System

Abstract

An object of the system according to the embodiment is to collect and analyze information in a multilingual manner and generate a report.SOLUTION: A system includes an information collection part, an analysis part, and a report generation part. The information collection unit collects information in a multilingual manner. The analysis unit analyzes the information collected by the information collection unit. The report generation unit generates a report based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to collect and analyze information in multiple languages, and there was a problem that it was necessary to request specialized consulting, especially when researching overseas markets.

[0005] The system according to the embodiment aims to collect information, analyze it, and generate reports in multiple languages. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a report generation unit. The information collection unit collects information in multiple languages. The analysis unit analyzes the information collected by the information collection unit. The report generation unit generates a report based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect information, analyze it, and generate reports in multiple languages. [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 generation AI service according to an embodiment of the present invention is a system that efficiently collects information on Southeast Asia, Vietnam, Korea, and other regions where languages ​​other than Japanese and English are spoken, and automates overseas market research. In this system, the generation AI collects information based on desktop research and automatically generates reports. This allows the generation AI service to efficiently conduct overseas market research, greatly contributing to the selection of business partners and understanding market trends.

[0029] A generation AI service according to an embodiment includes an information collection unit, an analysis unit, and a report generation unit. The information collection unit collects information in multiple languages. For example, the generation AI collects and analyzes information in Vietnamese, Korean, Thai, and other languages. The information collection unit also collects information from official company websites, news articles, industry reports, and the like from various countries. For example, the generation AI investigates market trends for IT companies in Vietnam and creates a report. The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes the collected data and extracts necessary data. The analysis unit also evaluates reliability and performance based on the collected information. For example, the generation AI analyzes the latest news articles about the Korean automobile industry and evaluates their content. The report generation unit generates a report based on the information analyzed by the analysis unit. For example, the generation AI organizes the collected data and summarizes it visually using graphs and tables. The report generation unit can also adjust the structure and content of the report based on user instructions. For example, the generation AI organizes collected data and generates a report based on an instruction to "create a report on the market trends of IT companies in Vietnam." This allows the generation AI service according to the embodiment to collect, analyze, and generate reports in multiple languages, enabling efficient market research. For example, a user can research the market trends of IT companies in Vietnam and find a reliable business partner. Or, a user can gather the latest information on the Korean automobile industry and understand the trends of competitors.

[0030] The information collection unit collects information from social media or forums in each country and can also analyze data from unofficial sources. For example, the generation AI collects information from social media in each country (e.g., Facebook, Twitter, Weibo, etc.) and analyzes the content of posts. For example, it collects user opinions and reviews about Vietnamese IT companies and stores them in a database. The information collection unit also collects information from forums in each country (e.g., Reddit, Naver, Baidu Tieba, etc.), and the generation AI analyzes the content. For example, it collects discussions about the Korean automobile industry and identifies trends. The information collection unit also collects data from unofficial sources (e.g., blogs, personal websites, review sites, etc.), and the generation AI evaluates their reliability. For example, it analyzes blog posts about Thailand's tourism industry to identify tourism trends. This allows data from unofficial sources to be analyzed by collecting information from social media and forums.

[0031] The information collection unit can collect technical information from patent databases in each country and analyze technological trends. For example, the generation AI in the information collection unit collects technical information from patent databases in each country (e.g., USPTO, EPO, JPO, etc.) and analyzes the content of the patents. For example, it analyzes patents filed by Vietnamese IT companies to understand technological trends. The generation AI also analyzes technological trends based on the technical information collected from patent databases in each country. For example, it analyzes patents for the latest technology in the Korean automobile industry to understand technological trends. The information collection unit also analyzes technological evolution and competitors' technological strategies based on the information collected from patent databases by the generation AI. For example, it analyzes patents for new technology in the Thai tourism industry to understand trends in technological innovation. This makes it possible to collect technical information from patent databases and analyze technological trends.

[0032] The information collection unit can collect information from video platforms in each country, analyze video content, and convert it into text. For example, the generation AI in the information collection unit collects information from video platforms in each country (e.g., YouTube, Vimeo, Bilibili, etc.) and analyzes video content to convert it into text. For example, a video about IT companies in Vietnam is analyzed and saved as text data. The information collection unit also analyzes videos collected from video platforms in each country, and the generation AI converts the content into text. For example, a video about the automobile industry in South Korea is analyzed and saved as text data. The information collection unit also builds a system that analyzes video content and converts it into text based on the information collected by the generation AI from video platforms. For example, a video about the tourism industry in Thailand is analyzed and saved as text data. This makes it possible to collect information from video platforms and convert video content into text.

[0033] The information collection unit can collect information from academic paper databases in each country and analyze the latest research trends. For example, the generation AI collects information from each country's academic paper databases (e.g., Google Scholar, PubMed, IEEE Xplore, etc.) and analyzes the content of the papers. For example, the generation AI collects and analyzes the latest research papers on Vietnamese IT companies. The information collection unit also analyzes the latest research trends based on the information collected from each country's academic paper databases. For example, the generation AI collects and analyzes the latest research papers on South Korea's automobile industry. The information collection unit also builds a system that analyzes the latest research trends based on the information the generation AI collects from the academic paper databases. For example, the generation AI collects and analyzes the latest research papers on Thailand's tourism industry. This makes it possible to collect information from academic paper databases and analyze the latest research trends.

[0034] The information collection unit can collect economic indicators and policy information from government databases of each country and analyze the policy information. For example, the generation AI collects economic indicators and policy information from government databases of each country (e.g., IMF, World Bank, OECD, etc.) and analyzes the content. For example, the generation AI collects and analyzes economic indicators and policy information for Vietnam. The information collection unit also analyzes the content of economic indicators and policy information collected from government databases of each country using the generation AI. For example, the generation AI collects and analyzes economic indicators and policy information for South Korea. The information collection unit also builds a system that analyzes economic indicators and policy information based on the information collected by the generation AI from government databases. For example, the generation AI collects and analyzes economic indicators and policy information for Thailand. This makes it possible to collect and analyze economic indicators and policy information from government databases.

[0035] The information collection unit can collect product reviews from online marketplaces in each country and analyze consumer opinions. For example, the information collection unit uses a generation AI to collect product reviews from online marketplaces in each country (e.g., Amazon, eBay, Alibaba, etc.) and analyze their content. For example, product reviews about IT products in Vietnam are collected and analyzed. The information collection unit also uses a generation AI to analyze consumer opinions based on the product reviews collected from online marketplaces in each country. For example, product reviews about automobiles in South Korea are collected and analyzed. The information collection unit also builds a system that analyzes product reviews based on the information collected by the generation AI from online marketplaces. For example, reviews about tourism products in Thailand are collected and analyzed. This makes it possible to collect product reviews from online marketplaces and analyze consumer opinions.

[0036] The information collection unit can collect job data from job information sites in each country and analyze labor market trends. For example, the information collection unit uses the generation AI to collect job data from job information sites in each country (e.g., Indeed, LinkedIn, Glassdoor, etc.) and analyze the content. For example, the information collection unit collects job information from IT companies in Vietnam and analyzes labor market trends. The information collection unit also uses the generation AI to analyze labor market trends based on the job data collected from job information sites in each country. For example, the information collection unit collects job information related to the automobile industry in South Korea and analyzes labor market trends. The information collection unit also builds a system that analyzes labor market trends based on the information the generation AI collects from job information sites. For example, the information collection unit collects job information related to the tourism industry in Thailand and analyzes labor market trends. This makes it possible to collect job data from job information sites and analyze labor market trends.

[0037] The information collection unit can collect data from real estate information sites in each country and analyze real estate market trends. For example, the generation AI collects data from real estate information sites in each country (e.g., Zillow, Realtor.com, Rightmove, etc.) and analyzes the content of the data. For example, the generation AI collects and analyzes data related to the real estate market in Vietnam. The information collection unit also analyzes real estate market trends based on the data collected from real estate information sites in each country. For example, the generation AI collects and analyzes data related to the real estate market in South Korea. The information collection unit also builds a system that analyzes real estate market trends based on the information collected by the generation AI from real estate information sites. For example, the generation AI collects and analyzes data related to the real estate market in Thailand. This makes it possible to collect data from real estate information sites and analyze real estate market trends.

[0038] The analysis unit can build a predictive model based on the collected data and forecast market trends. For example, the analysis unit builds a predictive model based on data collected by the generation AI and predicts future market trends. For example, it predicts market trends for IT companies in Vietnam and evaluates future growth potential. The analysis unit also collects market data for each country, and the generation AI builds a predictive model based on that data. For example, it predicts future market trends based on data on the automobile industry in South Korea. The analysis unit also builds a predictive model based on data collected by the generation AI and constructs a system that forecasts future market trends. For example, it predicts future market trends based on data on the tourism industry in Thailand. This makes it possible to build a predictive model based on the collected data and forecast future market trends.

[0039] The analysis unit can conduct competitive analysis based on the collected data and analyze the strengths and weaknesses of competitors. For example, the analysis unit conducts competitive analysis based on data collected by the generation AI and analyzes the strengths and weaknesses of competitors. For example, it analyzes competitors of a Vietnamese IT company and evaluates their strengths and weaknesses. The analysis unit also collects market data from each country, and the generation AI conducts competitive analysis based on that data. For example, it analyzes competitors in the Korean automobile industry and evaluates their strengths and weaknesses. The analysis unit also builds a system that conducts competitive analysis based on the data collected by the generation AI and analyzes the strengths and weaknesses of competitors. For example, it analyzes competitors in the Thai tourism industry and evaluates their strengths and weaknesses. This makes it possible to conduct competitive analysis based on the collected data and analyze the strengths and weaknesses of competitors.

[0040] The analysis unit can perform scenario analysis based on the collected data and generate reports based on multiple scenarios. For example, the analysis unit performs scenario analysis based on data collected by the generation AI and generates reports based on multiple scenarios. For example, it analyzes multiple scenarios related to market trends for IT companies in Vietnam and generates a report. The analysis unit also collects market data for each country, and the generation AI performs scenario analysis based on that data. For example, it analyzes multiple scenarios related to the Korean automobile industry and generates a report. The analysis unit also builds a system that performs scenario analysis based on data collected by the generation AI and generates reports based on multiple scenarios. For example, it analyzes multiple scenarios related to the tourism industry in Thailand and generates a report. This makes it possible to perform scenario analysis based on collected data and generate reports based on multiple scenarios.

[0041] The analysis unit can perform risk analysis based on the collected data and identify potential risks. For example, the analysis unit can perform risk analysis based on data collected by the generation AI and identify potential risks. For example, it can analyze risks related to IT companies in Vietnam and compile the results into a report. The analysis unit also collects market data from each country, and the generation AI performs risk analysis based on that data. For example, it can analyze risks related to the automobile industry in South Korea and compile the results into a report. The analysis unit can also build a system that performs risk analysis based on data collected by the generation AI and identifies potential risks. For example, it can analyze risks related to the tourism industry in Thailand and compile the results into a report. This makes it possible to perform risk analysis based on the collected data and identify potential risks.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The information gathering unit can also collect information on cultural events and festivals in various countries and analyze cultural trends. For example, the generation AI can collect information on Vietnam's Tet Festival and analyze its impact. It can also collect information on South Korea's Chuseok Festival to understand regional consumption trends. It can also collect information on Thailand's Songkran Festival and analyze its impact on the tourism industry. This allows it to collect information on cultural events and understand regional cultural trends.

[0044] The information collection unit can also collect data from educational institutions in each country and analyze educational trends. For example, the generative AI can collect data on the number of students enrolled at universities in Vietnam and the career paths of graduates to analyze educational trends. It can also collect data on the enrollment rates and academic achievement test results of high schools in South Korea to evaluate the quality of education. It can also collect information on the curricula and educational policies of educational institutions in Thailand to understand educational trends. This allows data on educational institutions to be collected and educational trends to be analyzed.

[0045] The information collection unit can also collect health information from medical databases in various countries and analyze health trends. For example, the generation AI can collect health information from a medical database in Vietnam and analyze health trends. The generation AI can also analyze health trends based on health information collected from a medical database in South Korea. Furthermore, the generation AI can analyze health trends based on health information collected from a medical database in Thailand. This makes it possible to collect health information from medical databases and analyze health trends.

[0046] The information collection unit can also collect energy consumption data from each country's energy database and analyze energy consumption trends. For example, the generation AI collects energy consumption data from the Vietnam energy database and analyzes energy consumption trends. The generation AI can also analyze energy consumption trends based on energy consumption data collected from the South Korea energy database. Furthermore, the generation AI can analyze energy consumption trends based on energy consumption data collected from the Thailand energy database. In this way, energy consumption data can be collected from energy databases and energy consumption trends can be analyzed.

[0047] The information collection unit can also collect environmental data from each country's environmental database and analyze environmental trends. For example, the generation AI can collect environmental data from Vietnam's environmental database and analyze environmental trends. The generation AI can also analyze environmental trends based on environmental data collected from South Korea's environmental database. Furthermore, the generation AI can analyze environmental trends based on environmental data collected from Thailand's environmental database. In this way, environmental data can be collected from environmental databases and environmental trends can be analyzed.

[0048] The information collection unit can also collect traffic data from traffic databases in each country and analyze traffic trends. For example, the generation AI collects traffic data from a traffic database in Vietnam and analyzes traffic trends. The generation AI can also analyze traffic trends based on traffic data collected from a traffic database in South Korea. Furthermore, the generation AI can analyze traffic trends based on traffic data collected from a traffic database in Thailand. This makes it possible to collect traffic data from traffic databases and analyze traffic trends.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The information gathering unit collects information in multiple languages. For example, the generation AI collects and analyzes information in Vietnamese, Korean, Thai, and other languages. The information gathering unit also collects information from official company websites, news articles, industry reports, and other sources in each country. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes the collected data and extracts the necessary data. The analysis unit also evaluates reliability and performance based on the collected information. Step 3: The report generation unit generates a report based on the information analyzed by the analysis unit. For example, the generation AI organizes the collected data and summarizes it visually in an easy-to-understand manner using graphs and tables. The report generation unit can also adjust the structure and content of the report based on user instructions.

[0051] (Example 2) The generation AI service according to an embodiment of the present invention is a system that efficiently collects information on Southeast Asia, Vietnam, Korea, and other regions where languages ​​other than Japanese and English are spoken, and automates overseas market research. In this system, the generation AI collects information based on desktop research and automatically generates reports. This allows the generation AI service to efficiently conduct overseas market research, greatly contributing to the selection of business partners and understanding market trends.

[0052] A generation AI service according to an embodiment includes an information collection unit, an analysis unit, and a report generation unit. The information collection unit collects information in multiple languages. For example, the generation AI collects and analyzes information in Vietnamese, Korean, Thai, and other languages. The information collection unit also collects information from official company websites, news articles, industry reports, and the like from various countries. For example, the generation AI investigates market trends for IT companies in Vietnam and creates a report. The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes the collected data and extracts necessary data. The analysis unit also evaluates reliability and performance based on the collected information. For example, the generation AI analyzes the latest news articles about the Korean automobile industry and evaluates their content. The report generation unit generates a report based on the information analyzed by the analysis unit. For example, the generation AI organizes the collected data and summarizes it visually using graphs and tables. The report generation unit can also adjust the structure and content of the report based on user instructions. For example, the generation AI organizes collected data and generates a report based on an instruction to "create a report on the market trends of IT companies in Vietnam." This allows the generation AI service according to the embodiment to collect, analyze, and generate reports in multiple languages, enabling efficient market research. For example, a user can research the market trends of IT companies in Vietnam and find a reliable business partner. Or, a user can gather the latest information on the Korean automobile industry and understand the trends of competitors.

[0053] The information collection unit collects information from social media or forums in each country and can also analyze data from unofficial sources. For example, the generation AI collects information from social media in each country (e.g., Facebook, Twitter, Weibo, etc.) and analyzes the content of posts. For example, it collects user opinions and reviews about Vietnamese IT companies and stores them in a database. The information collection unit also collects information from forums in each country (e.g., Reddit, Naver, Baidu Tieba, etc.), and the generation AI analyzes the content. For example, it collects discussions about the Korean automobile industry and identifies trends. The information collection unit also collects data from unofficial sources (e.g., blogs, personal websites, review sites, etc.), and the generation AI evaluates their reliability. For example, it analyzes blog posts about Thailand's tourism industry to identify tourism trends. This allows data from unofficial sources to be analyzed by collecting information from social media and forums.

[0054] The information collection unit can collect technical information from patent databases in each country and analyze technological trends. For example, the generation AI in the information collection unit collects technical information from patent databases in each country (e.g., USPTO, EPO, JPO, etc.) and analyzes the content of the patents. For example, it analyzes patents filed by Vietnamese IT companies to understand technological trends. The generation AI also analyzes technological trends based on the technical information collected from patent databases in each country. For example, it analyzes patents for the latest technology in the Korean automobile industry to understand technological trends. The information collection unit also analyzes technological evolution and competitors' technological strategies based on the information collected from patent databases by the generation AI. For example, it analyzes patents for new technology in the Thai tourism industry to understand trends in technological innovation. This makes it possible to collect technical information from patent databases and analyze technological trends.

[0055] The information collection unit can use the emotion estimation function to analyze the emotions in news articles or social media posts from each country and prioritize collecting positive information. For example, the information collection unit uses a generation AI to collect news articles from each country and analyze the emotions in the articles using the emotion estimation function. For example, it prioritizes collecting positive news articles about IT companies in Vietnam. The information collection unit also collects social media posts from each country and analyzes the emotions in the posts using the emotion estimation function. For example, it prioritizes collecting positive posts about South Korea's automobile industry. The information collection unit also uses the emotion estimation function to analyze the emotions in news articles and social media posts from each country and builds a system that prioritizes collecting positive information. For example, it prioritizes collecting positive information about Thailand's tourism industry. This makes it possible to prioritize collecting positive information using the emotion estimation function.

[0056] The information collection unit can collect information from video platforms in each country, analyze video content, and convert it into text. For example, the generation AI in the information collection unit collects information from video platforms in each country (e.g., YouTube, Vimeo, Bilibili, etc.) and analyzes video content to convert it into text. For example, a video about IT companies in Vietnam is analyzed and saved as text data. The information collection unit also analyzes videos collected from video platforms in each country, and the generation AI converts the content into text. For example, a video about the automobile industry in South Korea is analyzed and saved as text data. The information collection unit also builds a system that analyzes video content and converts it into text based on the information collected by the generation AI from video platforms. For example, a video about the tourism industry in Thailand is analyzed and saved as text data. This makes it possible to collect information from video platforms and convert video content into text.

[0057] The information collection unit can collect information from academic paper databases in each country and analyze the latest research trends. For example, the generation AI collects information from each country's academic paper databases (e.g., Google Scholar, PubMed, IEEE Xplore, etc.) and analyzes the content of the papers. For example, the generation AI collects and analyzes the latest research papers on Vietnamese IT companies. The information collection unit also analyzes the latest research trends based on the information collected from each country's academic paper databases. For example, the generation AI collects and analyzes the latest research papers on South Korea's automobile industry. The information collection unit also builds a system that analyzes the latest research trends based on the information the generation AI collects from the academic paper databases. For example, the generation AI collects and analyzes the latest research papers on Thailand's tourism industry. This makes it possible to collect information from academic paper databases and analyze the latest research trends.

[0058] The information collection unit uses the emotion estimation function to analyze the emotions in press releases or official announcements from companies in each country and extract reliable information. For example, the information collection unit uses a generation AI to collect press releases from companies in each country and analyzes the emotions using the emotion estimation function. For example, it analyzes press releases from IT companies in Vietnam and extracts reliable information. The information collection unit also collects official announcements from companies in each country and analyzes the emotions using the emotion estimation function. For example, it analyzes official announcements related to the Korean automobile industry and extracts reliable information. The information collection unit also uses the emotion estimation function to build a system that analyzes the emotions in press releases and official announcements from companies in each country and extracts reliable information. For example, it analyzes official announcements related to Thailand's tourism industry and extracts reliable information. This makes it possible to analyze the emotions in press releases and official announcements and extract reliable information.

[0059] The information collection unit can collect economic indicators and policy information from government databases of each country and analyze the policy information. For example, the generation AI collects economic indicators and policy information from government databases of each country (e.g., IMF, World Bank, OECD, etc.) and analyzes the content. For example, the generation AI collects and analyzes economic indicators and policy information for Vietnam. The information collection unit also analyzes the content of economic indicators and policy information collected from government databases of each country using the generation AI. For example, the generation AI collects and analyzes economic indicators and policy information for South Korea. The information collection unit also builds a system that analyzes economic indicators and policy information based on the information collected by the generation AI from government databases. For example, the generation AI collects and analyzes economic indicators and policy information for Thailand. This makes it possible to collect and analyze economic indicators and policy information from government databases.

[0060] The information collection unit can collect product reviews from online marketplaces in each country and analyze consumer opinions. For example, the information collection unit uses a generation AI to collect product reviews from online marketplaces in each country (e.g., Amazon, eBay, Alibaba, etc.) and analyze their content. For example, product reviews about IT products in Vietnam are collected and analyzed. The information collection unit also uses a generation AI to analyze consumer opinions based on the product reviews collected from online marketplaces in each country. For example, product reviews about automobiles in South Korea are collected and analyzed. The information collection unit also builds a system that analyzes product reviews based on the information collected by the generation AI from online marketplaces. For example, reviews about tourism products in Thailand are collected and analyzed. This makes it possible to collect product reviews from online marketplaces and analyze consumer opinions.

[0061] The information collection unit can collect job data from job information sites in each country and analyze labor market trends. For example, the information collection unit uses the generation AI to collect job data from job information sites in each country (e.g., Indeed, LinkedIn, Glassdoor, etc.) and analyze the content. For example, the information collection unit collects job information from IT companies in Vietnam and analyzes labor market trends. The information collection unit also uses the generation AI to analyze labor market trends based on the job data collected from job information sites in each country. For example, the information collection unit collects job information related to the automobile industry in South Korea and analyzes labor market trends. The information collection unit also builds a system that analyzes labor market trends based on the information the generation AI collects from job information sites. For example, the information collection unit collects job information related to the tourism industry in Thailand and analyzes labor market trends. This makes it possible to collect job data from job information sites and analyze labor market trends.

[0062] The information collection unit can collect data from real estate information sites in each country and analyze real estate market trends. For example, the generation AI collects data from real estate information sites in each country (e.g., Zillow, Realtor.com, Rightmove, etc.) and analyzes the content of the data. For example, the generation AI collects and analyzes data related to the real estate market in Vietnam. The information collection unit also analyzes real estate market trends based on the data collected from real estate information sites in each country. For example, the generation AI collects and analyzes data related to the real estate market in South Korea. The information collection unit also builds a system that analyzes real estate market trends based on the information collected by the generation AI from real estate information sites. For example, the generation AI collects and analyzes data related to the real estate market in Thailand. This makes it possible to collect data from real estate information sites and analyze real estate market trends.

[0063] The information collection unit can use the emotion estimation function to analyze the emotions of consumer reviews from each country and prioritize collecting positive reviews. For example, the information collection unit uses a generation AI to collect consumer reviews from each country and analyzes the emotions using the emotion estimation function. For example, it prioritizes collecting positive reviews about IT products from Vietnam. The information collection unit also builds a system that collects consumer reviews from each country and uses the emotion estimation function to prioritize collecting positive reviews. For example, it prioritizes collecting positive reviews about cars from South Korea. The information collection unit also uses the emotion estimation function to analyze the emotions of consumer reviews from each country and prioritize collecting positive reviews. For example, it prioritizes collecting positive reviews about tourism products from Thailand. In this way, it is possible to analyze the emotions of consumer reviews and prioritize collecting positive reviews.

[0064] The analysis unit can build a predictive model based on the collected data and forecast market trends. For example, the analysis unit builds a predictive model based on data collected by the generation AI and predicts future market trends. For example, it predicts market trends for IT companies in Vietnam and evaluates future growth potential. The analysis unit also collects market data for each country, and the generation AI builds a predictive model based on that data. For example, it predicts future market trends based on data on the automobile industry in South Korea. The analysis unit also builds a predictive model based on data collected by the generation AI and constructs a system that forecasts future market trends. For example, it predicts future market trends based on data on the tourism industry in Thailand. This makes it possible to build a predictive model based on the collected data and forecast future market trends.

[0065] The analysis unit can conduct competitive analysis based on the collected data and analyze the strengths and weaknesses of competitors. For example, the analysis unit conducts competitive analysis based on data collected by the generation AI and analyzes the strengths and weaknesses of competitors. For example, it analyzes competitors of a Vietnamese IT company and evaluates their strengths and weaknesses. The analysis unit also collects market data from each country, and the generation AI conducts competitive analysis based on that data. For example, it analyzes competitors in the Korean automobile industry and evaluates their strengths and weaknesses. The analysis unit also builds a system that conducts competitive analysis based on the data collected by the generation AI and analyzes the strengths and weaknesses of competitors. For example, it analyzes competitors in the Thai tourism industry and evaluates their strengths and weaknesses. This makes it possible to conduct competitive analysis based on the collected data and analyze the strengths and weaknesses of competitors.

[0066] The analysis unit can use the emotion estimation function to analyze the user's emotional response to the report content and adjust the report to elicit a positive response. For example, the analysis unit generates a report based on data collected by the generation AI and uses the emotion estimation function to analyze the user's emotional response. For example, a report on IT companies in Vietnam is generated and adjusted to elicit a positive response. The analysis unit also generates a report based on market data for each country and uses the emotion estimation function to analyze the user's emotional response. For example, a report on the Korean automobile industry is generated and adjusted to elicit a positive response. The analysis unit also uses the emotion estimation function to build a system that analyzes the user's emotional response to the report content and adjusts the report to elicit a positive response. For example, a report on the tourism industry in Thailand is generated and adjusted to elicit a positive response. This allows the analysis of the user's emotional response to the report content and adjustment of the report to elicit a positive response.

[0067] The analysis unit can perform scenario analysis based on the collected data and generate reports based on multiple scenarios. For example, the analysis unit performs scenario analysis based on data collected by the generation AI and generates reports based on multiple scenarios. For example, it analyzes multiple scenarios related to market trends for IT companies in Vietnam and generates a report. The analysis unit also collects market data for each country, and the generation AI performs scenario analysis based on that data. For example, it analyzes multiple scenarios related to the Korean automobile industry and generates a report. The analysis unit also builds a system that performs scenario analysis based on data collected by the generation AI and generates reports based on multiple scenarios. For example, it analyzes multiple scenarios related to the tourism industry in Thailand and generates a report. This makes it possible to perform scenario analysis based on collected data and generate reports based on multiple scenarios.

[0068] The analysis unit can perform risk analysis based on the collected data and identify potential risks. For example, the analysis unit can perform risk analysis based on data collected by the generation AI and identify potential risks. For example, it can analyze risks related to IT companies in Vietnam and compile the results into a report. The analysis unit also collects market data from each country, and the generation AI performs risk analysis based on that data. For example, it can analyze risks related to the automobile industry in South Korea and compile the results into a report. The analysis unit can also build a system that performs risk analysis based on data collected by the generation AI and identifies potential risks. For example, it can analyze risks related to the tourism industry in Thailand and compile the results into a report. This makes it possible to perform risk analysis based on the collected data and identify potential risks.

[0069] The analysis unit can use the emotion estimation function to analyze the emotional impact of the visual elements of a report and generate a visually appealing report. For example, the analysis unit generates a report based on data collected by the generation AI and uses the emotion estimation function to analyze the emotional impact of the visual elements. For example, the analysis unit analyzes the graphs and tables of a report on IT companies in Vietnam and generates a visually appealing report. The analysis unit also generates a report based on market data for each country and uses the emotion estimation function to analyze the emotional impact of the visual elements. For example, the analysis unit analyzes the graphs and tables of a report on the automobile industry in South Korea and generates a visually appealing report. The analysis unit also uses the emotion estimation function to analyze the emotional impact of the visual elements of a report and build a system that generates a visually appealing report. For example, the analysis unit analyzes the graphs and tables of a report on the tourism industry in Thailand and generates a visually appealing report. This makes it possible to analyze the emotional impact of the visual elements of a report and generate a visually appealing report.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The information gathering unit can also collect information on cultural events and festivals in various countries and analyze cultural trends. For example, the generation AI can collect information on Vietnam's Tet Festival and analyze its impact. It can also collect information on South Korea's Chuseok Festival to understand regional consumption trends. It can also collect information on Thailand's Songkran Festival and analyze its impact on the tourism industry. This allows it to collect information on cultural events and understand regional cultural trends.

[0072] The information collection unit can also collect data from educational institutions in each country and analyze educational trends. For example, the generative AI can collect data on the number of students enrolled at universities in Vietnam and the career paths of graduates to analyze educational trends. It can also collect data on the enrollment rates and academic achievement test results of high schools in South Korea to evaluate the quality of education. It can also collect information on the curricula and educational policies of educational institutions in Thailand to understand educational trends. This allows data on educational institutions to be collected and educational trends to be analyzed.

[0073] The information gathering unit can also use the emotion estimation function to analyze the purchasing intent of consumers in each country and identify products with high purchasing intent. For example, the generation AI can analyze the purchasing intent of Vietnamese consumers and identify popular products. It can also analyze the purchasing intent of Korean consumers and identify best-selling products. It can also analyze the purchasing intent of Thai consumers and identify products with high demand. This makes it possible to analyze consumer purchasing intent and identify products with high purchasing intent.

[0074] The information collection unit can also collect health information from medical databases in various countries and analyze health trends. For example, the generation AI can collect health information from a medical database in Vietnam and analyze health trends. The generation AI can also analyze health trends based on health information collected from a medical database in South Korea. Furthermore, the generation AI can analyze health trends based on health information collected from a medical database in Thailand. This makes it possible to collect health information from medical databases and analyze health trends.

[0075] The information collection unit can also use the emotion estimation function to analyze the emotions in travel reviews for each country and identify positive tourist destinations. For example, the generation AI can collect travel reviews for Vietnam and use the emotion estimation function to identify positive tourist destinations. It can also collect travel reviews for South Korea and use the emotion estimation function to identify positive tourist destinations. It can also collect travel reviews for Thailand and use the emotion estimation function to identify positive tourist destinations. This makes it possible to analyze the emotions in travel reviews and identify positive tourist destinations.

[0076] The information collection unit can also collect energy consumption data from each country's energy database and analyze energy consumption trends. For example, the generation AI collects energy consumption data from the Vietnam energy database and analyzes energy consumption trends. The generation AI can also analyze energy consumption trends based on energy consumption data collected from the South Korea energy database. Furthermore, the generation AI can analyze energy consumption trends based on energy consumption data collected from the Thailand energy database. In this way, energy consumption data can be collected from energy databases and energy consumption trends can be analyzed.

[0077] The information gathering unit can also use the emotion estimation function to analyze the emotions in political news from each country and grasp political trends. For example, the generation AI collects political news from Vietnam and uses the emotion estimation function to grasp political trends. It can also collect political news from South Korea and use the emotion estimation function to grasp political trends. It can also collect political news from Thailand and use the emotion estimation function to grasp political trends. This makes it possible to analyze the emotions in political news and grasp political trends.

[0078] The information collection unit can also collect environmental data from each country's environmental database and analyze environmental trends. For example, the generation AI can collect environmental data from Vietnam's environmental database and analyze environmental trends. The generation AI can also analyze environmental trends based on environmental data collected from South Korea's environmental database. Furthermore, the generation AI can analyze environmental trends based on environmental data collected from Thailand's environmental database. In this way, environmental data can be collected from environmental databases and environmental trends can be analyzed.

[0079] The information collection unit can also use the emotion estimation function to analyze the emotions in sports news from each country and identify popular sports events. For example, the generation AI collects sports news from Vietnam and uses the emotion estimation function to identify popular sports events. It can also collect sports news from South Korea and use the emotion estimation function to identify popular sports events. It can also collect sports news from Thailand and use the emotion estimation function to identify popular sports events. In this way, it is possible to analyze the emotions in sports news and identify popular sports events.

[0080] The information collection unit can also collect traffic data from traffic databases in each country and analyze traffic trends. For example, the generation AI collects traffic data from a traffic database in Vietnam and analyzes traffic trends. The generation AI can also analyze traffic trends based on traffic data collected from a traffic database in South Korea. Furthermore, the generation AI can analyze traffic trends based on traffic data collected from a traffic database in Thailand. This makes it possible to collect traffic data from traffic databases and analyze traffic trends.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The information gathering unit collects information in multiple languages. For example, the generation AI collects and analyzes information in Vietnamese, Korean, Thai, and other languages. The information gathering unit also collects information from official company websites, news articles, industry reports, and other sources in each country. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, the generation AI analyzes the collected data and extracts the necessary data. The analysis unit also evaluates reliability and performance based on the collected information. Step 3: The report generation unit generates a report based on the information analyzed by the analysis unit. For example, the generation AI organizes the collected data and summarizes it visually in an easy-to-understand manner using graphs and tables. The report generation unit can also adjust the structure and content of the report based on user instructions.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 gathering department that collects information in multiple languages, an analysis unit that analyzes the information collected by the information collection unit; a report generation unit that generates a report based on the information analyzed by the analysis unit. A system characterized by:

2. The information collecting unit Collect information from social media or forums in each country and also analyze data from unofficial sources 2. The system of claim 1.

3. The information collecting unit Collecting technical information from patent databases in various countries and analyzing technological trends 2. The system of claim 1.

4. The information collecting unit Analyze the sentiment of news articles or social media posts from each country and prioritize collecting positive information 2. The system of claim 1.

5. The information collecting unit Collect information from video platforms in each country, analyze the video content, and convert it into text 2. The system of claim 1.

6. The information collecting unit Collect information from academic paper databases in various countries and analyze research trends 2. The system of claim 1.

7. The information collecting unit Analyze the sentiment of press releases or official announcements from companies in each country and extract reliable information 2. The system of claim 1.

8. The information collecting unit Collect economic indicators and policy information from government databases in each country and analyze said policy information 2. The system of claim 1.

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