System

Abstract

An object of the system according to the embodiment is to recommend an optimal candidate based on a user's opinion or political tendency and to promote political participation.SOLUTION: A system includes an opinion input part, an analysis part, a recommendation part, and a voting part. The opinion input unit inputs a user's opinion and political tendency. The analysis part analyzes the data input by the opinion input part. The recommendation unit recommends a candidate who best matches the intention of the user based on the data analyzed by the analysis unit. The voting unit provides a virtual voting process.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not done enough to promote political participation among young people who have little interest in politics or to improve information transparency, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend the most suitable candidate based on the user's opinions and political tendencies, thereby promoting political participation. [Means for solving the problem]

[0006] The system according to the embodiment includes an opinion input unit, an analysis unit, a recommendation unit, and a voting unit. The opinion input unit inputs a user's opinions and political tendencies. The analysis unit analyzes the data input by the opinion input unit. The recommendation unit recommends a candidate who best matches the user's preferences based on the data analyzed by the analysis unit. The voting unit provides a virtual voting process. [Effects of the Invention]

[0007] The system according to the embodiment can recommend the most suitable candidate based on the user's opinions and political tendencies, thereby promoting political participation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​political party system according to an embodiment of the present invention is a system in which users input their opinions and political tendencies, and a generating AI analyzes them to propose optimal policies and provide a virtual voting process. This enables the AI ​​political party system to promote users' political participation, improve information transparency, and mitigate social divisions.

[0029] The AI ​​political party system according to the embodiment includes an opinion input unit, an analysis unit, a recommendation unit, and a voting unit. The opinion input unit inputs a user's opinions and political tendencies. For example, the user inputs their opinions by answering a questionnaire. The opinion input unit can also collect social media posts to understand the user's political tendencies. The opinion input unit can also collect past voting histories to analyze the user's political tendencies. The analysis unit analyzes the data input by the opinion input unit. For example, the analysis unit can analyze the user's opinions using data mining technology. The analysis unit can also analyze the user's opinions using natural language processing technology. The analysis unit can also analyze the user's opinions using statistical analysis technology. The recommendation unit recommends a candidate who best matches the user's preferences based on the data analyzed by the analysis unit. For example, the recommendation unit can recommend candidates using a matching algorithm. The recommendation unit can also recommend candidates using a scoring system. The recommendation unit can also recommend candidates based on the user's preferences. The voting unit provides a virtual voting process. For example, the voting unit may provide a virtual voting process using a simulation. The voting unit may also provide a virtual voting process using an interactive voting system. The voting unit may also allow users to check voting results. This allows the AI ​​political party system according to the embodiment to promote users' political participation, improve information transparency, and mitigate social division. For example, the AI ​​political party system may make it easier for users to take an interest in politics and provide political information in an easy-to-understand manner. The AI ​​political party system may also make proposals that allow people with different positions to agree on common solutions. Furthermore, the AI ​​political party system may filter out false information and create a healthy political environment.

[0030] The analysis unit can analyze a user's past statements and behavioral history to generate policy proposals optimized for each individual user. For example, the generation AI analyzes a user's past statements and behavioral history to generate policy proposals optimized for each individual user. For example, if a user has made many statements about education policy in the past, the generation AI will make specific proposals regarding education policy to that user. The analysis unit can also analyze social media posts to generate policy proposals based on the user's interests. The analysis unit can also analyze survey results to generate policy proposals based on the user's intentions. This makes it possible to generate optimal policy proposals based on the user's past statements and behavioral history, thereby making it possible to make proposals that meet the user's interests.

[0031] The analysis unit can analyze the progress of the discussion in real time and suggest new topics or perspectives to prevent the discussion from stagnating. For example, the generation AI of the analysis unit analyzes the progress of the discussion in real time and suggests new topics or perspectives to prevent the discussion from stagnating. For example, if the discussion is stagnating, the generation AI suggests a new related topic. The analysis unit can also analyze the progress of the discussion and suggest perspectives to attract users' attention. The analysis unit can also analyze the flow of the discussion and make suggestions to revitalize the discussion. In this way, the progress of the discussion can be analyzed in real time and new topics or perspectives can be suggested to prevent the discussion from stagnating, thereby revitalizing the discussion.

[0032] The analysis unit can automatically translate discussions in different languages ​​and generate policy proposals from an international perspective. In the analysis unit, for example, the generation AI can automatically translate discussions in different languages ​​and generate policy proposals from an international perspective. For example, the generation AI can translate discussions in English into Japanese and make policy proposals from a Japanese perspective. The analysis unit can also translate discussions in different languages ​​and make policy proposals from an international perspective. The analysis unit can also build a system in which the generation AI can translate discussions in different languages ​​and make policy proposals from an international perspective. This makes it possible to automatically translate discussions in different languages ​​and generate policy proposals from an international perspective, making it possible to make policy proposals that incorporate a global perspective.

[0033] The analysis unit can generate policy proposals from a professional perspective, taking into account the user's expertise and professional background. For example, the generation AI of the analysis unit generates policy proposals from a professional perspective, taking into account the user's expertise and professional background. For example, specific proposals regarding medical policy are made to a user with expertise in the medical field. The analysis unit can also analyze the user's occupational history and make policy proposals from a professional perspective. The analysis unit can also analyze the user's qualification information in the user's field of expertise and make policy proposals from a professional perspective. In this way, by generating policy proposals that take into account the user's expertise and professional background, more professional and effective proposals can be made.

[0034] The voting department can analyze the voting results and conduct a detailed analysis taking into account the voter's background information. For example, the voting department uses a generation AI to analyze the voting results and conduct a detailed analysis taking into account the voter's background information (age, gender, region, etc.). For example, it can analyze the voting trends of young people and clarify why a particular policy is supported. The voting department can also analyze the voter's background information and conduct a detailed analysis of the voting results. The voting department can also conduct a detailed analysis of the voting results based on the voter's background information. This allows for a deeper understanding of the voting results by conducting a detailed analysis taking into account the voter's background information.

[0035] The voting department can build a model that predicts future elections and voting behavior based on voting results. For example, the generation AI in the voting department builds a model that predicts future elections and voting behavior based on voting results. For example, it analyzes past voting data and predicts voting trends in the next election. The voting department can also build a model that predicts future elections and voting behavior based on voting results. The voting department can also build a system in which the generation AI predicts future elections and voting behavior based on voting results. This can be useful in formulating election strategies by predicting future elections and voting behavior based on voting results.

[0036] The voting unit can simulate different voting systems and propose the optimal voting system. For example, the generation AI simulates different voting systems (e.g., ranked-choice voting or proportional representation) and proposes the optimal voting system. For example, the voting unit simulates ranked-choice voting and proposes the optimal voting system based on the results. The voting unit can also simulate different voting systems and propose the optimal voting system. The voting unit can also build a system in which the generation AI simulates different voting systems and proposes the optimal voting system. In this way, by simulating different voting systems and proposing the optimal voting system, the voting process can be improved.

[0037] The voting unit can visualize the voting results so that the user can intuitively understand them. For example, the voting unit can have a generation AI visualize the voting results so that the user can intuitively understand them. For example, the voting results can be displayed in a graph or chart so that the user can understand them at a glance. The voting unit can also visualize the voting results so that the user can intuitively understand them. The voting unit can also build a system in which the generation AI visualizes the voting results so that the user can intuitively understand them. In this way, visualizing the voting results makes it easier for the user to understand intuitively.

[0038] The recommendation unit can analyze a user's past voting history and political statements to make more accurate candidate recommendations. For example, the recommendation unit uses a generation AI to analyze a user's past voting history and political statements to make more accurate candidate recommendations. For example, the recommendation unit recommends candidates with similar policies based on the policies of candidates the user has supported in the past. The recommendation unit can also analyze a user's voting history to make more accurate candidate recommendations. The recommendation unit can also analyze a user's political statements to make more accurate candidate recommendations. In this way, by analyzing a user's past voting history and political statements, more accurate candidate recommendations are possible.

[0039] The recommendation unit can recommend candidates from different countries and regions, promoting political participation from an international perspective. For example, the generation AI can recommend candidates from different countries and regions, promoting political participation from an international perspective. For example, the recommendation unit can recommend candidates from countries in which the user is interested. The recommendation unit can also recommend candidates from different countries and regions, promoting political participation from an international perspective. The recommendation unit can also build a system in which the generation AI can recommend candidates from different countries and regions, promoting political participation from an international perspective. In this way, by recommending candidates from different countries and regions, political participation from an international perspective is promoted.

[0040] The recommendation unit can recommend candidates with relevant policies by taking into account the user's personal interests, such as their occupation and hobbies. For example, the generation AI of the recommendation unit can recommend candidates with relevant policies by taking into account the user's personal interests, such as their occupation and hobbies. For example, if the user is interested in the medical field, it can recommend candidates with medical policies. The recommendation unit can also analyze the user's occupation and hobbies and recommend candidates with relevant policies. The recommendation unit can also analyze the user's personal interests and recommend candidates with relevant policies. This makes it possible to recommend candidates with higher interest by taking into account the user's personal interests, such as their occupation and hobbies.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The analysis unit can analyze the user's health condition and lifestyle habits and make suggestions regarding health policies. For example, if the user inputs the results of a health checkup, the analysis unit can make specific suggestions regarding health policies based on that data. The analysis unit can also analyze the user's lifestyle habits (diet, exercise, sleep, etc.) and make suggestions regarding health policies. The analysis unit can also monitor the user's health condition in real time and make suggestions regarding health policies. This makes it possible to propose specific health policies based on the user's health condition and lifestyle habits.

[0043] The analysis unit can analyze the user's purchasing history and make suggestions regarding economic policies. For example, the analysis unit can make specific suggestions regarding economic policies to the user based on data on products purchased by the user in the past. The analysis unit can also analyze the user's purchasing history and make suggestions regarding economic policies based on consumption trends. The analysis unit can also monitor the user's purchasing history in real time and make suggestions regarding economic policies. This makes it possible to make specific suggestions regarding economic policies based on the user's purchasing history.

[0044] The analysis unit can analyze the user's educational history and make suggestions regarding educational policies. For example, the analysis unit can make specific suggestions regarding educational policies to the user based on data on the education the user has received in the past. The analysis unit can also analyze the user's educational history and make suggestions regarding educational policies based on educational trends. The analysis unit can also monitor the user's educational history in real time and make suggestions regarding educational policies. This makes it possible to propose specific educational policies based on the user's educational history.

[0045] The analysis unit can analyze the user's hobbies and interests and make suggestions regarding cultural policies. For example, the analysis unit can make specific suggestions regarding cultural policies to the user based on data on cultural events the user has attended in the past. The analysis unit can also analyze the user's hobbies and interests and make suggestions regarding cultural policies based on cultural trends. The analysis unit can also monitor the user's hobbies and interests in real time and make suggestions regarding cultural policies. This makes it possible to make specific suggestions regarding cultural policies based on the user's hobbies and interests.

[0046] The analysis unit can analyze the user's travel history and make suggestions regarding tourism policies. For example, the analysis unit can make specific suggestions regarding tourism policies to the user based on data on tourist destinations the user has visited in the past. The analysis unit can also analyze the user's travel history and make suggestions regarding tourism policies based on tourism trends. The analysis unit can also monitor the user's travel history in real time and make suggestions regarding tourism policies. This makes it possible to propose specific tourism policies based on the user's travel history.

[0047] The analysis unit can analyze the user's exercise history and make suggestions regarding sports policies. For example, the analysis unit can make specific sports policy suggestions to the user based on data on sporting events the user has participated in in the past. The analysis unit can also analyze the user's exercise history and make suggestions regarding sports policies based on the user's sports tendencies. The analysis unit can also monitor the user's exercise history in real time and make suggestions regarding sports policies. This makes it possible to propose specific sports policies based on the user's exercise history.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The opinion input unit inputs the user's opinions and political tendencies. For example, the user inputs their opinions by answering a questionnaire. The opinion input unit can also collect social media posts to understand the user's political tendencies. Furthermore, the opinion input unit can collect past voting history and analyze the user's political tendencies. Step 2: The analysis unit analyzes the data input by the opinion input unit. For example, the analysis unit analyzes the user's opinion using data mining technology, natural language processing technology, and statistical analysis technology. Step 3: The recommendation unit recommends candidates who best match the user's preferences based on the data analyzed by the analysis unit. For example, the recommendation unit recommends candidates using a matching algorithm or a scoring system. Step 4: The voting unit provides a virtual voting process. For example, the voting unit provides a virtual voting process using a simulation or an interactive voting system, allowing users to check the voting results.

[0050] (Example 2) The AI ​​political party system according to an embodiment of the present invention is a system in which users input their opinions and political tendencies, and a generating AI analyzes them to propose optimal policies and provide a virtual voting process. This enables the AI ​​political party system to promote users' political participation, improve information transparency, and mitigate social divisions.

[0051] The AI ​​political party system according to the embodiment includes an opinion input unit, an analysis unit, a recommendation unit, and a voting unit. The opinion input unit inputs a user's opinions and political tendencies. For example, the user inputs their opinions by answering a questionnaire. The opinion input unit can also collect social media posts to understand the user's political tendencies. The opinion input unit can also collect past voting histories to analyze the user's political tendencies. The analysis unit analyzes the data input by the opinion input unit. For example, the analysis unit can analyze the user's opinions using data mining technology. The analysis unit can also analyze the user's opinions using natural language processing technology. The analysis unit can also analyze the user's opinions using statistical analysis technology. The recommendation unit recommends a candidate who best matches the user's preferences based on the data analyzed by the analysis unit. For example, the recommendation unit can recommend candidates using a matching algorithm. The recommendation unit can also recommend candidates using a scoring system. The recommendation unit can also recommend candidates based on the user's preferences. The voting unit provides a virtual voting process. For example, the voting unit may provide a virtual voting process using a simulation. The voting unit may also provide a virtual voting process using an interactive voting system. The voting unit may also allow users to check voting results. This allows the AI ​​political party system according to the embodiment to promote users' political participation, improve information transparency, and mitigate social division. For example, the AI ​​political party system may make it easier for users to take an interest in politics and provide political information in an easy-to-understand manner. The AI ​​political party system may also make proposals that allow people with different positions to agree on common solutions. Furthermore, the AI ​​political party system may filter out false information and create a healthy political environment.

[0052] The analysis unit can analyze a user's past statements and behavioral history to generate policy proposals optimized for each individual user. For example, the generation AI analyzes a user's past statements and behavioral history to generate policy proposals optimized for each individual user. For example, if a user has made many statements about education policy in the past, the generation AI will make specific proposals regarding education policy to that user. The analysis unit can also analyze social media posts to generate policy proposals based on the user's interests. The analysis unit can also analyze survey results to generate policy proposals based on the user's intentions. This makes it possible to generate optimal policy proposals based on the user's past statements and behavioral history, thereby making it possible to make proposals that meet the user's interests.

[0053] The analysis unit can analyze the progress of the discussion in real time and suggest new topics or perspectives to prevent the discussion from stagnating. For example, the generation AI of the analysis unit analyzes the progress of the discussion in real time and suggests new topics or perspectives to prevent the discussion from stagnating. For example, if the discussion is stagnating, the generation AI suggests a new related topic. The analysis unit can also analyze the progress of the discussion and suggest perspectives to attract users' attention. The analysis unit can also analyze the flow of the discussion and make suggestions to revitalize the discussion. In this way, the progress of the discussion can be analyzed in real time and new topics or perspectives can be suggested to prevent the discussion from stagnating, thereby revitalizing the discussion.

[0054] The analysis unit can use the emotion estimation function to analyze the emotions of users during a discussion and generate policy proposals that are likely to resonate emotionally. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of users during a discussion and generate policy proposals that are likely to resonate emotionally. For example, if a user is emotionally excited, the generation AI will make policy proposals that resonate with that emotion. The analysis unit can also analyze the emotions of users and make policy proposals that are likely to resonate emotionally. The analysis unit can also use the emotion estimation function to generate policy proposals based on the users' emotions. In this way, by analyzing the emotions of users during a discussion and generating policy proposals that are likely to resonate emotionally, it is easier to attract users' attention.

[0055] The analysis unit can automatically translate discussions in different languages ​​and generate policy proposals from an international perspective. In the analysis unit, for example, the generation AI can automatically translate discussions in different languages ​​and generate policy proposals from an international perspective. For example, the generation AI can translate discussions in English into Japanese and make policy proposals from a Japanese perspective. The analysis unit can also translate discussions in different languages ​​and make policy proposals from an international perspective. The analysis unit can also build a system in which the generation AI can translate discussions in different languages ​​and make policy proposals from an international perspective. This makes it possible to automatically translate discussions in different languages ​​and generate policy proposals from an international perspective, making it possible to make policy proposals that incorporate a global perspective.

[0056] The analysis unit can generate policy proposals from a professional perspective, taking into account the user's expertise and professional background. For example, the generation AI of the analysis unit generates policy proposals from a professional perspective, taking into account the user's expertise and professional background. For example, specific proposals regarding medical policy are made to a user with expertise in the medical field. The analysis unit can also analyze the user's occupational history and make policy proposals from a professional perspective. The analysis unit can also analyze the user's qualification information in the user's field of expertise and make policy proposals from a professional perspective. In this way, by generating policy proposals that take into account the user's expertise and professional background, more professional and effective proposals can be made.

[0057] The analysis unit can use the emotion estimation function to monitor the emotions of users during a discussion in real time and make suggestions to elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to monitor the emotions of users during a discussion in real time and make suggestions to elicit positive emotions. For example, if a user is feeling down, the generation AI suggests an encouraging message. The analysis unit can also monitor the emotions of users in real time and make suggestions to elicit positive emotions. The analysis unit can also use the emotion estimation function to make suggestions based on the user's emotions. In this way, the quality of the discussion is improved by monitoring the emotions of users during a discussion in real time and making suggestions to elicit positive emotions.

[0058] The voting department can analyze the voting results and conduct a detailed analysis taking into account the voter's background information. For example, the voting department uses a generation AI to analyze the voting results and conduct a detailed analysis taking into account the voter's background information (age, gender, region, etc.). For example, it can analyze the voting trends of young people and clarify why a particular policy is supported. The voting department can also analyze the voter's background information and conduct a detailed analysis of the voting results. The voting department can also conduct a detailed analysis of the voting results based on the voter's background information. This allows for a deeper understanding of the voting results by conducting a detailed analysis taking into account the voter's background information.

[0059] The voting department can build a model that predicts future elections and voting behavior based on voting results. For example, the generation AI in the voting department builds a model that predicts future elections and voting behavior based on voting results. For example, it analyzes past voting data and predicts voting trends in the next election. The voting department can also build a model that predicts future elections and voting behavior based on voting results. The voting department can also build a system in which the generation AI predicts future elections and voting behavior based on voting results. This can be useful in formulating election strategies by predicting future elections and voting behavior based on voting results.

[0060] The voting unit can use the emotion estimation function to analyze voters' emotions and identify policies and candidates that are likely to resonate emotionally. The voting unit, for example, uses the emotion estimation function to analyze voters' emotions and identify policies and candidates that are likely to resonate emotionally. For example, if a voter is emotionally aroused, the voting unit identifies policies and candidates that resonate with that emotion. The voting unit can also analyze voters' emotions and identify policies and candidates that are likely to resonate emotionally. The voting unit can also use the emotion estimation function to identify policies and candidates based on the voter's emotions. In this way, by analyzing voters' emotions and identifying policies and candidates that are likely to resonate emotionally, a deeper understanding of voting behavior can be achieved.

[0061] The voting unit can simulate different voting systems and propose the optimal voting system. For example, the generation AI simulates different voting systems (e.g., ranked-choice voting or proportional representation) and proposes the optimal voting system. For example, the voting unit simulates ranked-choice voting and proposes the optimal voting system based on the results. The voting unit can also simulate different voting systems and propose the optimal voting system. The voting unit can also build a system in which the generation AI simulates different voting systems and proposes the optimal voting system. In this way, by simulating different voting systems and proposing the optimal voting system, the voting process can be improved.

[0062] The voting unit can visualize the voting results so that the user can intuitively understand them. For example, the voting unit can have a generation AI visualize the voting results so that the user can intuitively understand them. For example, the voting results can be displayed in a graph or chart so that the user can understand them at a glance. The voting unit can also visualize the voting results so that the user can intuitively understand them. The voting unit can also build a system in which the generation AI visualizes the voting results so that the user can intuitively understand them. In this way, visualizing the voting results makes it easier for the user to understand intuitively.

[0063] The voting unit can use the emotion estimation function to monitor the emotional responses of voters in real time and provide feedback to improve the voting process. For example, the voting unit can use the emotion estimation function to monitor the emotional responses of voters in real time and provide feedback to improve the voting process. For example, if a voter is emotionally dissatisfied, the voting unit can identify the cause and propose improvements. The voting unit can also monitor the emotional responses of voters in real time and provide feedback to improve the voting process. The voting unit can also use the emotion estimation function to provide feedback based on the voter's emotions. This improves the quality of the voting experience by monitoring the emotional responses of voters in real time and providing feedback to improve the voting process.

[0064] The recommendation unit can analyze a user's past voting history and political statements to make more accurate candidate recommendations. For example, the recommendation unit uses a generation AI to analyze a user's past voting history and political statements to make more accurate candidate recommendations. For example, the recommendation unit recommends candidates with similar policies based on the policies of candidates the user has supported in the past. The recommendation unit can also analyze a user's voting history to make more accurate candidate recommendations. The recommendation unit can also analyze a user's political statements to make more accurate candidate recommendations. In this way, by analyzing a user's past voting history and political statements, more accurate candidate recommendations are possible.

[0065] The recommendation unit can use the emotion estimation function to analyze the user's emotions and recommend candidates who are easy to empathize with emotionally. The recommendation unit, for example, uses the emotion estimation function to analyze the user's emotions and recommend candidates who are easy to empathize with emotionally. For example, if the user is emotionally excited, the recommendation unit recommends candidates who empathize with those emotions. The recommendation unit can also analyze the user's emotions and recommend candidates who are easy to empathize with emotionally. The recommendation unit can also use the emotion estimation function to recommend candidates based on the user's emotions. In this way, analyzing the user's emotions and recommending candidates who are easy to empathize with emotionally makes it easier to attract the user's attention.

[0066] The recommendation unit can recommend candidates from different countries and regions, promoting political participation from an international perspective. For example, the generation AI can recommend candidates from different countries and regions, promoting political participation from an international perspective. For example, the recommendation unit can recommend candidates from countries in which the user is interested. The recommendation unit can also recommend candidates from different countries and regions, promoting political participation from an international perspective. The recommendation unit can also build a system in which the generation AI can recommend candidates from different countries and regions, promoting political participation from an international perspective. In this way, by recommending candidates from different countries and regions, political participation from an international perspective is promoted.

[0067] The recommendation unit can recommend candidates with relevant policies by taking into account the user's personal interests, such as their occupation and hobbies. For example, the generation AI of the recommendation unit can recommend candidates with relevant policies by taking into account the user's personal interests, such as their occupation and hobbies. For example, if the user is interested in the medical field, it can recommend candidates with medical policies. The recommendation unit can also analyze the user's occupation and hobbies and recommend candidates with relevant policies. The recommendation unit can also analyze the user's personal interests and recommend candidates with relevant policies. This makes it possible to recommend candidates with higher interest by taking into account the user's personal interests, such as their occupation and hobbies.

[0068] The recommendation unit can use the emotion estimation function to monitor the user's emotional reactions in real time and continuously recommend optimal candidates. The recommendation unit can, for example, use the emotion estimation function to monitor the user's emotional reactions in real time and continuously recommend optimal candidates. For example, the recommendation unit updates the candidate recommendations every time the user's emotions change. The recommendation unit can also monitor the user's emotional reactions in real time and continuously recommend optimal candidates. The recommendation unit can also use the emotion estimation function to continuously recommend candidates based on the user's emotions. This makes it easier to maintain the user's interest by monitoring the user's emotional reactions in real time and continuously recommending optimal candidates.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The analysis unit can analyze the user's health condition and lifestyle habits and make suggestions regarding health policies. For example, if the user inputs the results of a health checkup, the analysis unit can make specific suggestions regarding health policies based on that data. The analysis unit can also analyze the user's lifestyle habits (diet, exercise, sleep, etc.) and make suggestions regarding health policies. The analysis unit can also monitor the user's health condition in real time and make suggestions regarding health policies. This makes it possible to propose specific health policies based on the user's health condition and lifestyle habits.

[0071] The analysis unit can analyze the user's purchasing history and make suggestions regarding economic policies. For example, the analysis unit can make specific suggestions regarding economic policies to the user based on data on products purchased by the user in the past. The analysis unit can also analyze the user's purchasing history and make suggestions regarding economic policies based on consumption trends. The analysis unit can also monitor the user's purchasing history in real time and make suggestions regarding economic policies. This makes it possible to make specific suggestions regarding economic policies based on the user's purchasing history.

[0072] The analysis unit can use the emotion estimation function to analyze the user's emotions and propose environmental policies that are likely to resonate with the user emotionally. For example, if the user has strong emotions about environmental issues, the analysis unit can propose environmental policies that resonate with those emotions. The analysis unit can also analyze the user's emotions and propose environmental policies that are likely to resonate with the user emotionally. The analysis unit can also use the emotion estimation function to propose environmental policies based on the user's emotions. This makes it possible to propose specific environmental policies based on the user's emotions.

[0073] The analysis unit can analyze the user's educational history and make suggestions regarding educational policies. For example, the analysis unit can make specific suggestions regarding educational policies to the user based on data on the education the user has received in the past. The analysis unit can also analyze the user's educational history and make suggestions regarding educational policies based on educational trends. The analysis unit can also monitor the user's educational history in real time and make suggestions regarding educational policies. This makes it possible to propose specific educational policies based on the user's educational history.

[0074] The analysis unit can use the emotion estimation function to analyze the user's emotions and propose welfare policies that are likely to resonate with the user emotionally. For example, if the user has strong emotions about a welfare issue, the analysis unit can propose welfare policies that resonate with those emotions. The analysis unit can also analyze the user's emotions and propose welfare policies that are likely to resonate with the user emotionally. The analysis unit can also use the emotion estimation function to propose welfare policies based on the user's emotions. This makes it possible to propose specific welfare policies based on the user's emotions.

[0075] The analysis unit can analyze the user's hobbies and interests and make suggestions regarding cultural policies. For example, the analysis unit can make specific suggestions regarding cultural policies to the user based on data on cultural events the user has attended in the past. The analysis unit can also analyze the user's hobbies and interests and make suggestions regarding cultural policies based on cultural trends. The analysis unit can also monitor the user's hobbies and interests in real time and make suggestions regarding cultural policies. This makes it possible to make specific suggestions regarding cultural policies based on the user's hobbies and interests.

[0076] The analysis unit can use the emotion estimation function to analyze the user's emotions and propose labor policies that are likely to resonate with the user emotionally. For example, if the user has strong emotions about a labor issue, the analysis unit can propose labor policies that resonate with those emotions. The analysis unit can also analyze the user's emotions and propose labor policies that are likely to resonate with the user emotionally. The analysis unit can also use the emotion estimation function to propose labor policies based on the user's emotions. This makes it possible to propose specific labor policies based on the user's emotions.

[0077] The analysis unit can analyze the user's travel history and make suggestions regarding tourism policies. For example, the analysis unit can make specific suggestions regarding tourism policies to the user based on data on tourist destinations the user has visited in the past. The analysis unit can also analyze the user's travel history and make suggestions regarding tourism policies based on tourism trends. The analysis unit can also monitor the user's travel history in real time and make suggestions regarding tourism policies. This makes it possible to propose specific tourism policies based on the user's travel history.

[0078] The analysis unit can use the emotion estimation function to analyze the user's emotions and propose disaster prevention policies that are likely to resonate with the user emotionally. For example, if the user has strong emotions about disaster prevention issues, the analysis unit can propose disaster prevention policies that resonate with those emotions. The analysis unit can also analyze the user's emotions and propose disaster prevention policies that are likely to resonate with the user emotionally. The analysis unit can also use the emotion estimation function to propose disaster prevention policies based on the user's emotions. This makes it possible to propose specific disaster prevention policies based on the user's emotions.

[0079] The analysis unit can analyze the user's exercise history and make suggestions regarding sports policies. For example, the analysis unit can make specific sports policy suggestions to the user based on data on sporting events the user has participated in in the past. The analysis unit can also analyze the user's exercise history and make suggestions regarding sports policies based on the user's sports tendencies. The analysis unit can also monitor the user's exercise history in real time and make suggestions regarding sports policies. This makes it possible to propose specific sports policies based on the user's exercise history.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The opinion input unit inputs the user's opinions and political tendencies. For example, the user inputs their opinions by answering a questionnaire. The opinion input unit can also collect social media posts to understand the user's political tendencies. Furthermore, the opinion input unit can collect past voting history and analyze the user's political tendencies. Step 2: The analysis unit analyzes the data input by the opinion input unit. For example, the analysis unit analyzes the user's opinion using data mining technology, natural language processing technology, and statistical analysis technology. Step 3: The recommendation unit recommends candidates who best match the user's preferences based on the data analyzed by the analysis unit. For example, the recommendation unit recommends candidates using a matching algorithm or a scoring system. Step 4: The voting unit provides a virtual voting process. For example, the voting unit provides a virtual voting process using a simulation or an interactive voting system, allowing users to check the voting results.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an opinion input section for inputting a user's opinions and political tendencies; an analysis unit that analyzes the data input by the opinion input unit; a recommendation unit that recommends a candidate who best matches the user's intentions based on the data analyzed by the analysis unit; a voting unit that provides a virtual voting process; A system characterized by:

2. The analysis unit Analyze the progress of discussions in real time and suggest new topics and perspectives to prevent discussions from stagnating.

2. The system of claim 1.

3. The analysis unit Automatically translate discussions in different languages ​​and generate policy proposals from an international perspective 2. The system of claim 1.

4. The voting unit: Analyze the results of the polls and conduct detailed analysis taking into account the voter's background information 2. The system of claim 1.

5. The analysis unit Using emotion estimation function, the emotions of the users during the discussion are analyzed and policy proposals that are likely to be emotionally relatable are generated.

2. The system of claim 1.

6. The voting unit: Using emotion estimation, we analyze voter sentiment and identify policies and candidates that resonate with voters emotionally.

2. The system of claim 1.

7. The recommendation unit Using emotion estimation function, the emotional response of the user is monitored in real time, and optimal candidates are continuously recommended.

2. The system of claim 1.

8. The recommendation unit Analyzing the user's past voting history and political statements will enable more accurate candidate recommendations.

2. The system of claim 1.

Images