system

Abstract

The system according to this embodiment aims to allow voters to find a politician whose interests and values ​​best match their own and to engage in direct dialogue with them. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a presentation unit, and a dialogue unit. The reception unit receives input from the user regarding their interests, opinions, and values. The analysis unit analyzes the information input by the reception unit to understand the politician's profile, vision, beliefs, policies, etc. The presentation unit presents the user with the most suitable politician based on the information understood by the analysis unit. The dialogue unit facilitates an online dialogue between the user and the politician presented by the presentation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for the right holder to find a politician who best matches their interests and values.

[0005] The system according to the embodiment aims to enable the right holder to find a politician who best matches their interests and values and directly communicate with them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a presentation unit, and a dialogue unit. The reception unit receives input from the user regarding their interests, opinions, and values. The analysis unit analyzes the information input by the reception unit to understand the politician's profile, vision, beliefs, policies, etc. The presentation unit presents the user with the most suitable politician based on the information understood by the analysis unit. The dialogue unit facilitates an online dialogue between the user and the politician presented by the presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows voters to find a politician whose interests and values ​​best match their own and to engage in direct dialogue with them. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that utilizes AI technology to enable voters to find the most suitable politician and engage in direct dialogue with them. In this system, the user inputs their interests, opinions, and values, and the AI ​​analyzes the input information to gain a deep understanding of the politician's profile, vision, beliefs, and policies. The AI ​​analyzes each politician's information in detail and matches it with the user's interests, opinions, and values. The AI ​​presents the user with the most suitable politician, and the user can review the presented politician's profile, vision, beliefs, and policies. Furthermore, the AI ​​enables online dialogue between the user and the politician. The user can directly interact with the presented politician. This mechanism allows voters to vote for the candidate who best aligns with their ideology and interests, enabling them to make a satisfying political choice. In addition, the AI ​​can effectively convey the politician's proposals and stances during the election period. This allows voters to accurately grasp the politician's information and identify the politician who will meet their expectations. This system is effective for promoting political participation among voters, general citizens interested in politics, young people, and first-time voters. For example, when a user inputs their interests, opinions, and values, the AI ​​analyzes the user's input to understand the politician's profile, vision, beliefs, and policies. The AI ​​uses methods such as text analysis, data mining, and machine learning algorithms to gain a deep understanding of the politician's information. Next, the AI ​​matches the user's interests, opinions, and values ​​with the politician's information. For example, the AI ​​presents the user with the most suitable politician based on similarity measures and the algorithms used. Furthermore, the AI ​​facilitates online dialogue between the user and the politician. For example, the AI ​​provides an online dialogue platform so that the user can directly interact with the presented politician. This allows the user to vote for the candidate who most closely aligns with their ideology and interests, enabling them to make a satisfying political choice. The AI ​​also communicates the politician's proposals and stances to voters during election periods. For example, the AI ​​accurately conveys information about politicians using different types of media and communication methods. This allows voters to accurately grasp information about politicians and identify those who will meet their expectations.This allows the system to enable voters to find the most suitable politicians and engage in direct dialogue with them.

[0029] The system according to this embodiment comprises a reception unit, an analysis unit, a presentation unit, and a dialogue unit. The reception unit inputs the user's interests, opinions, and values. The user's interests, opinions, and values ​​include, but are not limited to, political interests, social opinions, and personal values. For example, the user inputs their interests, opinions, and values ​​in text format into the reception unit. The reception unit can also input the user's interests, opinions, and values ​​using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice into text data. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of inputting interests, opinions, and values ​​based on the estimated emotions of the user. For example, if the user is feeling stressed, the reception unit may simplify the input and adjust it to be completed in a short time. Also, if the user is relaxed, the reception unit may encourage detailed input and adjust it to collect more information. The analysis unit analyzes the information input by the reception unit to understand the politician's profile, vision, beliefs, policies, etc. The analysis unit deeply understands information about politicians, for example, using text analysis, data mining, and machine learning algorithms. For example, the analysis unit understands information about politicians using detailed profile analysis and belief analysis methods. Furthermore, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will provide detailed analysis results to deepen understanding. Alternatively, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. The presentation unit presents the most suitable politicians to the user based on the information understood by the analysis unit. For example, the presentation unit matches the user's interests, opinions, and values ​​with information about politicians to present the most suitable politicians. For example, the presentation unit presents the most suitable politicians to the user based on similarity measures and the algorithms used. Furthermore, the presentation unit can estimate the user's emotions and adjust the presentation of the presentation based on the estimated user emotions. For example, if the user is relaxed, the presentation unit will provide a presentation method that includes detailed information. Furthermore, the presentation section can provide a concise and to-the-point presentation method when the user is in a hurry.The dialogue unit facilitates online dialogue between the user and the politician presented by the presentation unit. The dialogue unit provides an online dialogue platform, for example, enabling the user to directly interact with the presented politician. The dialogue unit facilitates the dialogue between the user and the politician using, for example, video calls or chat functions. Furthermore, the dialogue unit can estimate the user's emotions and adjust the dialogue method based on the estimated emotions. For example, if the user is nervous, the dialogue unit will conduct the dialogue in a calm tone. Conversely, if the user is relaxed, the dialogue unit can conduct the dialogue in a friendly tone. This allows the system according to the embodiment to find the most suitable politician based on the user's interests, opinions, and values, and to facilitate online dialogue.

[0030] The reception unit inputs the user's interests, opinions, and values. These include, but are not limited to, political interests, social opinions, and personal values. The reception unit accepts user input in text format, for example. It can also accept voice input. For instance, it uses speech recognition technology to convert the user's voice into text data. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of input based on these estimations. For example, if the user is stressed, it may simplify the input process to allow for quicker completion. Conversely, if the user is relaxed, it may encourage more detailed input to gather more information. The reception unit centrally manages the text and voice data entered by the user and stores it in a database. This allows for efficient collection of information regarding the user's interests, opinions, and values, making it accessible to the analysis and presentation units. Additionally, the reception unit can process user input data in real time and immediately transmit it to the analysis unit. This allows for a quicker response to user input and improves the overall responsiveness of the system. The reception department securely manages user input data and takes appropriate measures to protect privacy. For example, it implements data encryption and access control to protect users' personal information. The reception department also regularly backs up user input data to prepare for data loss or corruption. This allows the reception department to safely and efficiently collect information about users' interests, opinions, and values, improving the reliability of the overall system.

[0031] The analysis unit analyzes the information entered by the reception unit to understand politicians' profiles, visions, beliefs, and policies. The analysis unit uses methods such as text analysis, data mining, and machine learning algorithms to gain a deep understanding of politicians. For example, the analysis unit uses detailed profile analysis and belief analysis methods to understand politicians. Furthermore, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. Conversely, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. The analysis unit uses natural language processing techniques to analyze the user's input data and extract their interests, opinions, and values. For example, it uses keyword extraction and topic modeling to identify the user's interests, opinions, and values ​​and matches them with politician information. The analysis unit uses machine learning algorithms to evaluate the relevance between the user's interests, opinions, and values ​​and politician information to identify the most suitable politician. For example, similarity scales and clustering algorithms are used to select the politician best suited to the user's interests, opinions, and values. Furthermore, the analysis unit estimates the user's emotions and adjusts the presentation of the analysis results based on those emotions. For instance, if the user is relaxed, detailed analysis results are provided to deepen their understanding. Conversely, if the user is in a hurry, concise analysis results focusing on the key points can be provided. In this way, the analysis unit can identify the most suitable politician based on the user's interests, opinions, and values ​​and provide the user with appropriate information.

[0032] The presentation unit presents the most suitable politician to the user based on the information understood by the analysis unit. For example, the presentation unit matches the user's interests, opinions, and values ​​with information about politicians to present the most suitable politician. For example, the presentation unit presents the most suitable politician to the user based on similarity measures and the algorithms used. Furthermore, the presentation unit can estimate the user's emotions and adjust the presentation style based on the estimated emotions. For example, if the user is relaxed, the presentation unit provides a presentation style that includes detailed information. Alternatively, if the user is in a hurry, the presentation unit can provide a concise presentation style that gets straight to the point. The presentation unit visually presents the most suitable politician in an easy-to-understand way based on the user's interests, opinions, and values. For example, it visually represents information such as politician profiles, policies, and visions using graphs, charts, infographics, etc., to allow the user to intuitively understand. The presentation unit estimates the user's emotions and adjusts the presentation style based on the estimated emotions. For example, if the user is relaxed, it provides a presentation style that includes detailed information to deepen understanding. Alternatively, if the user is in a hurry, it can provide a concise presentation style that gets straight to the point. The presentation unit collects user feedback and continuously improves the accuracy and effectiveness of the presented content. For example, it analyzes how users reacted to the presented information and optimizes the presentation method and content. This allows the presentation unit to effectively present the most suitable politicians to users and provide information based on users' interests, opinions, and values.

[0033] The Dialogue Unit facilitates online dialogues between users and politicians presented by the Presentation Unit. For example, the Dialogue Unit provides an online dialogue platform, enabling users to directly interact with presented politicians. The Dialogue Unit facilitates these dialogues using features such as video calls and chat. Furthermore, the Dialogue Unit can estimate the user's emotions and adjust the dialogue method based on that estimation. For example, if the user is nervous, the Dialogue Unit will conduct the dialogue in a calm tone. Conversely, if the user is relaxed, the Dialogue Unit can conduct the dialogue in a friendly tone. The Dialogue Unit provides support functions to ensure smooth dialogue between users and politicians. For example, users can pre-enter questions they wish to ask during the dialogue, allowing for smoother questioning during the conversation. The Dialogue Unit also monitors the progress of the dialogue in real time and provides appropriate support as needed. For example, if the dialogue is interrupted or the user is having difficulty, the Dialogue Unit can intervene and provide support. The Dialogue Unit records the content of the dialogue so that users can review it later. For example, it can save recordings of the dialogue and chat logs for users to review later. Furthermore, the dialogue unit can analyze the content of the conversation and provide feedback based on the user's interests, opinions, and values. This allows the dialogue unit to facilitate online conversations between users and politicians, and to realize conversations that are based on the user's interests, opinions, and values.

[0034] The analytics unit can gain a deep understanding of politicians' profiles, visions, beliefs, and policies. For example, the analytics unit uses text analysis, data mining, and machine learning algorithms to gain a deep understanding of politicians' information. For instance, it uses detailed profile analysis and belief analysis methods to understand politicians' information. For example, it uses text analysis techniques to analyze politicians' statements and writings to understand their beliefs and visions. Furthermore, it can use data mining techniques to analyze politicians' past actions and achievements to understand their policies and beliefs. In addition, the analytics unit can use machine learning algorithms to automatically analyze politicians' information and gain a deep understanding. For example, it uses machine learning models to analyze politicians' profiles and visions to understand their beliefs and policies. This allows the analytics unit to gain a deeper understanding of politicians, improving its accuracy in recommending the most suitable politicians to users.

[0035] The matching unit can match the user's interests, opinions, and values ​​with information about politicians. For example, the matching unit presents the user with the most suitable politician based on similarity measures and the algorithms used. The matching unit uses methods such as cosine similarity and Jaccard coefficients to match the user's interests, opinions, and values ​​with information about politicians. For example, the matching unit can use cosine similarity to calculate the similarity between the user's interests, opinions, and values ​​and the politician's information. The matching unit can also use the Jaccard coefficient to calculate the similarity between the user's interests, opinions, and values ​​and the politician's information. Furthermore, the matching unit can use machine learning algorithms to match the user's interests, opinions, and values ​​with information about politicians. For example, the matching unit uses machine learning models to match the user's interests, opinions, and values ​​with information about politicians and presents the most suitable politician. This allows the matching unit to match the user with the most suitable politician based on their interests, opinions, and values.

[0036] The communication department can convey politicians' proposals and stances to voters during the election period. The communication department accurately conveys information about politicians using various media and means of communication. For example, the communication department can convey politicians' proposals and stances to voters using media such as television, radio, and the internet. For instance, the communication department can convey politicians' proposals and stances to voters through television and radio programs. Furthermore, the communication department can also convey politicians' proposals and stances to voters via the internet. For example, the communication department can convey politicians' information to voters through websites and social media. In addition, the communication department can convey politicians' proposals and stances to voters using means of communication such as printed materials and posters. For example, the communication department can convey politicians' information to voters by publishing articles in newspapers and magazines. In this way, the communication department can convey politicians' proposals and stances to voters during the election period, enabling voters to accurately understand information about politicians.

[0037] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display as suggestions the user has frequently entered in the past regarding their interests, opinions, and values. The reception desk can also prioritize suggesting input methods the user has used in the past (e.g., voice, text). Furthermore, the reception desk can predict and suggest interests, opinions, and values ​​that the user will use at specific times based on their past input history. For example, the reception desk can analyze the user's past input data to predict topics of interest at specific times and suggest input items. In this way, the reception desk can provide the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0038] The reception unit can filter the input of interests, opinions, and values ​​based on the user's current life situation and areas of interest. For example, the reception unit can prioritize displaying relevant input items based on topics the user is currently interested in. The reception unit can also suggest appropriate input items based on the user's life situation (work, family, hobbies, etc.). Furthermore, the reception unit can filter input items based on the user's areas of interest (politics, economics, environment, etc.) and collect highly relevant information. For example, the reception unit can identify the user's areas of interest and prioritize displaying relevant input items. This allows the reception unit to collect highly relevant information by filtering based on the user's life situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's life situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The reception system can prioritize inputting highly relevant information by considering the user's geographical location when they input their interests, opinions, and values. For example, if the user lives in a specific region, the reception system can prioritize inputting information about politicians and policies related to that region. If the user is traveling, the reception system can also prioritize inputting information related to their current location. Furthermore, if the user is participating in a specific event, the reception system can prioritize inputting information related to that event. For example, the reception system considers the user's geographical location and prioritizes inputting relevant information. This allows the reception system to prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant information.

[0040] The reception desk can analyze the user's social media activity and input relevant information when the user inputs their interests, opinions, and values. For example, the reception desk can suggest relevant input fields based on topics the user frequently mentions on social media. The reception desk can also analyze the user's social media activity history and prioritize inputting information of high interest. Furthermore, the reception desk can input relevant information based on accounts and groups the user follows. For example, the reception desk analyzes the user's social media activity and inputs relevant information. In this way, the reception desk can collect highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input relevant information.

[0041] The analysis unit can adjust the level of detail in its analysis based on the importance of the politicians. For example, the analysis unit can analyze information about major politicians in detail and provide it to the user. The analysis unit can also analyze information about local politicians concisely and provide only the essentials. Furthermore, the analysis unit can analyze information about politicians of particular interest to the user in detail to facilitate a deeper understanding. For example, the analysis unit adjusts the level of detail in its analysis based on the importance of the politicians. This allows the analysis unit to provide information that is important to the user by adjusting the level of detail in its analysis based on the importance of the politicians. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input politician importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in its analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the politician's category during analysis. For example, the analysis unit can apply an analysis algorithm that focuses on policies and visions to information about members of parliament. For example, the analysis unit can also apply an analysis algorithm that focuses on community-based activities and achievements to information about local councilors. Furthermore, the analysis unit can apply an analysis algorithm that focuses on past experience and future visions to information about newly elected politicians. For example, the analysis unit applies different analysis algorithms depending on the politician's category. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the politician's category. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input politician category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0043] The analysis unit can prioritize analysis based on the timing of politicians' submissions. For example, the analysis unit prioritizes analyzing and providing information on the latest policy proposals and statements to the user. The analysis unit can also analyze and provide information on past performance and statements as needed. Furthermore, the analysis unit can prioritize analyzing information from periods of particular interest to the user to facilitate a deeper understanding. For example, the analysis unit prioritizes analysis based on the timing of politicians' submissions. This allows the analysis unit to prioritize providing the latest information by prioritizing analysis based on the timing of politicians' submissions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input politician submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0044] The analysis unit can adjust the order of analysis based on the relevance of politicians during the analysis process. For example, the analysis unit can prioritize the analysis and provision of information about politicians of particular interest to the user. The analysis unit can also prioritize the analysis and provision of information with high relevance to politicians. Furthermore, the analysis unit can analyze and provide information with low relevance to politicians as needed. For example, the analysis unit adjusts the order of analysis based on the relevance of politicians. This allows the analysis unit to prioritize the provision of highly relevant information by adjusting the order of analysis based on the relevance of politicians. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input politician relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The presentation unit can adjust the level of detail in its presentation based on the importance of the politicians. For example, it may present information about major politicians in detail and provide it to the user. It may also present information about local politicians concisely, providing only the essentials. Furthermore, it may present information about politicians of particular interest to the user in detail to facilitate a deeper understanding. For example, the presentation unit adjusts the level of detail in its presentation based on the importance of the politicians. This allows the presentation unit to provide information that is important to the user by adjusting the level of detail in its presentation based on the importance of the politicians. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input politician importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in its presentation.

[0046] The presentation unit can apply different presentation algorithms depending on the politician's category when presenting information. For example, for information about members of parliament, the presentation unit can apply a presentation algorithm that focuses on policies and visions. For example, for information about local councilors, the presentation unit can apply a presentation algorithm that focuses on community-based activities and achievements. Furthermore, for information about newly elected politicians, the presentation unit can apply a presentation algorithm that focuses on past experience and future visions. For example, the presentation unit applies different presentation algorithms depending on the politician's category. This allows the presentation unit to provide more appropriate information by applying different presentation algorithms depending on the politician's category. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input politician category data into a generating AI and have the generating AI execute the application of different presentation algorithms.

[0047] The presentation unit can determine the priority of presentations based on the timing of politicians' submissions. For example, the presentation unit can prioritize and provide users with information on the latest policy proposals and statements. The presentation unit can also, for example, present and provide information on past achievements and statements as needed. Furthermore, the presentation unit can prioritize and provide information on periods of particular interest to the user to facilitate deeper understanding. For example, the presentation unit determines the priority of presentations based on the timing of politicians' submissions. This allows the presentation unit to prioritize and provide the latest information by determining the priority of presentations based on the timing of politicians' submissions. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input politicians' submission timing data into a generating AI and have the generating AI perform the determination of presentation priorities.

[0048] The presentation unit can adjust the order of presentations based on the relevance of the politicians. For example, the presentation unit can prioritize and provide information about politicians that the user is particularly interested in. The presentation unit can also prioritize and provide information that is highly relevant to a politician. Furthermore, the presentation unit can also prioritize and provide information that is less relevant to a politician as needed. For example, the presentation unit adjusts the order of presentations based on the relevance of the politicians. In this way, the presentation unit can prioritize and provide information that is highly relevant by adjusting the order of presentations based on the relevance of the politicians. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input politician relevance data into a generating AI and have the generating AI perform the adjustment of the presentation order.

[0049] The dialogue unit can analyze the user's past dialogue history during a conversation to select the optimal dialogue method. For example, the dialogue unit can suggest the optimal dialogue method based on the user's preferred dialogue style in the past. The dialogue unit can also prioritize conversations on specific topics based on the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue history to select the most effective dialogue method. For example, the dialogue unit analyzes the user's past dialogue data to select the optimal dialogue method. In this way, the dialogue unit can provide the optimal dialogue method by analyzing the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's past dialogue data into a generating AI and have the generating AI select the optimal dialogue method.

[0050] The dialogue unit can customize the means of dialogue based on the user's current living situation during a conversation. For example, if the user is at work, the dialogue unit will conduct a short, concise conversation. If the user is relaxed, the dialogue unit can conduct a conversation that includes detailed information. Furthermore, if the user is on the move, the dialogue unit can prioritize voice dialogue and proceed with the conversation accordingly. For example, the dialogue unit customizes the means of dialogue based on the user's living situation. This allows the dialogue unit to provide a more appropriate conversation by customizing the means of dialogue based on the user's living situation. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of dialogue.

[0051] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location. For example, if the user lives in a specific region, the dialogue unit will prioritize topics related to that region. If the user is traveling, the dialogue unit can also prioritize information related to the user's current location. Furthermore, if the user is participating in a specific event, the dialogue unit can prioritize information related to that event. For example, the dialogue unit considers the user's geographical location to select the optimal dialogue method. This allows the dialogue unit to prioritize highly relevant information by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's geographical location into a generating AI and have the generating AI select the optimal dialogue method.

[0052] The dialogue unit can analyze the user's social media activity during a conversation and suggest a means of dialogue. For example, the dialogue unit can suggest relevant dialogues based on topics the user frequently mentions on social media. The dialogue unit can also analyze the user's social media activity history and prioritize information of high interest to the user. Furthermore, the dialogue unit can suggest relevant dialogues based on accounts and groups the user follows. For example, the dialogue unit analyzes the user's social media activity and suggests relevant dialogues. This allows the dialogue unit to prioritize information of high relevance by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI suggest a means of dialogue.

[0053] The matching unit can analyze the user's past interests, opinions, and values ​​during the matching process to select the optimal matching method. For example, the matching unit can propose the optimal matching method based on topics the user has been interested in in the past. The matching unit can also analyze the user's past opinions and values ​​to propose the most compatible politician. Furthermore, the matching unit can prioritize matching with highly relevant politicians based on the user's past interests. For example, the matching unit analyzes the user's past data to select the optimal matching method. In this way, the matching unit can provide the optimal matching method by analyzing the user's past interests, opinions, and values. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the user's past data into a generating AI and have the generating AI select the optimal matching method.

[0054] The matching unit can customize the matching method based on the user's current living situation during the matching process. For example, if the user is at work, the matching unit can perform a quick, concise match. If the user is relaxed, the matching unit can also perform a more detailed match. Furthermore, if the user is on the move, the matching unit can perform a voice-based match. For example, the matching unit customizes the matching method based on the user's living situation. This allows the matching unit to provide a more appropriate match by customizing the matching method based on the user's living situation. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input user living situation data into a generating AI and have the generating AI perform the customization of the matching method.

[0055] The matching unit can select the optimal matching method by considering the user's geographical location information during the matching process. For example, if the user lives in a specific region, the matching unit will prioritize matching them with politicians associated with that region. For example, if the user is traveling, the matching unit can also prioritize matching them with politicians associated with their current location. Furthermore, if the user is participating in a specific event, the matching unit can also prioritize matching them with politicians associated with that event. For example, the matching unit considers the user's geographical location information to select the optimal matching method. This allows the matching unit to prioritize matching users with highly relevant politicians by considering their geographical location information. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal matching method.

[0056] The matching unit can analyze a user's social media activity and propose matching methods during the matching process. For example, the matching unit can propose relevant politicians based on topics frequently mentioned by the user on social media. The matching unit can also analyze a user's social media activity history and prioritize matching them with politicians of high interest. Furthermore, the matching unit can propose relevant politicians based on accounts and groups followed by the user. For example, the matching unit analyzes a user's social media activity and proposes relevant politicians. This allows the matching unit to prioritize matching users with highly relevant politicians by analyzing their social media activity. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the user's social media activity data into a generating AI and have the generating AI propose matching methods.

[0057] The communication unit can analyze the user's past communication history to select the optimal communication method during communication. For example, the communication unit can suggest the optimal method based on the communication methods the user has preferred in the past. The communication unit can also prioritize the communication of information on specific topics based on the user's past communication history. Furthermore, the communication unit can analyze the user's past communication history to select the most effective communication method. For example, the communication unit analyzes the user's past data to select the optimal communication method. In this way, the communication unit can provide the optimal communication method by analyzing the user's past communication history. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input the user's past data into a generating AI and have the generating AI select the optimal communication method.

[0058] The communication unit can customize the means of communication based on the user's current living situation at the time of communication. For example, if the user is at work, the communication unit can provide concise and to-the-point communication in a short amount of time. If the user is relaxed, the communication unit can also provide communication that includes detailed information. Furthermore, if the user is on the move, the communication unit can provide voice communication. For example, the communication unit customizes the means of communication based on the user's living situation. This allows the communication unit to deliver more appropriate information by customizing the means of communication based on the user's living situation. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of communication.

[0059] The communication unit can select the optimal communication method by considering the user's geographical location information during communication. For example, if the user lives in a specific region, the communication unit can prioritize communicating information related to that region. For example, if the user is traveling, the communication unit can also prioritize communicating information related to the user's current location. Furthermore, if the communication unit is participating in a specific event, the communication unit can also prioritize communicating information related to that event. For example, the communication unit selects the optimal communication method by considering the user's geographical location information. This allows the communication unit to prioritize communicating highly relevant information by considering the user's geographical location information. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal communication method.

[0060] The communication unit can analyze the user's social media activity and propose means of communication during the communication process. For example, the communication unit can communicate relevant information based on topics that the user frequently mentions on social media. The communication unit can also analyze the user's social media activity history and prioritize the communication of information of high interest. Furthermore, the communication unit can communicate relevant information based on accounts and groups that the user follows. For example, the communication unit analyzes the user's social media activity and communicates relevant information. This allows the communication unit to prioritize the communication of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input the user's social media activity data into a generating AI and have the generating AI propose means of communication.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The system can suggest more appropriate politicians by considering the user's past voting history when they input their interests, opinions, and values. For example, the reception unit analyzes information on politicians the user has voted for in the past and suggests the most suitable politician by comparing it with the user's current interests, opinions, and values. The analysis unit analyzes the user's political tendencies based on past voting history, enabling more accurate matching. Furthermore, the presentation unit can prioritize and present information that is important to the user based on past voting history. In this way, the system can suggest more appropriate politicians by considering the user's past voting history and support the user's political choices.

[0063] The system can adjust the input method when users input their interests, opinions, and values, taking into account their current health condition. For example, the reception section can provide a simplified input method if the user is tired, allowing for quick completion. The analysis section can also adjust the presentation of the analysis based on the user's health condition, providing information in an easy-to-understand format. Furthermore, the presentation section can adjust the way information is presented according to the user's health condition, providing information in a less burdensome way. In this way, the system can provide more appropriate information and reduce the user's burden by taking their health condition into consideration.

[0064] The system can adjust the input method when users input their interests, opinions, and values, taking into account their learning history. For example, the reception unit suggests relevant input items based on what the user has learned in the past. The analysis unit can adjust the presentation of the analysis based on the user's learning history, providing information in an easy-to-understand format. Furthermore, the presentation unit can adjust the way information is presented according to the user's learning history, providing information in a way that enhances learning effectiveness. In this way, the system can provide more appropriate information by considering the user's learning history, thereby improving the user's learning effectiveness.

[0065] The system can adjust the input method when users input their interests, opinions, and values, taking into account their cultural background. For example, the reception unit can suggest appropriate input fields based on the user's cultural background. The analysis unit can also adjust the presentation of the analysis based on the user's cultural background, providing information in an easily understandable format. Furthermore, the presentation unit can adjust the way information is presented according to the user's cultural background, providing information in a culturally appropriate manner. As a result, the system can provide more appropriate information and deepen user understanding by considering the user's cultural background.

[0066] The system can adjust the input method when users input their interests, opinions, and values, taking their occupation into consideration. For example, the reception section can suggest relevant input fields based on the user's occupation. The analysis section can also adjust the presentation of the analysis based on the user's occupation, providing information in an easily understandable format. Furthermore, the presentation section can adjust the way information is presented according to the user's occupation, providing information in a way that is relevant to their occupation. In this way, the system can provide more appropriate information and deepen the user's understanding by taking their occupation into consideration.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk inputs the user's interests, opinions, and values. These include, for example, political interests, social opinions, and personal values. The reception desk allows users to input their interests, opinions, and values ​​in text format. Users can also input their interests, opinions, and values ​​using voice input, and speech recognition technology is used to convert the user's voice into text data. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of inputting interests, opinions, and values ​​based on the estimated emotions. For example, if the user is stressed, the input may be simplified and adjusted to be completed in a short time. Conversely, if the user is relaxed, it may be encouraged to provide detailed input and adjust to gather more information. Step 2: The analysis unit analyzes the information entered by the reception unit to understand the politician's profile, vision, beliefs, policies, etc. The analysis unit uses text analysis, data mining, and machine learning algorithms to gain a deep understanding of the politician's information. For example, it uses detailed profile analysis and belief analysis methods to understand the politician's information. Furthermore, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, it can provide detailed analysis results to deepen understanding. Conversely, if the user is in a hurry, it can provide concise analysis results that get straight to the point. Step 3: The presentation unit presents the most suitable politician to the user based on the information understood by the analysis unit. The presentation unit matches the user's interests, opinions, and values ​​with information about politicians to present the most suitable politician. The presentation unit presents the most suitable politician to the user based on the similarity scale and the algorithm used. Furthermore, the presentation unit can estimate the user's emotions and adjust the presentation style based on the estimated emotions of the user. For example, if the user is relaxed, it can provide a presentation style that includes detailed information. If the user is in a hurry, it can provide a concise presentation style that gets straight to the point. Step 4: The Dialogue Unit facilitates online dialogue between the user and the politician presented by the Presentation Unit. The Dialogue Unit provides an online dialogue platform so that the user can directly interact with the presented politician. The Dialogue Unit facilitates the dialogue between the user and the politician using video calls and chat functions. Furthermore, the Dialogue Unit can also estimate the user's emotions and adjust the dialogue method based on the estimated emotions of the user. For example, if the user is nervous, the dialogue can proceed in a calm tone. Conversely, if the user is relaxed, the dialogue can proceed in a friendly tone.

[0069] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes AI technology to enable voters to find the most suitable politician and engage in direct dialogue with them. In this system, the user inputs their interests, opinions, and values, and the AI ​​analyzes the input information to gain a deep understanding of the politician's profile, vision, beliefs, and policies. The AI ​​analyzes each politician's information in detail and matches it with the user's interests, opinions, and values. The AI ​​presents the user with the most suitable politician, and the user can review the presented politician's profile, vision, beliefs, and policies. Furthermore, the AI ​​enables online dialogue between the user and the politician. The user can directly interact with the presented politician. This mechanism allows voters to vote for the candidate who best aligns with their ideology and interests, enabling them to make a satisfying political choice. In addition, the AI ​​can effectively convey the politician's proposals and stances during the election period. This allows voters to accurately grasp the politician's information and identify the politician who will meet their expectations. This system is effective for promoting political participation among voters, general citizens interested in politics, young people, and first-time voters. For example, when a user inputs their interests, opinions, and values, the AI ​​analyzes the user's input to understand the politician's profile, vision, beliefs, and policies. The AI ​​uses methods such as text analysis, data mining, and machine learning algorithms to gain a deep understanding of the politician's information. Next, the AI ​​matches the user's interests, opinions, and values ​​with the politician's information. For example, the AI ​​presents the user with the most suitable politician based on similarity measures and the algorithms used. Furthermore, the AI ​​facilitates online dialogue between the user and the politician. For example, the AI ​​provides an online dialogue platform so that the user can directly interact with the presented politician. This allows the user to vote for the candidate who most closely aligns with their ideology and interests, enabling them to make a satisfying political choice. The AI ​​also communicates the politician's proposals and stances to voters during election periods. For example, the AI ​​accurately conveys information about politicians using different types of media and communication methods. This allows voters to accurately grasp information about politicians and identify those who will meet their expectations.This allows the system to enable voters to find the most suitable politicians and engage in direct dialogue with them.

[0070] The system according to this embodiment comprises a reception unit, an analysis unit, a presentation unit, and a dialogue unit. The reception unit inputs the user's interests, opinions, and values. The user's interests, opinions, and values ​​include, but are not limited to, political interests, social opinions, and personal values. For example, the user inputs their interests, opinions, and values ​​in text format into the reception unit. The reception unit can also input the user's interests, opinions, and values ​​using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice into text data. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of inputting interests, opinions, and values ​​based on the estimated emotions of the user. For example, if the user is feeling stressed, the reception unit may simplify the input and adjust it to be completed in a short time. Also, if the user is relaxed, the reception unit may encourage detailed input and adjust it to collect more information. The analysis unit analyzes the information input by the reception unit to understand the politician's profile, vision, beliefs, policies, etc. The analysis unit deeply understands information about politicians, for example, using text analysis, data mining, and machine learning algorithms. For example, the analysis unit understands information about politicians using detailed profile analysis and belief analysis methods. Furthermore, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will provide detailed analysis results to deepen understanding. Alternatively, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. The presentation unit presents the most suitable politicians to the user based on the information understood by the analysis unit. For example, the presentation unit matches the user's interests, opinions, and values ​​with information about politicians to present the most suitable politicians. For example, the presentation unit presents the most suitable politicians to the user based on similarity measures and the algorithms used. Furthermore, the presentation unit can estimate the user's emotions and adjust the presentation of the presentation based on the estimated user emotions. For example, if the user is relaxed, the presentation unit will provide a presentation method that includes detailed information. Furthermore, the presentation section can provide a concise and to-the-point presentation method when the user is in a hurry.The dialogue unit facilitates online dialogue between the user and the politician presented by the presentation unit. The dialogue unit provides an online dialogue platform, for example, enabling the user to directly interact with the presented politician. The dialogue unit facilitates the dialogue between the user and the politician using, for example, video calls or chat functions. Furthermore, the dialogue unit can estimate the user's emotions and adjust the dialogue method based on the estimated emotions. For example, if the user is nervous, the dialogue unit will conduct the dialogue in a calm tone. Conversely, if the user is relaxed, the dialogue unit can conduct the dialogue in a friendly tone. This allows the system according to the embodiment to find the most suitable politician based on the user's interests, opinions, and values, and to facilitate online dialogue.

[0071] The reception unit inputs the user's interests, opinions, and values. These include, but are not limited to, political interests, social opinions, and personal values. The reception unit accepts user input in text format, for example. It can also accept voice input. For instance, it uses speech recognition technology to convert the user's voice into text data. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of input based on these estimations. For example, if the user is stressed, it may simplify the input process to allow for quicker completion. Conversely, if the user is relaxed, it may encourage more detailed input to gather more information. The reception unit centrally manages the text and voice data entered by the user and stores it in a database. This allows for efficient collection of information regarding the user's interests, opinions, and values, making it accessible to the analysis and presentation units. Additionally, the reception unit can process user input data in real time and immediately transmit it to the analysis unit. This allows for a quicker response to user input and improves the overall responsiveness of the system. The reception department securely manages user input data and takes appropriate measures to protect privacy. For example, it implements data encryption and access control to protect users' personal information. The reception department also regularly backs up user input data to prepare for data loss or corruption. This allows the reception department to safely and efficiently collect information about users' interests, opinions, and values, improving the reliability of the overall system.

[0072] The analysis unit analyzes the information entered by the reception unit to understand politicians' profiles, visions, beliefs, and policies. The analysis unit uses methods such as text analysis, data mining, and machine learning algorithms to gain a deep understanding of politicians. For example, the analysis unit uses detailed profile analysis and belief analysis methods to understand politicians. Furthermore, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. Conversely, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. The analysis unit uses natural language processing techniques to analyze the user's input data and extract their interests, opinions, and values. For example, it uses keyword extraction and topic modeling to identify the user's interests, opinions, and values ​​and matches them with politician information. The analysis unit uses machine learning algorithms to evaluate the relevance between the user's interests, opinions, and values ​​and politician information to identify the most suitable politician. For example, similarity scales and clustering algorithms are used to select the politician best suited to the user's interests, opinions, and values. Furthermore, the analysis unit estimates the user's emotions and adjusts the presentation of the analysis results based on those emotions. For instance, if the user is relaxed, detailed analysis results are provided to deepen their understanding. Conversely, if the user is in a hurry, concise analysis results focusing on the key points can be provided. In this way, the analysis unit can identify the most suitable politician based on the user's interests, opinions, and values ​​and provide the user with appropriate information.

[0073] The presentation unit presents the most suitable politician to the user based on the information understood by the analysis unit. For example, the presentation unit matches the user's interests, opinions, and values ​​with information about politicians to present the most suitable politician. For example, the presentation unit presents the most suitable politician to the user based on similarity measures and the algorithms used. Furthermore, the presentation unit can estimate the user's emotions and adjust the presentation style based on the estimated emotions. For example, if the user is relaxed, the presentation unit provides a presentation style that includes detailed information. Alternatively, if the user is in a hurry, the presentation unit can provide a concise presentation style that gets straight to the point. The presentation unit visually presents the most suitable politician in an easy-to-understand way based on the user's interests, opinions, and values. For example, it visually represents information such as politician profiles, policies, and visions using graphs, charts, infographics, etc., to allow the user to intuitively understand. The presentation unit estimates the user's emotions and adjusts the presentation style based on the estimated emotions. For example, if the user is relaxed, it provides a presentation style that includes detailed information to deepen understanding. Alternatively, if the user is in a hurry, it can provide a concise presentation style that gets straight to the point. The presentation unit collects user feedback and continuously improves the accuracy and effectiveness of the presented content. For example, it analyzes how users reacted to the presented information and optimizes the presentation method and content. This allows the presentation unit to effectively present the most suitable politicians to users and provide information based on users' interests, opinions, and values.

[0074] The Dialogue Unit facilitates online dialogues between users and politicians presented by the Presentation Unit. For example, the Dialogue Unit provides an online dialogue platform, enabling users to directly interact with presented politicians. The Dialogue Unit facilitates these dialogues using features such as video calls and chat. Furthermore, the Dialogue Unit can estimate the user's emotions and adjust the dialogue method based on that estimation. For example, if the user is nervous, the Dialogue Unit will conduct the dialogue in a calm tone. Conversely, if the user is relaxed, the Dialogue Unit can conduct the dialogue in a friendly tone. The Dialogue Unit provides support functions to ensure smooth dialogue between users and politicians. For example, users can pre-enter questions they wish to ask during the dialogue, allowing for smoother questioning during the conversation. The Dialogue Unit also monitors the progress of the dialogue in real time and provides appropriate support as needed. For example, if the dialogue is interrupted or the user is having difficulty, the Dialogue Unit can intervene and provide support. The Dialogue Unit records the content of the dialogue so that users can review it later. For example, it can save recordings of the dialogue and chat logs for users to review later. Furthermore, the dialogue unit can analyze the content of the conversation and provide feedback based on the user's interests, opinions, and values. This allows the dialogue unit to facilitate online conversations between users and politicians, and to realize conversations that are based on the user's interests, opinions, and values.

[0075] The analytics unit can gain a deep understanding of politicians' profiles, visions, beliefs, and policies. For example, the analytics unit uses text analysis, data mining, and machine learning algorithms to gain a deep understanding of politicians' information. For instance, it uses detailed profile analysis and belief analysis methods to understand politicians' information. For example, it uses text analysis techniques to analyze politicians' statements and writings to understand their beliefs and visions. Furthermore, it can use data mining techniques to analyze politicians' past actions and achievements to understand their policies and beliefs. In addition, the analytics unit can use machine learning algorithms to automatically analyze politicians' information and gain a deep understanding. For example, it uses machine learning models to analyze politicians' profiles and visions to understand their beliefs and policies. This allows the analytics unit to gain a deeper understanding of politicians, improving its accuracy in recommending the most suitable politicians to users.

[0076] The matching unit can match the user's interests, opinions, and values ​​with information about politicians. For example, the matching unit presents the user with the most suitable politician based on similarity measures and the algorithms used. The matching unit uses methods such as cosine similarity and Jaccard coefficients to match the user's interests, opinions, and values ​​with information about politicians. For example, the matching unit can use cosine similarity to calculate the similarity between the user's interests, opinions, and values ​​and the politician's information. The matching unit can also use the Jaccard coefficient to calculate the similarity between the user's interests, opinions, and values ​​and the politician's information. Furthermore, the matching unit can use machine learning algorithms to match the user's interests, opinions, and values ​​with information about politicians. For example, the matching unit uses machine learning models to match the user's interests, opinions, and values ​​with information about politicians and presents the most suitable politician. This allows the matching unit to match the user with the most suitable politician based on their interests, opinions, and values.

[0077] The communication department can convey politicians' proposals and stances to voters during the election period. The communication department accurately conveys information about politicians using various media and means of communication. For example, the communication department can convey politicians' proposals and stances to voters using media such as television, radio, and the internet. For instance, the communication department can convey politicians' proposals and stances to voters through television and radio programs. Furthermore, the communication department can also convey politicians' proposals and stances to voters via the internet. For example, the communication department can convey politicians' information to voters through websites and social media. In addition, the communication department can convey politicians' proposals and stances to voters using means of communication such as printed materials and posters. For example, the communication department can convey politicians' information to voters by publishing articles in newspapers and magazines. In this way, the communication department can convey politicians' proposals and stances to voters during the election period, enabling voters to accurately understand information about politicians.

[0078] The reception desk can estimate the user's emotions and adjust the timing of input regarding interests, opinions, and values ​​based on the estimated emotions. For example, if the user is stressed, the reception desk can simplify the input and adjust it to be completed in a short time. For example, if the user is relaxed, the reception desk can encourage detailed input and adjust it to collect more information. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and adjust it to complete the input quickly. For example, the reception desk can use speech recognition technology to convert the user's voice into text data and complete the input quickly. This allows the reception desk to collect more relevant information by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display as suggestions the user has frequently entered in the past regarding their interests, opinions, and values. The reception desk can also prioritize suggesting input methods the user has used in the past (e.g., voice, text). Furthermore, the reception desk can predict and suggest interests, opinions, and values ​​that the user will use at specific times based on their past input history. For example, the reception desk can analyze the user's past input data to predict topics of interest at specific times and suggest input items. In this way, the reception desk can provide the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0080] The reception unit can filter the input of interests, opinions, and values ​​based on the user's current life situation and areas of interest. For example, the reception unit can prioritize displaying relevant input items based on topics the user is currently interested in. The reception unit can also suggest appropriate input items based on the user's life situation (work, family, hobbies, etc.). Furthermore, the reception unit can filter input items based on the user's areas of interest (politics, economics, environment, etc.) and collect highly relevant information. For example, the reception unit can identify the user's areas of interest and prioritize displaying relevant input items. This allows the reception unit to collect highly relevant information by filtering based on the user's life situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the user's life situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0081] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize the input of important information and postpone other information. If the user is relaxed, the reception desk may also prioritize the input of detailed information and collect overall information. Furthermore, if the user is in a hurry, the reception desk may prioritize the input of only the most important information and complete the process quickly. For example, the reception desk estimates the user's emotions and prioritizes the input of important information. This allows the reception desk to prioritize the collection of important information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception system can prioritize inputting highly relevant information by considering the user's geographical location when they input their interests, opinions, and values. For example, if the user lives in a specific region, the reception system can prioritize inputting information about politicians and policies related to that region. If the user is traveling, the reception system can also prioritize inputting information related to their current location. Furthermore, if the user is participating in a specific event, the reception system can prioritize inputting information related to that event. For example, the reception system considers the user's geographical location and prioritizes inputting relevant information. This allows the reception system to prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant information.

[0083] The reception desk can analyze the user's social media activity and input relevant information when the user inputs their interests, opinions, and values. For example, the reception desk can suggest relevant input fields based on topics the user frequently mentions on social media. The reception desk can also analyze the user's social media activity history and prioritize inputting information of high interest. Furthermore, the reception desk can input relevant information based on accounts and groups the user follows. For example, the reception desk analyzes the user's social media activity and inputs relevant information. In this way, the reception desk can collect highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input relevant information.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen understanding. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results to capture their interest. For example, the analysis unit estimates the user's emotions and adjusts the presentation of the analysis. This allows the analysis unit to provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail in its analysis based on the importance of the politicians. For example, the analysis unit can analyze information about major politicians in detail and provide it to the user. The analysis unit can also analyze information about local politicians concisely and provide only the essentials. Furthermore, the analysis unit can analyze information about politicians of particular interest to the user in detail to facilitate a deeper understanding. For example, the analysis unit adjusts the level of detail in its analysis based on the importance of the politicians. This allows the analysis unit to provide information that is important to the user by adjusting the level of detail in its analysis based on the importance of the politicians. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input politician importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in its analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the politician's category during analysis. For example, the analysis unit can apply an analysis algorithm that focuses on policies and visions to information about members of parliament. For example, the analysis unit can also apply an analysis algorithm that focuses on community-based activities and achievements to information about local councilors. Furthermore, the analysis unit can apply an analysis algorithm that focuses on past experience and future visions to information about newly elected politicians. For example, the analysis unit applies different analysis algorithms depending on the politician's category. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the politician's category. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input politician category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis unit can prioritize analysis based on the timing of politicians' submissions. For example, the analysis unit prioritizes analyzing and providing information on the latest policy proposals and statements to the user. The analysis unit can also analyze and provide information on past performance and statements as needed. Furthermore, the analysis unit can prioritize analyzing information from periods of particular interest to the user to facilitate a deeper understanding. For example, the analysis unit prioritizes analysis based on the timing of politicians' submissions. This allows the analysis unit to prioritize providing the latest information by prioritizing analysis based on the timing of politicians' submissions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input politician submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0089] The analysis unit can adjust the order of analysis based on the relevance of politicians during the analysis process. For example, the analysis unit can prioritize the analysis and provision of information about politicians of particular interest to the user. The analysis unit can also prioritize the analysis and provision of information with high relevance to politicians. Furthermore, the analysis unit can analyze and provide information with low relevance to politicians as needed. For example, the analysis unit adjusts the order of analysis based on the relevance of politicians. This allows the analysis unit to prioritize the provision of highly relevant information by adjusting the order of analysis based on the relevance of politicians. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input politician relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The presentation unit can estimate the user's emotions and adjust the presentation based on the estimated emotions. For example, if the user is relaxed, the presentation unit may provide a presentation that includes detailed information. For example, if the user is in a hurry, the presentation unit may provide a concise presentation that gets straight to the point. Furthermore, if the user is excited, the presentation unit may provide a visually appealing presentation to capture their interest. For example, the presentation unit estimates the user's emotions and adjusts the presentation. This allows the presentation unit to provide more appropriate information by adjusting the presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the presentation.

[0091] The presentation unit can adjust the level of detail in its presentation based on the importance of the politicians. For example, it may present information about major politicians in detail and provide it to the user. It may also present information about local politicians concisely, providing only the essentials. Furthermore, it may present information about politicians of particular interest to the user in detail to facilitate a deeper understanding. For example, the presentation unit adjusts the level of detail in its presentation based on the importance of the politicians. This allows the presentation unit to provide information that is important to the user by adjusting the level of detail in its presentation based on the importance of the politicians. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input politician importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in its presentation.

[0092] The presentation unit can apply different presentation algorithms depending on the politician's category when presenting information. For example, for information about members of parliament, the presentation unit can apply a presentation algorithm that focuses on policies and visions. For example, for information about local councilors, the presentation unit can apply a presentation algorithm that focuses on community-based activities and achievements. Furthermore, for information about newly elected politicians, the presentation unit can apply a presentation algorithm that focuses on past experience and future visions. For example, the presentation unit applies different presentation algorithms depending on the politician's category. This allows the presentation unit to provide more appropriate information by applying different presentation algorithms depending on the politician's category. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input politician category data into a generating AI and have the generating AI execute the application of different presentation algorithms.

[0093] The presentation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated emotions. For example, if the user is in a hurry, the presentation unit can provide a short, concise presentation. For example, if the user is relaxed, the presentation unit can provide a longer presentation that includes detailed explanations. Furthermore, if the user is excited, the presentation unit can provide a presentation with visually stimulating effects. For example, the presentation unit estimates the user's emotions and adjusts the length of the presentation. This allows the presentation unit to provide more appropriate information by adjusting the length of the presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the presentation.

[0094] The presentation unit can determine the priority of presentations based on the timing of politicians' submissions. For example, the presentation unit can prioritize and provide users with information on the latest policy proposals and statements. The presentation unit can also, for example, present and provide information on past achievements and statements as needed. Furthermore, the presentation unit can prioritize and provide information on periods of particular interest to the user to facilitate deeper understanding. For example, the presentation unit determines the priority of presentations based on the timing of politicians' submissions. This allows the presentation unit to prioritize and provide the latest information by determining the priority of presentations based on the timing of politicians' submissions. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input politicians' submission timing data into a generating AI and have the generating AI perform the determination of presentation priorities.

[0095] The presentation unit can adjust the order of presentations based on the relevance of the politicians. For example, the presentation unit can prioritize and provide information about politicians that the user is particularly interested in. The presentation unit can also prioritize and provide information that is highly relevant to a politician. Furthermore, the presentation unit can also prioritize and provide information that is less relevant to a politician as needed. For example, the presentation unit adjusts the order of presentations based on the relevance of the politicians. In this way, the presentation unit can prioritize and provide information that is highly relevant by adjusting the order of presentations based on the relevance of the politicians. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input politician relevance data into a generating AI and have the generating AI perform the adjustment of the presentation order.

[0096] The dialogue unit can estimate the user's emotions and adjust the dialogue method based on the estimated emotions. For example, if the user is nervous, the dialogue unit can proceed with the dialogue in a calm tone. For example, if the user is relaxed, the dialogue unit can proceed with the dialogue in a friendly tone. Furthermore, if the user is in a hurry, the dialogue unit can proceed with a quick and concise dialogue. For example, the dialogue unit estimates the user's emotions and adjusts the dialogue method. This allows the dialogue unit to provide a more appropriate dialogue by adjusting the dialogue method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not using AI. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the dialogue method.

[0097] The dialogue unit can analyze the user's past dialogue history during a conversation to select the optimal dialogue method. For example, the dialogue unit can suggest the optimal dialogue method based on the user's preferred dialogue style in the past. The dialogue unit can also prioritize conversations on specific topics based on the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue history to select the most effective dialogue method. For example, the dialogue unit analyzes the user's past dialogue data to select the optimal dialogue method. In this way, the dialogue unit can provide the optimal dialogue method by analyzing the user's past dialogue history. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's past dialogue data into a generating AI and have the generating AI select the optimal dialogue method.

[0098] The dialogue unit can customize the means of dialogue based on the user's current living situation during a conversation. For example, if the user is at work, the dialogue unit will conduct a short, concise conversation. If the user is relaxed, the dialogue unit can conduct a conversation that includes detailed information. Furthermore, if the user is on the move, the dialogue unit can prioritize voice dialogue and proceed with the conversation accordingly. For example, the dialogue unit customizes the means of dialogue based on the user's living situation. This allows the dialogue unit to provide a more appropriate conversation by customizing the means of dialogue based on the user's living situation. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of dialogue.

[0099] The dialogue unit can estimate the user's emotions and determine the priority of the conversation based on the estimated emotions. For example, if the user is nervous, the dialogue unit will prioritize important topics. If the user is relaxed, the dialogue unit may also prioritize conversations containing detailed information. Furthermore, if the user is in a hurry, the dialogue unit may prioritize only the most important topics. For example, the dialogue unit estimates the user's emotions and determines the priority of the conversation. This allows the dialogue unit to prioritize important information by determining the priority of the conversation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input user emotion data into a generative AI and have the generative AI perform the determination of conversation priorities.

[0100] The dialogue unit can select the optimal dialogue method during a conversation, taking into account the user's geographical location. For example, if the user lives in a specific region, the dialogue unit will prioritize topics related to that region. If the user is traveling, the dialogue unit can also prioritize information related to the user's current location. Furthermore, if the user is participating in a specific event, the dialogue unit can prioritize information related to that event. For example, the dialogue unit considers the user's geographical location to select the optimal dialogue method. This allows the dialogue unit to prioritize highly relevant information by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's geographical location into a generating AI and have the generating AI select the optimal dialogue method.

[0101] The dialogue unit can analyze the user's social media activity during a conversation and suggest a means of dialogue. For example, the dialogue unit can suggest relevant dialogues based on topics the user frequently mentions on social media. The dialogue unit can also analyze the user's social media activity history and prioritize information of high interest to the user. Furthermore, the dialogue unit can suggest relevant dialogues based on accounts and groups the user follows. For example, the dialogue unit analyzes the user's social media activity and suggests relevant dialogues. This allows the dialogue unit to prioritize information of high relevance by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's social media activity data into a generating AI and have the generating AI suggest a means of dialogue.

[0102] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit can perform matching based on detailed information. If the user is in a hurry, the matching unit can also perform matching based on concise information that gets straight to the point. Furthermore, if the user is excited, the matching unit can also perform matching based on visually appealing information. For example, the matching unit estimates the user's emotions and adjusts the matching criteria. This allows the matching unit to provide more appropriate matches by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the matching criteria.

[0103] The matching unit can analyze the user's past interests, opinions, and values ​​during the matching process to select the optimal matching method. For example, the matching unit can propose the optimal matching method based on topics the user has been interested in in the past. The matching unit can also analyze the user's past opinions and values ​​to propose the most compatible politician. Furthermore, the matching unit can prioritize matching with highly relevant politicians based on the user's past interests. For example, the matching unit analyzes the user's past data to select the optimal matching method. In this way, the matching unit can provide the optimal matching method by analyzing the user's past interests, opinions, and values. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the user's past data into a generating AI and have the generating AI select the optimal matching method.

[0104] The matching unit can customize the matching method based on the user's current living situation during the matching process. For example, if the user is at work, the matching unit can perform a quick, concise match. If the user is relaxed, the matching unit can also perform a more detailed match. Furthermore, if the user is on the move, the matching unit can perform a voice-based match. For example, the matching unit customizes the matching method based on the user's living situation. This allows the matching unit to provide a more appropriate match by customizing the matching method based on the user's living situation. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input user living situation data into a generating AI and have the generating AI perform the customization of the matching method.

[0105] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated emotions. For example, if the user is nervous, the matching unit may prioritize matching based on important information. If the user is relaxed, the matching unit may also prioritize matching based on detailed information. Furthermore, if the user is in a hurry, the matching unit may prioritize matching based only on the most important information. For example, the matching unit estimates the user's emotions and determines matching priorities. This allows the matching unit to prioritize matching based on important information by determining matching priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input user emotion data into a generative AI and have the generative AI determine the matching priorities.

[0106] The matching unit can select the optimal matching method by considering the user's geographical location information during the matching process. For example, if the user lives in a specific region, the matching unit will prioritize matching them with politicians associated with that region. For example, if the user is traveling, the matching unit can also prioritize matching them with politicians associated with their current location. Furthermore, if the user is participating in a specific event, the matching unit can also prioritize matching them with politicians associated with that event. For example, the matching unit considers the user's geographical location information to select the optimal matching method. This allows the matching unit to prioritize matching users with highly relevant politicians by considering their geographical location information. Some or all of the above-described processes in the matching unit may be performed using AI or not. For example, the matching unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal matching method.

[0107] The matching unit can analyze a user's social media activity and propose matching methods during the matching process. For example, the matching unit can propose relevant politicians based on topics frequently mentioned by the user on social media. The matching unit can also analyze a user's social media activity history and prioritize matching them with politicians of high interest. Furthermore, the matching unit can propose relevant politicians based on accounts and groups followed by the user. For example, the matching unit analyzes a user's social media activity and proposes relevant politicians. This allows the matching unit to prioritize matching users with highly relevant politicians by analyzing their social media activity. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the user's social media activity data into a generating AI and have the generating AI propose matching methods.

[0108] The communication unit can estimate the user's emotions and adjust its communication method based on the estimated emotions. For example, if the user is tense, the communication unit can communicate information in a calm tone. For example, if the user is relaxed, the communication unit can also communicate information in a friendly tone. Furthermore, if the user is in a hurry, the communication unit can communicate information in a quick and concise manner. For example, the communication unit estimates the user's emotions and adjusts its communication method. This allows the communication unit to deliver more appropriate information by adjusting its communication method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the communication method.

[0109] The communication unit can analyze the user's past communication history to select the optimal communication method during communication. For example, the communication unit can suggest the optimal method based on the communication methods the user has preferred in the past. The communication unit can also prioritize the communication of information on specific topics based on the user's past communication history. Furthermore, the communication unit can analyze the user's past communication history to select the most effective communication method. For example, the communication unit analyzes the user's past data to select the optimal communication method. In this way, the communication unit can provide the optimal communication method by analyzing the user's past communication history. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input the user's past data into a generating AI and have the generating AI select the optimal communication method.

[0110] The communication unit can customize the means of communication based on the user's current living situation at the time of communication. For example, if the user is at work, the communication unit can provide concise and to-the-point communication in a short amount of time. If the user is relaxed, the communication unit can also provide communication that includes detailed information. Furthermore, if the user is on the move, the communication unit can provide voice communication. For example, the communication unit customizes the means of communication based on the user's living situation. This allows the communication unit to deliver more appropriate information by customizing the means of communication based on the user's living situation. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of communication.

[0111] The communication unit can estimate the user's emotions and determine the priority of communication based on the estimated emotions. For example, if the user is tense, the communication unit will prioritize the delivery of important information. For example, if the user is relaxed, the communication unit may also prioritize the delivery of detailed information. Furthermore, if the user is in a hurry, the communication unit may prioritize the delivery of only the most important information. For example, the communication unit estimates the user's emotions and determines the priority of communication. This allows the communication unit to prioritize the delivery of important information by determining the priority of communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input user emotion data into a generative AI and have the generative AI perform the determination of communication priorities.

[0112] The communication unit can select the optimal communication method by considering the user's geographical location information during communication. For example, if the user lives in a specific region, the communication unit can prioritize communicating information related to that region. For example, if the user is traveling, the communication unit can also prioritize communicating information related to the user's current location. Furthermore, if the communication unit is participating in a specific event, the communication unit can also prioritize communicating information related to that event. For example, the communication unit selects the optimal communication method by considering the user's geographical location information. This allows the communication unit to prioritize communicating highly relevant information by considering the user's geographical location information. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal communication method.

[0113] The communication unit can analyze the user's social media activity and propose means of communication during the communication process. For example, the communication unit can communicate relevant information based on topics that the user frequently mentions on social media. The communication unit can also analyze the user's social media activity history and prioritize the communication of information of high interest. Furthermore, the communication unit can communicate relevant information based on accounts and groups that the user follows. For example, the communication unit analyzes the user's social media activity and communicates relevant information. This allows the communication unit to prioritize the communication of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can input the user's social media activity data into a generating AI and have the generating AI propose means of communication.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The system can suggest more appropriate politicians by considering the user's past voting history when they input their interests, opinions, and values. For example, the reception unit analyzes information on politicians the user has voted for in the past and suggests the most suitable politician by comparing it with the user's current interests, opinions, and values. The analysis unit analyzes the user's political tendencies based on past voting history, enabling more accurate matching. Furthermore, the presentation unit can prioritize and present information that is important to the user based on past voting history. In this way, the system can suggest more appropriate politicians by considering the user's past voting history and support the user's political choices.

[0116] The system can adjust the input method when users input their interests, opinions, and values, taking into account their current health condition. For example, the reception section can provide a simplified input method if the user is tired, allowing for quick completion. The analysis section can also adjust the presentation of the analysis based on the user's health condition, providing information in an easy-to-understand format. Furthermore, the presentation section can adjust the way information is presented according to the user's health condition, providing information in a less burdensome way. In this way, the system can provide more appropriate information and reduce the user's burden by taking their health condition into consideration.

[0117] The system can adjust the input method when users input their interests, opinions, and values, taking into account their learning history. For example, the reception unit suggests relevant input items based on what the user has learned in the past. The analysis unit can adjust the presentation of the analysis based on the user's learning history, providing information in an easy-to-understand format. Furthermore, the presentation unit can adjust the way information is presented according to the user's learning history, providing information in a way that enhances learning effectiveness. In this way, the system can provide more appropriate information by considering the user's learning history, thereby improving the user's learning effectiveness.

[0118] The system can adjust the input method when users input their interests, opinions, and values, taking into account their cultural background. For example, the reception unit can suggest appropriate input fields based on the user's cultural background. The analysis unit can also adjust the presentation of the analysis based on the user's cultural background, providing information in an easily understandable format. Furthermore, the presentation unit can adjust the way information is presented according to the user's cultural background, providing information in a culturally appropriate manner. As a result, the system can provide more appropriate information and deepen user understanding by considering the user's cultural background.

[0119] The system can adjust the input method when users input their interests, opinions, and values, taking their occupation into consideration. For example, the reception section can suggest relevant input fields based on the user's occupation. The analysis section can also adjust the presentation of the analysis based on the user's occupation, providing information in an easily understandable format. Furthermore, the presentation section can adjust the way information is presented according to the user's occupation, providing information in a way that is relevant to their occupation. In this way, the system can provide more appropriate information and deepen the user's understanding by taking their occupation into consideration.

[0120] The system can estimate the user's emotions and adjust the way it provides feedback based on those emotions. For example, the analysis unit provides concise and to-the-point feedback if the user is stressed. The presentation unit provides detailed feedback to deepen understanding if the user is relaxed. Furthermore, the dialogue unit provides visually engaging feedback to capture the user's interest if they are excited. In this way, the system can provide more appropriate feedback by adjusting the feedback method according to the user's emotions.

[0121] The system can estimate the user's emotions and adjust the order of questions asked based on those emotions. For example, if the reception desk is nervous, it will start with simple questions and gradually increase the difficulty. If the user is relaxed, the analysis desk will prioritize detailed questions to gather deeper information. Furthermore, if the user is in a hurry, the presentation desk will prioritize important questions to gather information quickly. In this way, the system can gather more appropriate information by adjusting the order of questions according to the user's emotions.

[0122] The system can estimate the user's emotions and adjust how information is presented to the user based on those emotions. For example, if the user is relaxed, the presentation unit can provide a detailed presentation. If the user is in a hurry, the presentation unit can provide a concise presentation that gets straight to the point. Furthermore, if the user is excited, the presentation unit can provide a visually appealing presentation to capture their interest. In this way, the system can provide more appropriate information by adjusting how information is presented according to the user's emotions.

[0123] The system can estimate the user's emotions and adjust the way it conducts the conversation based on those emotions. For example, if the user is tense, the conversation will proceed in a calm tone. If the user is relaxed, the conversation will proceed in a friendly tone. Furthermore, if the user is in a hurry, the conversation will proceed quickly and concisely. In this way, the system can provide a more appropriate conversation by adjusting the way it conducts the conversation according to the user's emotions.

[0124] The system can estimate the user's emotions and adjust the priority of information presented to the user based on those emotions. For example, if the user is stressed, the system will prioritize presenting important information. If the user is relaxed, the system can prioritize presenting detailed information. Furthermore, if the user is in a hurry, the system can prioritize presenting only the most important information. In this way, the system can prioritize providing important information by adjusting the priority of information according to the user's emotions.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The reception desk inputs the user's interests, opinions, and values. These include, for example, political interests, social opinions, and personal values. The reception desk allows users to input their interests, opinions, and values ​​in text format. Users can also input their interests, opinions, and values ​​using voice input, and speech recognition technology is used to convert the user's voice into text data. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of inputting interests, opinions, and values ​​based on the estimated emotions. For example, if the user is stressed, the input may be simplified and adjusted to be completed in a short time. Conversely, if the user is relaxed, it may be encouraged to provide detailed input and adjust to gather more information. Step 2: The analysis unit analyzes the information entered by the reception unit to understand the politician's profile, vision, beliefs, policies, etc. The analysis unit uses text analysis, data mining, and machine learning algorithms to gain a deep understanding of the politician's information. For example, it uses detailed profile analysis and belief analysis methods to understand the politician's information. Furthermore, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, it can provide detailed analysis results to deepen understanding. Conversely, if the user is in a hurry, it can provide concise analysis results that get straight to the point. Step 3: The presentation unit presents the most suitable politician to the user based on the information understood by the analysis unit. The presentation unit matches the user's interests, opinions, and values ​​with information about politicians to present the most suitable politician. The presentation unit presents the most suitable politician to the user based on the similarity scale and the algorithm used. Furthermore, the presentation unit can estimate the user's emotions and adjust the presentation style based on the estimated emotions of the user. For example, if the user is relaxed, it can provide a presentation style that includes detailed information. If the user is in a hurry, it can provide a concise presentation style that gets straight to the point. Step 4: The Dialogue Unit facilitates online dialogue between the user and the politician presented by the Presentation Unit. The Dialogue Unit provides an online dialogue platform so that the user can directly interact with the presented politician. The Dialogue Unit facilitates the dialogue between the user and the politician using video calls and chat functions. Furthermore, the Dialogue Unit can also estimate the user's emotions and adjust the dialogue method based on the estimated emotions of the user. For example, if the user is nervous, the dialogue can proceed in a calm tone. Conversely, if the user is relaxed, the dialogue can proceed in a friendly tone.

[0127] 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.

[0128] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, presentation unit, dialogue unit, matching unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user regarding their interests, opinions, and values. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents the user with the most suitable politician. The dialogue unit is implemented by the control unit 46A of the smart device 14 and provides online dialogue between the user and the politician. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the user's interests, opinions, and values ​​with information on politicians. The communication unit is implemented by the specific processing unit 290 of the data processing unit 12 and conveys the politicians' proposals and stances to voters during the election period. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0134] 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.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0136] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] 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.

[0138] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, presentation unit, dialogue unit, matching unit, and communication unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and takes input of the user's interests, opinions, and values. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The presentation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and presents the user with the most suitable politician. The dialogue unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides online dialogue between the user and the politician. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches the user's interests, opinions, and values ​​with information on politicians. The communication unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and conveys the politicians' proposals and stances to voters during the election period. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0150] 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.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0152] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] 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.

[0154] 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.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, presentation unit, dialogue unit, matching unit, and transmission unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the user regarding their interests, opinions, and values. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The presentation unit is implemented, for example, by the control unit 46A of the headset terminal 314 and presents the user with the most suitable politician. The dialogue unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides online dialogue between the user and the politician. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches the user's interests, opinions, and values ​​with information on politicians. The transmission unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and conveys the politicians' proposals and stances to voters during the election period. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0166] 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.

[0167] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0168] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] 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.

[0170] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] 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.

[0172] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] 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.

[0177] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the reception unit, analysis unit, presentation unit, dialogue unit, matching unit, and communication unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user regarding their interests, opinions, and values. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information. The presentation unit is implemented by, for example, the control unit 46A of the robot 414 and presents the user with the most suitable politician. The dialogue unit is implemented by, for example, the control unit 46A of the robot 414 and provides online dialogue between the user and the politician. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches the user's interests, opinions, and values ​​with information on politicians. The communication unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conveys the politicians' proposals and stances to voters during the election period. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0180] 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.

[0181] Figure 9 shows the 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.

[0182] 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.

[0183] 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.

[0184] 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, and motorcycles, 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 based, for example, 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.

[0185] 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."

[0186] 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.

[0187] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] 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 other things 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.

[0197] 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.

[0198] (Note 1) A reception area where users input their interests, opinions, and values, The analysis unit analyzes the information entered by the reception unit to understand the politician's profile, vision, beliefs, policies, etc. Based on the information understood by the analysis unit, the presentation unit presents the most suitable politician to the user, The system includes a dialogue unit that enables online dialogue between politicians and users as presented by the presentation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, To gain a deep understanding of politicians' profiles, visions, beliefs, and policies. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a matching section that matches users' interests, opinions, and values ​​with information about politicians. The system described in Appendix 1, characterized by the features described herein. (Note 4) During the election period, they have a communication center to convey politicians' proposals and stances to voters. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting interests, opinions, and values ​​based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users input their interests, opinions, and values, the system filters the information based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input their interests, opinions, and values, the system prioritizes inputting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input their interests, opinions, and values, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of the politicians. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, different analysis algorithms are applied depending on the politician's category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when politicians submitted their proposals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relevance of the politicians. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is, It estimates the user's emotions and adjusts the presentation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is, When presenting information, adjust the level of detail based on the importance of the politician. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, When presenting, different presentation algorithms are applied depending on the politician's category. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, It estimates the user's emotions and adjusts the length of the presentation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When presenting proposals, the priority of presentations will be determined based on when each politician submitted their proposal. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, When presenting, adjust the order of presentation based on the relevance of the politicians. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way it interacts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, During a conversation, the system analyzes the user's past conversation history to select the most appropriate conversation method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, During conversations, the means of communication are customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During the interaction, the system selects the optimal interaction method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, During the conversation, we analyze the user's social media activity and suggest ways to communicate. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is During the matching process, the system analyzes the user's past interests, opinions, and values ​​to select the most suitable matching method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The matching unit is During the matching process, the matching method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 32) The matching unit is The system estimates the user's emotions and determines matching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The matching unit is During the matching process, the system selects the optimal matching method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The matching unit is During the matching process, we analyze the user's social media activity and suggest matching methods. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned transmission unit is It estimates the user's emotions and adjusts the communication method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned transmission unit is During communication, the system analyzes the user's past communication history to select the most suitable communication method. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned transmission unit is During communication, the means of communication are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned transmission unit is It estimates the user's emotions and determines the priority of communication based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned transmission unit is When communicating, the optimal communication method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned transmission unit is During communication, we analyze users' social media activity and propose communication methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their interests, opinions, and values, The analysis unit analyzes the information entered by the reception unit to understand the politician's profile, vision, beliefs, policies, etc. Based on the information understood by the analysis unit, the presentation unit presents the most suitable politician to the user, The system includes a dialogue unit that enables online dialogue between politicians and users as presented by the presentation unit. A system characterized by the following features.

2. The aforementioned analysis unit, To gain a deep understanding of politicians' profiles, visions, beliefs, and policies. The system according to feature 1.

3. It features a matching section that matches users' interests, opinions, and values ​​with information about politicians. The system according to feature 1.

4. During the election period, they have a communication center to convey politicians' proposals and stances to voters. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting interests, opinions, and values ​​based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When users input their interests, opinions, and values, the system filters the information based on their current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When users input their interests, opinions, and values, the system prioritizes inputting highly relevant information by considering the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When users input their interests, opinions, and values, the system analyzes their social media activity and inputs relevant information. The system according to feature 1.

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