System

Abstract

An object of the system according to the embodiment is to provide objective information by analyzing the minutes of the Congress and comparing the minutes with the official commitments of the members.SOLUTION: A system includes a minutes analysis part, a summary generation part, a common agreement comparison part, and an analysis result provision part. The minutes analysis unit analyzes the minutes of the Diet. A summary generation part generates a summary on the basis of a result analyzed by the minutes analysis part. A common commitment comparison part compares the common commitment of the member with the minutes. The analysis result providing unit provides the analysis result obtained by the common divisor comparison unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, analyzing parliamentary minutes and comparing them with lawmakers' campaign promises was time-consuming and laborious, making it difficult to provide objective information.

[0005] The system according to the embodiment aims to analyze the minutes of the Diet, compare them with the pledges of Diet members, and provide objective information. [Means for solving the problem]

[0006] The system according to the embodiment includes a minutes analysis unit, a summary generation unit, a promise comparison unit, and an analysis result provision unit. The minutes analysis unit analyzes the minutes of a Diet session. The summary generation unit generates a summary based on the results of the analysis by the minutes analysis unit. The promise comparison unit compares the promises of the Diet members with the minutes. The analysis result provision unit provides the analysis results obtained by the promise comparison unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the minutes of the Diet, compare them with the pledges of Diet members, and provide objective information. [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) The Diet record analysis system according to an embodiment of the present invention summarizes Diet records and clarifies how each Diet member is active in the Diet. Through the summary and analysis of Diet records, the Diet record analysis system can clarify the activities of Diet members, the degree to which they fulfill their campaign promises, and the consistency of their words and actions, thereby providing objective information to voters and the media.

[0029] The parliamentary minutes analysis system according to the embodiment includes a minutes analysis unit, a summary generation unit, a pledge comparison unit, and an analysis result provision unit. The minutes analysis unit analyzes parliamentary minutes. For example, the minutes analysis unit analyzes the contents of the minutes using text mining technology. The minutes analysis unit can also extract important parts of the minutes using natural language processing technology. The minutes analysis unit can also analyze the entire text of the minutes and extract information related to specific topics. The summary generation unit generates a summary based on the results of the analysis by the minutes analysis unit. For example, the summary generation unit generates a summary of the minutes using a generation AI (e.g., a text generation AI or a multimodal generation AI). The summary generation unit can also generate a summary by extracting important remarks and points of discussion. The summary generation unit can also concisely summarize long passages in the minutes. The pledge comparison unit compares the minutes with the pledges of members of parliament. For example, the pledge comparison unit analyzes election pledges, website information, and the contents of lectures, and compares them with the minutes. The pledge comparison unit can also analyze the degree of agreement between a member's statements and actions. The pledge comparison unit can also evaluate the degree to which a member fulfills their pledges. The analysis result providing unit provides the analysis results obtained by the pledge comparison unit. For example, the analysis result providing unit quantifies the analysis results and displays them in graph or table format. The analysis result providing unit can also provide the analysis results through a website or app. The analysis result providing unit can also provide the analysis results to the media. This enables the Diet minutes analysis system according to the embodiment to summarize Diet minutes and analyze the degree to which pledges are fulfilled. For example, the output unit displays the analysis results to voters and the media via a web application or mobile application. If feedback in paper form is desired, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to voters and the media.

[0030] In addition to summarizing the minutes, the summary generation unit can automatically add the background to the discussion and detailed information about related bills. For example, the summary generation unit uses a generation AI to analyze the minutes and add background information about the discussion to the summary. For example, it may briefly explain the incident or issue that sparked the discussion. The summary generation unit also automatically incorporates detailed information about related bills into the summary. For example, it may briefly explain the purpose and main clauses of a bill. The summary generation unit also adds past discussions and decisions related to the summary of the minutes to clarify the flow of the discussion. For example, it may reflect key statements and decisions made in past discussions in the summary. This allows for a deeper understanding by adding background information and detailed information about bills to the summary of the minutes.

[0031] The summary generation unit can automatically explain technical terms and abbreviations used in the minutes to aid understanding. For example, the summary generation unit uses a generation AI to analyze the minutes, automatically detect technical terms and abbreviations, and add their explanations to the summary. For example, "TPP" is explained as "Trans-Pacific Partnership Agreement." The summary generation unit also adds explanations of technical terms and abbreviations to the summary as footnotes to make it easier for readers to understand. For example, "GDP" is explained as "Gross Domestic Product" in a footnote. The summary generation unit also incorporates explanations of technical terms and abbreviations into the summary, providing information in a natural way. For example, "ICT" is incorporated into the sentence as "Information and Communication Technology." This makes the minutes easier to understand thanks to the explanations of technical terms and abbreviations.

[0032] The analysis result providing unit can also provide summaries of Diet minutes in audio or video format, allowing users to obtain information visually and aurally. For example, the analysis result providing unit converts the summary generated by the generation AI into audio format using speech synthesis technology, allowing users to obtain information auditorily. For example, the summary may be read aloud as a narration. The analysis result providing unit may also generate videos based on the summaries to provide information visually. For example, it may create videos that combine images and graphs related to the summaries. The analysis result providing unit may also distribute the audio summaries as podcasts, allowing users to listen to them while traveling or working. For example, it may provide podcasts that summarize the minutes weekly. In this way, information can be obtained visually and aurally by providing it in audio or video format.

[0033] The analysis results providing unit can summarize parliamentary minutes from different countries in the same way, enabling comparative analysis of international debates. For example, the analysis results providing unit uses generative AI to summarize parliamentary minutes from different countries and conduct comparative analysis of international debates. For example, it summarizes parliamentary minutes from Japan and the United States and identifies the differences in key discussions. The analysis results providing unit also summarizes parliamentary minutes from each country and extracts common themes and issues. For example, it summarizes discussions on environmental issues from each country's minutes and analyzes similarities and differences. The analysis results providing unit also summarizes parliamentary minutes from each country and creates comparison tables to conduct comparative analysis of international debates. For example, it summarizes key statements and decisions made in each country's parliament and compiles them in a comparison table. This enables summarizing parliamentary minutes from different countries and conducting comparative analysis of international debates.

[0034] When analyzing the consistency between a lawmaker's statements and actions, the campaign promise comparison unit can evaluate the long-term consistency, including past election promises and statements. For example, the generation AI analyzes past election promises and statements and compares them with current behavior to evaluate the long-term consistency. For example, it compares promises and actions from the past 10 years. The campaign promise comparison unit also registers past election promises and statements in a database, and the generation AI builds a system to evaluate the consistency based on these. For example, it automatically compares past promises with current behavior. Furthermore, to evaluate the long-term consistency, the campaign promise comparison unit analyzes past election promises and statements chronologically to identify trends of change. For example, it evaluates the extent to which past promises have been implemented. This makes it possible to understand the consistency of a lawmaker's actions by evaluating the long-term consistency, including past election promises and statements.

[0035] The campaign promise comparison unit displays the changes in a lawmaker's statements and actions over time, making it possible to clarify trends in change. For example, the generation AI in the campaign promise comparison unit analyzes the changes in a lawmaker's statements and actions over time and displays the results in graph or table format. For example, it shows changes in the frequency and content of statements over time. The campaign promise comparison unit also builds a system that displays the changes in a lawmaker's statements and actions over time, allowing users to easily grasp trends in change. For example, it displays changes in the content of statements in a line graph. Furthermore, when the generation AI provides the analysis results, the campaign promise comparison unit displays the changes in a lawmaker's statements and actions over time, emphasizing important changes. For example, it highlights periods when the content of statements changed significantly. In this way, trends in change can be grasped by displaying the changes in a lawmaker's statements and actions over time.

[0036] The pledge comparison unit can compare the analysis results of the degree of promise fulfillment and the consistency of words and actions between different lawmakers and political parties, and provide a relative evaluation. For example, the generation AI in the pledge comparison unit compares the analysis results of the degree of promise fulfillment and the consistency of words and actions between different lawmakers and political parties, and displays the results in graph or table format. For example, it compares the degree of promise fulfillment for each lawmaker using a bar graph. The pledge comparison unit also builds a system that compares the degree of promise fulfillment and the consistency of words and actions between different lawmakers and political parties, allowing users to easily understand relative evaluations. For example, it compares the degree of promise fulfillment for each political party. Furthermore, when the generation AI provides the analysis results, the pledge comparison unit compares different lawmakers and political parties and highlights important differences. For example, it highlights lawmakers with particularly high degrees of promise fulfillment. This makes it possible to provide a relative evaluation by comparing different lawmakers and political parties.

[0037] The campaign promise comparison unit classifies the analysis results by region and policy, making it possible to clarify how lawmakers respond to specific regions and policies. For example, the campaign promise comparison unit classifies the analysis results of the generation AI's campaign promise fulfillment and consistency between words and actions by region and policy, and displays the results in graph or table format. For example, it displays the campaign promise fulfillment level for each region on a map. The campaign promise comparison unit also builds a system to classify the analysis results by region and policy, making it easy for users to understand how lawmakers respond to specific regions and policies. For example, it compares responses to education policy. The campaign promise comparison unit also classifies the analysis results by region and policy when the generation AI provides them, emphasizing important differences. For example, it highlights lawmakers who have a high degree of campaign promise fulfillment in a specific region. In this way, by classifying the analysis results by region and policy, it is possible to clarify how lawmakers respond to specific regions and policies.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The analysis result provider can provide detailed analysis results on specific topics that interest the user. For example, if the user is interested in environmental issues, the analysis result provider can provide a summary of discussions on environmental issues and the progress of related bills. The analysis result provider can also generate detailed reports including past discussions and decisions on topics specified by the user. Furthermore, the analysis result provider can provide the latest news and related media reports on topics that interest the user. This allows the user to obtain detailed information on specific topics.

[0040] The summary generation unit can collect user feedback on the summary of the minutes and improve the quality of the summary. For example, an interface is provided that allows users to provide comments and ratings on the summary. The summary generation unit can also analyze the user feedback and identify areas for improvement in the summary. Furthermore, the summary generation unit can automatically revise the summary based on the user feedback to provide a more accurate and easy-to-understand summary. This makes it possible to utilize user feedback to improve the quality of the summary.

[0041] The analysis result provider can generate infographics to make the summary of the minutes easier to understand visually. For example, it can create graphs and charts that visually represent the main points of the discussion and statistical data. The analysis result provider can also add images and icons related to the summary text to provide a visually appealing summary. Furthermore, the analysis result provider can make the infographics interactive, allowing users to click to view more detailed information. This makes it possible to provide a summary that is easy to understand visually.

[0042] The analysis result providing unit can translate the summary of the minutes into different languages, enabling multilingual support. For example, the generation AI can automatically translate the summary into English, Chinese, etc., to provide information to users who speak different languages. The analysis result providing unit can also evaluate the quality of the translated summary and correct it as necessary. Furthermore, the analysis result providing unit can provide a multilingual interface, allowing users to select a language to view the summary. This multilingual support makes it possible to provide information to users who speak different languages.

[0043] The analysis result providing unit can provide summaries of the minutes in different formats, thereby providing information tailored to the user's needs. For example, the summary can be provided in PDF format, allowing the user to download it and view it offline. The analysis result providing unit can also provide the summary in slide format, allowing it to be used in presentations. Furthermore, the analysis result providing unit can provide the summary in a format that can be shared via email or social media, allowing the user to easily share information. This makes it possible to provide information tailored to the user's needs by providing information in different formats.

[0044] The analysis result providing unit can provide a summary of the minutes optimized for different devices, allowing users to comfortably view information on any device. For example, the summary can be optimized for smartphones and tablets, making it easier to view on mobile devices. The analysis result providing unit can also optimize the summary for desktops and laptops, making it easier to view on large screens. Furthermore, the analysis result providing unit can provide summaries for new devices such as smart watches and smart speakers, allowing users to obtain information on a variety of devices. In this way, by providing information optimized for different devices, it is possible to enable users to comfortably view information on any device.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The minutes analysis unit analyzes the minutes of the Diet. For example, it uses text mining and natural language processing techniques to analyze the contents of the minutes and extract important parts and information related to specific topics. Step 2: The summary generation unit generates a summary based on the results of the analysis by the minutes analysis unit. For example, a generation AI can be used to generate a summary of the minutes, extracting important remarks and key points of discussion and summarizing them concisely. Step 3: The campaign promise comparison section compares the lawmaker's campaign promises with the minutes. For example, it analyzes election manifestos, website information, and lecture content, compares them with the minutes, and evaluates the degree of agreement between the lawmaker's statements and actions and the degree to which the lawmaker has fulfilled their campaign promises. Step 4: The analysis results provider provides the analysis results obtained by the pledge comparison component. For example, the analysis results may be quantified, displayed in graphs or tables, and provided via a website or app. The analysis results may also be provided to the media.

[0047] (Example 2) The Diet record analysis system according to an embodiment of the present invention summarizes Diet records and clarifies how each Diet member is active in the Diet. Through the summary and analysis of Diet records, the Diet record analysis system can clarify the activities of Diet members, the degree to which they fulfill their campaign promises, and the consistency of their words and actions, thereby providing objective information to voters and the media.

[0048] The parliamentary minutes analysis system according to the embodiment includes a minutes analysis unit, a summary generation unit, a pledge comparison unit, and an analysis result provision unit. The minutes analysis unit analyzes parliamentary minutes. For example, the minutes analysis unit analyzes the contents of the minutes using text mining technology. The minutes analysis unit can also extract important parts of the minutes using natural language processing technology. The minutes analysis unit can also analyze the entire text of the minutes and extract information related to specific topics. The summary generation unit generates a summary based on the results of the analysis by the minutes analysis unit. For example, the summary generation unit generates a summary of the minutes using a generation AI (e.g., a text generation AI or a multimodal generation AI). The summary generation unit can also generate a summary by extracting important remarks and points of discussion. The summary generation unit can also concisely summarize long passages in the minutes. The pledge comparison unit compares the minutes with the pledges of members of parliament. For example, the pledge comparison unit analyzes election pledges, website information, and the contents of lectures, and compares them with the minutes. The pledge comparison unit can also analyze the degree of agreement between a member's statements and actions. The pledge comparison unit can also evaluate the degree to which a member fulfills their pledges. The analysis result providing unit provides the analysis results obtained by the pledge comparison unit. For example, the analysis result providing unit quantifies the analysis results and displays them in graph or table format. The analysis result providing unit can also provide the analysis results through a website or app. The analysis result providing unit can also provide the analysis results to the media. This enables the Diet minutes analysis system according to the embodiment to summarize Diet minutes and analyze the degree to which pledges are fulfilled. For example, the output unit displays the analysis results to voters and the media via a web application or mobile application. If feedback in paper form is desired, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to voters and the media.

[0049] In addition to summarizing the minutes, the summary generation unit can automatically add the background to the discussion and detailed information about related bills. For example, the summary generation unit uses a generation AI to analyze the minutes and add background information about the discussion to the summary. For example, it may briefly explain the incident or issue that sparked the discussion. The summary generation unit also automatically incorporates detailed information about related bills into the summary. For example, it may briefly explain the purpose and main clauses of a bill. The summary generation unit also adds past discussions and decisions related to the summary of the minutes to clarify the flow of the discussion. For example, it may reflect key statements and decisions made in past discussions in the summary. This allows for a deeper understanding by adding background information and detailed information about bills to the summary of the minutes.

[0050] The summary generation unit can automatically explain technical terms and abbreviations used in the minutes to aid understanding. For example, the summary generation unit uses a generation AI to analyze the minutes, automatically detect technical terms and abbreviations, and add their explanations to the summary. For example, "TPP" is explained as "Trans-Pacific Partnership Agreement." The summary generation unit also adds explanations of technical terms and abbreviations to the summary as footnotes to make it easier for readers to understand. For example, "GDP" is explained as "Gross Domestic Product" in a footnote. The summary generation unit also incorporates explanations of technical terms and abbreviations into the summary, providing information in a natural way. For example, "ICT" is incorporated into the sentence as "Information and Communication Technology." This makes the minutes easier to understand thanks to the explanations of technical terms and abbreviations.

[0051] The summary generation unit can use the emotion estimation function to estimate the emotions of speakers in the minutes and reflect changes in emotion in the summary. For example, the summary generation unit uses a generation AI to analyze the minutes, estimate the emotions of the speakers, and reflect these changes in emotion in the summary. For example, a scene in which a speaker shows anger may be summarized as "a heated discussion took place." The summary generation unit also uses the emotion estimation function to reflect changes in the speaker's emotion in the summary. For example, if a speaker was initially calm but later became excited, the summary may state that "their emotions grew as the discussion progressed." The summary generation unit also estimates the speaker's emotion and reflects the intensity of that emotion in the summary. For example, a scene in which strong emotion was shown may be summarized as "a strong opposing opinion was expressed." In this way, by reflecting changes in the speaker's emotion, the summary of the minutes becomes more detailed.

[0052] The analysis result providing unit can also provide summaries of Diet minutes in audio or video format, allowing users to obtain information visually and aurally. For example, the analysis result providing unit converts the summary generated by the generation AI into audio format using speech synthesis technology, allowing users to obtain information auditorily. For example, the summary may be read aloud as a narration. The analysis result providing unit may also generate videos based on the summaries to provide information visually. For example, it may create videos that combine images and graphs related to the summaries. The analysis result providing unit may also distribute the audio summaries as podcasts, allowing users to listen to them while traveling or working. For example, it may provide podcasts that summarize the minutes weekly. In this way, information can be obtained visually and aurally by providing it in audio or video format.

[0053] The analysis results providing unit can summarize parliamentary minutes from different countries in the same way, enabling comparative analysis of international debates. For example, the analysis results providing unit uses generative AI to summarize parliamentary minutes from different countries and conduct comparative analysis of international debates. For example, it summarizes parliamentary minutes from Japan and the United States and identifies the differences in key discussions. The analysis results providing unit also summarizes parliamentary minutes from each country and extracts common themes and issues. For example, it summarizes discussions on environmental issues from each country's minutes and analyzes similarities and differences. The analysis results providing unit also summarizes parliamentary minutes from each country and creates comparison tables to conduct comparative analysis of international debates. For example, it summarizes key statements and decisions made in each country's parliament and compiles them in a comparison table. This enables summarizing parliamentary minutes from different countries and conducting comparative analysis of international debates.

[0054] The analysis result providing unit uses the emotion estimation function to collect users' emotional reactions to the summary of the minutes, which can be used to improve the summary. For example, the analysis result providing unit collects users' emotional reactions to the summary generated by the generation AI and improves the summary based on that data. For example, it prioritizes the adoption of summaries that receive a lot of positive reactions. The analysis result providing unit also uses the emotion estimation function to collect users' emotional reactions in real time and identify areas for improvement in the summary. For example, it corrects parts that receive a lot of negative reactions. The analysis result providing unit also analyzes users' emotional reaction data and makes suggestions for improving the summary based on the results. For example, it makes specific suggestions for correcting parts with low emotion scores. In this way, the analysis result providing unit can collect users' emotional reactions and use them to improve the summary.

[0055] When analyzing the consistency between a lawmaker's statements and actions, the campaign promise comparison unit can evaluate the long-term consistency, including past election promises and statements. For example, the generation AI analyzes past election promises and statements and compares them with current behavior to evaluate the long-term consistency. For example, it compares promises and actions from the past 10 years. The campaign promise comparison unit also registers past election promises and statements in a database, and the generation AI builds a system to evaluate the consistency based on these. For example, it automatically compares past promises with current behavior. Furthermore, to evaluate the long-term consistency, the campaign promise comparison unit analyzes past election promises and statements chronologically to identify trends of change. For example, it evaluates the extent to which past promises have been implemented. This makes it possible to understand the consistency of a lawmaker's actions by evaluating the long-term consistency, including past election promises and statements.

[0056] The campaign promise comparison unit displays the changes in a lawmaker's statements and actions over time, making it possible to clarify trends in change. For example, the generation AI in the campaign promise comparison unit analyzes the changes in a lawmaker's statements and actions over time and displays the results in graph or table format. For example, it shows changes in the frequency and content of statements over time. The campaign promise comparison unit also builds a system that displays the changes in a lawmaker's statements and actions over time, allowing users to easily grasp trends in change. For example, it displays changes in the content of statements in a line graph. Furthermore, when the generation AI provides the analysis results, the campaign promise comparison unit displays the changes in a lawmaker's statements and actions over time, emphasizing important changes. For example, it highlights periods when the content of statements changed significantly. In this way, trends in change can be grasped by displaying the changes in a lawmaker's statements and actions over time.

[0057] The pledge comparison unit can compare the analysis results of the degree of promise fulfillment and the consistency of words and actions between different lawmakers and political parties, and provide a relative evaluation. For example, the generation AI in the pledge comparison unit compares the analysis results of the degree of promise fulfillment and the consistency of words and actions between different lawmakers and political parties, and displays the results in graph or table format. For example, it compares the degree of promise fulfillment for each lawmaker using a bar graph. The pledge comparison unit also builds a system that compares the degree of promise fulfillment and the consistency of words and actions between different lawmakers and political parties, allowing users to easily understand relative evaluations. For example, it compares the degree of promise fulfillment for each political party. Furthermore, when the generation AI provides the analysis results, the pledge comparison unit compares different lawmakers and political parties and highlights important differences. For example, it highlights lawmakers with particularly high degrees of promise fulfillment. This makes it possible to provide a relative evaluation by comparing different lawmakers and political parties.

[0058] The campaign promise comparison unit classifies the analysis results by region and policy, making it possible to clarify how lawmakers respond to specific regions and policies. For example, the campaign promise comparison unit classifies the analysis results of the generation AI's campaign promise fulfillment and consistency between words and actions by region and policy, and displays the results in graph or table format. For example, it displays the campaign promise fulfillment level for each region on a map. The campaign promise comparison unit also builds a system to classify the analysis results by region and policy, making it easy for users to understand how lawmakers respond to specific regions and policies. For example, it compares responses to education policy. The campaign promise comparison unit also classifies the analysis results by region and policy when the generation AI provides them, emphasizing important differences. For example, it highlights lawmakers who have a high degree of campaign promise fulfillment in a specific region. In this way, by classifying the analysis results by region and policy, it is possible to clarify how lawmakers respond to specific regions and policies.

[0059] The promise comparison unit uses the emotion estimation function to collect the user's emotional reactions to the analysis results, which can be used to improve the accuracy of the analysis. For example, the promise comparison unit collects the user's emotional reactions to the analysis results of the generation AI, such as the degree of promise fulfillment and the consistency between words and actions, and uses that data to improve the accuracy of the analysis. For example, it prioritizes the adoption of analysis results with a high number of positive reactions. The promise comparison unit also uses the emotion estimation function to collect the user's emotional reactions in real time and identify areas for improvement in the analysis results. For example, it corrects parts with a high number of negative reactions. The promise comparison unit also analyzes the user's emotional reaction data and makes suggestions for improving the analysis results based on the results. For example, it makes specific suggestions for correcting parts with low emotion scores. In this way, the user's emotional reactions can be collected and used to improve the accuracy of the analysis.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The analysis result provider can provide detailed analysis results on specific topics that interest the user. For example, if the user is interested in environmental issues, the analysis result provider can provide a summary of discussions on environmental issues and the progress of related bills. The analysis result provider can also generate detailed reports including past discussions and decisions on topics specified by the user. Furthermore, the analysis result provider can provide the latest news and related media reports on topics that interest the user. This allows the user to obtain detailed information on specific topics.

[0062] The summary generation unit can collect user feedback on the summary of the minutes and improve the quality of the summary. For example, an interface is provided that allows users to provide comments and ratings on the summary. The summary generation unit can also analyze the user feedback and identify areas for improvement in the summary. Furthermore, the summary generation unit can automatically revise the summary based on the user feedback to provide a more accurate and easy-to-understand summary. This makes it possible to utilize user feedback to improve the quality of the summary.

[0063] The summary generation unit can collect the user's emotional response to the summary of the minutes and improve the quality of the summary. For example, an interface is provided that allows the user to evaluate their feelings after reading the summary. The summary generation unit can also analyze the user's emotional response and identify areas for improvement in the summary. Furthermore, the summary generation unit can automatically revise the summary based on the user's emotional response to provide a more emotionally appealing summary. This makes it possible to improve the quality of the summary by utilizing the user's emotional response.

[0064] The analysis result provider can generate infographics to make the summary of the minutes easier to understand visually. For example, it can create graphs and charts that visually represent the main points of the discussion and statistical data. The analysis result provider can also add images and icons related to the summary text to provide a visually appealing summary. Furthermore, the analysis result provider can make the infographics interactive, allowing users to click to view more detailed information. This makes it possible to provide a summary that is easy to understand visually.

[0065] The analysis result providing unit can use the emotion estimation function to collect the user's emotional reactions to the summary of the minutes and improve the quality of the summary. For example, an interface is provided that allows the user to evaluate their emotions after reading the summary. The analysis result providing unit can also analyze the user's emotional reactions and identify areas for improvement in the summary. Furthermore, the analysis result providing unit can automatically revise the summary based on the user's emotional reactions and provide a more emotionally appealing summary. This makes it possible to improve the quality of the summary by utilizing the user's emotional reactions.

[0066] The analysis result providing unit can translate the summary of the minutes into different languages, enabling multilingual support. For example, the generation AI can automatically translate the summary into English, Chinese, etc., to provide information to users who speak different languages. The analysis result providing unit can also evaluate the quality of the translated summary and correct it as necessary. Furthermore, the analysis result providing unit can provide a multilingual interface, allowing users to select a language to view the summary. This multilingual support makes it possible to provide information to users who speak different languages.

[0067] The analysis result providing unit can use the emotion estimation function to collect the user's emotional reactions to the summary of the minutes and improve the quality of the summary. For example, an interface is provided that allows the user to evaluate their emotions after reading the summary. The analysis result providing unit can also analyze the user's emotional reactions and identify areas for improvement in the summary. Furthermore, the analysis result providing unit can automatically revise the summary based on the user's emotional reactions and provide a more emotionally appealing summary. This makes it possible to improve the quality of the summary by utilizing the user's emotional reactions.

[0068] The analysis result providing unit can provide summaries of the minutes in different formats, thereby providing information tailored to the user's needs. For example, the summary can be provided in PDF format, allowing the user to download it and view it offline. The analysis result providing unit can also provide the summary in slide format, allowing it to be used in presentations. Furthermore, the analysis result providing unit can provide the summary in a format that can be shared via email or social media, allowing the user to easily share information. This makes it possible to provide information tailored to the user's needs by providing information in different formats.

[0069] The analysis result providing unit can use the emotion estimation function to collect the user's emotional reactions to the summary of the minutes and improve the quality of the summary. For example, an interface is provided that allows the user to evaluate their emotions after reading the summary. The analysis result providing unit can also analyze the user's emotional reactions and identify areas for improvement in the summary. Furthermore, the analysis result providing unit can automatically revise the summary based on the user's emotional reactions and provide a more emotionally appealing summary. This makes it possible to improve the quality of the summary by utilizing the user's emotional reactions.

[0070] The analysis result providing unit can provide a summary of the minutes optimized for different devices, allowing users to comfortably view information on any device. For example, the summary can be optimized for smartphones and tablets, making it easier to view on mobile devices. The analysis result providing unit can also optimize the summary for desktops and laptops, making it easier to view on large screens. Furthermore, the analysis result providing unit can provide summaries for new devices such as smart watches and smart speakers, allowing users to obtain information on a variety of devices. In this way, by providing information optimized for different devices, it is possible to enable users to comfortably view information on any device.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The minutes analysis unit analyzes the minutes of the Diet. For example, it uses text mining and natural language processing techniques to analyze the contents of the minutes and extract important parts and information related to specific topics. Step 2: The summary generation unit generates a summary based on the results of the analysis by the minutes analysis unit. For example, a generation AI can be used to generate a summary of the minutes, extracting important remarks and key points of discussion and summarizing them concisely. Step 3: The campaign promise comparison section compares the lawmaker's campaign promises with the minutes. For example, it analyzes election manifestos, website information, and lecture content, compares them with the minutes, and evaluates the degree of agreement between the lawmaker's statements and actions and the degree to which the lawmaker has fulfilled their campaign promises. Step 4: The analysis results provider provides the analysis results obtained by the pledge comparison component. For example, the analysis results may be quantified, displayed in graphs or tables, and provided via a website or app. The analysis results may also be provided to the media.

[0073] 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.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] In the robot 414, 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 robot 414 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 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 minutes analysis department that analyzes parliamentary minutes; a summary generation unit that generates a summary based on the analysis result by the minutes analysis unit; A pledge comparison section that compares the pledges of members of parliament with the minutes of meetings; an analysis result providing unit that provides the analysis result obtained by the promise comparison unit; A system characterized by:

2. The summary generation unit In addition to the summary of the minutes, the system automatically adds detailed information about the background of the discussion and related bills.

2. The system of claim 1.

3. The summary generation unit Automatically explains technical terms and abbreviations used in the minutes to aid understanding 2. The system of claim 1.

4. The summary generation unit The emotions of the speakers in the minutes are estimated, and changes in the emotions are reflected in the summary.

2. The system of claim 1.

5. The analysis result providing unit The summaries of the Diet minutes will also be provided in audio and video formats, allowing information to be obtained visually and aurally.

2. The system of claim 1.

6. The analysis result providing unit Similar summaries of parliamentary proceedings from different countries allow for comparative analysis of international debates 2. The system of claim 1.

7. The analysis result providing unit Collecting users' emotional responses to the summary of the minutes to help improve the summary 2. The system of claim 1.

8. The promise comparison unit When analyzing the consistency between the statements and actions of the lawmaker, we evaluate the long-term consistency, including past election pledges and statements.

2. The system of claim 1.

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