System

Abstract

A system according to an embodiment aims to accurately summarize the information source and point out the differences between the original utterances.SOLUTION: A system according to an embodiment includes a collection part, a summarization part, and a difference indication part. The collection unit collects information sources. The summarizing unit summarizes the information collected by the collecting unit. The difference indicating unit indicates a difference between the summary generated by the summarizing unit and the original speech.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques can sometimes produce inaccurate summaries of information sources or differ from the original statements, which can lead to misunderstandings.

[0005] The system according to the embodiment aims to accurately summarize the information source and point out the differences between the original statement. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a summarization unit, and a difference indication unit. The collection unit collects information sources. The summarization unit summarizes the information collected by the collection unit. The difference indication unit indicates differences between the summary generated by the summarization unit and the original utterance. [Effects of the Invention]

[0007] The system according to the embodiment can accurately summarize the information source and point out the differences between the original statement. [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) An information summarization system according to an embodiment of the present invention appropriately summarizes statements from information sources and prevents misleading reporting. The information summarization system collects information sources, generates summaries, and identifies differences between the generated summaries and the original statements, thereby providing users with an overall picture of the information and supporting accurate information judgment. For example, the information summarization system collects statements from interviews, press conferences, and other sources, and uses AI to summarize the content. The AI-generated summary is then compared with the original statements to check for missing important information or misleading expressions. This provides users with an overall picture of the information and supports accurate information judgment. This allows the information summarization system to appropriately summarize information sources and prevent misleading reporting. For example, users can prevent misleading reporting by referring to the AI-generated summary and the identified differences. This allows users to grasp the overall picture of the information and make accurate information judgments.

[0029] An information summarization system according to an embodiment includes a collection unit, a summarization unit, and a difference indication unit. The collection unit collects information sources. Examples of information sources include, but are not limited to, news articles, interviews, and press conferences. The collection unit collects, for example, statements from interviews, press conferences, and the like. The collection unit can use AI to collect detailed information about the content and context of statements. For example, the collection unit can collect audio data from interviews and convert it into text data. The collection unit can also collect video data from press conferences and extract important statements. The summarization unit generates a summary based on the information collected by the collection unit. The summarization is performed based on, for example, the length of the sentences and the importance of the information to be summarized, but is not limited to, examples. For example, the summarization unit can use AI to extract important statements from long interviews and generate a concise summary. The summarization unit can also use AI to extract key points from news articles and generate a short summary. The difference indication unit indicates differences between the summary generated by the summarization unit and the original statements. Differences are pointed out based on criteria such as, for example, the degree of similarity of content or whether important information is missing, but are not limited to such examples. For example, the difference pointing unit may use AI to compare the summary with the original statement and check whether important information is missing. The difference pointing unit may also use AI to check whether the summary contains misleading expressions. This allows the information summarization system according to the embodiment to appropriately summarize the information source and prevent misleading reports. For example, a user can prevent misleading reports by referring to the summary generated by AI and the difference points. This allows a user to grasp the overall picture of the information and make accurate information judgments.

[0030] The collection unit can collect statements made in interviews or press conferences. For example, the collection unit collects audio data of interviews and converts it into text data. The collection unit can also collect video data of press conferences and extract important statements. For example, the collection unit converts audio data of interviews into text data using highly accurate speech recognition technology. Important statements can be extracted from video data of press conferences using video analysis technology. By collecting statements made in interviews, press conferences, etc., diversity of information sources can be ensured. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input audio data of interviews into AI and have the AI ​​convert it into text data.

[0031] The summarization unit can generate a summary based on the information collected by the collection unit. For example, the summarization unit can extract important statements from a long interview and generate a concise summary. The summarization unit can also extract the main points of a news article and generate a short summary. For example, the summarization unit can use AI to extract important statements from an interview and generate a summary. The summarization unit can also use AI to extract the main points of a news article and generate a summary. Furthermore, the summarization unit can use AI to pick out particularly important information from the collected information and generate a summary based on that information. In this way, by generating a summary based on the collected information, the main points of the information can be concisely conveyed. Some or all of the above-mentioned processing in the summarization unit may be performed using AI, for example, or may be performed without using AI. For example, the summarization unit can input the collected information into AI and have the AI ​​generate a summary.

[0032] The difference detection unit can compare the summary generated by the summarization unit with the original utterance and point out any differences. For example, the difference detection unit can compare the summary with the original utterance to check whether important information is missing. The difference detection unit can also check whether the summary contains any misleading expressions. For example, the difference detection unit can use AI to compare the summary with the original utterance to check whether important information is missing. The difference detection unit can also use AI to check whether the summary contains any misleading expressions. Furthermore, the difference detection unit can use AI to evaluate the degree of similarity between the content of the summary and the original utterance and point out any differences. In this way, by comparing the summary with the original utterance and pointing out any differences, misunderstandings can be prevented. Some or all of the above-mentioned processing in the difference detection unit can be performed using AI, for example, or without AI. For example, the difference detection unit can input the summary and the original utterance into AI and have the AI ​​point out any differences.

[0033] The collection unit can analyze the user's past information collection history and select an appropriate collection method. For example, the collection unit prioritizes collection of information sources that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method based on the user's past information collection history. The collection unit can also analyze the user's past information collection history and select the optimal collection method for a specific time period. This enables efficient information collection by analyzing the past information collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information collection history data into AI and have the AI ​​select the optimal collection method.

[0034] The collection unit can filter information sources based on the user's current areas of interest when collecting them. For example, the collection unit preferentially collects information sources related to topics in which the user is currently interested. The collection unit can also filter unnecessary information based on the user's current areas of interest. The collection unit can also automatically select highly relevant information sources based on the user's areas of interest. This makes it possible to provide highly relevant information by filtering information based on the user's areas of interest. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's area of ​​interest data into AI and have the AI ​​perform the filtering of information sources.

[0035] When collecting information sources, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is using image input, the collection unit can prioritize collecting image data. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data into AI and have the AI ​​select the optimal collection means.

[0036] When collecting information sources, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information sources related to the user's current location. The collection unit can also collect news and event information related to the area based on the user's geographical location information. The collection unit can also prioritize collecting nearby information sources by taking into account the user's location information. This makes it possible to provide information related to the area by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0037] When collecting information sources, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects comments from accounts the user follows on social media. The collection unit can also analyze the content of the user's social media posts to collect related information sources. The collection unit can also collect related information sources by referring to the activities of the user's friends on social media. This makes it possible to provide highly relevant information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​collect related information.

[0038] When collecting information sources, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects information sources that the user has previously rated highly. The collection unit can also optimize the collection method based on the user's past feedback. The collection unit can also adjust the types of information sources to be collected by reflecting the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0039] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the information. For example, the summarization unit summarizes important information in detail and summarizes less important information briefly. The summarization unit can also adjust the length of the summary based on the importance of the information. The summarization unit can also generate and provide multiple summaries when there is a lot of important information. In this way, by adjusting the level of detail of the summary based on the importance of the information, important information can be conveyed in detail. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input information importance data into AI and have the AI ​​adjust the level of detail of the summary.

[0040] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of information. For example, in the case of a news article, the summarization unit uses an algorithm that extracts key points. In addition, in the case of a scientific paper, the summarization unit can use a summarization algorithm that includes detailed information. In addition, in the case of a social media post, the summarization unit can use a concise summarization algorithm. In this way, by applying a summarization algorithm depending on the category of information, an optimal summary can be provided. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information category data into AI and have the AI ​​apply the summarization algorithm.

[0041] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. For example, the summarization unit can improve the accuracy of the summary by referring to summary results that the user has previously given high ratings. The summarization unit can also analyze the user's past summarization results and suggest an optimal summarization method. The summarization unit can also adjust the summarization algorithm based on the user's past summarization results. In this way, the accuracy of the summary can be improved by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's past summarization result data into AI and have the AI ​​improve the accuracy of the summary.

[0042] When generating summaries, the summarizing unit can determine the priority of summaries based on the time of submission of information. For example, the summarizing unit prioritizes summarizing the most recent information. The summarizing unit can also summarize information that was submitted earlier more concisely. The summarizing unit can also adjust the order of summaries based on the time of submission. In this way, by determining the priority of summaries based on the time of submission of information, the most recent information can be provided preferentially. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of summaries.

[0043] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the information. For example, the summarization unit prioritizes summarization of highly relevant information. The summarization unit can also briefly summarize less relevant information. The summarization unit can also adjust the order of summaries based on the relevance of the information. In this way, by adjusting the order of summaries based on the relevance of the information, highly relevant information can be provided preferentially. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information relevance data into AI and have the AI ​​adjust the order of summaries.

[0044] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the summarization unit can provide a summary that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the summarization unit can provide a summary in simple language. The summarization unit can also adjust the way the summary is expressed according to the user's level of expertise. This allows for the provision of an easy-to-understand summary by adjusting the technical terms in the summary according to the user's level of expertise. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's level of expertise data into AI and have the AI ​​control the use of technical terms in the summary.

[0045] When pointing out differences, the difference pointing unit can adjust the level of detail of the differences based on the importance of the information. For example, the difference pointing unit points out important differences in detail and less important differences in brief. The difference pointing unit can also adjust the length of the difference pointing depending on the importance of the information. The difference pointing unit can also generate and provide multiple difference points when there are many important differences. In this way, by adjusting the level of detail of the differences based on the importance of the information, important differences can be pointed out in detail. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, or without, AI. For example, the difference pointing unit can input information importance data into AI and have the AI ​​adjust the level of detail of the differences.

[0046] When detecting differences, the difference detection unit can apply different difference detection algorithms depending on the category of information. For example, in the case of a news article, the difference detection unit uses a difference detection algorithm that extracts important points. In addition, in the case of a scientific paper, the difference detection unit can use a difference detection algorithm that includes detailed information. In addition, in the case of a social media post, the difference detection unit can use a concise difference detection algorithm. In this way, by applying a difference detection algorithm depending on the category of information, optimal difference detection can be provided. Some or all of the above-mentioned processing in the difference detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the difference detection unit can input information category data into AI and have the AI ​​apply the difference detection algorithm.

[0047] When pointing out differences, the difference pointing unit can improve the accuracy of the pointing by referring to the user's past difference pointing results. The difference pointing unit can improve the accuracy of the pointing by referring to, for example, difference pointing results that the user has previously given high ratings. The difference pointing unit can also analyze the user's past difference pointing results and propose an optimal pointing method. The difference pointing unit can also adjust the difference pointing algorithm based on the user's past difference pointing results. In this way, the accuracy of the pointing can be improved by referring to the user's past difference pointing results. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, for example, AI, or may be performed without using AI. For example, the difference pointing unit can input the user's past difference pointing result data into AI and have the AI ​​improve the accuracy of the pointing.

[0048] When pointing out differences, the difference pointing unit can adjust the importance of the differences based on the time of submission of the information. For example, the difference pointing unit prioritizes pointing out differences based on the most recent information. The difference pointing unit can also briefly point out differences based on information that was submitted earlier. The difference pointing unit can also adjust the order in which differences are pointed out based on the time of submission. In this way, by adjusting the importance of differences based on the time of submission of the information, differences based on the most recent information can be prioritized. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, for example, AI, or may be performed without using AI. For example, the difference pointing unit can input data on the time of submission of information into AI and have the AI ​​adjust the importance of the differences.

[0049] When pointing out differences, the difference pointing unit can adjust the display order of differences based on the relevance of the information. For example, the difference pointing unit prioritizes pointing out differences based on highly relevant information. The difference pointing unit can also briefly point out differences based on less relevant information. The difference pointing unit can also adjust the display order of pointed out differences based on the relevance of the information. In this way, by adjusting the display order of differences based on the relevance of the information, it is possible to prioritize point out highly relevant differences. Some or all of the above-mentioned processing in the difference pointing unit may be performed using AI, for example, or may be performed without using AI. For example, the difference pointing unit can input information relevance data into AI and cause the AI ​​to adjust the display order of differences.

[0050] When pointing out differences, the difference pointing unit can adjust the method of explaining the differences according to the user's level of expertise. For example, if the user has specialized knowledge, the difference pointing unit can point out differences using a lot of technical terminology. Furthermore, if the user does not have specialized knowledge, the difference pointing unit can also point out differences in simple language. Furthermore, the difference pointing unit can adjust the way the differences are expressed according to the user's level of expertise. In this way, by adjusting the method of explaining differences according to the user's level of expertise, it is possible to provide easy-to-understand difference points. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, for example, AI, or may be performed without using AI. For example, the difference pointing unit can input the user's level of expertise data into AI and have the AI ​​adjust the method of explaining the differences.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze the user's past search history and prioritize collection of highly relevant information sources. For example, it can prioritize collection of information sources related to keywords that the user has frequently searched for in the past. The collection unit can also select the optimal collection method for a specific time period from the user's past search history. Furthermore, the collection unit can also suggest the most efficient collection method based on the user's past search history. This enables efficient information collection by analyzing past search history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past search history data into AI and have the AI ​​select the optimal collection method.

[0053] The summarization unit can analyze the user's past browsing history and prioritize summarizing highly relevant information. For example, it can prioritize summarizing information related to topics that the user has frequently viewed in the past. The summarization unit can also select the optimal summarization method for a specific time period based on the user's past browsing history. Furthermore, the summarization unit can also suggest the most efficient summarization method based on the user's past browsing history. This enables efficient summarization by analyzing the past browsing history. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's past browsing history data into AI and have the AI ​​select the optimal summarization method.

[0054] The difference pointing unit can analyze the user's past feedback and improve the accuracy of difference pointing. For example, the difference pointing unit can improve the accuracy of pointing by referring to difference pointing results that the user has previously given high ratings. The difference pointing unit can also suggest an optimal pointing method based on the user's past feedback. Furthermore, the difference pointing unit can also adjust the difference pointing algorithm by reflecting the user's past feedback. In this way, the accuracy of pointing can be improved by referring to the user's past feedback. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, or without, AI, for example. For example, the difference pointing unit can input the user's past feedback data into AI and have the AI ​​improve the accuracy of pointing.

[0055] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, it can prioritize collecting information sources related to the user's current location. The collection unit can also collect news and event information related to the area based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting nearby information sources by taking into account the user's location information. This makes it possible to provide information related to the area by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0056] The summarization unit can apply different summarization algorithms depending on the category of information. For example, for news articles, an algorithm that extracts key points is used. For scientific papers, the summarization unit can also use a summarization algorithm that includes detailed information. Furthermore, for social media posts, the summarization unit can also use a concise summarization algorithm. This allows the application of a summarization algorithm depending on the category of information to provide an optimal summary. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information category data into AI and have the AI ​​apply the summarization algorithm.

[0057] The difference pointing unit can adjust the level of detail of the differences based on the importance of the information. For example, important differences are pointed out in detail, and less important differences are pointed out briefly. The difference pointing unit can also adjust the length of the difference pointing depending on the importance of the information. Furthermore, if there are many important differences, the difference pointing unit can generate and provide multiple difference points. In this way, by adjusting the level of detail of the differences based on the importance of the information, important differences can be pointed out in detail. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, or without, AI. For example, the difference pointing unit can input information importance data into AI and have the AI ​​adjust the level of detail of the differences.

[0058] The collection unit can analyze the user's social media activity and collect related information. For example, it can collect comments from accounts the user follows on social media. The collection unit can also analyze the content of the user's social media posts and collect related information sources. Furthermore, the collection unit can also collect related information sources by referring to the activities of the user's friends on social media. This makes it possible to provide highly relevant information by analyzing the user's social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​collect related information.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection department collects information sources. Information sources include news articles, interviews, and press conferences. The collection department collects statements from interviews and press conferences, and uses AI to collect detailed information about the content and context of the statements. For example, the collection department can collect audio data from interviews and convert it into text data. It can also collect video data from press conferences and extract important statements. Step 2: The summarization unit generates a summary based on the information collected by the collection unit. The summarization is based on the length of the text and the importance of the information being summarized. For example, the summarization unit uses AI to extract important statements from a long interview and generate a concise summary. It can also extract the main points of a news article and generate a short summary. Step 3: The difference detector identifies differences between the summary generated by the summarizer and the original speech. Differences are identified based on criteria such as the degree of similarity of content and whether important information is missing. For example, the difference detector can use AI to compare the summary with the original speech to ensure that important information is not missing. It can also check for misleading statements in the summary.

[0061] (Example 2) An information summarization system according to an embodiment of the present invention appropriately summarizes statements from information sources and prevents misleading reporting. The information summarization system collects information sources, generates summaries, and identifies differences between the generated summaries and the original statements, thereby providing users with an overall picture of the information and supporting accurate information judgment. For example, the information summarization system collects statements from interviews, press conferences, and other sources, and uses AI to summarize the content. The AI-generated summary is then compared with the original statements to check for missing important information or misleading expressions. This provides users with an overall picture of the information and supports accurate information judgment. This allows the information summarization system to appropriately summarize information sources and prevent misleading reporting. For example, users can prevent misleading reporting by referring to the AI-generated summary and the identified differences. This allows users to grasp the overall picture of the information and make accurate information judgments.

[0062] An information summarization system according to an embodiment includes a collection unit, a summarization unit, and a difference indication unit. The collection unit collects information sources. Examples of information sources include, but are not limited to, news articles, interviews, and press conferences. The collection unit collects, for example, statements from interviews, press conferences, and the like. The collection unit can use AI to collect detailed information about the content and context of statements. For example, the collection unit can collect audio data from interviews and convert it into text data. The collection unit can also collect video data from press conferences and extract important statements. The summarization unit generates a summary based on the information collected by the collection unit. The summarization is performed based on, for example, the length of the sentences and the importance of the information to be summarized, but is not limited to, examples. For example, the summarization unit can use AI to extract important statements from long interviews and generate a concise summary. The summarization unit can also use AI to extract key points from news articles and generate a short summary. The difference indication unit indicates differences between the summary generated by the summarization unit and the original statements. Differences are pointed out based on criteria such as, for example, the degree of similarity of content or whether important information is missing, but are not limited to such examples. For example, the difference pointing unit may use AI to compare the summary with the original statement and check whether important information is missing. The difference pointing unit may also use AI to check whether the summary contains misleading expressions. This allows the information summarization system according to the embodiment to appropriately summarize the information source and prevent misleading reports. For example, a user can prevent misleading reports by referring to the summary generated by AI and the difference points. This allows a user to grasp the overall picture of the information and make accurate information judgments.

[0063] The collection unit can collect statements made in interviews or press conferences. For example, the collection unit collects audio data of interviews and converts it into text data. The collection unit can also collect video data of press conferences and extract important statements. For example, the collection unit converts audio data of interviews into text data using highly accurate speech recognition technology. Important statements can be extracted from video data of press conferences using video analysis technology. By collecting statements made in interviews, press conferences, etc., diversity of information sources can be ensured. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input audio data of interviews into AI and have the AI ​​convert it into text data.

[0064] The summarization unit can generate a summary based on the information collected by the collection unit. For example, the summarization unit can extract important statements from a long interview and generate a concise summary. The summarization unit can also extract the main points of a news article and generate a short summary. For example, the summarization unit can use AI to extract important statements from an interview and generate a summary. The summarization unit can also use AI to extract the main points of a news article and generate a summary. Furthermore, the summarization unit can use AI to pick out particularly important information from the collected information and generate a summary based on that information. In this way, by generating a summary based on the collected information, the main points of the information can be concisely conveyed. Some or all of the above-mentioned processing in the summarization unit may be performed using AI, for example, or may be performed without using AI. For example, the summarization unit can input the collected information into AI and have the AI ​​generate a summary.

[0065] The difference detection unit can compare the summary generated by the summarization unit with the original utterance and point out any differences. For example, the difference detection unit can compare the summary with the original utterance to check whether important information is missing. The difference detection unit can also check whether the summary contains any misleading expressions. For example, the difference detection unit can use AI to compare the summary with the original utterance to check whether important information is missing. The difference detection unit can also use AI to check whether the summary contains any misleading expressions. Furthermore, the difference detection unit can use AI to evaluate the degree of similarity between the content of the summary and the original utterance and point out any differences. In this way, by comparing the summary with the original utterance and pointing out any differences, misunderstandings can be prevented. Some or all of the above-mentioned processing in the difference detection unit can be performed using AI, for example, or without AI. For example, the difference detection unit can input the summary and the original utterance into AI and have the AI ​​point out any differences.

[0066] The collection unit can estimate the user's emotions and adjust the collection timing of the information source based on the estimated user's emotions. For example, if the user is stressed, the collection unit can delay the collection timing and collect information when the user is relaxed. Furthermore, if the user is excited, the collection unit can immediately collect information and provide a real-time summary. Furthermore, if the user is tired, the collection unit can adjust the collection timing and collect information after the user has rested. This allows the user's stress to be reduced by adjusting the collection timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the collection timing.

[0067] The collection unit can analyze the user's past information collection history and select an appropriate collection method. For example, the collection unit prioritizes collection of information sources that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method based on the user's past information collection history. The collection unit can also analyze the user's past information collection history and select the optimal collection method for a specific time period. This enables efficient information collection by analyzing the past information collection history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information collection history data into AI and have the AI ​​select the optimal collection method.

[0068] The collection unit can filter information sources based on the user's current areas of interest when collecting them. For example, the collection unit preferentially collects information sources related to topics in which the user is currently interested. The collection unit can also filter unnecessary information based on the user's current areas of interest. The collection unit can also automatically select highly relevant information sources based on the user's areas of interest. This makes it possible to provide highly relevant information by filtering information based on the user's areas of interest. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's area of ​​interest data into AI and have the AI ​​perform the filtering of information sources.

[0069] When collecting information sources, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is using image input, the collection unit can prioritize collecting image data. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data into AI and have the AI ​​select the optimal collection means.

[0070] The collection unit can estimate the user's emotions and prioritize the information sources to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit can prioritize collecting information sources that help the user relax. Furthermore, if the user is excited, the collection unit can prioritize collecting information sources that are interesting. Furthermore, if the user is tired, the collection unit can prioritize collecting information sources that are easy to understand. By prioritizing information sources according to the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the information sources.

[0071] When collecting information sources, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information sources related to the user's current location. The collection unit can also collect news and event information related to the area based on the user's geographical location information. The collection unit can also prioritize collecting nearby information sources by taking into account the user's location information. This makes it possible to provide information related to the area by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0072] When collecting information sources, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects comments from accounts the user follows on social media. The collection unit can also analyze the content of the user's social media posts to collect related information sources. The collection unit can also collect related information sources by referring to the activities of the user's friends on social media. This makes it possible to provide highly relevant information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​collect related information.

[0073] When collecting information sources, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects information sources that the user has previously rated highly. The collection unit can also optimize the collection method based on the user's past feedback. The collection unit can also adjust the types of information sources to be collected by reflecting the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI and have the AI ​​customize the collection method.

[0074] The summarization unit can estimate the user's emotions and adjust the presentation style of the summary based on the estimated user emotions. For example, if the user is nervous, the summarization unit can use a concise and clear presentation style. If the user is relaxed, the summarization unit can use a presentation style that includes detailed information. If the user is excited, the summarization unit can use a visually appealing presentation style. By adjusting the presentation style of the summary according to the user's emotions, a summary that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the summarization unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the summarization unit can input the user's emotion data into an AI and have the AI ​​adjust the presentation style of the summary.

[0075] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the information. For example, the summarization unit summarizes important information in detail and summarizes less important information briefly. The summarization unit can also adjust the length of the summary based on the importance of the information. The summarization unit can also generate and provide multiple summaries when there is a lot of important information. In this way, by adjusting the level of detail of the summary based on the importance of the information, important information can be conveyed in detail. Some or all of the above-mentioned processing in the summarization unit may be performed using, or without, AI. For example, the summarization unit can input information importance data into AI and have the AI ​​adjust the level of detail of the summary.

[0076] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of information. For example, in the case of a news article, the summarization unit uses an algorithm that extracts key points. In addition, in the case of a scientific paper, the summarization unit can use a summarization algorithm that includes detailed information. In addition, in the case of a social media post, the summarization unit can use a concise summarization algorithm. In this way, by applying a summarization algorithm depending on the category of information, an optimal summary can be provided. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information category data into AI and have the AI ​​apply the summarization algorithm.

[0077] When generating a summary, the summarization unit can improve the accuracy of the summary by referring to the user's past summarization results. For example, the summarization unit can improve the accuracy of the summary by referring to summary results that the user has previously given high ratings. The summarization unit can also analyze the user's past summarization results and suggest an optimal summarization method. The summarization unit can also adjust the summarization algorithm based on the user's past summarization results. In this way, the accuracy of the summary can be improved by referring to the user's past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's past summarization result data into AI and have the AI ​​improve the accuracy of the summary.

[0078] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated user emotions. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a longer summary with more detailed information. If the user is excited, the summarization unit can provide a visually appealing summary. By adjusting the length of the summary according to the user's emotions, the summary can be optimally provided for the user. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the summarization unit can be performed using, for example, an AI, or without an AI. For example, the summarization unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the summary.

[0079] When generating summaries, the summarizing unit can determine the priority of summaries based on the time of submission of information. For example, the summarizing unit prioritizes summarizing the most recent information. The summarizing unit can also summarize information that was submitted earlier more concisely. The summarizing unit can also adjust the order of summaries based on the time of submission. In this way, by determining the priority of summaries based on the time of submission of information, the most recent information can be provided preferentially. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of summaries.

[0080] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the information. For example, the summarization unit prioritizes summarization of highly relevant information. The summarization unit can also briefly summarize less relevant information. The summarization unit can also adjust the order of summaries based on the relevance of the information. In this way, by adjusting the order of summaries based on the relevance of the information, highly relevant information can be provided preferentially. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information relevance data into AI and have the AI ​​adjust the order of summaries.

[0081] When generating a summary, the summarization unit can adjust the use of technical terms in the summary according to the user's level of expertise. For example, if the user has technical expertise, the summarization unit can provide a summary that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the summarization unit can provide a summary in simple language. The summarization unit can also adjust the way the summary is expressed according to the user's level of expertise. This allows for the provision of an easy-to-understand summary by adjusting the technical terms in the summary according to the user's level of expertise. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's level of expertise data into AI and have the AI ​​control the use of technical terms in the summary.

[0082] The difference pointing unit can estimate the user's emotions and adjust the difference pointing method based on the estimated user emotions. For example, if the user is nervous, the difference pointing unit can provide concise and clear difference pointing. Furthermore, if the user is relaxed, the difference pointing unit can provide detailed difference pointing. Furthermore, if the user is excited, the difference pointing unit can provide visually appealing difference pointing. By adjusting the difference pointing method according to the user's emotions, it is possible to provide difference pointing that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the difference pointing unit may be performed using an AI, or may be performed without using an AI. For example, the difference pointing unit can input the user's emotion data into an AI and have the AI ​​adjust the difference pointing method.

[0083] When pointing out differences, the difference pointing unit can adjust the level of detail of the differences based on the importance of the information. For example, the difference pointing unit points out important differences in detail and less important differences in brief. The difference pointing unit can also adjust the length of the difference pointing depending on the importance of the information. The difference pointing unit can also generate and provide multiple difference points when there are many important differences. In this way, by adjusting the level of detail of the differences based on the importance of the information, important differences can be pointed out in detail. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, or without, AI. For example, the difference pointing unit can input information importance data into AI and have the AI ​​adjust the level of detail of the differences.

[0084] When detecting differences, the difference detection unit can apply different difference detection algorithms depending on the category of information. For example, in the case of a news article, the difference detection unit uses a difference detection algorithm that extracts important points. In addition, in the case of a scientific paper, the difference detection unit can use a difference detection algorithm that includes detailed information. In addition, in the case of a social media post, the difference detection unit can use a concise difference detection algorithm. In this way, by applying a difference detection algorithm depending on the category of information, optimal difference detection can be provided. Some or all of the above-mentioned processing in the difference detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the difference detection unit can input information category data into AI and have the AI ​​apply the difference detection algorithm.

[0085] When pointing out differences, the difference pointing unit can improve the accuracy of the pointing by referring to the user's past difference pointing results. The difference pointing unit can improve the accuracy of the pointing by referring to, for example, difference pointing results that the user has previously given high ratings. The difference pointing unit can also analyze the user's past difference pointing results and propose an optimal pointing method. The difference pointing unit can also adjust the difference pointing algorithm based on the user's past difference pointing results. In this way, the accuracy of the pointing can be improved by referring to the user's past difference pointing results. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, for example, AI, or may be performed without using AI. For example, the difference pointing unit can input the user's past difference pointing result data into AI and have the AI ​​improve the accuracy of the pointing.

[0086] The difference pointing unit can estimate the user's emotions and determine the priority of difference pointing based on the estimated user's emotions. For example, if the user is nervous, the difference pointing unit can prioritize pointing out important differences. Furthermore, if the user is relaxed, the difference pointing unit can prioritize pointing out detailed differences. Furthermore, if the user is excited, the difference pointing unit can prioritize pointing out visually appealing differences. Thus, by determining the priority of difference pointing according to the user's emotions, important differences can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the difference pointing unit can be performed using, for example, an AI, or without an AI. For example, the difference pointing unit can input the user's emotion data into an AI and have the AI ​​determine the priority of difference pointing.

[0087] When pointing out differences, the difference pointing unit can adjust the importance of the differences based on the time of submission of the information. For example, the difference pointing unit prioritizes pointing out differences based on the most recent information. The difference pointing unit can also briefly point out differences based on information that was submitted earlier. The difference pointing unit can also adjust the order in which differences are pointed out based on the time of submission. In this way, by adjusting the importance of differences based on the time of submission of the information, differences based on the most recent information can be prioritized. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, for example, AI, or may be performed without using AI. For example, the difference pointing unit can input data on the time of submission of information into AI and have the AI ​​adjust the importance of the differences.

[0088] When pointing out differences, the difference pointing unit can adjust the display order of differences based on the relevance of the information. For example, the difference pointing unit prioritizes pointing out differences based on highly relevant information. The difference pointing unit can also briefly point out differences based on less relevant information. The difference pointing unit can also adjust the display order of pointed out differences based on the relevance of the information. In this way, by adjusting the display order of differences based on the relevance of the information, it is possible to prioritize point out highly relevant differences. Some or all of the above-mentioned processing in the difference pointing unit may be performed using AI, for example, or may be performed without using AI. For example, the difference pointing unit can input information relevance data into AI and cause the AI ​​to adjust the display order of differences.

[0089] When pointing out differences, the difference pointing unit can adjust the method of explaining the differences according to the user's level of expertise. For example, if the user has specialized knowledge, the difference pointing unit can point out differences using a lot of technical terminology. Furthermore, if the user does not have specialized knowledge, the difference pointing unit can also point out differences in simple language. Furthermore, the difference pointing unit can adjust the way the differences are expressed according to the user's level of expertise. In this way, by adjusting the method of explaining differences according to the user's level of expertise, it is possible to provide easy-to-understand difference points. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, for example, AI, or may be performed without using AI. For example, the difference pointing unit can input the user's level of expertise data into AI and have the AI ​​adjust the method of explaining the differences. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, summarization unit, and difference indication unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects statements from interviews and press conferences using the camera 42 and microphone 38B of the smart device 14, and the collected statements are converted into text data by the specific processing unit 290 of the data processing device 12. The summarization unit generates a summary based on the information collected by, for example, the specific processing unit 290 of the data processing device 12. The difference indication unit indicates differences between the summary and the original statements by, for example, the specific processing unit 290 of the data processing device 12. This allows the information summarization system to appropriately summarize the information source and prevent misleading reporting. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, summarization unit, and difference indication unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects statements from interviews or press conferences using the camera 42 or microphone 238 of the smart glasses 214, and the collected statements are converted into text data by the specific processing unit 290 of the data processing device 12. The summarization unit generates a summary based on the information collected by, for example, the specific processing unit 290 of the data processing device 12. The difference indication unit indicates differences between the summary and the original statements by, for example, the specific processing unit 290 of the data processing device 12. This allows the information summarization system to appropriately summarize the information source and prevent misleading reporting. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, summarization unit, and difference indication unit described above is realized, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects statements from interviews and press conferences using the camera 42 and microphone 238 of the headset-type terminal 314, and the collected statements are converted into text data by the specific processing unit 290 of the data processing device 12. For example, the summarization unit generates a summary based on the information collected by the specific processing unit 290 of the data processing device 12. For example, the difference indication unit indicates differences between the summary and the original statements by the specific processing unit 290 of the data processing device 12. This allows the information summarization system to appropriately summarize the information source and prevent misleading reporting. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, summarization unit, and difference indication unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects statements from interviews and press conferences using the camera 42 and microphone 238 of the robot 414, and the collected statements are converted into text data by the specific processing unit 290 of the data processing device 12. For example, the summarization unit generates a summary based on the information collected by the specific processing unit 290 of the data processing device 12. For example, the difference indication unit indicates differences between the summary and the original statements by the specific processing unit 290 of the data processing device 12. This allows the information summarization system to appropriately summarize the information source and prevent misleading reporting.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The collection unit can analyze the user's past search history and prioritize collection of highly relevant information sources. For example, it can prioritize collection of information sources related to keywords that the user has frequently searched for in the past. The collection unit can also select the optimal collection method for a specific time period from the user's past search history. Furthermore, the collection unit can also suggest the most efficient collection method based on the user's past search history. This enables efficient information collection by analyzing past search history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past search history data into AI and have the AI ​​select the optimal collection method.

[0092] The summarization unit can analyze the user's past browsing history and prioritize summarizing highly relevant information. For example, it can prioritize summarizing information related to topics that the user has frequently viewed in the past. The summarization unit can also select the optimal summarization method for a specific time period based on the user's past browsing history. Furthermore, the summarization unit can also suggest the most efficient summarization method based on the user's past browsing history. This enables efficient summarization by analyzing the past browsing history. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input the user's past browsing history data into AI and have the AI ​​select the optimal summarization method.

[0093] The difference pointing unit can analyze the user's past feedback and improve the accuracy of difference pointing. For example, the difference pointing unit can improve the accuracy of pointing by referring to difference pointing results that the user has previously given high ratings. The difference pointing unit can also suggest an optimal pointing method based on the user's past feedback. Furthermore, the difference pointing unit can also adjust the difference pointing algorithm by reflecting the user's past feedback. In this way, the accuracy of pointing can be improved by referring to the user's past feedback. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, or without, AI, for example. For example, the difference pointing unit can input the user's past feedback data into AI and have the AI ​​improve the accuracy of pointing.

[0094] The collection unit can estimate the user's emotions and prioritize the information sources to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit can prioritize collecting information sources that are relaxing. Furthermore, if the user is excited, the collection unit can prioritize collecting information sources that are interesting. Furthermore, if the user is tired, the collection unit can prioritize collecting information sources that are easy to understand. By prioritizing information sources according to the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the information sources.

[0095] The summarization unit can estimate the user's emotions and adjust the summary presentation style based on the estimated user emotions. For example, if the user is nervous, the summarization unit can use a concise and clear presentation style. If the user is relaxed, the summarization unit can use a presentation style that includes detailed information. Furthermore, if the user is excited, the summarization unit can use a visually appealing presentation style. By adjusting the summary presentation style according to the user's emotions, a summary that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the summarization unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the summarization unit can input the user's emotion data into an AI and have the AI ​​adjust the summary presentation style.

[0096] The difference pointing unit can estimate the user's emotions and adjust the difference pointing method based on the estimated user's emotions. For example, if the user is nervous, the difference pointing unit can provide concise and clear difference pointing. Furthermore, if the user is relaxed, the difference pointing unit can provide detailed difference pointing. Furthermore, if the user is excited, the difference pointing unit can provide visually appealing difference pointing. By adjusting the difference pointing method according to the user's emotions, it is possible to provide difference pointing that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the difference pointing unit may be performed using an AI, or may be performed without using an AI. For example, the difference pointing unit can input the user's emotion data into an AI and have the AI ​​adjust the difference pointing method.

[0097] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, it can prioritize collecting information sources related to the user's current location. The collection unit can also collect news and event information related to the area based on the user's geographical location information. Furthermore, the collection unit can prioritize collecting nearby information sources by taking into account the user's location information. This makes it possible to provide information related to the area by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0098] The summarization unit can apply different summarization algorithms depending on the category of information. For example, for news articles, an algorithm that extracts key points is used. For scientific papers, the summarization unit can also use a summarization algorithm that includes detailed information. Furthermore, for social media posts, the summarization unit can also use a concise summarization algorithm. This allows the application of a summarization algorithm depending on the category of information to provide an optimal summary. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input information category data into AI and have the AI ​​apply the summarization algorithm.

[0099] The difference pointing unit can adjust the level of detail of the differences based on the importance of the information. For example, important differences are pointed out in detail, and less important differences are pointed out briefly. The difference pointing unit can also adjust the length of the difference pointing depending on the importance of the information. Furthermore, if there are many important differences, the difference pointing unit can generate and provide multiple difference points. In this way, by adjusting the level of detail of the differences based on the importance of the information, important differences can be pointed out in detail. Some or all of the above-mentioned processing in the difference pointing unit may be performed using, or without, AI. For example, the difference pointing unit can input information importance data into AI and have the AI ​​adjust the level of detail of the differences.

[0100] The collection unit can analyze the user's social media activity and collect related information. For example, it can collect comments from accounts the user follows on social media. The collection unit can also analyze the content of the user's social media posts and collect related information sources. Furthermore, the collection unit can also collect related information sources by referring to the activities of the user's friends on social media. This makes it possible to provide highly relevant information by analyzing the user's social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​collect related information.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection department collects information sources. Information sources include news articles, interviews, and press conferences. The collection department collects statements from interviews and press conferences, and uses AI to collect detailed information about the content and context of the statements. For example, the collection department can collect audio data from interviews and convert it into text data. It can also collect video data from press conferences and extract important statements. Step 2: The summarization unit generates a summary based on the information collected by the collection unit. The summarization is based on the length of the text and the importance of the information being summarized. For example, the summarization unit uses AI to extract important statements from a long interview and generate a concise summary. It can also extract the main points of a news article and generate a short summary. Step 3: The difference detector identifies differences between the summary generated by the summarizer and the original speech. Differences are identified based on criteria such as the degree of similarity of content and whether important information is missing. For example, the difference detector can use AI to compare the summary with the original speech to ensure that important information is not missing. It can also check for misleading statements in the summary.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[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 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.

[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 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.

[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 AI 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 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.

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

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 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.

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

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 AI 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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, in order to avoid confusion and to 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 collection unit that collects information sources; a summarizing unit that summarizes the information collected by the collecting unit; a difference indicating unit that indicates differences between the summary generated by the summarizing unit and the original utterance; Equipped with A system characterized by:

2. The collecting unit Collect statements from interviews or press conferences 2. The system of claim 1.

3. The summary section A summary is generated based on the information collected by the collection unit.

2. The system of claim 1.

4. The difference indicating unit Comparing the summary generated by the summarizing unit with the original utterance and notifying any differences 2. The system of claim 1.

5. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting information sources based on the estimated user emotions.

2. The system of claim 1.

6. The collecting unit Analyze the user's past information collection history and select the appropriate collection method 2. The system of claim 1.

7. The collecting unit When collecting information sources, filtering is performed based on the user's current areas of interest.

2. The system of claim 1.

8. The collecting unit When collecting information sources, select the optimal collection method depending on the user's input method.

2. The system of claim 1.

Images