system

Abstract

The system according to the embodiment aims to make it easier to collect and understand election information and to increase voter turnout. [Solution] A system according to an embodiment includes a reception unit, a collection unit, a generation unit, and an explanation unit. The reception unit receives searches for election information from users. The collection unit collects election information based on the information received by the reception unit. The generation unit analyzes the information collected by the collection unit and generates a customized summary of the election information. The explanation unit provides explanations as an expert assistant based on the summary generated by the generation unit.

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 technology has made it difficult to collect and understand election information, resulting in low voter turnout, especially among young people.

[0005] The system according to the embodiment aims to make it easier to collect and understand election information and to increase voter turnout. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, and an explanation unit. The reception unit receives election information searches from users. The collection unit collects election information based on the information received by the reception unit. The generation unit analyzes the information collected by the collection unit and generates a customized summary of the election information. The explanation unit provides explanations as an expert assistant based on the summary generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate the collection and understanding of election information and increase voter turnout. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A portal site according to an embodiment of the present invention is a system that utilizes generative AI to address low voter turnout in elections such as the House of Representatives election. In this system, users access the portal site and collect election-related information. The generative AI provides a customized summary of election information based on the user's interests and provides easy-to-understand explanations as an expert assistant. For example, a user can access the portal site and search for information on a specific candidate or policy. The generative AI analyzes the input information and provides the user with a customized summary of election information. Furthermore, the generative AI provides explanations about the background and impact of policies, providing information in an easy-to-understand format. This eliminates the need for users to gather election information, potentially increasing voter turnout. This allows the portal site to provide customized summaries and explanations of election information based on the user's interests and contributes to increased voter turnout. This is particularly effective among young people in their teens, twenties, and thirties, and can increase their interest in politics. Furthermore, in a world where distrust in politics is on the rise, proposing an electoral revolution using generative AI can make the process by which citizens choose politicians more transparent and understandable.

[0029] An election information provision system according to an embodiment includes a reception unit, a collection unit, a generation unit, and an explanation unit. The reception unit accepts searches for election information from users. For example, users can search for information about specific candidates or policies. The collection unit collects election information based on the information accepted by the reception unit. For example, the collection unit collects data from sources such as newspapers, TV, social media, election bulletins, and street speeches. The generation unit analyzes the information collected by the collection unit and generates customized summaries of election information. For example, the generation AI generates summaries of specific candidates' policies and past performance based on the user's interests and concerns. The explanation unit acts as an expert assistant and provides commentary based on the summaries generated by the generation unit. For example, the explanation unit provides commentary on the background and impact of policies, providing information in a format that is easy for users to understand. As a result, the election information provision system according to an embodiment provides election information summaries and explanations customized based on the user's interests and concerns, thereby increasing voter turnout.

[0030] The collection unit can collect election information from a variety of sources. The collection unit collects data from sources such as newspapers, TV, social media, election bulletins, and street speeches. For example, the collection unit collects newspaper articles and extracts information related to elections. The collection unit can also analyze TV news to collect important information related to elections. Furthermore, the collection unit can analyze social media posts to collect trend information related to elections. In this way, the collection unit can provide comprehensive information by collecting election information from a variety of sources.

[0031] The generation unit can generate a customized summary of election information based on the user's interests and concerns. For example, the generation unit generates a summary of the policies and past achievements of a specific candidate based on the user's interests and concerns. For example, the generation AI summarizes the policies of a candidate in which the user is interested and briefly summarizes their past achievements. The generation unit can also generate a customized summary based on the user's search history and survey results. For example, the generation AI summarizes related election information by referring to information the user has previously searched for. In this way, the generation unit provides a summary based on the user's interests and concerns, thereby deepening the user's understanding of the information.

[0032] The commentary unit can provide commentary on the background and impact of the policy based on the generated summary. The commentary unit, for example, provides commentary on the background and impact of the policy based on the generated summary. For example, the commentary unit provides commentary on the economic impact of a particular policy, providing information in a form that is easy for the user to understand. The commentary unit can also provide commentary on the social impact of the policy. For example, the commentary unit explains the impact of the policy on society using specific examples. Furthermore, the commentary unit can also provide commentary on the historical background of the policy. For example, the commentary unit explains how the policy was enacted. In this way, the commentary unit can deepen the user's understanding by providing commentary on the background and impact of the policy.

[0033] The collection unit can collect data from information sources such as newspapers, TV, social media, election bulletins, and street speeches. The collection unit, for example, collects newspaper articles and extracts information related to elections. For example, the collection unit automatically extracts information related to elections from newspaper articles and stores it in a database. The collection unit can also analyze TV news and collect important information related to elections. For example, the collection unit analyzes video and audio from TV news and extracts information related to elections. Furthermore, the collection unit can analyze posts on social media and collect trend information related to elections. For example, the collection unit analyzes posts on social media in real time to grasp trends related to elections. In this way, the collection unit can provide comprehensive election information by collecting data from information sources such as newspapers, TV, social media, election bulletins, and street speeches.

[0034] The generation unit can generate a summary of the policies and past achievements of a specific candidate. The generation unit generates a summary of the policies and past achievements of a specific candidate, for example. For example, the generation AI summarizes the candidate's policies and briefly summarizes their past achievements. The generation unit can also generate a summary based on the candidate's campaign promises and speech content. For example, the generation AI analyzes the candidate's campaign promises and extracts and summarizes important points. The generation unit can also generate a summary based on the candidate's past activities and achievements. For example, the generation AI analyzes the candidate's past activity records and summarizes their important achievements. In this way, the generation unit can assist the user in making a selection by providing a summary of the candidate's policies and past achievements.

[0035] The reception unit can analyze the user's past search history and suggest optimal search keywords. The reception unit, for example, analyzes the user's past search history and suggests optimal search keywords. For example, the reception unit can suggest related keywords based on the names of candidates or policies that the user has searched for in the past. The reception unit can also analyze trends in information the user has searched for in the past and suggest keywords that the user may be interested in. Furthermore, the reception unit can suggest optimal keywords based on the time period and frequency of searches the user has performed in the past. For example, the reception unit analyzes the user's search history and suggests optimal search keywords. In this way, the reception unit can improve search efficiency by suggesting optimal keywords based on the user's past search history.

[0036] The reception unit can filter search results based on the user's current interests and trends during a search. For example, the reception unit filters search results based on the user's current interests and trends during a search. For example, the reception unit can prioritize displaying information related to policies in which the user is currently interested. The reception unit can also display related election information based on trend information from social media accounts the user follows. Furthermore, the reception unit can prioritize displaying election information related to topics recently searched by the user. For example, the reception unit analyzes the user's current interests and trends in real time and displays optimal search results. In this way, the reception unit can provide highly relevant information by filtering search results based on the user's current interests and trends.

[0037] The reception unit can select the optimal search means according to the user's input method during a search. The reception unit, for example, selects the optimal search means according to the user's input method during a search. For example, when the user uses voice input, the reception unit displays optimal search results using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also display optimal search results based on the input keywords. Furthermore, when the user uses image input, the reception unit can display related election information using image recognition technology. For example, the reception unit selects the optimal search means according to the user's input method and displays optimal search results. In this way, the reception unit can improve the convenience of searches by selecting the optimal search means according to the user's input method.

[0038] The reception unit can prioritize displaying highly relevant information based on the user's geographical location information during a search. For example, the reception unit prioritizes displaying highly relevant information based on the user's geographical location information during a search. For example, the reception unit prioritizes displaying information about local candidates and policies based on the user's current location. The reception unit can also display related election information based on the user's past location information. Furthermore, if the user is interested in a specific region, the reception unit can prioritize displaying election information related to that region. For example, the reception unit analyzes the user's geographical location information in real time and displays optimal search results. In this way, the reception unit can provide highly relevant information taking into account the user's geographical location information, thereby providing information that is closely tied to the region.

[0039] The reception unit can analyze the user's social media activity during a search and display relevant information. The reception unit, for example, analyzes the user's social media activity during a search and displays relevant information. For example, the reception unit prioritizes displaying information about candidates and policies that the user follows. The reception unit can also analyze the content of the user's social media posts and display relevant election information. Furthermore, the reception unit can display election information that the user's friends and followers are interested in. For example, the reception unit analyzes the user's social media activity in real time and displays optimal search results. In this way, the reception unit can provide highly relevant information by analyzing the user's social media activity.

[0040] The reception unit can customize the search method by reflecting the user's past feedback during a search. The reception unit, for example, customizes the search method by reflecting the user's past feedback during a search. For example, the reception unit preferentially displays information sources that the user has previously rated highly. The reception unit can also display search results while excluding information sources that the user has previously rated poorly. Furthermore, the reception unit can apply an optimal search algorithm based on the user's past feedback. For example, the reception unit analyzes the user's feedback in real time and displays optimal search results. In this way, the reception unit can optimize the search method and improve search accuracy by reflecting the user's past feedback.

[0041] The collection unit can evaluate the reliability of information sources at the time of collection and prioritize collecting highly reliable information. For example, the collection unit evaluates the reliability of information sources at the time of collection and prioritize collecting highly reliable information. For example, the collection unit prioritizes collecting highly reliable news sites and official announcements. The collection unit can also prioritize collecting highly reliable information based on past reliability evaluations of information sources. Furthermore, the collection unit can also prioritize collecting highly reliable information based on evaluations by experts on information sources. For example, the collection unit evaluates the reliability of information sources in real time and selects the most appropriate information source. In this way, the collection unit can provide highly reliable information by evaluating the reliability of information sources.

[0042] The collection unit can adjust the timing of collection based on the update frequency of the information when collecting the information. For example, the collection unit adjusts the timing of collection based on the update frequency of the information when collecting the information. For example, the collection unit periodically collects information from news sites that are updated frequently. The collection unit can also collect information as needed from information sources that are updated less frequently. Furthermore, the collection unit can monitor the update frequency of the information in real time and collect information at the optimal timing. For example, the collection unit analyzes the update frequency of the information and determines the optimal collection timing. As a result, the collection unit can provide the latest information by adjusting the collection timing based on the update frequency of the information.

[0043] The collection unit may collect information from different categories to ensure diversity of information sources during collection. For example, the collection unit may collect information from different categories to ensure diversity of information sources during collection. For example, the collection unit may collect information from different categories, such as news sites, social media, and official announcements. The collection unit may also collect information from sources with different perspectives and opinions. Furthermore, the collection unit may collect information from sources in different regions or countries. For example, the collection unit may select sources from different categories to ensure diversity of information sources. This allows the collection unit to provide comprehensive information by ensuring diversity of information sources.

[0044] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting the information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting the information. For example, the collection unit prioritizes collecting information about local candidates and policies based on the user's current location. The collection unit can also collect related election information based on the user's past location information. Furthermore, if the user is interested in a specific region, the collection unit can prioritize collecting election information related to that region. For example, the collection unit analyzes the user's geographical location information in real time and collects optimal information. As a result, the collection unit can provide highly relevant information by taking into account the user's geographical location information, thereby providing information that is closely tied to the region.

[0045] The collection unit can analyze the user's social media activity and collect relevant information at the time of collection. For example, the collection unit can analyze the user's social media activity and collect relevant information at the time of collection. For example, the collection unit can prioritize collecting information about candidates and policies that the user follows. The collection unit can also analyze the content of the user's social media posts and collect relevant election information. Furthermore, the collection unit can collect election information that the user's friends and followers are interested in. For example, the collection unit can analyze the user's social media activity in real time and collect optimal information. This allows the collection unit to provide highly relevant information by analyzing the user's social media activity.

[0046] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit prioritizes collecting information sources that the user has previously rated highly. The collection unit can also collect information by excluding information sources that the user has previously rated poorly. Furthermore, the collection unit can apply an optimal collection algorithm based on the user's past feedback. For example, the collection unit analyzes the user's feedback in real time and selects the optimal information source. In this way, the collection unit can optimize the collection method and improve collection accuracy by reflecting the user's past feedback.

[0047] The generation unit can adjust the level of detail of the summary based on the importance of the information when generating the summary. For example, the generation unit adjusts the level of detail of the summary based on the importance of the information when generating the summary. For example, the generation unit summarizes information of high importance in detail and summarizes information of low importance in brief. The generation unit can also evaluate the importance of the information in real time and generate a summary with an optimal level of detail. Furthermore, the generation unit can prioritize summarizing information of high importance based on the user's interests. For example, the generation unit analyzes the importance of the information and generates an optimal summary. As a result, the generation unit can provide important information in detail by adjusting the level of detail of the summary based on the importance of the information.

[0048] The generation unit can apply different summarization algorithms depending on the category of information when generating summaries. For example, the generation unit can generate summaries that include detailed analysis for information about policies. The generation unit can also generate summaries that include past performance for information about candidates. Furthermore, the generation unit can generate summaries that emphasize timeliness for information about news. For example, the generation unit applies the optimal summarization algorithm depending on the category of information. In this way, the generation unit can provide more appropriate summaries by applying a summarization algorithm depending on the category of information.

[0049] The generation unit can improve the accuracy of the summary by referring to the user's past summarization results when generating a summary. For example, the generation unit can improve the accuracy of the summary by referring to the user's past summarization results when generating a summary. For example, the generation unit generates a summary by referring to a summary style that the user has previously rated highly. The generation unit can also generate a summary by avoiding a summary style that the user has previously rated poorly. Furthermore, the generation unit can analyze the user's past summarization results and apply an optimal summarization algorithm. For example, the generation unit analyzes the user's past summarization results and generates an optimal summary. In this way, the generation unit can improve the accuracy of the summary by referring to the user's past summarization results.

[0050] The generation unit can determine the priority of summaries based on the time of submission of information when generating summaries. For example, the generation unit determines the priority of summaries based on the time of submission of information when generating summaries. For example, the generation unit prioritizes summarizing the latest information and postpones older information. The generation unit can also evaluate the time of submission of information in real time and generate summaries with optimal priority. Furthermore, the generation unit can prioritize summarizing recently submitted information based on the user's interests. For example, the generation unit analyzes the time of submission of information and generates optimal summaries. In this way, the generation unit can provide the latest information by determining the priority of summaries based on the time of submission of information.

[0051] The generation unit can adjust the order of summaries based on the relevance of information when generating summaries. For example, the generation unit adjusts the order of summaries based on the relevance of information when generating summaries. For example, the generation unit prioritizes summarizing highly relevant information based on the user's interests. The generation unit can also evaluate the relevance of information in real time and generate summaries in an optimal order. Furthermore, the generation unit can prioritize summarizing highly relevant information based on the user's past search history. For example, the generation unit analyzes the relevance of information and generates an optimal summary. As a result, the generation unit can provide highly relevant information by adjusting the order of summaries based on the relevance of information.

[0052] The generation unit can adjust the use of technical terms in the summary according to the user's level of expertise when generating a summary. For example, the generation unit adjusts the use of technical terms in the summary according to the user's level of expertise when generating a summary. For example, if the user has technical expertise, the generation unit generates a summary that uses a lot of technical terms. Also, if the user does not have technical expertise, the generation unit can generate a summary that explains things in simple terms. Furthermore, the generation unit can adjust the use of optimal technical terms based on the user's past summarization results. For example, the generation unit analyzes the user's level of expertise and generates an optimal summary. In this way, the generation unit can provide a summary that is easier to understand by adjusting the use of technical terms according to the user's level of expertise.

[0053] The commentary unit can provide a detailed explanation of the background and impact of the information when providing the explanation. For example, the commentary unit provides a detailed explanation of the background and impact of the information when providing the explanation. For example, the commentary unit provides an explanation of the background of a policy, including historical details. The commentary unit can also provide an explanation of the impact of a policy, including economic impacts. Furthermore, the commentary unit can provide an explanation of a candidate's past performance, including specific examples. For example, the commentary unit analyzes the background and impact of a policy in real time and provides an optimal explanation. In this way, the commentary unit can deepen the user's understanding by providing a detailed explanation of the background and impact of the information.

[0054] The commentary unit can improve the accuracy of the commentary by referring to the user's past commentary results when providing commentary. For example, the commentary unit can improve the accuracy of the commentary by referring to the user's past commentary results when providing commentary. For example, the commentary unit provides commentary by referring to commentary styles that the user has previously rated highly. The commentary unit can also provide commentary by avoiding commentary styles that the user has previously rated poorly. Furthermore, the commentary unit can analyze the user's past commentary results and apply an optimal commentary algorithm. For example, the commentary unit analyzes the user's past commentary results and provides an optimal commentary. This allows the commentary unit to improve the accuracy of the commentary by referring to the user's past commentary results.

[0055] The commentary unit can apply different commentary algorithms depending on the category of information when providing commentary. For example, the commentary unit applies different commentary algorithms depending on the category of information when providing commentary. For example, the commentary unit provides commentary including detailed analysis for information related to policies. The commentary unit can also provide commentary including past achievements for information related to candidates. Furthermore, the commentary unit can provide commentary that emphasizes timeliness for information related to news. For example, the commentary unit applies the optimal commentary algorithm depending on the category of information. In this way, the commentary unit can provide more appropriate commentary by applying a commentary algorithm depending on the category of information.

[0056] The commentary unit can determine the priority of the commentary based on the time of submission of the information when providing the commentary. For example, the commentary unit determines the priority of the commentary based on the time of submission of the information when providing the commentary. For example, the commentary unit prioritizes explaining the latest information and postpones older information. The commentary unit can also evaluate the time of submission of the information in real time and provide explanations with optimal priority. Furthermore, the commentary unit can prioritize explaining newest information based on the user's interests. For example, the commentary unit analyzes the time of submission of the information and provides optimal explanations. As a result, the commentary unit can provide the latest information by determining the priority of the commentary based on the time of submission of the information.

[0057] The commentary unit can adjust the order of commentary based on the relevance of the information when providing commentary. For example, the commentary unit adjusts the order of commentary based on the relevance of the information when providing commentary. For example, the commentary unit prioritizes explaining highly relevant information based on the user's interests. The commentary unit can also evaluate the relevance of information in real time and provide explanations in an optimal order. Furthermore, the commentary unit can prioritize explaining highly relevant information based on the user's past search history. For example, the commentary unit analyzes the relevance of information and provides an optimal explanation. As a result, the commentary unit can provide highly relevant information by adjusting the order of commentary based on the relevance of the information.

[0058] The explanation unit can adjust the use of technical terms in the explanation according to the user's level of expertise when providing an explanation. For example, the explanation unit adjusts the use of technical terms in the explanation according to the user's level of expertise when providing an explanation. For example, if the user has technical expertise, the explanation unit provides an explanation that uses a lot of technical terms. Also, if the user does not have technical expertise, the explanation unit can provide an explanation that explains things in simple terms. Furthermore, the explanation unit can adjust the use of optimal technical terms based on the user's past explanation results. For example, the explanation unit analyzes the user's level of expertise and provides an optimal explanation. As a result, the explanation unit can provide an explanation that is easier to understand by adjusting the use of technical terms according to the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can also analyze the user's past voting history and provide voting reminders. For example, the reception unit can remind the user of the next election date based on data on elections in which the user has voted in the past. The reception unit can also provide the user with the latest information on candidates and political parties for which the user has voted in the past. Furthermore, the reception unit can notify the user of any changes to the electoral district in which the user voted in the past. In this way, the reception unit can utilize the user's past voting history to provide voting reminders, thereby helping to increase voter turnout.

[0061] The collection unit can also calculate a reliability score for each source to evaluate the reliability of election information. For example, the collection unit calculates a reliability score based on the past accuracy of each source. The collection unit can also adjust the reliability score by incorporating expert evaluations of the source. Furthermore, the collection unit can update the reliability score taking into account the update frequency and transparency of the source. This allows the collection unit to provide accurate election information to users by preferentially collecting reliable information.

[0062] The collection unit can also use the user's geographic location information to collect region-specific election information. For example, the collection unit can prioritize collection of information about local candidates and policies based on the user's current location. The collection unit can also collect election information for regions that the user has previously visited. Furthermore, the collection unit can collect election information for regions in which the user is interested. In this way, the collection unit can use the user's geographic location information to provide region-specific election information.

[0063] The generator can also adjust the level of detail of the summary according to the user's level of expertise. For example, if the user has specialized knowledge, the generator can generate a summary that includes a detailed analysis. Alternatively, if the user does not have specialized knowledge, the generator can generate a summary that explains things in simple terms. Furthermore, the generator can generate a summary with an optimal level of detail based on the user's past summarization results. This allows the generator to provide a summary that matches the user's level of expertise, thereby deepening the user's understanding of the information.

[0064] When collecting election information, the collection unit can also adjust the timing of collection taking into account the frequency of information updates. For example, the collection unit periodically collects information from news sites that are updated frequently. The collection unit can also collect information as needed from sources that are updated less frequently. Furthermore, the collection unit can monitor the frequency of information updates in real time and collect information at the optimal timing. This allows the collection unit to provide the latest information by adjusting the timing of collection based on the frequency of information updates.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit accepts a search for election information from a user. For example, a user can search for information about a specific candidate or policy. Step 2: The collection unit collects election information based on the information received by the reception unit. For example, the collection unit collects data from sources such as newspapers, TV, social media, election bulletins, and street speeches. Step 3: The generator analyzes the information collected by the collector and generates a customized summary of election information. For example, the generator generates a summary of a specific candidate's policies and past performance based on the user's interests. Step 4: The commentary unit acts as an expert assistant and provides commentary based on the summary generated by the generation unit. For example, the commentary unit may provide commentary on the background and impact of the policy, providing information in a format that is easy for users to understand.

[0067] (Example 2) A portal site according to an embodiment of the present invention is a system that utilizes generative AI to address low voter turnout in elections such as the House of Representatives election. In this system, users access the portal site and collect election-related information. The generative AI provides a customized summary of election information based on the user's interests and provides easy-to-understand explanations as an expert assistant. For example, a user can access the portal site and search for information on a specific candidate or policy. The generative AI analyzes the input information and provides the user with a customized summary of election information. Furthermore, the generative AI provides explanations about the background and impact of policies, providing information in an easy-to-understand format. This eliminates the need for users to gather election information, potentially increasing voter turnout. This allows the portal site to provide customized summaries and explanations of election information based on the user's interests and contributes to increased voter turnout. This is particularly effective among young people in their teens, twenties, and thirties, and can increase their interest in politics. Furthermore, in a world where distrust in politics is on the rise, proposing an electoral revolution using generative AI can make the process by which citizens choose politicians more transparent and understandable.

[0068] An election information provision system according to an embodiment includes a reception unit, a collection unit, a generation unit, and an explanation unit. The reception unit accepts searches for election information from users. For example, users can search for information about specific candidates or policies. The collection unit collects election information based on the information accepted by the reception unit. For example, the collection unit collects data from sources such as newspapers, TV, social media, election bulletins, and street speeches. The generation unit analyzes the information collected by the collection unit and generates customized summaries of election information. For example, the generation AI generates summaries of specific candidates' policies and past performance based on the user's interests and concerns. The explanation unit acts as an expert assistant and provides commentary based on the summaries generated by the generation unit. For example, the explanation unit provides commentary on the background and impact of policies, providing information in a format that is easy for users to understand. As a result, the election information provision system according to an embodiment provides election information summaries and explanations customized based on the user's interests and concerns, thereby increasing voter turnout.

[0069] The collection unit can collect election information from a variety of sources. The collection unit collects data from sources such as newspapers, TV, social media, election bulletins, and street speeches. For example, the collection unit collects newspaper articles and extracts information related to elections. The collection unit can also analyze TV news to collect important information related to elections. Furthermore, the collection unit can analyze social media posts to collect trend information related to elections. In this way, the collection unit can provide comprehensive information by collecting election information from a variety of sources.

[0070] The generation unit can generate a customized summary of election information based on the user's interests and concerns. For example, the generation unit generates a summary of the policies and past achievements of a specific candidate based on the user's interests and concerns. For example, the generation AI summarizes the policies of a candidate in which the user is interested and briefly summarizes their past achievements. The generation unit can also generate a customized summary based on the user's search history and survey results. For example, the generation AI summarizes related election information by referring to information the user has previously searched for. In this way, the generation unit provides a summary based on the user's interests and concerns, thereby deepening the user's understanding of the information.

[0071] The commentary unit can provide commentary on the background and impact of the policy based on the generated summary. The commentary unit, for example, provides commentary on the background and impact of the policy based on the generated summary. For example, the commentary unit provides commentary on the economic impact of a particular policy, providing information in a form that is easy for the user to understand. The commentary unit can also provide commentary on the social impact of the policy. For example, the commentary unit explains the impact of the policy on society using specific examples. Furthermore, the commentary unit can also provide commentary on the historical background of the policy. For example, the commentary unit explains how the policy was enacted. In this way, the commentary unit can deepen the user's understanding by providing commentary on the background and impact of the policy.

[0072] The collection unit can collect data from information sources such as newspapers, TV, social media, election bulletins, and street speeches. The collection unit, for example, collects newspaper articles and extracts information related to elections. For example, the collection unit automatically extracts information related to elections from newspaper articles and stores it in a database. The collection unit can also analyze TV news and collect important information related to elections. For example, the collection unit analyzes video and audio from TV news and extracts information related to elections. Furthermore, the collection unit can analyze posts on social media and collect trend information related to elections. For example, the collection unit analyzes posts on social media in real time to grasp trends related to elections. In this way, the collection unit can provide comprehensive election information by collecting data from information sources such as newspapers, TV, social media, election bulletins, and street speeches.

[0073] The generation unit can generate a summary of the policies and past achievements of a specific candidate. The generation unit generates a summary of the policies and past achievements of a specific candidate, for example. For example, the generation AI summarizes the candidate's policies and briefly summarizes their past achievements. The generation unit can also generate a summary based on the candidate's campaign promises and speech content. For example, the generation AI analyzes the candidate's campaign promises and extracts and summarizes important points. The generation unit can also generate a summary based on the candidate's past activities and achievements. For example, the generation AI analyzes the candidate's past activity records and summarizes their important achievements. In this way, the generation unit can assist the user in making a selection by providing a summary of the candidate's policies and past achievements.

[0074] The reception unit can estimate the user's emotions and adjust the display order of search results based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the display order of search results based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize displaying the latest election information. Furthermore, if the user is relaxed, the reception unit can prioritize displaying information including detailed explanations. Furthermore, if the user is stressed, the reception unit can prioritize displaying concise, to-the-point information. For example, the reception unit analyzes the user's emotions in real time and displays optimal search results. This allows the reception unit to provide more appropriate information by adjusting the display order of search results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The reception unit can analyze the user's past search history and suggest optimal search keywords. The reception unit, for example, analyzes the user's past search history and suggests optimal search keywords. For example, the reception unit can suggest related keywords based on the names of candidates or policies that the user has searched for in the past. The reception unit can also analyze trends in information the user has searched for in the past and suggest keywords that the user may be interested in. Furthermore, the reception unit can suggest optimal keywords based on the time period and frequency of searches the user has performed in the past. For example, the reception unit analyzes the user's search history and suggests optimal search keywords. In this way, the reception unit can improve search efficiency by suggesting optimal keywords based on the user's past search history.

[0076] The reception unit can filter search results based on the user's current interests and trends during a search. For example, the reception unit filters search results based on the user's current interests and trends during a search. For example, the reception unit can prioritize displaying information related to policies in which the user is currently interested. The reception unit can also display related election information based on trend information from social media accounts the user follows. Furthermore, the reception unit can prioritize displaying election information related to topics recently searched by the user. For example, the reception unit analyzes the user's current interests and trends in real time and displays optimal search results. In this way, the reception unit can provide highly relevant information by filtering search results based on the user's current interests and trends.

[0077] The reception unit can select the optimal search means according to the user's input method during a search. The reception unit, for example, selects the optimal search means according to the user's input method during a search. For example, when the user uses voice input, the reception unit displays optimal search results using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also display optimal search results based on the input keywords. Furthermore, when the user uses image input, the reception unit can display related election information using image recognition technology. For example, the reception unit selects the optimal search means according to the user's input method and displays optimal search results. In this way, the reception unit can improve the convenience of searches by selecting the optimal search means according to the user's input method.

[0078] The reception unit can estimate the user's emotions and prioritize search results based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes search results based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize displaying the latest election information. Furthermore, if the user is relaxed, the reception unit can prioritize displaying information including detailed explanations. Furthermore, if the user is stressed, the reception unit can prioritize displaying concise, to-the-point information. For example, the reception unit analyzes the user's emotions in real time and displays optimal search results. This allows the reception unit to prioritize search results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The reception unit can prioritize displaying highly relevant information based on the user's geographical location information during a search. For example, the reception unit prioritizes displaying highly relevant information based on the user's geographical location information during a search. For example, the reception unit prioritizes displaying information about local candidates and policies based on the user's current location. The reception unit can also display related election information based on the user's past location information. Furthermore, if the user is interested in a specific region, the reception unit can prioritize displaying election information related to that region. For example, the reception unit analyzes the user's geographical location information in real time and displays optimal search results. In this way, the reception unit can provide highly relevant information taking into account the user's geographical location information, thereby providing information that is closely tied to the region.

[0080] The reception unit can analyze the user's social media activity during a search and display relevant information. The reception unit, for example, analyzes the user's social media activity during a search and displays relevant information. For example, the reception unit prioritizes displaying information about candidates and policies that the user follows. The reception unit can also analyze the content of the user's social media posts and display relevant election information. Furthermore, the reception unit can display election information that the user's friends and followers are interested in. For example, the reception unit analyzes the user's social media activity in real time and displays optimal search results. In this way, the reception unit can provide highly relevant information by analyzing the user's social media activity.

[0081] The reception unit can customize the search method by reflecting the user's past feedback during a search. The reception unit, for example, customizes the search method by reflecting the user's past feedback during a search. For example, the reception unit preferentially displays information sources that the user has previously rated highly. The reception unit can also display search results while excluding information sources that the user has previously rated poorly. Furthermore, the reception unit can apply an optimal search algorithm based on the user's past feedback. For example, the reception unit analyzes the user's feedback in real time and displays optimal search results. In this way, the reception unit can optimize the search method and improve search accuracy by reflecting the user's past feedback.

[0082] The collection unit can estimate the user's emotions and determine the priority of information sources to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of information sources to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting the latest news sources. Furthermore, if the user is relaxed, the collection unit can prioritize collecting information sources that include detailed commentary. Furthermore, if the user is stressed, the collection unit can prioritize collecting information sources that are concise and to the point. For example, the collection unit can analyze the user's emotions in real time and select the optimal information source. This allows the collection unit to prioritize information sources according to the user's emotions, thereby collecting more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The collection unit can evaluate the reliability of information sources at the time of collection and prioritize collecting highly reliable information. For example, the collection unit evaluates the reliability of information sources at the time of collection and prioritize collecting highly reliable information. For example, the collection unit prioritizes collecting highly reliable news sites and official announcements. The collection unit can also prioritize collecting highly reliable information based on past reliability evaluations of information sources. Furthermore, the collection unit can also prioritize collecting highly reliable information based on evaluations by experts on information sources. For example, the collection unit evaluates the reliability of information sources in real time and selects the most appropriate information source. In this way, the collection unit can provide highly reliable information by evaluating the reliability of information sources.

[0084] The collection unit can adjust the timing of collection based on the update frequency of the information when collecting the information. For example, the collection unit adjusts the timing of collection based on the update frequency of the information when collecting the information. For example, the collection unit periodically collects information from news sites that are updated frequently. The collection unit can also collect information as needed from information sources that are updated less frequently. Furthermore, the collection unit can monitor the update frequency of the information in real time and collect information at the optimal timing. For example, the collection unit analyzes the update frequency of the information and determines the optimal collection timing. As a result, the collection unit can provide the latest information by adjusting the collection timing based on the update frequency of the information.

[0085] The collection unit may collect information from different categories to ensure diversity of information sources during collection. For example, the collection unit may collect information from different categories to ensure diversity of information sources during collection. For example, the collection unit may collect information from different categories, such as news sites, social media, and official announcements. The collection unit may also collect information from sources with different perspectives and opinions. Furthermore, the collection unit may collect information from sources in different regions or countries. For example, the collection unit may select sources from different categories to ensure diversity of information sources. This allows the collection unit to provide comprehensive information by ensuring diversity of information sources.

[0086] The collection unit can estimate the user's emotions and adjust the type of information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the type of information to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting the latest news and breaking news. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed commentary and analytical articles. Furthermore, if the user is stressed, the collection unit can prioritize collecting concise, to-the-point information. For example, the collection unit can analyze the user's emotions in real time and select the optimal type of information. This allows the collection unit to provide more appropriate information by adjusting the type of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting the information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting the information. For example, the collection unit prioritizes collecting information about local candidates and policies based on the user's current location. The collection unit can also collect related election information based on the user's past location information. Furthermore, if the user is interested in a specific region, the collection unit can prioritize collecting election information related to that region. For example, the collection unit analyzes the user's geographical location information in real time and collects optimal information. As a result, the collection unit can provide highly relevant information by taking into account the user's geographical location information, thereby providing information that is closely tied to the region.

[0088] The collection unit can analyze the user's social media activity and collect relevant information at the time of collection. For example, the collection unit can analyze the user's social media activity and collect relevant information at the time of collection. For example, the collection unit can prioritize collecting information about candidates and policies that the user follows. The collection unit can also analyze the content of the user's social media posts and collect relevant election information. Furthermore, the collection unit can collect election information that the user's friends and followers are interested in. For example, the collection unit can analyze the user's social media activity in real time and collect optimal information. This allows the collection unit to provide highly relevant information by analyzing the user's social media activity.

[0089] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit prioritizes collecting information sources that the user has previously rated highly. The collection unit can also collect information by excluding information sources that the user has previously rated poorly. Furthermore, the collection unit can apply an optimal collection algorithm based on the user's past feedback. For example, the collection unit analyzes the user's feedback in real time and selects the optimal information source. In this way, the collection unit can optimize the collection method and improve collection accuracy by reflecting the user's past feedback.

[0090] The generation unit can estimate the user's emotion and adjust the summary presentation style based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the summary presentation style based on the estimated user emotion. For example, if the user is relaxed, the generation unit generates a summary with detailed explanations. If the user is in a hurry, the generation unit can generate a concise summary that focuses on the main points. Furthermore, if the user is excited, the generation unit can generate a summary with visually stimulating effects. For example, the generation unit can analyze the user's emotion in real time and generate an optimal summary. This allows the generation unit to provide more appropriate information by adjusting the summary presentation style according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The generation unit can adjust the level of detail of the summary based on the importance of the information when generating the summary. For example, the generation unit adjusts the level of detail of the summary based on the importance of the information when generating the summary. For example, the generation unit summarizes information of high importance in detail and summarizes information of low importance in brief. The generation unit can also evaluate the importance of the information in real time and generate a summary with an optimal level of detail. Furthermore, the generation unit can prioritize summarizing information of high importance based on the user's interests. For example, the generation unit analyzes the importance of the information and generates an optimal summary. As a result, the generation unit can provide important information in detail by adjusting the level of detail of the summary based on the importance of the information.

[0092] The generation unit can apply different summarization algorithms depending on the category of information when generating summaries. For example, the generation unit can generate summaries that include detailed analysis for information about policies. The generation unit can also generate summaries that include past performance for information about candidates. Furthermore, the generation unit can generate summaries that emphasize timeliness for information about news. For example, the generation unit applies the optimal summarization algorithm depending on the category of information. In this way, the generation unit can provide more appropriate summaries by applying a summarization algorithm depending on the category of information.

[0093] The generation unit can improve the accuracy of the summary by referring to the user's past summarization results when generating a summary. For example, the generation unit can improve the accuracy of the summary by referring to the user's past summarization results when generating a summary. For example, the generation unit generates a summary by referring to a summary style that the user has previously rated highly. The generation unit can also generate a summary by avoiding a summary style that the user has previously rated poorly. Furthermore, the generation unit can analyze the user's past summarization results and apply an optimal summarization algorithm. For example, the generation unit analyzes the user's past summarization results and generates an optimal summary. In this way, the generation unit can improve the accuracy of the summary by referring to the user's past summarization results.

[0094] The generation unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise summary. If the user is relaxed, the generation unit can generate a longer summary with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a summary with visually stimulating effects. For example, the generation unit can analyze the user's emotions in real time and generate an optimal summary. This allows the generation unit to provide more appropriate information by adjusting the length of the summary according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The generation unit can determine the priority of summaries based on the time of submission of information when generating summaries. For example, the generation unit determines the priority of summaries based on the time of submission of information when generating summaries. For example, the generation unit prioritizes summarizing the latest information and postpones older information. The generation unit can also evaluate the time of submission of information in real time and generate summaries with optimal priority. Furthermore, the generation unit can prioritize summarizing recently submitted information based on the user's interests. For example, the generation unit analyzes the time of submission of information and generates optimal summaries. In this way, the generation unit can provide the latest information by determining the priority of summaries based on the time of submission of information.

[0096] The generation unit can adjust the order of summaries based on the relevance of information when generating summaries. For example, the generation unit adjusts the order of summaries based on the relevance of information when generating summaries. For example, the generation unit prioritizes summarizing highly relevant information based on the user's interests. The generation unit can also evaluate the relevance of information in real time and generate summaries in an optimal order. Furthermore, the generation unit can prioritize summarizing highly relevant information based on the user's past search history. For example, the generation unit analyzes the relevance of information and generates an optimal summary. As a result, the generation unit can provide highly relevant information by adjusting the order of summaries based on the relevance of information.

[0097] The generation unit can adjust the use of technical terms in the summary according to the user's level of expertise when generating a summary. For example, the generation unit adjusts the use of technical terms in the summary according to the user's level of expertise when generating a summary. For example, if the user has technical expertise, the generation unit generates a summary that uses a lot of technical terms. Also, if the user does not have technical expertise, the generation unit can generate a summary that explains things in simple terms. Furthermore, the generation unit can adjust the use of optimal technical terms based on the user's past summarization results. For example, the generation unit analyzes the user's level of expertise and generates an optimal summary. In this way, the generation unit can provide a summary that is easier to understand by adjusting the use of technical terms according to the user's level of expertise.

[0098] The commentary unit can estimate the user's emotions and adjust the way in which the commentary is expressed based on the estimated user's emotions. The commentary unit, for example, estimates the user's emotions and adjusts the way in which the commentary is expressed based on the estimated user's emotions. For example, if the user is nervous, the commentary unit can provide commentary in a calm tone. Furthermore, if the user is relaxed, the commentary unit can provide commentary in a friendly tone. Furthermore, if the user is excited, the commentary unit can provide commentary in an energetic tone. For example, the commentary unit analyzes the user's emotions in real time and provides optimal commentary. This allows the commentary unit to provide more appropriate information by adjusting the way in which the commentary is expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The commentary unit can provide a detailed explanation of the background and impact of the information when providing the explanation. For example, the commentary unit provides a detailed explanation of the background and impact of the information when providing the explanation. For example, the commentary unit provides an explanation of the background of a policy, including historical details. The commentary unit can also provide an explanation of the impact of a policy, including economic impacts. Furthermore, the commentary unit can provide an explanation of a candidate's past performance, including specific examples. For example, the commentary unit analyzes the background and impact of a policy in real time and provides an optimal explanation. In this way, the commentary unit can deepen the user's understanding by providing a detailed explanation of the background and impact of the information.

[0100] The commentary unit can improve the accuracy of the commentary by referring to the user's past commentary results when providing commentary. For example, the commentary unit can improve the accuracy of the commentary by referring to the user's past commentary results when providing commentary. For example, the commentary unit provides commentary by referring to commentary styles that the user has previously rated highly. The commentary unit can also provide commentary by avoiding commentary styles that the user has previously rated poorly. Furthermore, the commentary unit can analyze the user's past commentary results and apply an optimal commentary algorithm. For example, the commentary unit analyzes the user's past commentary results and provides an optimal commentary. This allows the commentary unit to improve the accuracy of the commentary by referring to the user's past commentary results.

[0101] The commentary unit can apply different commentary algorithms depending on the category of information when providing commentary. For example, the commentary unit applies different commentary algorithms depending on the category of information when providing commentary. For example, the commentary unit provides commentary including detailed analysis for information related to policies. The commentary unit can also provide commentary including past achievements for information related to candidates. Furthermore, the commentary unit can provide commentary that emphasizes timeliness for information related to news. For example, the commentary unit applies the optimal commentary algorithm depending on the category of information. In this way, the commentary unit can provide more appropriate commentary by applying a commentary algorithm depending on the category of information.

[0102] The commentary unit can estimate the user's emotions and adjust the length of the commentary based on the estimated user emotions. For example, the commentary unit can estimate the user's emotions and adjust the length of the commentary based on the estimated user emotions. For example, if the user is in a hurry, the commentary unit can provide a short, to-the-point commentary. Furthermore, if the user is relaxed, the commentary unit can provide a longer commentary with detailed explanations. Furthermore, if the user is excited, the commentary unit can provide a commentary with visually stimulating effects. For example, the commentary unit can analyze the user's emotions in real time and provide an optimal commentary. This allows the commentary unit to provide more appropriate information by adjusting the length of the commentary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The commentary unit can determine the priority of the commentary based on the time of submission of the information when providing the commentary. For example, the commentary unit determines the priority of the commentary based on the time of submission of the information when providing the commentary. For example, the commentary unit prioritizes explaining the latest information and postpones older information. The commentary unit can also evaluate the time of submission of the information in real time and provide explanations with optimal priority. Furthermore, the commentary unit can prioritize explaining newest information based on the user's interests. For example, the commentary unit analyzes the time of submission of the information and provides optimal explanations. As a result, the commentary unit can provide the latest information by determining the priority of the commentary based on the time of submission of the information.

[0104] The commentary unit can adjust the order of commentary based on the relevance of the information when providing commentary. For example, the commentary unit adjusts the order of commentary based on the relevance of the information when providing commentary. For example, the commentary unit prioritizes explaining highly relevant information based on the user's interests. The commentary unit can also evaluate the relevance of information in real time and provide explanations in an optimal order. Furthermore, the commentary unit can prioritize explaining highly relevant information based on the user's past search history. For example, the commentary unit analyzes the relevance of information and provides an optimal explanation. As a result, the commentary unit can provide highly relevant information by adjusting the order of commentary based on the relevance of the information.

[0105] The explanation unit can adjust the use of technical terms in the explanation according to the user's level of expertise when providing an explanation. For example, the explanation unit adjusts the use of technical terms in the explanation according to the user's level of expertise when providing an explanation. For example, if the user has technical expertise, the explanation unit provides an explanation that uses a lot of technical terms. Also, if the user does not have technical expertise, the explanation unit can provide an explanation that explains things in simple terms. Furthermore, the explanation unit can adjust the use of optimal technical terms based on the user's past explanation results. For example, the explanation unit analyzes the user's level of expertise and provides an optimal explanation. As a result, the explanation unit can provide an explanation that is easier to understand by adjusting the use of technical terms according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, collection unit, generation unit, and commentary unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives election information searches from users. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects data from information sources such as newspapers, TV, social media, election bulletins, and street speeches. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate a customized summary of election information. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides commentary as an expert assistant based on the generated summary. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, and commentary unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a search for election information from a user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects data from information sources such as newspapers, TV, social media, election bulletins, and street speeches. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate a customized summary of election information. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides commentary as an expert assistant based on the generated summary. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, and commentary unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives election information searches from users. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects data from information sources such as newspapers, TV, social media, election bulletins, and street speeches. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate a customized summary of election information. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides commentary as an expert assistant based on the generated summary. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, generation unit, and commentary unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives election information searches from users. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects data from information sources such as newspapers, TV, social media, election bulletins, and street speeches. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to generate a customized summary of election information. The commentary unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides commentary as an expert assistant based on the generated summary.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also analyze the user's past voting history and provide voting reminders. For example, the reception unit can remind the user of the next election date based on data on elections in which the user has voted in the past. The reception unit can also provide the user with the latest information on candidates and political parties for which the user has voted in the past. Furthermore, the reception unit can notify the user of any changes to the electoral district in which the user voted in the past. In this way, the reception unit can utilize the user's past voting history to provide voting reminders, thereby helping to increase voter turnout.

[0108] The collection unit can also calculate a reliability score for each source to evaluate the reliability of election information. For example, the collection unit calculates a reliability score based on the past accuracy of each source. The collection unit can also adjust the reliability score by incorporating expert evaluations of the source. Furthermore, the collection unit can update the reliability score taking into account the update frequency and transparency of the source. This allows the collection unit to provide accurate election information to users by preferentially collecting reliable information.

[0109] The generation unit can also estimate the user's emotion and adjust the tone of the summary based on the estimated user's emotion. For example, if the user is feeling anxious, the generation unit can generate the summary in a reassuring tone. If the user is excited, the generation unit can also generate the summary in an energetic tone. Furthermore, if the user is relaxed, the generation unit can also generate the summary in a calm tone. In this way, the generation unit can improve the ease of understanding of information by providing the summary in a tone that corresponds to the user's emotion.

[0110] The commentary unit can also estimate the user's emotions and adjust the style of commentary based on the estimated user's emotions. For example, if the user is nervous, the commentary unit can provide a humorous commentary to relax the user. If the user is excited, the commentary unit can also provide a calm commentary. If the user is depressed, the commentary unit can also provide a commentary that includes words of encouragement. In this way, the commentary unit can provide a commentary style that corresponds to the user's emotions, thereby deepening the user's understanding of the information.

[0111] The collection unit can also use the user's geographic location information to collect region-specific election information. For example, the collection unit can prioritize collection of information about local candidates and policies based on the user's current location. The collection unit can also collect election information for regions that the user has previously visited. Furthermore, the collection unit can collect election information for regions in which the user is interested. In this way, the collection unit can use the user's geographic location information to provide region-specific election information.

[0112] The generator can also adjust the level of detail of the summary according to the user's level of expertise. For example, if the user has specialized knowledge, the generator can generate a summary that includes a detailed analysis. Alternatively, if the user does not have specialized knowledge, the generator can generate a summary that explains things in simple terms. Furthermore, the generator can generate a summary with an optimal level of detail based on the user's past summarization results. This allows the generator to provide a summary that matches the user's level of expertise, thereby deepening the user's understanding of the information.

[0113] The reception unit can also estimate the user's emotions and adjust the display order of search results based on the estimated user's emotions. For example, if the user is excited, the reception unit can prioritize displaying the latest election information. If the user is relaxed, the reception unit can also prioritize displaying information including detailed explanations. Furthermore, if the user is stressed, the reception unit can also prioritize displaying concise information that focuses on the main points. In this way, the reception unit can provide more appropriate information by adjusting the display order of search results according to the user's emotions.

[0114] When collecting election information, the collection unit can also adjust the timing of collection taking into account the frequency of information updates. For example, the collection unit periodically collects information from news sites that are updated frequently. The collection unit can also collect information as needed from sources that are updated less frequently. Furthermore, the collection unit can monitor the frequency of information updates in real time and collect information at the optimal timing. This allows the collection unit to provide the latest information by adjusting the timing of collection based on the frequency of information updates.

[0115] The generation unit can also estimate the user's emotions and adjust the length of the summary based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can generate a short summary that covers the main points. If the user is relaxed, the generation unit can generate a longer summary that includes detailed explanations. Furthermore, if the user is excited, the generation unit can generate a summary that adds visually stimulating effects. In this way, the generation unit can provide more appropriate information by adjusting the length of the summary according to the user's emotions.

[0116] The commentary unit can also estimate the user's emotions and adjust the way the commentary is expressed based on the estimated user's emotions. For example, if the user is nervous, the commentary unit can provide the commentary in a calm tone. If the user is relaxed, the commentary unit can also provide the commentary in a friendly tone. Furthermore, if the user is excited, the commentary unit can also provide the commentary in an energetic tone. In this way, the commentary unit can provide more appropriate information by adjusting the way the commentary is expressed in accordance with the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit accepts a search for election information from a user. For example, a user can search for information about a specific candidate or policy. Step 2: The collection unit collects election information based on the information received by the reception unit. For example, the collection unit collects data from sources such as newspapers, TV, social media, election bulletins, and street speeches. Step 3: The generator analyzes the information collected by the collector and generates a customized summary of election information. For example, the generator generates a summary of a specific candidate's policies and past performance based on the user's interests. Step 4: The commentary unit acts as an expert assistant and provides commentary based on the summary generated by the generation unit. For example, the commentary unit may provide commentary on the background and impact of the policy, providing information in a format that is easy for users to understand.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. a reception unit that receives election information searches from users; a collection unit that collects election information based on the information received by the reception unit; a generation unit that analyzes the information collected by the collection unit and generates a customized summary of election information; a commentary unit that provides commentary as an expert assistant based on the summary generated by the generation unit. A system characterized by:

2. The collecting unit Collect election information from a variety of sources 2. The system of claim 1.

3. The generation unit Generate customized election information summaries based on user interests 2. The system of claim 1.

4. The commentary section Provide commentary on the background and impact of policies based on the generated summaries 2. The system of claim 1.

5. The collecting unit Data is collected from sources such as newspapers, TV, social media, election bulletins, and street speeches.

2. The system of claim 1.

6. The generation unit Generate summaries of a particular candidate's policies and past performance 2. The system of claim 1.

7. The reception unit Inferring user sentiment and adjusting the display order of search results based on the estimated user sentiment 2. The system of claim 1.

8. The reception unit Analyzes users' past search history and suggests optimal search keywords 2. The system of claim 1.

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