System

Abstract

An object of a system according to an embodiment is to unify and aggregate information expressed in different formats.SOLUTION: A system includes an information collection part, an analysis part, an integration part, an aggregation part, and a provision part. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit. The identification unit identifies the information analyzed by the analysis unit. The aggregation unit aggregates the information unified by the unification unit. The providing unit provides the information aggregated by the aggregating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to efficiently unify and aggregate information that expresses the same content in different formats.

[0005] The system according to the embodiment aims to unify and aggregate information expressed in different formats. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, an identification unit, an aggregation unit, and a provision unit. The information collection unit collects information. The analysis unit analyzes the information collected by the information collection unit. The identification unit identifies the information analyzed by the analysis unit. The aggregation unit aggregates the information identified by the identification unit. The provision unit provides the information aggregated by the aggregation unit. [Effects of the Invention]

[0007] The system according to the embodiment can unify and aggregate information expressed in different formats. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The information aggregation system according to an embodiment of the present invention is a system that uses AI technology to unify and aggregate information that has the same content expressed in different forms or formats, thereby enabling the information aggregation system to organize and efficiently use information.

[0029] The information aggregation system according to the embodiment includes an information collection unit, an analysis unit, an identification unit, an aggregation unit, and a provision unit. The information collection unit collects information. For example, the information collection unit collects information from the Internet or a database. The information collection unit can also collect information using web scraping technology. For example, the information collection unit can automatically acquire data from specific websites. The information collection unit can also acquire data through an API. For example, the information collection unit collects data using a publicly available API. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit can analyze text data using text mining technology. The analysis unit can also analyze image data using image analysis technology. For example, the analysis unit can analyze the content of images using an image recognition algorithm. The analysis unit can also analyze audio data using voice analysis technology. For example, the analysis unit can convert audio data into text data using voice recognition technology. The identification unit identifies the information analyzed by the analysis unit. For example, the analysis unit can recognize different expressions as having the same meaning using natural language processing technology and identify them. The identification unit can also calculate the degree of agreement or similarity of data to identify them. For example, the analysis unit can calculate the similarity of text data using cosine similarity. The identification unit can also calculate the similarity of data using the Jaccard coefficient. The aggregation unit aggregates the information identified by the identification unit. For example, it aggregates reviews of the same product and compiles an overall rating. The aggregation unit can also aggregate different news articles to provide comprehensive information about the same event. For example, it can integrate multiple news articles to create a single comprehensive article. The providing unit provides the information aggregated by the aggregation unit. For example, it provides aggregated review information when a user purchases a product. The providing unit can also provide comprehensive information about the same event when reading a news article. For example, it provides information to a user through a news app. As a result, the information aggregation system according to the embodiment can efficiently identify and aggregate information that expresses the same content in different forms or formats. For example, by aggregating product reviews, users can obtain more accurate information and make purchasing decisions.Additionally, news article aggregation allows users to get multiple perspectives on the same event.

[0030] The information collecting unit can assign a reliability score by referring to past reliability data of the information source to evaluate the reliability of the collected information. The information collecting unit assigns a reliability score by referring to past reliability data of, for example, a product review site. For example, reviews from highly reliable sites are preferentially analyzed. The information collecting unit also assigns a reliability score based on past reliability data of news sites. For example, articles from highly reliable news sites are preferentially analyzed. The information collecting unit also assigns a reliability score by referring to past reliability data of social media posters. For example, information from highly reliable posters is preferentially analyzed. In this way, by evaluating the reliability of the information source, highly reliable information can be preferentially analyzed.

[0031] The information collection unit can analyze background information of the collected information and evaluate the quality of the information. For example, the information collection unit analyzes the expertise and experience of a product review poster and evaluates the quality of the information. For example, it prioritizes analysis of reviews by posters with specialized knowledge. The information collection unit also analyzes the expertise and experience of news article reporters and evaluates the quality of the information. For example, it prioritizes analysis of articles by reporters with specialized knowledge. The information collection unit also analyzes the expertise and experience of social media posters and evaluates the quality of the information. For example, it prioritizes analysis of information by posters with specialized knowledge. In this way, by analyzing the background information, the quality of the information can be evaluated and high-quality information can be analyzed preferentially.

[0032] The information collection unit can expand the types of information it collects to multimodal information that includes not only text but also images and audio data, allowing it to analyze a wider variety of data. For example, the information collection unit collects and analyzes image and audio data in addition to text data from product reviews. For example, it analyzes product photos and audio descriptions to evaluate the quality of the information. The information collection unit also collects and analyzes image and audio data in addition to text data from news articles. For example, it analyzes news footage and audio interviews to evaluate the quality of the information. The information collection unit also collects and analyzes image and audio data in addition to text data from social media posts. For example, it analyzes the poster's photos and audio messages to evaluate the quality of the information. This allows it to analyze a wider variety of data by analyzing multimodal information that includes not only text but also images and audio data.

[0033] The information gathering unit can obtain new insights by collecting information from different industries and fields and performing cross-domain analysis. For example, in addition to product reviews, the information gathering unit can collect related technical articles and market reports and perform cross-domain analysis. For example, technological trends and market movements can be analyzed to obtain new insights. The information gathering unit can also collect related academic papers and policy reports in addition to news articles and perform cross-domain analysis. For example, the information gathering unit can analyze the latest research results and policy trends to obtain new insights. The information gathering unit can also collect related blog articles and forum posts in addition to social media posts and perform cross-domain analysis. For example, user opinions and trends can be analyzed to obtain new insights. In this way, new insights can be obtained by collecting information from different industries and fields and performing cross-domain analysis.

[0034] The identification unit can automatically translate and identify different languages ​​and dialects in order to improve the accuracy of identification. For example, the identification unit automatically translates and identifies different languages ​​and dialects of product reviews. For example, it translates and identifies reviews in English and Spanish. The identification unit also automatically translates and identifies different languages ​​and dialects of news articles. For example, it translates and identifies articles in French and Chinese. The identification unit also automatically translates and identifies different languages ​​and dialects of social media posts. For example, it translates and identifies posts in Japanese and Korean. In this way, by automatically translating and identifying different languages ​​and dialects, the accuracy of identification can be improved.

[0035] The identification unit can identify information of different formats (e.g., text, images, and audio) to achieve multimodal identification. For example, the identification unit may identify image and audio data in addition to text data of product reviews. For example, it may identify product photos and explanatory audio. The identification unit may also identify image and audio data in addition to text data of news articles. For example, it may identify news footage and audio interviews. The identification unit may also identify image and audio data in addition to text data for social media posts. For example, it may identify the poster's photos and audio messages. In this way, by identifying information of different formats, multimodal identification can be achieved.

[0036] The identification unit can apply the identification process to information from different industries and fields, enabling cross-domain identification. For example, the identification unit can identify related technical articles and market reports in addition to product reviews. For example, it can identify technological trends and market movements. The identification unit can also identify related academic papers and policy reports in addition to news articles. For example, it can identify the latest research results and policy trends. The identification unit can also identify related blog articles and forum posts in addition to social media posts. For example, it can identify user opinions and trends. In this way, cross-domain identification can be achieved by applying the identification process to information from different industries and fields.

[0037] In order to improve the accuracy of aggregation, the aggregating unit can evaluate the relevance of information from multiple angles and aggregate the most relevant information. For example, the aggregating unit evaluates the relevance of product reviews from multiple angles and aggregates the most relevant reviews. For example, it aggregates reviews about different features of the same product. The aggregating unit also evaluates the relevance of news articles from multiple angles and aggregates the most relevant articles. For example, it aggregates articles about the same event from different perspectives. The aggregating unit also evaluates the relevance of social media posts from multiple angles and aggregates the most relevant posts. For example, it aggregates posts with different opinions about the same topic. In this way, by evaluating the relevance of information from multiple angles, the most relevant information can be aggregated.

[0038] The aggregating unit can evaluate the reliability of information when aggregating, and prioritize aggregating highly reliable information. For example, the aggregating unit evaluates the reliability of product reviews, and prioritizes aggregating highly reliable reviews. For example, it aggregates reviews from highly reliable sites. The aggregating unit also evaluates the reliability of news articles, and prioritizes aggregating highly reliable articles. For example, it aggregates articles from highly reliable news sites. The aggregating unit also evaluates the reliability of social media posts, and prioritizes aggregating highly reliable posts. For example, it aggregates information from highly reliable posters. In this way, by evaluating the reliability of information, highly reliable information can be prioritized.

[0039] The aggregation unit can aggregate information of different formats (e.g., text, images, and audio) to achieve multimodal aggregation. For example, the aggregation unit aggregates image and audio data in addition to text data of product reviews. For example, it aggregates product photos and audio descriptions. The aggregation unit also aggregates image and audio data in addition to text data of news articles. For example, it aggregates news footage and audio interviews. The aggregation unit also aggregates image and audio data in addition to text data for social media posts. For example, it aggregates photos and audio messages from the poster. In this way, multimodal aggregation can be achieved by aggregating information of different formats.

[0040] The aggregation unit can apply the aggregation process to information from different industries and fields, allowing for cross-domain aggregation. For example, the aggregation unit aggregates related technical articles and market reports in addition to product reviews. For example, it aggregates technological trends and market movements. The aggregation unit can also aggregate related academic papers and policy reports in addition to news articles. For example, it can aggregate the latest research results and policy trends. The aggregation unit can also aggregate related blog articles and forum posts in addition to social media posts. For example, it can aggregate user opinions and trends. This allows for cross-domain aggregation by applying the aggregation process to information from different industries and fields.

[0041] The providing unit can evaluate the reliability of the information to be provided and provide highly reliable information preferentially. The providing unit, for example, evaluates the reliability of product reviews and provides highly reliable reviews preferentially. For example, it provides reviews from highly reliable sites. The providing unit also evaluates the reliability of news articles and provides highly reliable articles preferentially. For example, it provides articles from highly reliable news sites. The providing unit also evaluates the reliability of social media posts and provides highly reliable posts preferentially. For example, it provides information about highly reliable posters. In this way, by evaluating the reliability of information, highly reliable information can be provided preferentially.

[0042] The providing unit can provide information in different formats (e.g., text, images, audio) to realize multimodal information provision. For example, the providing unit can provide image and audio data in addition to text data of product reviews. For example, it can provide product photos and audio descriptions. The providing unit can also provide image and audio data in addition to text data of news articles. For example, it can provide news footage and audio interviews. The providing unit can also provide image and audio data for social media posts in addition to text data. For example, it can provide photos and audio messages from the poster. This makes it possible to provide multimodal information provision by providing information in different formats.

[0043] The provision unit can expand the information it provides to include information on different industries and fields, thereby providing cross-domain information. For example, the provision unit provides related technical articles and market reports in addition to product reviews. For example, it provides technological trends and market movements. Furthermore, the provision unit provides related academic papers and policy reports in addition to news articles. For example, it provides the latest research results and policy movements. Furthermore, the provision unit provides related blog articles and forum posts in addition to social media posts. For example, it provides user opinions and trends. This makes it possible to provide cross-domain information by expanding the information it provides to include information on different industries and fields.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The information collection unit can assign a reliability score by referring to past reliability data of the information source to evaluate the reliability of the collected information. For example, the information collection unit assigns a reliability score by referring to past reliability data of a product review site. For example, reviews from highly reliable sites are preferentially analyzed. The information collection unit also assigns a reliability score based on past reliability data of a news site. For example, articles from highly reliable news sites are preferentially analyzed. The information collection unit also assigns a reliability score by referring to past reliability data of posters on social media. For example, information from highly reliable posters is preferentially analyzed. In this way, by evaluating the reliability of the information source, highly reliable information can be preferentially analyzed.

[0046] The information gathering unit can analyze background information of the collected information and evaluate the quality of the information. For example, the information gathering unit analyzes the expertise and experience of the poster of a product review and evaluates the quality of the information. For example, reviews by posters with specialized knowledge are analyzed preferentially. The information gathering unit also analyzes the expertise and experience of reporters of news articles and evaluates the quality of the information. For example, articles by reporters with specialized knowledge are analyzed preferentially. The information gathering unit also analyzes the expertise and experience of posters of social media and evaluates the quality of the information. For example, information by posters with specialized knowledge is analyzed preferentially. In this way, the quality of information can be evaluated by analyzing the background information and high-quality information can be analyzed preferentially.

[0047] The information collection unit can expand the types of information it collects to multimodal information that includes not only text but also images and audio data, allowing it to analyze a wider variety of data. For example, in addition to text data from product reviews, it can also collect and analyze image and audio data. For example, it can analyze product photos and audio descriptions to evaluate the quality of the information. The information collection unit can also collect and analyze image and audio data in addition to text data from news articles. For example, it can analyze news footage and audio interviews to evaluate the quality of the information. The information collection unit can also collect and analyze image and audio data in addition to text data from social media posts. For example, it can analyze the poster's photos and audio messages to evaluate the quality of the information. This allows it to analyze a wider variety of data by analyzing multimodal information that includes not only text but also images and audio data.

[0048] The information gathering unit can obtain new insights by collecting information from different industries and fields and performing cross-domain analysis. For example, in addition to product reviews, it collects related technical articles and market reports and performs cross-domain analysis. For example, it analyzes technological trends and market movements to obtain new insights. The information gathering unit also collects related academic papers and policy reports in addition to news articles and performs cross-domain analysis. For example, it analyzes the latest research results and policy trends to obtain new insights. The information gathering unit also collects related blog articles and forum posts in addition to social media posts and performs cross-domain analysis. For example, it analyzes user opinions and trends to obtain new insights. In this way, it is possible to obtain new insights by collecting information from different industries and fields and performing cross-domain analysis.

[0049] The identification unit can automatically translate and identify different languages ​​and dialects to improve the accuracy of identification. For example, different languages ​​and dialects of product reviews are automatically translated and identified. For example, reviews in English and Spanish are translated and identified. The identification unit also automatically translates and identifies different languages ​​and dialects of news articles. For example, articles in French and Chinese are translated and identified. The identification unit also automatically translates and identifies different languages ​​and dialects of social media posts. For example, posts in Japanese and Korean are translated and identified. In this way, the accuracy of identification can be improved by automatically translating and identifying different languages ​​and dialects.

[0050] The identification unit can identify information of different formats (e.g., text, images, and audio) to achieve multimodal identification. For example, in addition to the text data of a product review, it can also identify image and audio data. For example, it can identify product photos and audio descriptions. The identification unit can also identify image and audio data in addition to the text data of a news article. For example, it can identify news footage and audio interviews. The identification unit can also identify image and audio data in addition to text data for social media posts. For example, it can identify the poster's photos and audio messages. In this way, it is possible to achieve multimodal identification by identifying information of different formats.

[0051] The identification unit can apply the identification process to information from different industries and fields, enabling cross-domain identification. For example, in addition to product reviews, it can identify related technical articles and market reports. For example, it can identify technological trends and market movements. In addition to news articles, it can identify related academic papers and policy reports. For example, it can identify the latest research results and policy trends. In addition to social media posts, it can identify related blog articles and forum posts. For example, it can identify user opinions and trends. In this way, by applying the identification process to information from different industries and fields, cross-domain identification can be performed.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The information collection unit collects information. For example, it collects information from the Internet or a database. The information collection unit can also collect information using web scraping technology. For example, it can automatically obtain data from a specific website. Furthermore, the information collection unit can also obtain data through an API. For example, it can collect data using a publicly available API. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit analyzes text data using text mining technology. The analysis unit can also analyze image data using image analysis technology. For example, the analysis unit can analyze the content of images using an image recognition algorithm. The analysis unit can also analyze voice data using voice analysis technology. For example, the analysis unit can convert voice data into text data using voice recognition technology. Step 3: The identification unit identifies the information analyzed by the analysis unit. For example, it uses natural language processing technology to recognize different expressions as having the same meaning and identify them. The identification unit can also calculate the degree of agreement or similarity of data to identify them. For example, it calculates the similarity of text data using cosine similarity. The identification unit can also calculate the similarity of data using Jaccard coefficients. Step 4: The aggregator aggregates the information that has been identified by the identity analyzer. For example, it may aggregate reviews of the same product and compile an overall rating. The aggregator may also aggregate different news articles to provide comprehensive information about the same event. For example, it may combine multiple news articles to create a single comprehensive article. Step 5: The providing unit provides the information aggregated by the aggregating unit. For example, when a user purchases a product, the providing unit provides aggregated review information. The providing unit can also provide comprehensive information about the same event when the user reads a news article. For example, the providing unit provides information to the user through a news app.

[0054] (Example 2) The information aggregation system according to an embodiment of the present invention is a system that uses AI technology to unify and aggregate information that has the same content expressed in different forms or formats, thereby enabling the information aggregation system to organize and efficiently use information.

[0055] The information aggregation system according to the embodiment includes an information collection unit, an analysis unit, an identification unit, an aggregation unit, and a provision unit. The information collection unit collects information. For example, the information collection unit collects information from the Internet or a database. The information collection unit can also collect information using web scraping technology. For example, the information collection unit can automatically acquire data from specific websites. The information collection unit can also acquire data through an API. For example, the information collection unit collects data using a publicly available API. The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit can analyze text data using text mining technology. The analysis unit can also analyze image data using image analysis technology. For example, the analysis unit can analyze the content of images using an image recognition algorithm. The analysis unit can also analyze audio data using voice analysis technology. For example, the analysis unit can convert audio data into text data using voice recognition technology. The identification unit identifies the information analyzed by the analysis unit. For example, the analysis unit can recognize different expressions as having the same meaning using natural language processing technology and identify them. The identification unit can also calculate the degree of agreement or similarity of data to identify them. For example, the analysis unit can calculate the similarity of text data using cosine similarity. The identification unit can also calculate the similarity of data using the Jaccard coefficient. The aggregation unit aggregates the information identified by the identification unit. For example, it aggregates reviews of the same product and compiles an overall rating. The aggregation unit can also aggregate different news articles to provide comprehensive information about the same event. For example, it can integrate multiple news articles to create a single comprehensive article. The providing unit provides the information aggregated by the aggregation unit. For example, it provides aggregated review information when a user purchases a product. The providing unit can also provide comprehensive information about the same event when reading a news article. For example, it provides information to a user through a news app. As a result, the information aggregation system according to the embodiment can efficiently identify and aggregate information that expresses the same content in different forms or formats. For example, by aggregating product reviews, users can obtain more accurate information and make purchasing decisions.Additionally, news article aggregation allows users to get multiple perspectives on the same event.

[0056] The information collection unit performs sentiment analysis on the collected information and can evaluate the importance of the information based on the intensity and type of emotion. For example, the information collection unit performs sentiment analysis on collected product reviews and prioritizes analysis of reviews with strong positive emotions. For example, reviews with high levels of joy or satisfaction are extracted and their importance is evaluated. The information collection unit also performs sentiment analysis on news articles and identifies important articles based on the intensity and type of emotion. For example, articles with high levels of surprise or interest are prioritized in analysis and their importance is evaluated. The information collection unit also performs sentiment analysis on social media posts and evaluates the importance of the information based on the intensity and type of emotion. For example, posts with strong anger or sadness are analyzed and treated as important information. In this way, important information can be prioritized in analysis by evaluating the importance of information based on the intensity and type of emotion.

[0057] The information collecting unit can assign a reliability score by referring to past reliability data of the information source to evaluate the reliability of the collected information. The information collecting unit assigns a reliability score by referring to past reliability data of, for example, a product review site. For example, reviews from highly reliable sites are preferentially analyzed. The information collecting unit also assigns a reliability score based on past reliability data of news sites. For example, articles from highly reliable news sites are preferentially analyzed. The information collecting unit also assigns a reliability score by referring to past reliability data of social media posters. For example, information from highly reliable posters is preferentially analyzed. In this way, by evaluating the reliability of the information source, highly reliable information can be preferentially analyzed.

[0058] The information collection unit can analyze background information of the collected information and evaluate the quality of the information. For example, the information collection unit analyzes the expertise and experience of a product review poster and evaluates the quality of the information. For example, it prioritizes analysis of reviews by posters with specialized knowledge. The information collection unit also analyzes the expertise and experience of news article reporters and evaluates the quality of the information. For example, it prioritizes analysis of articles by reporters with specialized knowledge. The information collection unit also analyzes the expertise and experience of social media posters and evaluates the quality of the information. For example, it prioritizes analysis of information by posters with specialized knowledge. In this way, by analyzing the background information, the quality of the information can be evaluated and high-quality information can be analyzed preferentially.

[0059] The information collection unit can expand the types of information it collects to multimodal information that includes not only text but also images and audio data, allowing it to analyze a wider variety of data. For example, the information collection unit collects and analyzes image and audio data in addition to text data from product reviews. For example, it analyzes product photos and audio descriptions to evaluate the quality of the information. The information collection unit also collects and analyzes image and audio data in addition to text data from news articles. For example, it analyzes news footage and audio interviews to evaluate the quality of the information. The information collection unit also collects and analyzes image and audio data in addition to text data from social media posts. For example, it analyzes the poster's photos and audio messages to evaluate the quality of the information. This allows it to analyze a wider variety of data by analyzing multimodal information that includes not only text but also images and audio data.

[0060] The information gathering unit can obtain new insights by collecting information from different industries and fields and performing cross-domain analysis. For example, in addition to product reviews, the information gathering unit can collect related technical articles and market reports and perform cross-domain analysis. For example, technological trends and market movements can be analyzed to obtain new insights. The information gathering unit can also collect related academic papers and policy reports in addition to news articles and perform cross-domain analysis. For example, the information gathering unit can analyze the latest research results and policy trends to obtain new insights. The information gathering unit can also collect related blog articles and forum posts in addition to social media posts and perform cross-domain analysis. For example, user opinions and trends can be analyzed to obtain new insights. In this way, new insights can be obtained by collecting information from different industries and fields and performing cross-domain analysis.

[0061] The information collection unit can use the emotion estimation function to analyze the emotional aspects of the collected information and prioritize collecting information that elicits positive emotions. The information collection unit, for example, performs emotion analysis on product reviews and prioritizes collecting reviews with strong positive emotions. For example, reviews with high levels of joy and satisfaction are extracted. The information collection unit also performs emotion analysis on news articles and prioritizes collecting articles with strong positive emotions. For example, articles with high hopes and expectations are extracted. The information collection unit also performs emotion analysis on social media posts and prioritizes collecting posts with strong positive emotions. For example, posts with strong expressions of gratitude and joy are extracted. In this way, by using the emotion estimation function to prioritize collecting information that elicits positive emotions, it is possible to provide useful information to users.

[0062] The identification unit performs a sentiment analysis during identification, and is able to prioritize identification of emotionally matching information. The identification unit, for example, performs a sentiment analysis of product reviews, and prioritizes identification of emotionally matching reviews. For example, reviews with strong positive sentiment are identified. The identification unit also performs a sentiment analysis of news articles, and prioritizes identification of emotionally matching articles. For example, articles with high hopes and expectations are identified. The identification unit also performs a sentiment analysis of social media posts, and prioritizes identification of emotionally matching posts. For example, posts with strong feelings of gratitude and joy are identified. In this way, by prioritizing identification of emotionally matching information, it is possible to provide information that is easy for users to empathize with.

[0063] The identification unit can automatically translate and identify different languages ​​and dialects in order to improve the accuracy of identification. For example, the identification unit automatically translates and identifies different languages ​​and dialects of product reviews. For example, it translates and identifies reviews in English and Spanish. The identification unit also automatically translates and identifies different languages ​​and dialects of news articles. For example, it translates and identifies articles in French and Chinese. The identification unit also automatically translates and identifies different languages ​​and dialects of social media posts. For example, it translates and identifies posts in Japanese and Korean. In this way, by automatically translating and identifying different languages ​​and dialects, the accuracy of identification can be improved.

[0064] The identification unit can identify information of different formats (e.g., text, images, and audio) to achieve multimodal identification. For example, the identification unit may identify image and audio data in addition to text data of product reviews. For example, it may identify product photos and explanatory audio. The identification unit may also identify image and audio data in addition to text data of news articles. For example, it may identify news footage and audio interviews. The identification unit may also identify image and audio data in addition to text data for social media posts. For example, it may identify the poster's photos and audio messages. In this way, by identifying information of different formats, multimodal identification can be achieved.

[0065] The identification unit can apply the identification process to information from different industries and fields, enabling cross-domain identification. For example, the identification unit can identify related technical articles and market reports in addition to product reviews. For example, it can identify technological trends and market movements. The identification unit can also identify related academic papers and policy reports in addition to news articles. For example, it can identify the latest research results and policy trends. The identification unit can also identify related blog articles and forum posts in addition to social media posts. For example, it can identify user opinions and trends. In this way, cross-domain identification can be achieved by applying the identification process to information from different industries and fields.

[0066] The identification unit can use the emotion estimation function to collect users' emotional reactions to the identified information and prioritize identifying information that is likely to resonate emotionally. The identification unit, for example, collects users' emotional reactions to identified product reviews and prioritizes identifying reviews that are likely to resonate emotionally. For example, it identifies reviews with strong positive emotions. The identification unit also collects users' emotional reactions to identified news articles and prioritizes identifying articles that are likely to resonate emotionally. For example, it identifies articles with high hopes and expectations. The identification unit also collects users' emotional reactions to identified social media posts and prioritizes identifying posts that are likely to resonate emotionally. For example, it identifies posts with strong feelings of gratitude and joy. In this way, by using the emotion estimation function to prioritize identifying information that is likely to resonate emotionally, it is possible to provide useful information to users.

[0067] The aggregating unit performs sentiment analysis when aggregating, and can prioritize aggregating emotionally positive information. For example, the aggregating unit performs sentiment analysis on product reviews, and prioritizes aggregating reviews with strong positive sentiment. For example, it aggregates reviews with high levels of joy and satisfaction. The aggregating unit also performs sentiment analysis on news articles, and prioritizes aggregating articles with strong positive sentiment. For example, it aggregates articles with high hopes and expectations. The aggregating unit also performs sentiment analysis on social media posts, and prioritizes aggregating posts with strong positive sentiment. For example, it aggregates posts with strong feelings of gratitude and joy. In this way, by prioritizing the aggregation of emotionally positive information, it is possible to provide useful information to users.

[0068] In order to improve the accuracy of aggregation, the aggregating unit can evaluate the relevance of information from multiple angles and aggregate the most relevant information. For example, the aggregating unit evaluates the relevance of product reviews from multiple angles and aggregates the most relevant reviews. For example, it aggregates reviews about different features of the same product. The aggregating unit also evaluates the relevance of news articles from multiple angles and aggregates the most relevant articles. For example, it aggregates articles about the same event from different perspectives. The aggregating unit also evaluates the relevance of social media posts from multiple angles and aggregates the most relevant posts. For example, it aggregates posts with different opinions about the same topic. In this way, by evaluating the relevance of information from multiple angles, the most relevant information can be aggregated.

[0069] The aggregating unit can evaluate the reliability of information when aggregating, and prioritize aggregating highly reliable information. For example, the aggregating unit evaluates the reliability of product reviews, and prioritizes aggregating highly reliable reviews. For example, it aggregates reviews from highly reliable sites. The aggregating unit also evaluates the reliability of news articles, and prioritizes aggregating highly reliable articles. For example, it aggregates articles from highly reliable news sites. The aggregating unit also evaluates the reliability of social media posts, and prioritizes aggregating highly reliable posts. For example, it aggregates information from highly reliable posters. In this way, by evaluating the reliability of information, highly reliable information can be prioritized.

[0070] The aggregation unit can aggregate information of different formats (e.g., text, images, and audio) to achieve multimodal aggregation. For example, the aggregation unit aggregates image and audio data in addition to text data of product reviews. For example, it aggregates product photos and audio descriptions. The aggregation unit also aggregates image and audio data in addition to text data of news articles. For example, it aggregates news footage and audio interviews. The aggregation unit also aggregates image and audio data in addition to text data for social media posts. For example, it aggregates photos and audio messages from the poster. In this way, multimodal aggregation can be achieved by aggregating information of different formats.

[0071] The aggregation unit can apply the aggregation process to information from different industries and fields, allowing for cross-domain aggregation. For example, the aggregation unit aggregates related technical articles and market reports in addition to product reviews. For example, it aggregates technological trends and market movements. The aggregation unit can also aggregate related academic papers and policy reports in addition to news articles. For example, it can aggregate the latest research results and policy trends. The aggregation unit can also aggregate related blog articles and forum posts in addition to social media posts. For example, it can aggregate user opinions and trends. This allows for cross-domain aggregation by applying the aggregation process to information from different industries and fields.

[0072] The aggregating unit can use the emotion estimation function to collect users' emotional reactions to the aggregated information and prioritize aggregating information that is likely to resonate emotionally. The aggregating unit, for example, collects users' emotional reactions to aggregated product reviews and prioritizes aggregating reviews that are likely to resonate emotionally. For example, it aggregates reviews with strong positive emotions. The aggregating unit also collects users' emotional reactions to aggregated news articles and prioritizes aggregating articles that are likely to resonate emotionally. For example, it aggregates articles with high hopes and expectations. The aggregating unit also collects users' emotional reactions to aggregated social media posts and prioritizes aggregating posts that are likely to resonate emotionally. For example, it aggregates posts with strong feelings of gratitude and joy. In this way, by using the emotion estimation function to prioritize aggregating information that is likely to resonate emotionally, it is possible to provide useful information to users.

[0073] The providing unit performs sentiment analysis of the information to be provided, and can provide emotionally positive information preferentially. For example, the providing unit performs sentiment analysis of product reviews, and provides reviews with strong positive sentiments preferentially. For example, reviews with high levels of joy and satisfaction are provided. The providing unit also performs sentiment analysis of news articles, and provides articles with strong positive sentiments preferentially. For example, articles with high hopes and expectations are provided. The providing unit also performs sentiment analysis of social media posts, and provides posts with strong positive sentiments preferentially. For example, posts with strong feelings of gratitude and joy are provided. In this way, by providing emotionally positive information preferentially, it is possible to provide useful information to the user.

[0074] The providing unit can evaluate the reliability of the information to be provided and provide highly reliable information preferentially. The providing unit, for example, evaluates the reliability of product reviews and provides highly reliable reviews preferentially. For example, it provides reviews from highly reliable sites. The providing unit also evaluates the reliability of news articles and provides highly reliable articles preferentially. For example, it provides articles from highly reliable news sites. The providing unit also evaluates the reliability of social media posts and provides highly reliable posts preferentially. For example, it provides information about highly reliable posters. In this way, by evaluating the reliability of information, highly reliable information can be provided preferentially.

[0075] The providing unit can provide information in different formats (e.g., text, images, audio) to realize multimodal information provision. For example, the providing unit can provide image and audio data in addition to text data of product reviews. For example, it can provide product photos and audio descriptions. The providing unit can also provide image and audio data in addition to text data of news articles. For example, it can provide news footage and audio interviews. The providing unit can also provide image and audio data for social media posts in addition to text data. For example, it can provide photos and audio messages from the poster. This makes it possible to provide multimodal information provision by providing information in different formats.

[0076] The provision unit can expand the information it provides to include information on different industries and fields, thereby providing cross-domain information. For example, the provision unit provides related technical articles and market reports in addition to product reviews. For example, it provides technological trends and market movements. Furthermore, the provision unit provides related academic papers and policy reports in addition to news articles. For example, it provides the latest research results and policy movements. Furthermore, the provision unit provides related blog articles and forum posts in addition to social media posts. For example, it provides user opinions and trends. This makes it possible to provide cross-domain information by expanding the information it provides to include information on different industries and fields.

[0077] The providing unit uses the emotion estimation function to monitor the user's emotional response to the provided information in real time, and can continuously provide optimal information. For example, the providing unit monitors the user's emotional response to provided product reviews in real time, and continuously provides optimal reviews. For example, reviews with strong positive emotions are provided preferentially. The providing unit also monitors the user's emotional response to provided news articles in real time, and continuously provides optimal articles. For example, articles with high hopes and expectations are provided preferentially. The providing unit also monitors the user's emotional response to provided social media posts in real time, and continuously provides optimal posts. For example, posts with strong gratitude and joy are provided preferentially. In this way, the emotion estimation function can be used to monitor the user's emotional response in real time, and can continuously provide optimal information.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The information collection unit can assign a reliability score by referring to past reliability data of the information source to evaluate the reliability of the collected information. For example, the information collection unit assigns a reliability score by referring to past reliability data of a product review site. For example, reviews from highly reliable sites are preferentially analyzed. The information collection unit also assigns a reliability score based on past reliability data of a news site. For example, articles from highly reliable news sites are preferentially analyzed. The information collection unit also assigns a reliability score by referring to past reliability data of posters on social media. For example, information from highly reliable posters is preferentially analyzed. In this way, by evaluating the reliability of the information source, highly reliable information can be preferentially analyzed.

[0080] The information gathering unit can analyze background information of the collected information and evaluate the quality of the information. For example, the information gathering unit analyzes the expertise and experience of the poster of a product review and evaluates the quality of the information. For example, reviews by posters with specialized knowledge are analyzed preferentially. The information gathering unit also analyzes the expertise and experience of reporters of news articles and evaluates the quality of the information. For example, articles by reporters with specialized knowledge are analyzed preferentially. The information gathering unit also analyzes the expertise and experience of posters of social media and evaluates the quality of the information. For example, information by posters with specialized knowledge is analyzed preferentially. In this way, the quality of information can be evaluated by analyzing the background information and high-quality information can be analyzed preferentially.

[0081] The information collection unit can expand the types of information it collects to multimodal information that includes not only text but also images and audio data, allowing it to analyze a wider variety of data. For example, in addition to text data from product reviews, it can also collect and analyze image and audio data. For example, it can analyze product photos and audio descriptions to evaluate the quality of the information. The information collection unit can also collect and analyze image and audio data in addition to text data from news articles. For example, it can analyze news footage and audio interviews to evaluate the quality of the information. The information collection unit can also collect and analyze image and audio data in addition to text data from social media posts. For example, it can analyze the poster's photos and audio messages to evaluate the quality of the information. This allows it to analyze a wider variety of data by analyzing multimodal information that includes not only text but also images and audio data.

[0082] The information gathering unit can obtain new insights by collecting information from different industries and fields and performing cross-domain analysis. For example, in addition to product reviews, it collects related technical articles and market reports and performs cross-domain analysis. For example, it analyzes technological trends and market movements to obtain new insights. The information gathering unit also collects related academic papers and policy reports in addition to news articles and performs cross-domain analysis. For example, it analyzes the latest research results and policy trends to obtain new insights. The information gathering unit also collects related blog articles and forum posts in addition to social media posts and performs cross-domain analysis. For example, it analyzes user opinions and trends to obtain new insights. In this way, it is possible to obtain new insights by collecting information from different industries and fields and performing cross-domain analysis.

[0083] The information collection unit can use the emotion estimation function to analyze the emotional aspects of the collected information and prioritize collecting information that elicits positive emotions. For example, it performs emotion analysis on product reviews and prioritizes collecting reviews with strong positive emotions. For example, it extracts reviews with high levels of joy and satisfaction. The information collection unit also performs emotion analysis on news articles and prioritizes collecting articles with strong positive emotions. For example, it extracts articles with high hopes and expectations. The information collection unit also performs emotion analysis on social media posts and prioritizes collecting posts with strong positive emotions. For example, it extracts posts with strong expressions of gratitude and joy. In this way, by using the emotion estimation function to prioritize collecting information that elicits positive emotions, it is possible to provide useful information to users.

[0084] The identification unit performs a sentiment analysis during identification, and can prioritize identification of emotionally matching information. For example, it performs a sentiment analysis of product reviews, and prioritizes identification of emotionally matching reviews. For example, it identifies reviews with strong positive sentiment. The identification unit also performs a sentiment analysis of news articles, and prioritizes identification of emotionally matching articles. For example, it identifies articles with high hopes and expectations. The identification unit also performs a sentiment analysis of social media posts, and prioritizes identification of emotionally matching posts. For example, it identifies posts with strong feelings of gratitude and joy. In this way, by prioritizing identification of emotionally matching information, it is possible to provide information that users can easily empathize with.

[0085] The identification unit can automatically translate and identify different languages ​​and dialects to improve the accuracy of identification. For example, different languages ​​and dialects of product reviews are automatically translated and identified. For example, reviews in English and Spanish are translated and identified. The identification unit also automatically translates and identifies different languages ​​and dialects of news articles. For example, articles in French and Chinese are translated and identified. The identification unit also automatically translates and identifies different languages ​​and dialects of social media posts. For example, posts in Japanese and Korean are translated and identified. In this way, the accuracy of identification can be improved by automatically translating and identifying different languages ​​and dialects.

[0086] The identification unit can identify information of different formats (e.g., text, images, and audio) to achieve multimodal identification. For example, in addition to the text data of a product review, it can also identify image and audio data. For example, it can identify product photos and audio descriptions. The identification unit can also identify image and audio data in addition to the text data of a news article. For example, it can identify news footage and audio interviews. The identification unit can also identify image and audio data in addition to text data for social media posts. For example, it can identify the poster's photos and audio messages. In this way, it is possible to achieve multimodal identification by identifying information of different formats.

[0087] The identification unit can apply the identification process to information from different industries and fields, enabling cross-domain identification. For example, in addition to product reviews, it can identify related technical articles and market reports. For example, it can identify technological trends and market movements. In addition to news articles, it can identify related academic papers and policy reports. For example, it can identify the latest research results and policy trends. In addition to social media posts, it can identify related blog articles and forum posts. For example, it can identify user opinions and trends. In this way, by applying the identification process to information from different industries and fields, cross-domain identification can be performed.

[0088] The identification unit can use the emotion estimation function to collect users' emotional reactions to the identified information and prioritize identifying information that is likely to resonate emotionally. For example, it collects users' emotional reactions to identified product reviews and prioritizes identifying reviews that are likely to resonate emotionally. For example, it identifies reviews with strong positive emotions. The identification unit also collects users' emotional reactions to identified news articles and prioritizes identifying articles that are likely to resonate emotionally. For example, it identifies articles with high hopes and expectations. The identification unit also collects users' emotional reactions to identified social media posts and prioritizes identifying posts that are likely to resonate emotionally. For example, it identifies posts with strong feelings of gratitude and joy. In this way, by using the emotion estimation function to prioritize identifying information that is likely to resonate emotionally, it is possible to provide useful information to users.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The information collection unit collects information. For example, it collects information from the Internet or a database. The information collection unit can also collect information using web scraping technology. For example, it can automatically obtain data from a specific website. Furthermore, the information collection unit can also obtain data through an API. For example, it can collect data using a publicly available API. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the analysis unit analyzes text data using text mining technology. The analysis unit can also analyze image data using image analysis technology. For example, the analysis unit can analyze the content of images using an image recognition algorithm. The analysis unit can also analyze voice data using voice analysis technology. For example, the analysis unit can convert voice data into text data using voice recognition technology. Step 3: The identification unit identifies the information analyzed by the analysis unit. For example, it uses natural language processing technology to recognize different expressions as having the same meaning and identify them. The identification unit can also calculate the degree of agreement or similarity of data to identify them. For example, it calculates the similarity of text data using cosine similarity. The identification unit can also calculate the similarity of data using Jaccard coefficients. Step 4: The aggregator aggregates the information that has been identified by the identity analyzer. For example, it may aggregate reviews of the same product and compile an overall rating. The aggregator may also aggregate different news articles to provide comprehensive information about the same event. For example, it may combine multiple news articles to create a single comprehensive article. Step 5: The providing unit provides the information aggregated by the aggregating unit. For example, when a user purchases a product, the providing unit provides aggregated review information. The providing unit can also provide comprehensive information about the same event when the user reads a news article. For example, the providing unit provides information to the user through a news app.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] 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.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects information; an analysis unit that analyzes the information collected by the information collection unit; an identification unit that identifies the information analyzed by the analysis unit; an aggregation unit that aggregates the information identified by the identification unit; a providing unit that provides the information aggregated by the aggregation unit. A system characterized by:

2. The information collecting unit Conducting sentiment analysis of collected information and assessing the importance of said information based on the intensity and type of sentiment 2. The system of claim 1.

3. The information collecting unit To assess the reliability of collected information, we refer to the source's past reliability data and assign a reliability score.

2. The system of claim 1.

4. The information collecting unit Analyze the context of the collected information and evaluate the quality of said information 2. The system of claim 1.

5. The information collecting unit Expand the types of information collected to include not only text but also multimodal information, including images and audio data, to analyze a wider variety of data.

2. The system of claim 1.

6. The information collecting unit Gain new insights by collecting information from different industries and fields and conducting cross-domain analysis.

2. The system of claim 1.

Images