System

Abstract

An object of the system according to the embodiment is to collect information from a plurality of information sources in real time and provide the information in an easy-to-understand form.SOLUTION: A system includes an information collection unit, an analysis unit, a generation unit, and a display unit. The information collection unit collects information from a plurality of information sources in real time. The analysis unit analyzes the information collected by the information collection unit. The generation unit compiles the information analyzed by the analysis unit into an easy-to-understand sentence. The display unit displays the text generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately collect information from multiple sources in real time and provide it in an easy-to-understand format, so there is room for improvement.

[0005] The system according to the embodiment aims to collect information in real time from multiple information sources and provide it in an easy-to-understand format. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a generation unit, and a display unit. The information collection unit collects information in real time from multiple information sources. The analysis unit analyzes and analyzes the information collected by the information collection unit. The generation unit compiles the information analyzed and analyzed by the analysis unit into easy-to-understand sentences. The display unit displays the sentences generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect information from multiple information sources in real time and provide it in an easy-to-understand format. [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 disaster prevention information provision system according to an embodiment of the present invention is a system that collects information in real time from a wide range of information sources, analyzes it using AI, and automatically compiles it into easy-to-understand sentences using a generation AI. This allows the disaster prevention information provision system to communicate the ever-changing situation to family members and related parties, and even people who cannot use their mobile phones can check the latest information through digital signs installed at evacuation centers.

[0029] A disaster prevention information provision system according to an embodiment includes an information collection unit, an analysis unit, a generation unit, and a display unit. The information collection unit collects information in real time from multiple information sources. For example, the information collection unit collects information from disaster prevention radio broadcasts, television, radio, internet news, user posts on social media, and the like. The information collection unit can also collect environmental data using sensors. For example, the information collection unit collects television news flashes in real time. The information collection unit collects radio emergency broadcasts. The information collection unit collects user posts on social media. The analysis unit analyzes and analyzes the information collected by the information collection unit. For example, the analysis unit classifies the collected information and evaluates its importance and reliability. The analysis unit can also analyze the content of social media posts to distinguish between highly reliable information and less reliable information. The analysis unit can also analyze the content of television and radio broadcasts to extract important information. For example, the analysis unit analyzes the collected information using natural language processing technology. The analysis unit evaluates the reliability of the information using a machine learning algorithm. The analysis unit extracts important information using data mining technology. The generation unit summarizes the information analyzed by the analysis unit into easy-to-understand sentences. For example, the generation unit uses a generation AI to organize the information and generate sentences. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI provides information in a format that is easy for users to understand. For example, the generation AI provides information in the form of, "You are currently instructed to head to a shelter. The nearest shelter is XX." The generation unit generates sentences based on prompts containing summaries of the information analyzed by the generation AI and instructions. The generation AI extracts and summarizes important parts of the sentences. The display unit displays the sentences generated by the generation unit. For example, the display unit provides information through digital signs installed at evacuation shelters. The display unit displays information updated in real time. The display unit can also send information via email or a messenger app. For example, the display unit installs digital signs at the entrances to evacuation shelters and in key locations, displaying the latest disaster prevention information. The display unit sends the generated sentences to family and related parties via email. The display unit provides information through a messenger app.As a result, the disaster prevention information provision system according to the embodiment can collect information in real time from a wide range of information sources, analyze it, and provide it in an easy-to-understand format. For example, people in evacuation shelters can quickly grasp the latest information and take appropriate action. In addition, family members and other related parties can feel reassured by knowing the latest situation.

[0030] The information gathering unit can develop an algorithm that compares information sources with past data to evaluate their reliability and prioritizes the selection of highly reliable information sources. The information gathering unit, for example, collects past data on each information source and develops an algorithm to evaluate reliability. For example, the accuracy of past news articles and social media posts is evaluated, and highly reliable information sources are selected with priority. The information gathering unit also develops an algorithm that compares the content of television and radio broadcasts with past broadcast content to evaluate their reliability. For example, the accuracy of past emergency broadcasts is evaluated, and highly reliable broadcast stations are selected with priority. The information gathering unit also develops an algorithm that compares the content of internet news articles with past news articles to evaluate their reliability. For example, the accuracy of past news articles is evaluated, and highly reliable news sites are selected with priority. In this way, accurate information can be collected by preferentially selecting highly reliable information sources.

[0031] The information gathering unit can analyze the correlation between different information sources and build a system that automatically eliminates duplicate or contradictory information. For example, the information gathering unit analyzes the correlation between data collected from different information sources and builds a system that automatically eliminates duplicate information. For example, when the same news article is collected from multiple information sources, the duplicates are eliminated. The information gathering unit also builds a system that analyzes the correlation between data collected from different information sources and automatically eliminates contradictory information. For example, when information collected from different information sources is contradictory, the more reliable information is selected preferentially. The information gathering unit also analyzes the correlation between data collected from different information sources and develops an algorithm that automatically eliminates duplicate or contradictory information. For example, when the same news article is collected from different information sources, the duplicates and contradictory information are eliminated. This eliminates duplicate and contradictory information, making it possible to provide accurate information.

[0032] The information collection unit can use voice recognition technology to convert radio or television audio data into text and use it for analysis. The information collection unit, for example, builds a system that converts radio or television audio data into text using voice recognition technology and uses it for analysis. For example, voice data of emergency broadcasts is converted into text and important information is extracted. The information collection unit also uses voice recognition technology to convert radio or television audio data into text in real time and uses it for analysis. For example, voice data of breaking news is converted into text and important information is extracted. The information collection unit also uses voice recognition technology to develop an algorithm that converts radio or television audio data into text and uses it for analysis. For example, voice data of emergency broadcasts is converted into text and important information is extracted. By converting the voice data into text, it can be used for analysis.

[0033] The information collection unit can collect data from information sources in different regions or countries and provide disaster prevention information from a global perspective. The information collection unit, for example, collects data from information sources in different regions or countries and builds a system that provides disaster prevention information from a global perspective. For example, it collects data from overseas news sites and social media. The information collection unit also collects data from information sources in different regions or countries and develops an algorithm that provides disaster prevention information from a global perspective. For example, it collects data from overseas news sites and social media and extracts important information. The information collection unit also collects data from information sources in different regions or countries and collects data from news sites and social media in each country to provide disaster prevention information from a global perspective. For example, it collects data from overseas news sites and social media and extracts important information. This makes it possible to provide disaster prevention information from a global perspective.

[0034] When analyzing information, the analysis unit can refer to past disaster data, detect similar patterns, and make predictions. For example, when AI analyzes information, the analysis unit builds a system that refers to past disaster data, detects similar patterns, and makes predictions. For example, it analyzes the current situation based on past disaster data and makes predictions. The analysis unit also refers to past disaster data and develops an algorithm that allows AI to detect similar patterns and make predictions. For example, it analyzes the current situation based on past disaster data and makes predictions. The analysis unit also collects past disaster data and uses it for analysis so that when AI analyzes information, it refers to past disaster data, detects similar patterns, and makes predictions. For example, it analyzes the current situation based on past disaster data and makes predictions. This makes it possible to provide more accurate information by making predictions based on past disaster data.

[0035] The analysis unit can generate an interactive dashboard to visualize the analysis results and enable the user to intuitively understand them. The analysis unit, for example, builds a system that generates an interactive dashboard to visualize the analysis results and enable the user to intuitively understand them. For example, the analysis results are displayed using graphs and charts. The analysis unit also generates an interactive dashboard to enable the user to intuitively understand the analysis results. For example, the analysis unit provides a dashboard that allows access to detailed information by clicking and zooming. The analysis unit also develops an algorithm that generates an interactive dashboard to visualize the analysis results and enable the user to intuitively understand them. For example, the analysis results are displayed using graphs and charts. In this way, by visualizing the analysis results, the user can intuitively understand the information.

[0036] The analysis unit automatically translates the results of the analysis into different languages, allowing for feedback from an international perspective. The analysis unit, for example, builds a system that automatically translates the results of the analysis into different languages ​​and allows for feedback from an international perspective. For example, it translates into multiple languages, such as English, French, and Chinese. The analysis unit also posts the automatically translated analysis results on a multilingual platform and receives feedback from users around the world. For example, it collects feedback in different languages. The analysis unit also collects advice and suggestions for improvement from an international perspective based on the analysis results translated into different languages, improving the quality of the analysis results. For example, it reflects feedback that takes cultural and market differences into account. This allows for feedback from an international perspective to be obtained by automatically translating into different languages.

[0037] The analysis unit can convert the analysis results into visual notes or mind maps to make them easier to understand visually. The analysis unit, for example, builds a system that converts the analysis results into visual notes to make them easier for users to understand visually. For example, it shows important points with diagrams or icons. The analysis unit also converts the analysis results into mind map format to visually organize related keywords and concepts. This allows users to understand the overall picture of the analysis results at a glance. The analysis unit also develops tools that automatically generate visual notes and mind maps to enable users to easily display the analysis results visually. For example, it provides a function to visualize the analysis results with drag and drop. This allows the analysis results to be converted into visual notes or mind maps to make them easier to understand visually.

[0038] The generation unit provides an option to select different writing styles or tones when generating text, enabling the provision of information tailored to the user's preferences. For example, the generation unit builds a system that provides an option to select different writing styles or tones when the generation AI generates text. For example, it makes it possible to select a formal style or a casual style. The generation unit also generates text in different writing styles and tones to suit the user's preferences. For example, it provides information based on the tone selected by the user. The generation unit also develops an algorithm that provides an option to select different writing styles or tones when the generation AI generates text. For example, it selects an appropriate writing style or tone based on the user's preferences. This makes it possible to provide information tailored to the user's preferences.

[0039] The generation unit can introduce an algorithm that learns from past generation results and user feedback to improve the accuracy of the generated sentences. The generation unit, for example, builds a system that introduces an algorithm that learns from past generation results and user feedback to improve the accuracy of the generated sentences. For example, the generation algorithm is improved based on user feedback. The generation unit also learns from past generation results and user feedback to improve the accuracy of the generated sentences. For example, the generation algorithm is improved based on sentences that are highly rated by users. The generation unit also develops an algorithm that learns from past generation results and user feedback to improve the accuracy of the generated sentences. For example, the generation algorithm is improved based on user feedback. In this way, the accuracy of the generated sentences can be improved by learning from past generation results and user feedback.

[0040] The generation unit provides the generated text audibly using speech synthesis technology, thereby enabling information to be conveyed to visually impaired persons or elderly persons. The generation unit, for example, builds a system that provides the generated text audibly using speech synthesis technology, thereby enabling information to be conveyed to visually impaired persons or elderly persons. For example, the generated text is read aloud. The generation unit also provides the generated text audibly using speech synthesis technology, thereby enabling information to be conveyed to visually impaired persons or elderly persons. For example, a device is provided that reads the generated text aloud. The generation unit also develops an algorithm that provides the generated text audibly using speech synthesis technology, thereby enabling information to be conveyed to visually impaired persons or elderly persons. For example, the generated text is read aloud. This enables information to be conveyed to visually impaired persons or elderly persons.

[0041] The generation unit can convert the generated text into different media to make it easier to understand visually. The generation unit, for example, builds a system that converts the generated text into different media to make it easier to understand visually. For example, it converts the generated text into videos or infographics. The generation unit also converts the generated text into different media to make it easier to understand visually. For example, it provides a tool that converts the generated text into videos or infographics. The generation unit also develops an algorithm that converts the generated text into different media to make it easier to understand visually. For example, it converts the generated text into videos or infographics. In this way, converting the generated text into different media makes it easier to understand visually.

[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 disaster prevention information system can also acquire the user's location information and provide disaster prevention information customized for each region. For example, the information collection unit acquires the user's GPS data and collects disaster prevention information specific to that region. The analysis unit evaluates the disaster risk for each region and provides appropriate evacuation instructions to the user. Furthermore, the generation unit generates text that takes into account the characteristics of each region and provides it to the user. This allows the user to obtain specific disaster prevention information based on their current location.

[0044] The disaster prevention information system can also monitor the user's health status and provide disaster prevention information according to health risks. For example, the information collection unit collects data such as heart rate and body temperature from the user's wearable device. The analysis unit analyzes the collected health data and evaluates health risks. The generation unit generates text including evacuation instructions and warnings according to health risks. This makes it possible to provide appropriate disaster prevention information according to the user's health status.

[0045] The disaster prevention information providing system can further provide customized disaster prevention information taking into consideration the user's family structure and whether or not they have pets. For example, the information collecting unit collects information about the user's family structure and pets. The analyzing unit suggests the selection of an evacuation shelter and an evacuation route based on the family structure and whether or not they have pets. The generating unit generates text that includes evacuation instructions that take into consideration the family and pets. This makes it possible to provide disaster prevention information tailored to the user's individual situation.

[0046] The disaster prevention information provision system can further propose an individual evacuation plan based on the user's past behavioral history. For example, the information collection unit collects the user's past evacuation behavior and movement history. The analysis unit analyzes the past behavioral history and proposes optimal evacuation routes and shelters. The generation unit generates text including a specific evacuation plan based on the user's past behavioral history. This makes it possible to provide an appropriate evacuation plan based on the user's past behavior.

[0047] The disaster prevention information provision system can further consider the user's hobbies and interests and provide relevant disaster prevention information. For example, the information collection unit collects the user's hobbies and interests from social media accounts and search histories. The analysis unit extracts disaster prevention information related to the hobbies and interests and provides it to the user. The generation unit generates text that includes specific disaster prevention measures related to the user's hobbies and interests. This makes it possible to provide disaster prevention information that matches the user's interests.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The information collection unit collects information in real time from multiple sources, such as disaster prevention radio, television, radio, internet news, and user posts on social media. It can also collect environmental data using sensors. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it classifies the collected information and evaluates its importance and reliability. It analyzes the content of social media posts and distinguishes between highly reliable and less reliable information. It analyzes the content of television and radio broadcasts and extracts important information. It performs analysis using natural language processing technology, machine learning algorithms, and data mining technology. Step 3: The generation unit compiles the information analyzed by the analysis unit into easy-to-understand sentences. For example, it uses a generation AI to organize the information and generate sentences. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to provide information in a form that is easy for the user to understand. The generation AI generates sentences based on prompts containing summaries and instructions of the analyzed information, extracting important parts and summarizing them. Step 4: The display unit displays the text generated by the generation unit. For example, information is provided through digital signs installed at evacuation centers. Information updated in real time can be displayed and sent via email or messenger apps. Digital signs are installed at the entrance to the evacuation center and in key locations, displaying the latest disaster prevention information. The generated text is sent to family members and related parties via email, and information is provided through messenger apps.

[0050] (Example 2) The disaster prevention information provision system according to an embodiment of the present invention is a system that collects information in real time from a wide range of information sources, analyzes it using AI, and automatically compiles it into easy-to-understand sentences using a generation AI. This allows the disaster prevention information provision system to communicate the ever-changing situation to family members and related parties, and even people who cannot use their mobile phones can check the latest information through digital signs installed at evacuation centers.

[0051] A disaster prevention information provision system according to an embodiment includes an information collection unit, an analysis unit, a generation unit, and a display unit. The information collection unit collects information in real time from multiple information sources. For example, the information collection unit collects information from disaster prevention radio broadcasts, television, radio, internet news, user posts on social media, and the like. The information collection unit can also collect environmental data using sensors. For example, the information collection unit collects television news flashes in real time. The information collection unit collects radio emergency broadcasts. The information collection unit collects user posts on social media. The analysis unit analyzes and analyzes the information collected by the information collection unit. For example, the analysis unit classifies the collected information and evaluates its importance and reliability. The analysis unit can also analyze the content of social media posts to distinguish between highly reliable information and less reliable information. The analysis unit can also analyze the content of television and radio broadcasts to extract important information. For example, the analysis unit analyzes the collected information using natural language processing technology. The analysis unit evaluates the reliability of the information using a machine learning algorithm. The analysis unit extracts important information using data mining technology. The generation unit summarizes the information analyzed by the analysis unit into easy-to-understand sentences. For example, the generation unit uses a generation AI to organize the information and generate sentences. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI provides information in a format that is easy for users to understand. For example, the generation AI provides information in the form of, "You are currently instructed to head to a shelter. The nearest shelter is XX." The generation unit generates sentences based on prompts containing summaries of the information analyzed by the generation AI and instructions. The generation AI extracts and summarizes important parts of the sentences. The display unit displays the sentences generated by the generation unit. For example, the display unit provides information through digital signs installed at evacuation shelters. The display unit displays information updated in real time. The display unit can also send information via email or a messenger app. For example, the display unit installs digital signs at the entrances to evacuation shelters and in key locations, displaying the latest disaster prevention information. The display unit sends the generated sentences to family and related parties via email. The display unit provides information through a messenger app.As a result, the disaster prevention information provision system according to the embodiment can collect information in real time from a wide range of information sources, analyze it, and provide it in an easy-to-understand format. For example, people in evacuation shelters can quickly grasp the latest information and take appropriate action. In addition, family members and other related parties can feel reassured by knowing the latest situation.

[0052] The information collection unit can perform sentiment analysis on data collected from each information source and filter the data based on the intensity or type of emotion. For example, the information collection unit performs sentiment analysis on data collected from each information source and quantifies the intensity and type of emotion. For example, the information collection unit analyzes the content of posts on social media, expresses emotions such as joy, sadness, and surprise as numerical values, and prioritizes analysis of information with a high specific emotional score. The information collection unit also performs sentiment analysis on the content of television and radio broadcasts and evaluates their importance based on the intensity and type of emotion. For example, the information collection unit analyzes the content of emergency broadcasts and prioritizes analysis of information with a high emotional intensity. The information collection unit also performs sentiment analysis on internet news articles and filters them based on the intensity and type of emotion. For example, the information collection unit analyzes news articles about disasters and prioritizes analysis of information with a high emotional intensity. In this way, by filtering information based on the intensity and type of emotion, important information can be prioritized for analysis.

[0053] The information gathering unit can develop an algorithm that compares information sources with past data to evaluate their reliability and prioritizes the selection of highly reliable information sources. The information gathering unit, for example, collects past data on each information source and develops an algorithm to evaluate reliability. For example, the accuracy of past news articles and social media posts is evaluated, and highly reliable information sources are selected with priority. The information gathering unit also develops an algorithm that compares the content of television and radio broadcasts with past broadcast content to evaluate their reliability. For example, the accuracy of past emergency broadcasts is evaluated, and highly reliable broadcast stations are selected with priority. The information gathering unit also develops an algorithm that compares the content of internet news articles with past news articles to evaluate their reliability. For example, the accuracy of past news articles is evaluated, and highly reliable news sites are selected with priority. In this way, accurate information can be collected by preferentially selecting highly reliable information sources.

[0054] The information gathering unit can analyze the correlation between different information sources and build a system that automatically eliminates duplicate or contradictory information. For example, the information gathering unit analyzes the correlation between data collected from different information sources and builds a system that automatically eliminates duplicate information. For example, when the same news article is collected from multiple information sources, the duplicates are eliminated. The information gathering unit also builds a system that analyzes the correlation between data collected from different information sources and automatically eliminates contradictory information. For example, when information collected from different information sources is contradictory, the more reliable information is selected preferentially. The information gathering unit also analyzes the correlation between data collected from different information sources and develops an algorithm that automatically eliminates duplicate or contradictory information. For example, when the same news article is collected from different information sources, the duplicates and contradictory information are eliminated. This eliminates duplicate and contradictory information, making it possible to provide accurate information.

[0055] The information collection unit can use voice recognition technology to convert radio or television audio data into text and use it for analysis. The information collection unit, for example, builds a system that converts radio or television audio data into text using voice recognition technology and uses it for analysis. For example, voice data of emergency broadcasts is converted into text and important information is extracted. The information collection unit also uses voice recognition technology to convert radio or television audio data into text in real time and uses it for analysis. For example, voice data of breaking news is converted into text and important information is extracted. The information collection unit also uses voice recognition technology to develop an algorithm that converts radio or television audio data into text and uses it for analysis. For example, voice data of emergency broadcasts is converted into text and important information is extracted. By converting the voice data into text, it can be used for analysis.

[0056] The information collection unit can collect data from information sources in different regions or countries and provide disaster prevention information from a global perspective. The information collection unit, for example, collects data from information sources in different regions or countries and builds a system that provides disaster prevention information from a global perspective. For example, it collects data from overseas news sites and social media. The information collection unit also collects data from information sources in different regions or countries and develops an algorithm that provides disaster prevention information from a global perspective. For example, it collects data from overseas news sites and social media and extracts important information. The information collection unit also collects data from information sources in different regions or countries and collects data from news sites and social media in each country to provide disaster prevention information from a global perspective. For example, it collects data from overseas news sites and social media and extracts important information. This makes it possible to provide disaster prevention information from a global perspective.

[0057] The information collection unit is equipped with an emotion estimation function, and can estimate the emotion a user is feeling when entering information in real time and make suggestions to elicit positive emotions. The information collection unit, for example, is equipped with an emotion estimation function and builds a system that estimates the emotion a user is feeling when entering information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information collection unit also uses the emotion estimation function to make suggestions to elicit positive emotions when the user enters information. For example, it presents encouraging messages and success stories. The information collection unit also uses the emotion estimation function to provide an interface to elicit positive emotions when the user enters information. For example, it displays appropriate encouragement or praise according to the input content. This makes it possible to provide information that takes the user's emotions into consideration.

[0058] The analysis unit can perform sentiment analysis on the collected information and prioritize analysis of emotionally significant information. The analysis unit, for example, builds a system that performs sentiment analysis on the collected information and prioritizes analysis of emotionally significant information. For example, it analyzes the content of social media posts and prioritizes analysis of information with high emotional intensity. The analysis unit also uses generative AI to perform sentiment analysis on the collected information and prioritize analysis of emotionally significant information. For example, it analyzes the content of news articles and emergency broadcasts and prioritizes analysis of information with high emotional intensity. The analysis unit also uses sentiment analysis to develop an algorithm that evaluates the importance of the collected information and prioritizes analysis of emotionally significant information. For example, it analyzes the content of social media posts and prioritizes analysis of information with high emotional intensity. As a result, by prioritizing analysis of emotionally significant information, highly urgent information can be provided quickly.

[0059] When analyzing information, the analysis unit can refer to past disaster data, detect similar patterns, and make predictions. For example, when AI analyzes information, the analysis unit builds a system that refers to past disaster data, detects similar patterns, and makes predictions. For example, it analyzes the current situation based on past disaster data and makes predictions. The analysis unit also refers to past disaster data and develops an algorithm that allows AI to detect similar patterns and make predictions. For example, it analyzes the current situation based on past disaster data and makes predictions. The analysis unit also collects past disaster data and uses it for analysis so that when AI analyzes information, it refers to past disaster data, detects similar patterns, and makes predictions. For example, it analyzes the current situation based on past disaster data and makes predictions. This makes it possible to provide more accurate information by making predictions based on past disaster data.

[0060] The analysis unit can generate an interactive dashboard to visualize the analysis results and enable the user to intuitively understand them. The analysis unit, for example, builds a system that generates an interactive dashboard to visualize the analysis results and enable the user to intuitively understand them. For example, the analysis results are displayed using graphs and charts. The analysis unit also generates an interactive dashboard to enable the user to intuitively understand the analysis results. For example, the analysis unit provides a dashboard that allows access to detailed information by clicking and zooming. The analysis unit also develops an algorithm that generates an interactive dashboard to visualize the analysis results and enable the user to intuitively understand them. For example, the analysis results are displayed using graphs and charts. In this way, by visualizing the analysis results, the user can intuitively understand the information.

[0061] The analysis unit automatically translates the results of the analysis into different languages, allowing for feedback from an international perspective. The analysis unit, for example, builds a system that automatically translates the results of the analysis into different languages ​​and allows for feedback from an international perspective. For example, it translates into multiple languages, such as English, French, and Chinese. The analysis unit also posts the automatically translated analysis results on a multilingual platform and receives feedback from users around the world. For example, it collects feedback in different languages. The analysis unit also collects advice and suggestions for improvement from an international perspective based on the analysis results translated into different languages, improving the quality of the analysis results. For example, it reflects feedback that takes cultural and market differences into account. This allows for feedback from an international perspective to be obtained by automatically translating into different languages.

[0062] The analysis unit can convert the analysis results into visual notes or mind maps to make them easier to understand visually. The analysis unit, for example, builds a system that converts the analysis results into visual notes to make them easier for users to understand visually. For example, it shows important points with diagrams or icons. The analysis unit also converts the analysis results into mind map format to visually organize related keywords and concepts. This allows users to understand the overall picture of the analysis results at a glance. The analysis unit also develops tools that automatically generate visual notes and mind maps to enable users to easily display the analysis results visually. For example, it provides a function to visualize the analysis results with drag and drop. This allows the analysis results to be converted into visual notes or mind maps to make them easier to understand visually.

[0063] The analysis unit can use the emotion estimation function to collect users' emotional reactions to the analysis results and improve the analysis algorithm based on that. The analysis unit, for example, collects users' emotional reactions to the analysis results in real time and builds a system that improves the analysis algorithm based on that data. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit also uses the emotion estimation function to collect users' emotional reactions to the analysis results and readjusts the analysis algorithm if there are many negative reactions. For example, it improves the analysis results based on user feedback. The analysis unit also analyzes users' emotional reaction data and identifies areas for improvement in the analysis algorithm based on the results. For example, it makes suggestions to correct parts with low emotion scores. In this way, by improving the analysis algorithm based on users' emotional reactions, it becomes possible to provide more appropriate information.

[0064] The generation unit can use an emotion estimation function to generate sentences that take the user's emotions into consideration when generating sentences. For example, the generation unit builds a system that uses the emotion estimation function to generate sentences that take the user's emotions into consideration when the generation AI generates sentences. For example, it uses a lot of positive expressions based on the user's emotion score. The generation unit also uses the emotion estimation function to generate sentences that take the user's emotions into consideration when the generation AI generates sentences. For example, it uses expressions to help maintain calm in an emergency. The generation unit also develops an algorithm that generates sentences that take the user's emotions into consideration based on emotion estimation data when the generation AI generates sentences. For example, it selects an appropriate tone and style based on the user's emotion score. This generates sentences that take the user's emotions into consideration, making it possible to provide more appropriate information.

[0065] The generation unit provides an option to select different writing styles or tones when generating text, enabling the provision of information tailored to the user's preferences. For example, the generation unit builds a system that provides an option to select different writing styles or tones when the generation AI generates text. For example, it makes it possible to select a formal style or a casual style. The generation unit also generates text in different writing styles and tones to suit the user's preferences. For example, it provides information based on the tone selected by the user. The generation unit also develops an algorithm that provides an option to select different writing styles or tones when the generation AI generates text. For example, it selects an appropriate writing style or tone based on the user's preferences. This makes it possible to provide information tailored to the user's preferences.

[0066] The generation unit can introduce an algorithm that learns from past generation results and user feedback to improve the accuracy of the generated sentences. The generation unit, for example, builds a system that introduces an algorithm that learns from past generation results and user feedback to improve the accuracy of the generated sentences. For example, the generation algorithm is improved based on user feedback. The generation unit also learns from past generation results and user feedback to improve the accuracy of the generated sentences. For example, the generation algorithm is improved based on sentences that are highly rated by users. The generation unit also develops an algorithm that learns from past generation results and user feedback to improve the accuracy of the generated sentences. For example, the generation algorithm is improved based on user feedback. In this way, the accuracy of the generated sentences can be improved by learning from past generation results and user feedback.

[0067] The generation unit provides the generated text audibly using speech synthesis technology, thereby enabling information to be conveyed to visually impaired persons or elderly persons. The generation unit, for example, builds a system that provides the generated text audibly using speech synthesis technology, thereby enabling information to be conveyed to visually impaired persons or elderly persons. For example, the generated text is read aloud. The generation unit also provides the generated text audibly using speech synthesis technology, thereby enabling information to be conveyed to visually impaired persons or elderly persons. For example, a device is provided that reads the generated text aloud. The generation unit also develops an algorithm that provides the generated text audibly using speech synthesis technology, thereby enabling information to be conveyed to visually impaired persons or elderly persons. For example, the generated text is read aloud. This enables information to be conveyed to visually impaired persons or elderly persons.

[0068] The generation unit can convert the generated text into different media to make it easier to understand visually. The generation unit, for example, builds a system that converts the generated text into different media to make it easier to understand visually. For example, it converts the generated text into videos or infographics. The generation unit also converts the generated text into different media to make it easier to understand visually. For example, it provides a tool that converts the generated text into videos or infographics. The generation unit also develops an algorithm that converts the generated text into different media to make it easier to understand visually. For example, it converts the generated text into videos or infographics. In this way, converting the generated text into different media makes it easier to understand visually.

[0069] The generation unit uses the emotion estimation function to monitor the user's emotional response to the generated sentences in real time, and can continuously generate optimal sentences. The generation unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to the generated sentences in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit also evaluates the generated sentences based on the user's emotional response data and continuously generates optimal sentences. For example, the generation unit prioritizes generating sentences with a high number of positive emotional responses. The generation unit also builds a system that collects emotion estimation data in real time and uses it to improve the generated sentences. For example, the generation algorithm is dynamically adjusted according to changes in the user's emotions. In this way, the user's emotional response can be monitored in real time, and optimal sentences can be continuously generated.

[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 disaster prevention information system can also acquire the user's location information and provide disaster prevention information customized for each region. For example, the information collection unit acquires the user's GPS data and collects disaster prevention information specific to that region. The analysis unit evaluates the disaster risk for each region and provides appropriate evacuation instructions to the user. Furthermore, the generation unit generates text that takes into account the characteristics of each region and provides it to the user. This allows the user to obtain specific disaster prevention information based on their current location.

[0072] The disaster prevention information provision system can further estimate the user's emotions and determine the priority of information based on the estimated emotions. For example, the information collection unit analyzes the user's social media posts and message content and calculates an emotion score. The analysis unit prioritizes analysis of information with a high emotion score and evaluates its importance. The generation unit provides information in a format that is easy for the user to understand based on the emotion score. This makes it possible to provide information that takes the user's emotions into consideration.

[0073] The disaster prevention information system can also monitor the user's health status and provide disaster prevention information according to health risks. For example, the information collection unit collects data such as heart rate and body temperature from the user's wearable device. The analysis unit analyzes the collected health data and evaluates health risks. The generation unit generates text including evacuation instructions and warnings according to health risks. This makes it possible to provide appropriate disaster prevention information according to the user's health status.

[0074] The disaster prevention information system can further estimate the user's emotions and customize the way information is displayed based on the estimated emotions. For example, the information collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit organizes information based on the emotion score so that the user does not feel stressed. The generation unit provides information in a tone that helps the user relax based on the emotion score. This makes it possible to display information that takes the user's emotions into consideration.

[0075] The disaster prevention information providing system can further provide customized disaster prevention information taking into consideration the user's family structure and whether or not they have pets. For example, the information collecting unit collects information about the user's family structure and pets. The analyzing unit suggests the selection of an evacuation shelter and an evacuation route based on the family structure and whether or not they have pets. The generating unit generates text that includes evacuation instructions that take into consideration the family and pets. This makes it possible to provide disaster prevention information tailored to the user's individual situation.

[0076] The disaster prevention information system can further estimate the user's emotions and provide mental health support in emergencies based on the estimated emotions. For example, the information collection unit analyzes the user's social media posts and message content to calculate an emotion score. If the emotion score is low, the analysis unit determines that mental health support is necessary. The generation unit provides the user with encouraging messages and relaxation techniques based on the emotion score. This enables support that takes into consideration the user's mental health.

[0077] The disaster prevention information provision system can further propose an individual evacuation plan based on the user's past behavioral history. For example, the information collection unit collects the user's past evacuation behavior and movement history. The analysis unit analyzes the past behavioral history and proposes optimal evacuation routes and shelters. The generation unit generates text including a specific evacuation plan based on the user's past behavioral history. This makes it possible to provide an appropriate evacuation plan based on the user's past behavior.

[0078] The disaster prevention information provision system can further estimate the user's emotions and adjust the timing of providing information based on the estimated emotions. For example, the information collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit determines the timing at which the user is most likely to receive information based on the emotion score. The generation unit provides information at an appropriate timing based on the emotion score. This makes it possible to provide information that takes the user's emotions into consideration.

[0079] The disaster prevention information provision system can further consider the user's hobbies and interests and provide relevant disaster prevention information. For example, the information collection unit collects the user's hobbies and interests from social media accounts and search histories. The analysis unit extracts disaster prevention information related to the hobbies and interests and provides it to the user. The generation unit generates text that includes specific disaster prevention measures related to the user's hobbies and interests. This makes it possible to provide disaster prevention information that matches the user's interests.

[0080] The disaster prevention information provision system can further estimate the user's emotions and customize the information format based on the estimated emotions. For example, the information collection unit analyzes the user's facial expressions and voice and calculates an emotion score. The analysis unit selects a format that is easy for the user to understand based on the emotion score. The generation unit provides the information in a format that allows the user to relax based on the emotion score. This makes it possible to create an information format that takes the user's emotions into consideration.

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

[0082] Step 1: The information collection unit collects information in real time from multiple sources, such as disaster prevention radio, television, radio, internet news, and user posts on social media. It can also collect environmental data using sensors. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it classifies the collected information and evaluates its importance and reliability. It analyzes the content of social media posts and distinguishes between highly reliable and less reliable information. It analyzes the content of television and radio broadcasts and extracts important information. It performs analysis using natural language processing technology, machine learning algorithms, and data mining technology. Step 3: The generation unit compiles the information analyzed by the analysis unit into easy-to-understand sentences. For example, it uses a generation AI to organize the information and generate sentences. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to provide information in a form that is easy for the user to understand. The generation AI generates sentences based on prompts containing summaries and instructions of the analyzed information, extracting important parts and summarizing them. Step 4: The display unit displays the text generated by the generation unit. For example, information is provided through digital signs installed at evacuation centers. Information updated in real time can be displayed and sent via email or messenger apps. Digital signs are installed at the entrance to the evacuation center and in key locations, displaying the latest disaster prevention information. The generated text is sent to family members and related parties via email, and information is provided through messenger apps.

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

[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 unit that gathers information from multiple information sources in real time; an analysis unit that analyzes the information collected by the information collection unit; a generation unit that compiles the information analyzed by the analysis unit into easy-to-understand sentences; a display unit that displays the sentence generated by the generation unit. A system characterized by:

2. The information collecting unit Performing sentiment analysis on the data collected from each source and filtering said data based on the intensity or type of sentiment 2. The system of claim 1.

3. The information collecting unit Develop an algorithm to evaluate the reliability of the information sources by comparing them with past data and prioritizing the most reliable information sources.

2. The system of claim 1.

4. The information collecting unit Build a system that analyzes correlations between different sources of information and automatically eliminates duplicate or contradictory information.

2. The system of claim 1.

5. The information collecting unit Using voice recognition technology, radio or television audio data is converted into text for analysis.

2. The system of claim 1.

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