both the user's interests and emotions, thereby supporting the decision-making process in voting.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The device displays a login screen to the user. The user enters their username and password to log in to their account. If successful, the device displays a survey form to the user. The user fills out the survey, selects their interests and values, and submits it.

[0575] Step 2:

[0576] The device transmits the user's facial expressions and voice, as they answer the survey, to an emotion engine via its camera and microphone. The emotion engine analyzes the data in real time and identifies the user's emotional state from their facial expressions and tone of voice. This information, along with an emotion label, is sent to the server along with the survey response.

[0577] Step 3:

[0578] The server stores survey data and sentiment information received from users in a database. During this process, the data is anonymized to protect user privacy. The server then collects the latest information on candidates and political parties from external data sources. This information includes campaign promises, social media posts, and speeches.

[0579] Step 4:

[0580] The server uses natural language processing technology to analyze candidate information. The goal of the analysis is to calculate a score indicating how each candidate's and party's policies relate to the user's interests. Furthermore, it incorporates user sentiment data to evaluate which candidates best meet the user's emotional needs.

[0581] Step 5:

[0582] Based on the analysis results, the server uses a recommendation algorithm to generate a list of suitable candidates for the user. Candidates are ranked in order of their level of interest and emotional affinity. The generated recommendation list is tailored to the user's individual needs.

[0583] Step 6:

[0584] The server sends a list of recommendations to the user's device. The device displays the list of recommended candidates to the user and allows them to view detailed information. The user can then use this information to decide on their voting behavior. The system collects feedback on the user's satisfaction with the recommendations and uses it to improve future algorithms.

[0585] (Example 2)

[0586] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0587] In today's information society, rationally and emotionally selecting who to vote for in an election is a challenging task. In particular, the sheer volume of information available about elections makes it difficult to gain a detailed understanding of individual candidates and political parties. Furthermore, there are few systems that can recommend voting options that take into account an individual's emotional state, resulting in low satisfaction with the chosen option.

[0588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0589] In this invention, the server includes means for collecting information about an individual's values ​​and interests, means for evaluating emotional information, and means for analyzing the collected information and making recommendations. This enables highly accurate recommendations for voting targets that take into account an individual's values ​​and emotional state.

[0590] A "user terminal" is an information processing device used by an individual, and is a device that displays questionnaires and checks recommendation results through an interface.

[0591] A "survey form" is a question-based input screen used to collect information about an individual's values ​​and interests.

[0592] "Information about an individual's values ​​and interests" refers to data that shows an individual's thoughts, beliefs, preferences, and areas of interest.

[0593] "Emotional analysis means" refers to a technology or device for detecting and evaluating an individual's emotional state from their facial expressions and tone of voice.

[0594] A "server" is an information processing device that analyzes received information and provides the processing results to the individual.

[0595] "Emotional information" refers to data about an individual's emotional state, and is the evaluation result obtained through emotion analysis methods.

[0596] "External information sources" refer to external databases and websites that provide information about candidates and political organizations.

[0597] "Natural language processing technology" is a technology that analyzes human language and extracts and organizes information from it.

[0598] A "recommendation algorithm" is a computational method for presenting the optimal choice based on an individual's values ​​and emotions.

[0599] "Recommendation results" refer to a list of information on candidates and political organizations presented to an individual based on analytical and computational methods.

[0600] "Feedback" refers to information about users' experiences and opinions, which is used to improve the recommendation system.

[0601] In order to implement this invention, the user terminal, the server, and the emotion analysis means must work together in coordination.

[0602] The user terminal presents each individual with a questionnaire form. This questionnaire investigates the individual's values ​​and interests and consists of multiple-choice and open-ended questions. The terminal also uses input devices such as a camera and microphone to record the individual's facial expressions and voice as they answer the questionnaire. This enables sentiment analysis.

[0603] The emotion analysis system works in conjunction with a server to detect an individual's emotional state from their facial expressions and voice. Specifically, it uses emotion analysis software (such as a common facial recognition API or voice analysis tool) to analyze an individual's emotions in real time as they answer a questionnaire. This evaluated emotion data is sent to the server along with the questionnaire data.

[0604] The server collects information about candidates and political organizations from external sources. It utilizes web scraping techniques and API connections for information gathering. The server analyzes the collected information using natural language processing techniques (e.g., common natural language processing libraries) and organizes it into categories based on individual values. Furthermore, the server uses a recommendation algorithm to generate recommendations for the most suitable candidates based on sentiment and interest information. This recommendation algorithm is built as a generative AI model and operates using prompts provided from outside the model.

[0605] As a concrete example, suppose a user has a strong interest in economic policy and is rated as "calm" in the sentiment analysis during the survey. In this case, the server will prioritize including in the recommendation list candidates who are predicted to have the most positive impact on the individual's emotional state among the candidates focused on economic policy.

[0606] Examples of prompt messages include the following:

[0607] User interests: Economic policy

[0608] User's emotional state: Calm

[0609] Target of recommendation: Candidate / Political Party

[0610] Objective: To recommend the best candidate based on their emotions and interests.

[0611] This invention enables highly accurate candidate recommendations that take into account individual emotions and values, thereby supporting better decision-making.

[0612] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0613] Step 1:

[0614] The device displays a survey and provides individuals with a form containing questions about their values ​​and interests.

[0615] Input: Question items

[0616] Output: User response data

[0617] Individuals answer questionnaire forms on their devices, providing information about their values ​​and interests. This process generates data about their values ​​and interests.

[0618] Step 2:

[0619] The device records the individual's facial expressions and voice and transmits them to an emotion analysis system.

[0620] Input: Individual facial expression and voice data

[0621] Output: Emotional information

[0622] The device uses a camera and microphone to record an individual's facial expressions and voice in real time. This data is then passed to emotion analysis software and used as foundational data for evaluating the individual's emotional state.

[0623] Step 3:

[0624] The emotion analysis system processes facial expression and voice data to identify the emotional state.

[0625] Input: Facial expression and voice data

[0626] Output: Evaluated sentiment information

[0627] The emotion analysis method uses specific algorithms (e.g., voice analysis tools or facial recognition APIs) to analyze and evaluate the emotional state from the input facial expressions and voice. As a result, the individual's emotional state is generated as data.

[0628] Step 4:

[0629] The device integrates survey data and sentiment information and sends it to the server.

[0630] Input: Survey response data, evaluated sentiment information

[0631] Output: Integrated personal information

[0632] The device integrates information about an individual's values, interests, and emotions into a single dataset and sends it to the server.

[0633] Step 5:

[0634] The server collects information about candidates and political organizations from external sources.

[0635] Input: External data source

[0636] Output: Information on candidates and political organizations

[0637] The server uses web scraping and API connections to collect information about candidates and political organizations. This information includes candidates' pledges, past achievements, and social standing.

[0638] Step 6:

[0639] The server uses natural language processing technology to analyze and organize the collected information.

[0640] Input: Information about candidates and political organizations

[0641] Output: Information organized by category

[0642] The server uses a common natural language processing library to analyze the collected information and organize it into categories based on the user's interests.

[0643] Step 7:

[0644] The server executes a recommendation algorithm based on the evaluated sentiment information and organized information.

[0645] Input: Integrated personal information, information organized by category

[0646] Output: Recommendation List

[0647] The server uses a generative AI model to select the most suitable candidates from the input personal information and organized data, and generates a recommendation list.

[0648] Step 8:

[0649] The device receives a list of recommendations and displays them to the individual.

[0650] Input: Recommendation list

[0651] Output: Displayed recommendation list

[0652] The device displays a list of recommendations received from the server to the individual, prompting them to consider them. This allows individuals to make more informed voting decisions.

[0653] (Application Example 2)

[0654] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0655] In today's consumer society, individual consumers often spend a great deal of time and effort selecting the most suitable products and services from a vast amount of information. Furthermore, traditional recommendation systems often fail to consider the consumer's emotional state, relying solely on past purchase and search history. This makes them incapable of suggesting truly valuable products and services to users.

[0656] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0657] In this invention, the server includes means for presenting an evaluation form to the user terminal and acquiring information on interests, values, and purchase history; means for transferring the acquired information to a communication device and storing it in a storage device; and means for acquiring information on products and services from external sources, analyzing it using natural language processing technology, and matching the acquired information with the user's interests and emotional information. This enables highly personalized recommendations that take emotional states into account.

[0658] A "user terminal" is an information processing device used by users to input or receive information. This device has functions for presenting evaluation forms and displaying recommendation results.

[0659] A "rating form" refers to a screen or interface used to obtain information related to a user's values ​​and purchase history. Users enter the necessary information here.

[0660] A "communication device" is a device used to transfer information acquired from a user terminal to a server. It typically includes network communication capabilities.

[0661] A "storage device" is a device that stores transferred data and keeps it in a state where it can be accessed as needed.

[0662] "Product and service information" refers to detailed information about a specific product or service. This information is obtained from external sources and is subject to analysis.

[0663] "External information sources" refer to external databases or information providers that offer data about products or services.

[0664] "Natural language processing technology" is a technology that enables machines to understand and process human language. This makes it possible to analyze acquired information and match it with the user's interests and emotional information.

[0665] "Emotional information" refers to data that indicates a user's emotional state. It is typically obtained from the user's facial expressions, tone of voice, and other similar data.

[0666] "Personalized recommendations" refer to suggestions for products and services optimized according to the individual user's characteristics. They are customized based on the user's purchase history and sentiment information.

[0667] The system for implementing this invention consists of a user terminal, a communication device, a storage device, and a server. The user terminal functions as an interface with the user and obtains information related to the user's interests, values, and purchase history through evaluation forms. This information is then transferred to the server via the communication device and stored in the storage device.

[0668] The server retrieves information about products and services from external sources based on information received from the user. This information is analyzed using natural language processing technology and matched with the user's interests and emotional information. In this process, the server utilizes emotional information such as the user's facial expressions and tone of voice to provide highly accurate, personalized recommendations.

[0669] As a concrete example, if a user is interested in relaxation products, the server analyzes related information using natural language processing technology and recommends appropriate products. In this process, the user's purchase history from when they were feeling stressed is taken into consideration, and new products best suited to their current emotional state can be suggested.

[0670] A concrete example of a prompt used when determining recommendations to present to a user using a generative AI model is, "Based on past data, please recommend products that users tend to purchase during stressful periods." Based on this prompt, the system can identify the most useful products and services for the user and present them as recommendations.

[0671] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0672] Step 1:

[0673] The user operates the device and enters information into an evaluation form. This includes the user's interests, values, and purchase history. This input data forms the basis for designing a personalized user experience. The device collects this data and prepares to send it to the server via a communication device.

[0674] Step 2:

[0675] The server receives information acquired from the terminal and stores it in storage. A database is used here to efficiently manage the input evaluation data. The input is data related to the user's interests and emotions, and the output is data in an organized, stored state.

[0676] Step 3:

[0677] The server collects information about products and services from external sources. It analyzes this information using natural language processing techniques and compares the retrieved information with user data. In this process, the server analyzes vast amounts of data, determining the category and importance of the information. The input is raw data from external sources, and the output is the analyzed data.

[0678] Step 4:

[0679] The server uses a generative AI model to create prompt messages that recommend the most suitable products and services to the user. Based on the user's input data and analyzed information, the model uses these prompts to formulate recommendations tailored to the user's needs. The input consists of user information and analyzed product data, and the output is a personalized recommendation list.

[0680] Step 5:

[0681] The server transfers a generated list of recommendations to the terminal, which then displays this information to the user. The user can then review the recommended products and services and proceed with a purchase or another action. The input is the list of recommendations, and the output is the displayed data presented to the user.

[0682] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0683] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0684] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0685] [Fourth Embodiment]

[0686] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0687] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0688] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0689] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0690] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0691] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0692] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0693] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0694] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0695] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0696] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0697] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0698] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] This invention is a system that recommends voting options based on the user's values ​​and interests, and primarily consists of communication between the user's terminal and the server. The operation of this system is carried out according to the following procedure.

[0700] First, a survey form is displayed on the user's device, allowing them to express their values ​​and interests in detail. By answering this form, the user communicates their political interests and important themes to the system. This information is sent from the device to a server, where it is stored in a database.

[0701] The server collects the latest information on candidates and political parties through external data sources. This information includes candidates' pledges, past achievements, speech content, and social media activity. This data is meticulously analyzed using natural language processing techniques. As a result of the analysis, the candidate and political party information is categorized by topic, and the degree to which it matches the user's response data is determined.

[0702] Based on the analysis results, the server generates a list of recommended candidates and political parties. The list is designed to prioritize candidates that align with the user's values, making it easy for the user to choose the candidate that best suits their values. The recommendations are sent to the user's device and presented visually on it.

[0703] For example, if a user considers environmental issues important, a survey response reflecting this interest is sent to the server. Through analysis, the server prioritizes listing candidates who focus on environmental policies and presents this candidate information to the user. The user can then use this information to inform their actual voting decisions.

[0704] In this way, the present invention is a system that enables flexible voting suggestions that respond to the diverse values ​​of users, thereby contributing to the development of democracy.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The device displays a login screen to the user. The user enters the appropriate authentication information and logs into their account. After logging in, the device displays a survey form. The user answers questions about policy themes and values ​​they are interested in in the survey. Once the user has completed the survey, the device sends this data to the server.

[0708] Step 2:

[0709] The server processes the user survey data it receives and stores it in a database. The server then utilizes external data sources to collect information about candidates and political parties related to the election. This includes data such as campaign promises, past performance, speech content, and social media ratings.

[0710] Step 3:

[0711] The server analyzes the candidate information it collects using natural language processing techniques. This analysis categorizes the information by policy and evaluates how well each candidate's policies match the areas of interest indicated in the user's responses. Based on this evaluation, a score is calculated for each candidate.

[0712] Step 4:

[0713] Based on the calculated score, the server generates a list of recommended candidates and political parties deemed most suitable for the user. This list is sorted in descending order of recommendation. The recommendation list includes each candidate's main pledges and brief background information.

[0714] Step 5:

[0715] The server generates a list of recommendations and sends it to the user's device in real time. The device retrieves this information and presents it to the user in an easy-to-understand visual format. The user can review the recommendations and view detailed information. This enables the user to make informed voting decisions.

[0716] (Example 1)

[0717] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0718] In the modern electoral system, there is a challenge in that it is difficult for individual voters to easily find candidates or organizations that align with their own values. Therefore, there is a need for methods to support voters with diverse values ​​in making the best choices.

[0719] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0720] In this invention, the server includes means for collecting information about the user's values ​​and interests, means for gathering and analyzing details of candidates and organizations from external sources, and means for recommending candidates and organizations that match the user's values. This makes it possible for users to easily identify candidates and organizations that are suitable for their own values.

[0721] A "user terminal" is a computer device that displays survey forms and collects information from users regarding their values ​​and interests.

[0722] A "server" is a computer system that receives information sent by a user and stores it in a memory device.

[0723] A "survey form" is a collection of questions designed to specifically investigate a user's values ​​and interests.

[0724] "External information sources" are external data providers that offer detailed information about candidates or organizations.

[0725] "Information processing technology" refers to the techniques used to analyze natural language and structured data.

[0726] "Recommended methods" refer to the methods used to present candidates or organizations that are a good match for the user based on the analysis results.

[0727] A "memory device" is a data storage device used to store received information.

[0728] "Values" refer to a collection of a user's beliefs and important principles.

[0729] "Candidates or organizations" refer to individuals or organizations that represent options in a political election.

[0730] "Social evaluation" refers to the overall assessment and opinion of a candidate or organization by society.

[0731] This invention consists of a system that recommends voting options that align with the user's values. The system operates primarily through communication between the user's terminal and the server.

[0732] Users input their values ​​and interests specifically through a survey form. The survey form is displayed on the user's device interface using technologies such as JavaScript and React. When a user enters information into the form and presses the submit button, that information is sent from the device to the server via an HTTP request.

[0733] The server processes the received information using Python frameworks such as Flask or Django and stores it in a database. PostgreSQL or MySQL are used for the database. The server validates user information and returns an error message to the user if invalid input is detected.

[0734] Furthermore, the server collects data about candidates and organizations from external sources. This operation is performed using web scraping tools such as Python's BeautifulSoup and Scrapy, or through provided APIs. The collected data includes candidates' policies, past achievements, speech content, and social media activity.

[0735] The server uses natural language processing techniques to analyze the collected data. Specifically, it uses Python's NLTK and spaCy to classify text data into topics. This process reveals how much emphasis each candidate places on specific policy areas.

[0736] The analysis results are matched with the user's values ​​on the server, and a recommendation list is generated that includes candidates and organizations with a high degree of match. This list generation algorithm is implemented using Python's Pandas and NumPy libraries. The generated list is sent to the user's device and displayed visually using a visualization library (e.g., Chart.js or D3.js).

[0737] As a concrete example, if a user is interested in environmental issues, they send a survey response based on that value to the server. The server analyzes the collected candidate information and prioritizes listing candidates who are focused on environmental policies. Based on this information, the user can choose the best candidate to vote for based on their own values.

[0738] An example of a prompt to a generative AI model is, "Based on this information, please generate a list of candidates that are best suited to users who are most interested in environmental issues."

[0739] Thus, this invention flexibly recommends voting options according to the diverse values ​​of users, thereby contributing to the realization of democracy.

[0740] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0741] Step 1:

[0742] Users receive a survey form on their device that allows them to express their values ​​and interests in detail. The survey form is displayed via a web interface, and users answer questions about political themes and issues that are important to them. The input for this step consists of human selection options, and the output is structured response data.

[0743] Step 2:

[0744] The terminal formats the user's response data and sends it to the server as an HTTP request. The input is the user's survey responses, and the output is information formatted into a data format for delivery to the server. This step also includes data format checks and validation.

[0745] Step 3:

[0746] The server stores the received data in a database. The input here is the user's response data sent from the terminal, and the output is the information recorded in the database. The server checks the data format and returns an error message to the terminal if necessary.

[0747] Step 4:

[0748] The server accesses external data sources to collect information about candidates and organizations. The input for this step is the address or API endpoint of the external information source, and the output is candidate data that is retrieved and stored as text information. The server uses web scraping tools or APIs.

[0749] Step 5:

[0750] The server analyzes the collected data using natural language processing techniques. The input is raw text data obtained from external sources, and the output is the analysis results classified by topic. Python's NLTK and spaCy are used to identify each candidate's policies and activities.

[0751] Step 6:

[0752] Based on the analysis results, the server generates a list of recommended candidates and organizations that match the user's values. The input consists of the user's survey data and the analysis results of candidate information; the output is a list of candidates suitable for the user. An algorithm that calculates the degree of data matching is used to generate the list.

[0753] Step 7:

[0754] The server sends the generated recommendation list to the user's terminal. The input for this step is candidate recommendation data, and the output is information visualized on the user's screen. The terminal uses a visualization library to present the information to the user in an easy-to-understand manner.

[0755] By coordinating each step, the system makes it easier for users to find candidates who match their values.

[0756] (Application Example 1)

[0757] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] In modern metropolitan areas, many residents lack adequate information regarding political participation, making it difficult for them to make informed decisions. In particular, information tailored to the individual values ​​and interests of residents is often insufficient in fostering public awareness within local communities and in selecting candidates during elections. This raises concerns that it will hinder residents' political expression and impede the development of democratic processes within their communities.

[0759] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0760] In this invention, the server includes means for displaying a questionnaire form on a user terminal and collecting data on the user's interests and values, means for transmitting the collected data to an information processing device and storing it in a data storage device, and means for providing information related to raising public awareness in the community and supporting election candidates. This makes it possible for residents to easily support candidates based on their own values ​​and make informed decisions.

[0761] A "user terminal" is an electronic device used by a user to input and retrieve information, and includes devices such as smartphones and computers.

[0762] A "survey form" is an electronic input screen with questions arranged on it, provided to collect data about users' interests and values.

[0763] An "information processing device" is a device consisting of a computer system or server that receives, analyzes, and stores collected data.

[0764] "Data storage devices" refer to devices, including storage devices and cloud storage, used to store collected data for the long term.

[0765] "External information sources" refer to sources of information, such as the internet and public databases, that provide information about candidates and organizations.

[0766] "Natural language processing technology" is a technology that uses computers to analyze the language that humans use on a daily basis, and is applied to the understanding and classification of text data.

[0767] "Individual targets or organizations" refers to groups or organizations with specific objectives, such as individual candidates or political parties involved in elections.

[0768] "Raising public awareness" is a concept that refers to activities and information provision aimed at promoting a sense of community and contribution among residents of a local area.

[0769] A "prompt" is input text used to give instructions to a generative AI model and generate responses or outputs.

[0770] The system for realizing this invention mainly consists of a user terminal and an information processing device (server). A questionnaire form for obtaining information about specific individuals or organizations related to the election is displayed on the user terminal. Data about the user's values ​​and interests is collected through this form. This data is transmitted to the information processing device via information communication means and stored in a data storage device.

[0771] The server retrieves information about individual subjects and organizations from external sources and analyzes that information using natural language processing technology. This analysis utilizes generative AI models specializing in understanding and classifying text data. The analyzed information is then tailored to align with the user's values ​​and interests. Based on the analysis results, the information processing device provides the user with information related to raising public awareness in the local community. This makes it easier for users to obtain information about candidates and organizations that align with their own values, thereby promoting informed decision-making.

[0772] For example, if a user has a strong interest in environmental protection, the information processing device will identify specific entities promoting environmental policies and provide that information to the user. This allows the user to receive information that aligns with their own values.

[0773] An example of a prompt is, "Please prioritize listing candidates related to the city's transportation problems. Provide information in a priority order that aligns with the user's values." By utilizing prompts in this way, the generative AI model can accurately provide the information the user is looking for.

[0774] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0775] Step 1:

[0776] The user accesses a survey form on their device and enters information about their interests and values. The entered data is sent from the device to the server and stored. The input here is the user's values ​​data, and the output is the user information stored on the server.

[0777] Step 2:

[0778] The server collects data about individual subjects and organizations from external sources. This data includes election candidates' policies and past performance. The input is data from external sources, and the output is new information stored on the server.

[0779] Step 3:

[0780] The server analyzes the collected data using a generating AI model and natural language processing. Here, all data stored on the server is analyzed as input, and its relationships are evaluated. The output is the analyzed information. Through this process, the data is classified by semantic units and filtered based on the user's values.

[0781] Step 4:

[0782] Based on the analysis results, the server generates a list of recommended individuals and organizations that align with the user's values. The input to the generation process is analysis data processed using natural language processing, and the output is recommended information that matches those values. This recommended information is prioritized.

[0783] Step 5:

[0784] The server sends the generated recommendation information to the user's terminal. The user's terminal receives this information and displays it visually. Here, the input is the recommendation information, and the output is what is displayed on the user's screen.

[0785] Step 6:

[0786] Users can review the displayed recommendations and send more detailed information or additional feedback to the server. This feedback is used to improve the accuracy of future recommendations. The input is user feedback, and the output is data used to improve the server's algorithms.

[0787] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0788] This invention is a system that improves recommendation accuracy by analyzing the user's emotional state, in addition to recommending voting options based on the user's values ​​and interests. This system mainly consists of a user terminal, a server, and an emotion engine.

[0789] First, the user's device presents them with a survey form. Here, the user answers questions about their values ​​and interests. The emotion engine analyzes the user's facial expressions and tone of voice while they answer the survey, and evaluates their emotional state. This evaluated emotional information is sent to the server along with the survey response data.

[0790] The server collects information on candidates and political parties using external data sources. This includes candidates' pledges, past achievements, and social media posts. The server analyzes this information using natural language processing techniques and organizes candidate information into categories based on user interests. At the same time, it takes into account the user's emotional state, evaluates the user's emotional response to each candidate, and incorporates this into the recommendation algorithm.

[0791] Based on the analysis, the server generates a list of recommended candidates and political parties suitable for the user. This list includes information on candidates deemed most suitable based on the user's interests and sentiment ratings. The recommendation list is quickly sent to the user's device and displayed to them. This allows the user to receive highly accurate recommendations, including those based on emotional satisfaction.

[0792] For example, suppose a user expresses interest in economic policy, and the emotion engine determines that the user's emotions are calm during the survey. In this case, the server prioritizes including candidates who prioritize economic policy and predicts they will have the most positive impact on the user's emotions in the recommendation list.

[0793] In this way, the present invention provides highly accurate voting recommendations that take into account both the user's interests and emotions, thereby supporting the decision-making process in voting.

[0794] The following describes the processing flow.

[0795] Step 1:

[0796] The device displays a login screen to the user. The user enters their username and password to log in to their account. If successful, the device displays a survey form to the user. The user fills out the survey, selects their interests and values, and submits it.

[0797] Step 2:

[0798] The device transmits the user's facial expressions and voice, as they answer the survey, to an emotion engine via its camera and microphone. The emotion engine analyzes the data in real time and identifies the user's emotional state from their facial expressions and tone of voice. This information, along with an emotion label, is sent to the server along with the survey response.

[0799] Step 3:

[0800] The server stores survey data and sentiment information received from users in a database. During this process, the data is anonymized to protect user privacy. The server then collects the latest information on candidates and political parties from external data sources. This information includes campaign promises, social media posts, and speeches.

[0801] Step 4:

[0802] The server uses natural language processing technology to analyze candidate information. The goal of the analysis is to calculate a score indicating how each candidate's and party's policies relate to the user's interests. Furthermore, it incorporates user sentiment data to evaluate which candidates best meet the user's emotional needs.

[0803] Step 5:

[0804] Based on the analysis results, the server uses a recommendation algorithm to generate a list of suitable candidates for the user. Candidates are ranked in order of their level of interest and emotional affinity. The generated recommendation list is tailored to the user's individual needs.

[0805] Step 6:

[0806] The server sends a list of recommendations to the user's device. The device displays the list of recommended candidates to the user and allows them to view detailed information. The user can then use this information to decide on their voting behavior. The system collects feedback on the user's satisfaction with the recommendations and uses it to improve future algorithms.

[0807] (Example 2)

[0808] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0809] In today's information society, rationally and emotionally selecting who to vote for in an election is a challenging task. In particular, the sheer volume of information available about elections makes it difficult to gain a detailed understanding of individual candidates and political parties. Furthermore, there are few systems that can recommend voting options that take into account an individual's emotional state, resulting in low satisfaction with the chosen option.

[0810] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0811] In this invention, the server includes means for collecting information about an individual's values ​​and interests, means for evaluating emotional information, and means for analyzing the collected information and making recommendations. This enables highly accurate recommendations for voting targets that take into account an individual's values ​​and emotional state.

[0812] A "user terminal" is an information processing device used by an individual, and is a device that displays questionnaires and checks recommendation results through an interface.

[0813] A "survey form" is a question-based input screen used to collect information about an individual's values ​​and interests.

[0814] "Information about an individual's values ​​and interests" refers to data that shows an individual's thoughts, beliefs, preferences, and areas of interest.

[0815] "Emotional analysis means" refers to a technology or device for detecting and evaluating an individual's emotional state from their facial expressions and tone of voice.

[0816] A "server" is an information processing device that analyzes received information and provides the processing results to the individual.

[0817] "Emotional information" refers to data about an individual's emotional state, and is the evaluation result obtained through emotion analysis methods.

[0818] "External information sources" refer to external databases and websites that provide information about candidates and political organizations.

[0819] "Natural language processing technology" is a technology that analyzes human language and extracts and organizes information from it.

[0820] A "recommendation algorithm" is a computational method for presenting the optimal choice based on an individual's values ​​and emotions.

[0821] "Recommendation results" refer to a list of information on candidates and political organizations presented to an individual based on analytical and computational methods.

[0822] "Feedback" refers to information about users' experiences and opinions, which is used to improve the recommendation system.

[0823] In order to implement this invention, the user terminal, the server, and the emotion analysis means must work together in coordination.

[0824] The user terminal presents each individual with a questionnaire form. This questionnaire investigates the individual's values ​​and interests and consists of multiple-choice and open-ended questions. The terminal also uses input devices such as a camera and microphone to record the individual's facial expressions and voice as they answer the questionnaire. This enables sentiment analysis.

[0825] The emotion analysis system works in conjunction with a server to detect an individual's emotional state from their facial expressions and voice. Specifically, it uses emotion analysis software (such as a common facial recognition API or voice analysis tool) to analyze an individual's emotions in real time as they answer a questionnaire. This evaluated emotion data is sent to the server along with the questionnaire data.

[0826] The server collects information about candidates and political organizations from external sources. It utilizes web scraping techniques and API connections for information gathering. The server analyzes the collected information using natural language processing techniques (e.g., common natural language processing libraries) and organizes it into categories based on individual values. Furthermore, the server uses a recommendation algorithm to generate recommendations for the most suitable candidates based on sentiment and interest information. This recommendation algorithm is built as a generative AI model and operates using prompts provided from outside the model.

[0827] As a concrete example, suppose a user has a strong interest in economic policy and is rated as "calm" in the sentiment analysis during the survey. In this case, the server will prioritize including in the recommendation list candidates who are predicted to have the most positive impact on the individual's emotional state among the candidates focused on economic policy.

[0828] Examples of prompt messages include the following:

[0829] User interests: Economic policy

[0830] User's emotional state: Calm

[0831] Target of recommendation: Candidate / Political Party

[0832] Objective: To recommend the best candidate based on their emotions and interests.

[0833] This invention enables highly accurate candidate recommendations that take into account individual emotions and values, thereby supporting better decision-making.

[0834] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0835] Step 1:

[0836] The device displays a survey and provides individuals with a form containing questions about their values ​​and interests.

[0837] Input: Question items

[0838] Output: User response data

[0839] Individuals answer questionnaire forms on their devices, providing information about their values ​​and interests. This process generates data about their values ​​and interests.

[0840] Step 2:

[0841] The device records the individual's facial expressions and voice and transmits them to an emotion analysis system.

[0842] Input: Individual facial expression and voice data

[0843] Output: Emotional information

[0844] The device uses a camera and microphone to record an individual's facial expressions and voice in real time. This data is then passed to emotion analysis software and used as foundational data for evaluating the individual's emotional state.

[0845] Step 3:

[0846] The emotion analysis system processes facial expression and voice data to identify the emotional state.

[0847] Input: Facial expression and voice data

[0848] Output: Evaluated sentiment information

[0849] The emotion analysis method uses specific algorithms (e.g., voice analysis tools or facial recognition APIs) to analyze and evaluate the emotional state from the input facial expressions and voice. As a result, the individual's emotional state is generated as data.

[0850] Step 4:

[0851] The device integrates survey data and sentiment information and sends it to the server.

[0852] Input: Survey response data, evaluated sentiment information

[0853] Output: Integrated personal information

[0854] The device integrates information about an individual's values, interests, and emotions into a single dataset and sends it to the server.

[0855] Step 5:

[0856] The server collects information about candidates and political organizations from external sources.

[0857] Input: External data source

[0858] Output: Information on candidates and political organizations

[0859] The server uses web scraping and API connections to collect information about candidates and political organizations. This information includes candidates' pledges, past achievements, and social standing.

[0860] Step 6:

[0861] The server uses natural language processing technology to analyze and organize the collected information.

[0862] Input: Information about candidates and political organizations

[0863] Output: Information organized by category

[0864] The server uses a common natural language processing library to analyze the collected information and organize it into categories based on the user's interests.

[0865] Step 7:

[0866] The server executes a recommendation algorithm based on the evaluated sentiment information and organized information.

[0867] Input: Integrated personal information, information organized by category

[0868] Output: Recommendation List

[0869] The server uses a generative AI model to select the most suitable candidates from the input personal information and organized data, and generates a recommendation list.

[0870] Step 8:

[0871] The device receives a list of recommendations and displays them to the individual.

[0872] Input: Recommendation list

[0873] Output: Displayed recommendation list

[0874] The device displays a list of recommendations received from the server to the individual, prompting them to consider them. This allows individuals to make more informed voting decisions.

[0875] (Application Example 2)

[0876] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0877] In today's consumer society, individual consumers often spend a great deal of time and effort selecting the most suitable products and services from a vast amount of information. Furthermore, traditional recommendation systems often fail to consider the consumer's emotional state, relying solely on past purchase and search history. This makes them incapable of suggesting truly valuable products and services to users.

[0878] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0879] In this invention, the server includes means for presenting an evaluation form to the user terminal and acquiring information on interests, values, and purchase history; means for transferring the acquired information to a communication device and storing it in a storage device; and means for acquiring information on products and services from external sources, analyzing it using natural language processing technology, and matching the acquired information with the user's interests and emotional information. This enables highly personalized recommendations that take emotional states into account.

[0880] A "user terminal" is an information processing device used by users to input or receive information. This device has functions for presenting evaluation forms and displaying recommendation results.

[0881] A "rating form" refers to a screen or interface used to obtain information related to a user's values ​​and purchase history. Users enter the necessary information here.

[0882] A "communication device" is a device used to transfer information acquired from a user terminal to a server. It typically includes network communication capabilities.

[0883] A "storage device" is a device that stores transferred data and keeps it in a state where it can be accessed as needed.

[0884] "Product and service information" refers to detailed information about a specific product or service. This information is obtained from external sources and is subject to analysis.

[0885] "External information sources" refer to external databases or information providers that offer data about products or services.

[0886] "Natural language processing technology" is a technology that enables machines to understand and process human language. This makes it possible to analyze acquired information and match it with the user's interests and emotional information.

[0887] "Emotional information" refers to data that indicates a user's emotional state. It is typically obtained from the user's facial expressions, tone of voice, and other similar data.

[0888] "Personalized recommendations" refer to suggestions for products and services optimized according to the individual user's characteristics. They are customized based on the user's purchase history and sentiment information.

[0889] The system for implementing this invention consists of a user terminal, a communication device, a storage device, and a server. The user terminal functions as an interface with the user and obtains information related to the user's interests, values, and purchase history through evaluation forms. This information is then transferred to the server via the communication device and stored in the storage device.

[0890] The server retrieves information about products and services from external sources based on information received from the user. This information is analyzed using natural language processing technology and matched with the user's interests and emotional information. In this process, the server utilizes emotional information such as the user's facial expressions and tone of voice to provide highly accurate, personalized recommendations.

[0891] As a concrete example, if a user is interested in relaxation products, the server analyzes related information using natural language processing technology and recommends appropriate products. In this process, the user's purchase history from when they were feeling stressed is taken into consideration, and new products best suited to their current emotional state can be suggested.

[0892] A concrete example of a prompt used when determining recommendations to present to a user using a generative AI model is, "Based on past data, please recommend products that users tend to purchase during stressful periods." Based on this prompt, the system can identify the most useful products and services for the user and present them as recommendations.

[0893] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0894] Step 1:

[0895] The user operates the device and enters information into an evaluation form. This includes the user's interests, values, and purchase history. This input data forms the basis for designing a personalized user experience. The device collects this data and prepares to send it to the server via a communication device.

[0896] Step 2:

[0897] The server receives information acquired from the terminal and stores it in storage. A database is used here to efficiently manage the input evaluation data. The input is data related to the user's interests and emotions, and the output is data in an organized, stored state.

[0898] Step 3:

[0899] The server collects information about products and services from external sources. It analyzes this information using natural language processing techniques and compares the retrieved information with user data. In this process, the server analyzes vast amounts of data, determining the category and importance of the information. The input is raw data from external sources, and the output is the analyzed data.

[0900] Step 4:

[0901] The server uses a generative AI model to create prompt messages that recommend the most suitable products and services to the user. Based on the user's input data and analyzed information, the model uses these prompts to formulate recommendations tailored to the user's needs. The input consists of user information and analyzed product data, and the output is a personalized recommendation list.

[0902] Step 5:

[0903] The server transfers a generated list of recommendations to the terminal, which then displays this information to the user. The user can then review the recommended products and services and proceed with a purchase or another action. The input is the list of recommendations, and the output is the displayed data presented to the user.

[0904] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0905] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0906] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0907] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0908] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0909] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0910] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0911] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0912] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0913] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0914] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0915] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0916] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0917] 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.

[0918] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0919] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0920] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0921] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0922] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0923] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0924] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0925] The following is further disclosed regarding the embodiments described above.

[0926] (Claim 1)

[0927] A means of displaying a survey form on the user's terminal and collecting data on the user's interests and values,

[0928] A means of sending the collected data to a server and storing it in a database,

[0929] A means of collecting information on candidates and political parties from external data sources, analyzing it using natural language processing technology, and matching the collected data with user interests,

[0930] Based on the analysis results, a means of recommending suitable candidates and political parties to users,

[0931] A means of displaying the recommended results on the user's terminal,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, further comprising means by which the server collects user feedback and uses it to improve the recommendation algorithm.

[0935] (Claim 3)

[0936] The system according to claim 1, further comprising means for analyzing the past performance and social media ratings of candidates and political parties in real time and providing users with rapid recommendations.

[0937] "Example 1"

[0938] (Claim 1)

[0939] A means of displaying a survey form on the user's terminal and collecting information about the user's values ​​and interests,

[0940] A means of sending the collected information to a server and storing it in a storage device,

[0941] A means of gathering details about candidates and organizations from external sources, analyzing them using information processing technology, and matching the collected information with user interests,

[0942] Based on the analysis results, we will develop methods to recommend candidates and organizations that align with the user's values,

[0943] A means of presenting recommended results to the user's device,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, further comprising means for a server to collect user feedback and use it to improve recommendation methods.

[0947] (Claim 3)

[0948] The system according to claim 1, further comprising means for rapidly analyzing the past achievements and social reputation of candidates and organizations and providing users with rapid recommendations.

[0949] "Application Example 1"

[0950] (Claim 1)

[0951] A means of displaying a survey form on the user's terminal and collecting data on the user's interests and values,

[0952] A means for transmitting collected data to an information processing device and storing it in a data storage device,

[0953] A means of collecting information on individual subjects or organizations from external sources, analyzing it using natural language processing technology, and matching the collected data with user interests,

[0954] Based on the analysis results, a means to recommend suitable individual targets or organizations for the user,

[0955] A means of displaying the recommended results on the user's terminal,

[0956] Means of providing information related to raising public awareness in local communities and supporting election candidates,

[0957] A system that includes this.

[0958] (Claim 2)

[0959] The system according to claim 1, further comprising means for the information processing device to collect user feedback and use it to improve the recommendation algorithm.

[0960] (Claim 3)

[0961] The system according to claim 1, further comprising means for analyzing the past performance and social reputation of individual subjects or organizations in real time and providing rapid recommendations to users.

[0962] "Example 2 of combining an emotion engine"

[0963] (Claim 1)

[0964] A means of displaying a survey form on a user's device and collecting information about an individual's values ​​and interests,

[0965] A means for transmitting collected information to an emotion analysis system and evaluating an individual's emotional state,

[0966] A means of sending the evaluated emotional information along with the survey information to a server and storing it in a database,

[0967] A means of collecting information on candidates and political organizations from external sources, analyzing it using natural language processing technology, and organizing the collected information into categories based on the individual's interests,

[0968] Based on the analysis results and sentiment evaluations, a means of recommending candidates and political organizations suitable for individuals,

[0969] A means of displaying the recommendation results on the user's terminal,

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, further comprising means for a server to collect feedback from individuals and use it to improve the recommendation algorithm.

[0973] (Claim 3)

[0974] The system according to claim 1, further comprising means for analyzing the past performance and social reputation of candidates and political organizations in real time and providing rapid recommendations to individuals.

[0975] "Application example 2 when combining with an emotional engine"

[0976] (Claim 1)

[0977] A means of presenting an evaluation form to the user's device and obtaining information about their interests, values, and purchase history,

[0978] A means for transferring the acquired information to a communication device and storing it in a memory device,

[0979] A means of obtaining information about products and services from external sources, analyzing it using natural language processing technology, and matching the obtained information with user interest and sentiment information.

[0980] Based on the analysis results, a means of recommending products and services suitable for the user,

[0981] A means of displaying the recommended results on the user's terminal,

[0982] A system that includes this.

[0983] (Claim 2)

[0984] The system according to claim 1, further comprising means for a communication device to collect user feedback and use it to improve a recommendation algorithm.

[0985] (Claim 3)

[0986] The system according to claim 1, further comprising means for immediately analyzing publicly available information and electronic feedback on products and services and providing prompt recommendations to users. [Explanation of symbols]

[0987] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of displaying a survey form on the user's terminal and collecting data on the user's interests and values, A means of sending the collected data to a server and storing it in a database, A means of collecting information on candidates and political parties from external data sources, analyzing it using natural language processing technology, and matching the collected data with user interests, Based on the analysis results, a means of recommending suitable candidates and political parties to users, A means of displaying the recommended results on the user's terminal, A system that includes this.

2. The system according to claim 1, further comprising means by which the server collects user feedback and uses it to improve the recommendation algorithm.

3. The system according to claim 1, further comprising means for analyzing the past performance and social media ratings of candidates and political parties in real time and providing users with rapid recommendations.

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