System

Abstract

An object of a system according to an embodiment is to analyze information on a network and identify and warn of a potential threat.SOLUTION: A system includes an information collection unit, an analysis unit, a warning unit, and a notification unit. The information collection unit collects information from a plurality of sources on a network. The analysis unit analyzes the information collected by the information collection unit. The warning unit specifies a potential threat based on the result analyzed by the analysis unit, and issues a warning. The notification unit notifies the warning issued by the warning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to identify the emergence of potential competitors in a timely manner in complex business environments.

[0005] The system according to the embodiment aims to analyze information on the Internet, identify potential threats, and issue warnings. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a warning unit, and a notification unit. The information collection unit collects information from multiple sources on the Internet. The analysis unit analyzes the information collected by the information collection unit. The warning unit identifies potential threats based on the results of the analysis by the analysis unit and issues a warning. The notification unit notifies the user of the warning issued by the warning unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information on the Internet, identify potential threats, and issue warnings. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 notification system according to an embodiment of the present invention collects information from multiple online sources, analyzes it using a generative AI, identifies potential threats, and prompts strategic responses. This allows company executives and marketers to quickly become aware of the emergence of potential competitors and take strategic responses.

[0029] A notification system according to an embodiment includes an information collection unit, an analysis unit, a warning unit, and a notification unit. The information collection unit collects information from multiple online sources. For example, it collects the latest industry trends from news sites. The information collection unit can also collect user reactions and trends from social media. The information collection unit can also collect information on new technologies and inventions from patent databases. For example, it collects articles on the latest industry trends and emerging companies from news sites and user reactions and trends from social media. It also collects information on new technologies and inventions from patent databases. The analysis unit uses a generation AI to analyze the information collected by the information collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze news articles and social media posts. If there is a sudden increase in mentions of a particular company or technology, it determines that the company or technology is likely to be a potential threat. The generation AI also analyzes patent information to understand how new technologies are evolving. For example, the generation AI analyzes news articles and social media posts. If there is a sudden increase in mentions of a particular company or technology, it determines that the company or technology is likely to be a potential threat. The generative AI also analyzes patent information to understand how new technologies are evolving. The alerting unit identifies potential threats based on the results of the analysis by the analysis unit and issues alerts. For example, if a specific startup company is growing rapidly, it will notify detailed information about that company. Also, if a new technology appears on the market and there is a possibility that that technology will compete, it will notify information about that technology. For example, if a specific startup company is growing rapidly, it will notify detailed information about that company. Also, if a new technology appears on the market and there is a possibility that that technology will compete, it will notify information about that technology. The notification unit notifies of alerts issued by the alerting unit. For example, the notification unit may send alerts to responsible personnel via email or push notification. The notification unit may also display alerts on a dashboard. For example, the notification unit may send alerts to responsible personnel via email or push notification. The notification unit may also display alerts on a dashboard.As a result, the notification system according to the embodiment can prompt strategic responses by identifying potential threats in a timely manner and issuing warnings. For example, by receiving a warning, a responsible person can take prompt strategic responses. In addition, the notification system can provide highly accurate analysis results because it analyzes information using generative AI. Furthermore, the notification system can provide comprehensive information because it collects information from multiple sources on the Internet.

[0030] The information gathering unit evaluates the reliability of the information to be collected and can prioritize the collection of only highly reliable information. For example, the information gathering unit calculates a reliability score for the information source in order for the generation AI to evaluate the reliability of information collected from news sites and social media. For example, it prioritizes the collection of information from highly reliable news sites and official accounts. In addition, the information gathering unit allows the generation AI to analyze the reliability of the source and author of the information in order to evaluate the reliability of the collected information. For example, it determines reliability based on past performance and evaluations. In addition, the information gathering unit analyzes the degree of agreement from multiple information sources in order for the generation AI to evaluate the reliability of the information. For example, if the same content is reported from multiple highly reliable information sources, it prioritizes the collection of that information. This prioritizes the collection of highly reliable information, improving the accuracy of the analysis results.

[0031] The information collection unit can also collect information from industry-specific forums or expert blogs. For example, the information collection unit monitors specific keywords and topics so that the generation AI can collect information from industry-specific forums. For example, it collects the latest discussions from technology and marketing forums. In addition, the information collection unit regularly checks the blogs of specific experts and influencers so that the generation AI can collect information from expert blogs. For example, it collects the opinions of industry leaders and experts. In addition, when the generation AI collects information from industry-specific forums and expert blogs, the information collection unit automatically extracts related topics and keywords and prioritizes the collection of important information. This allows the generation AI to obtain more specialized information by collecting information from industry-specific forums and expert blogs.

[0032] The information collection unit can also collect information from audio data or video data, and integrate it with text data for analysis. In the information collection unit, for example, the generation AI analyzes audio data and extracts important information. For example, the content of podcasts and webinars is converted into text data and used for analysis. In addition, to collect information from video data, the generation AI analyzes video subtitles and audio and extracts important information. For example, the content of YouTube videos and online seminars is analyzed. In addition, the information collection unit integrates audio data and video data with text data to perform comprehensive analysis. For example, the content of audio and video is combined with text data for analysis to identify important information. This enables more comprehensive information analysis by integrating audio data and video data with text data for analysis.

[0033] The information collection unit can automatically translate information in different languages ​​and collect information from a global perspective. For example, the generation AI automatically translates news articles and social media posts in different languages ​​to collect information from a global perspective. For example, it translates and analyzes information in English, Chinese, Spanish, etc. The information collection unit also uses the automatic translation function to collect patent information in different languages ​​and grasp global technology trends. For example, it collects information from patent databases in various countries, translates it, and analyzes it. The information collection unit also automatically translates information in different languages ​​using the generation AI to identify trends and threats from a global perspective. For example, it translates and analyzes international news and social media posts. This allows information to be collected from a global perspective by automatically translating information in different languages.

[0034] The analysis unit can identify potential threats by analyzing the frequency of specific keywords and phrases in the collected information. For example, the generation AI analyzes news articles and social media posts to calculate the frequency of specific keywords and phrases. For example, if there is a sudden increase in mentions of a startup or new technology, the analysis unit identifies that information as a potential threat. The analysis unit also analyzes patent information and analyzes the frequency of keywords related to specific technologies and inventions. For example, if a new technology is rapidly evolving, the analysis unit identifies that technology as a potential threat. The analysis unit also analyzes the frequency of specific keywords and phrases in the information collected by the generation AI to identify trends and threats. For example, if there is an increase in mentions of a specific company or technology, the analysis unit identifies that information as a potential threat. This makes it possible to identify potential threats by analyzing the frequency of specific keywords and phrases.

[0035] The analysis unit detects abnormal patterns by comparing it with past data and can identify potential threats. For example, the generation AI compares it with past news articles and social media posts to detect abnormal patterns. For example, if there is a sudden increase in mentions of a particular company or technology, the analysis unit identifies that information as a potential threat. The analysis unit also analyzes patent information and compares it with past data to detect abnormal patterns. For example, if a new technology is rapidly evolving, the analysis unit identifies that technology as a potential threat. The analysis unit also compares it with past data to detect abnormal patterns and identify potential threats. For example, if there is a sudden increase in the frequency of appearance of a particular keyword or phrase, the analysis unit identifies that information as a potential threat. This allows potential threats to be identified by detecting abnormal patterns by comparing it with past data.

[0036] The analysis unit can cross-reference information from different industries and fields to identify new threats. For example, the generation AI analyzes and cross-references news articles and social media posts from different industries. For example, it combines information from the technology field and the consumer market to identify new threats. The analysis unit also analyzes patent information and cross-references technologies and inventions from different fields. For example, it combines technologies from the medical and IT fields to identify new threats. The generation AI also cross-references information from different industries and fields to identify new threats. For example, it combines trends and technologies from different industries to identify new threats. This makes it possible to identify new threats by cross-referencing information from different industries and fields.

[0037] The analysis unit can integrate different data sources to perform more comprehensive analysis. For example, the generation AI analyzes economic and market data in addition to news articles and social media posts to perform a comprehensive analysis. For example, it identifies potential threats based on economic indicators and market trends. The analysis unit also integrates economic and market data when analyzing patent information to understand technological evolution and market trends. For example, it analyzes the impact of new technologies on the market. The analysis unit also integrates different data sources to perform a comprehensive analysis. For example, it combines news articles, social media posts, economic and market data to identify potential threats. This enables more comprehensive analysis by integrating different data sources.

[0038] The warning unit can automatically set the priority of warnings and give priority to the most important warnings. For example, the generation AI evaluates the importance of warnings based on the analysis results and automatically sets the priority. For example, the warning priority is determined based on the impact and urgency of potential threats. In addition, the warning unit takes into account past data and user feedback when the generation AI sets the priority of warnings. For example, it gives priority to warnings for threats that have had a high impact in the past. In addition, the warning unit builds a system in which the generation AI automatically sets the priority of warnings and gives priority to the most important warnings. For example, it immediately gives priority to warnings for threats with a high urgency. In this way, by automatically setting the priority of warnings, it is possible to give priority to the most important warnings.

[0039] The warning unit can evaluate the importance of a new threat by comparing it with past warning history. For example, the generation AI analyzes past warning history to evaluate the importance of a new threat. For example, it determines the importance by comparing it with similar threats that occurred in the past. Furthermore, when the generation AI issues a warning, the warning unit evaluates the impact of the new threat by comparing it with past warning history. For example, it determines the importance of the new threat based on the impact of past threats. Furthermore, the warning unit constructs a system in which the generation AI evaluates the importance of a new threat based on past warning history and issues a warning. For example, it sets the priority of the warning by referring to past data. This allows the importance of a new threat to be evaluated by comparing it with past warning history.

[0040] The warning unit can issue a customized warning based on the user's job title and job content. For example, the generation AI analyzes the user's job title and job content and issues a customized warning. For example, the generation AI issues strategic warnings to management and warnings including specific countermeasures to field personnel. The warning unit also customizes the content of the warning based on the user's job content. For example, the generation AI issues warnings about market trends to marketing personnel and warnings about technological threats to technical personnel. The warning unit also builds a system in which the generation AI issues customized warnings based on the user's job title and job content. For example, the content of the warning can be adjusted based on the user's profile information. This allows for customized warnings to be issued based on the user's job title and job content, enabling more appropriate responses.

[0041] The warning unit can provide notifications compatible with different devices. For example, when the generation AI issues a warning, the warning unit provides a notification compatible with the user's smartphone or wearable device. For example, the warning unit sends a push notification to the smartphone and transmits the warning by vibration to the wearable device. The warning unit also builds a warning notification system compatible with different devices, and the generation AI sends the warning to the optimal device. For example, the warning unit analyzes the user's device usage status and provides the notification to the optimal device. The warning unit also provides an interface for providing notifications compatible with different devices when the generation AI issues a warning. For example, the warning is notified through a smartphone app or an app for the wearable device. In this way, by providing notifications compatible with different devices, the user can receive the warning on the optimal device.

[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 notification system may further include a behavior analysis unit that analyzes a user's behavioral history. The behavior analysis unit, for example, analyzes what information the user has shown interest in in the past and prioritizes notification of information of high interest. For example, a user who is interested in a particular technology field may be prioritized in notification of the latest information related to that field. The behavior analysis unit may also analyze a user's browsing history and click history to predict the user's interests. For example, if the user frequently views information about a particular company or technology, new information about that company or technology may be prioritized in notification. The behavior analysis unit may also analyze a user's behavioral patterns and send notifications at the optimal time. For example, if a user tends to check information outside of normal business hours, notifications may be sent during those times. This allows for more personalized notifications by analyzing a user's behavioral history.

[0044] The notification system may further include a schedule adjustment unit that takes the user's schedule into consideration when sending notifications. The schedule adjustment unit, for example, acquires the user's calendar information and can avoid sending notifications before and after important meetings or events. For example, the schedule adjustment unit can prevent the user from receiving notifications during a meeting. The schedule adjustment unit can also refrain from sending notifications outside the user's working hours. For example, the schedule adjustment unit can prevent the user from receiving notifications while on vacation or late at night. The schedule adjustment unit can also set optimal notification timing based on the user's schedule. For example, the schedule adjustment unit can prevent the user from receiving notifications during normal working hours. In this way, by sending notifications taking the user's schedule into consideration, the user's work efficiency can be improved.

[0045] The notification system may further include a health monitoring unit that monitors the user's health state and sends a notification according to the health state. The health monitoring unit may, for example, monitor the user's heart rate and sleep state, and send a notification only when the user's health state is good. For example, a notification may be sent after the user has had enough sleep. The health monitoring unit may also adjust the content of the notification according to the user's health state. For example, a brief notification may be sent when the user is tired. The health monitoring unit may also monitor the user's health state in real time and adjust the timing of the notification according to the health state. For example, a notification may be sent when the user is relaxed. In this way, by sending notifications taking the user's health state into consideration, the user's health can be supported and stress reduced.

[0046] The notification system may further include a location information unit that takes into account the user's geographical location information when sending notifications. For example, when the user is in a specific area, the location information unit may prioritize notification of information related to that area. For example, the user may receive business news about that area while on a business trip. The location information unit may also analyze the user's movement patterns and send notifications at the optimal timing. For example, a notification may be sent while the user is commuting. The location information unit may also monitor the user's geographical location information in real time and adjust the content of the notification based on the location information. For example, when the user is at a specific event venue, information related to the event may be notified. In this way, by sending notifications taking into account the user's geographical location information, more relevant information can be provided.

[0047] The notification system may further include a behavior prediction unit that analyzes a user's past behavioral data and predicts future behavior. The behavior prediction unit, for example, analyzes what information a user has responded to in the past and predicts future behavior. For example, a user who is interested in a particular technology field may be prioritized in receiving notifications of the latest information related to that field. The behavior prediction unit may also analyze a user's past behavioral patterns and send notifications at the optimal time. For example, if a user tends to check information outside of normal business hours, notifications may be sent during those times. The behavior prediction unit may also monitor a user's behavioral data in real time, predict future behavior, and adjust notification content accordingly. For example, if a user frequently views information about a particular company or technology, new information about that company or technology may be prioritized in notifications. This enables more personalized notifications by analyzing a user's past behavioral data.

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

[0049] Step 1: The information gathering department collects information from multiple online sources. For example, they can gather the latest industry trends from news sites, user reactions and trends from social media, and information on new technologies and inventions from patent databases. Step 2: In the analysis section, the generation AI analyzes the information collected by the information collection section. For example, the generation AI uses text generation AI (e.g., LLM) to analyze news articles and social media posts, and if there is a sudden increase in mentions of a particular company or technology, it determines that the company or technology is likely to be a potential threat. The generation AI also analyzes patent information to understand how new technologies are evolving. Step 3: The warning unit identifies potential threats based on the results of the analysis by the analysis unit and issues a warning. For example, if a specific emerging company is growing rapidly, detailed information about that company will be notified. Also, if a new technology appears on the market and that technology may be competitive, information about that technology will be notified. Step 4: The notification unit notifies the user of the warning issued by the warning unit. For example, the notification unit may send the warning to the user by email or push notification. The notification unit may also display the warning on a dashboard.

[0050] (Example 2) The notification system according to an embodiment of the present invention collects information from multiple online sources, analyzes it using a generative AI, identifies potential threats, and prompts strategic responses. This allows company executives and marketers to quickly become aware of the emergence of potential competitors and take strategic responses.

[0051] A notification system according to an embodiment includes an information collection unit, an analysis unit, a warning unit, and a notification unit. The information collection unit collects information from multiple online sources. For example, it collects the latest industry trends from news sites. The information collection unit can also collect user reactions and trends from social media. The information collection unit can also collect information on new technologies and inventions from patent databases. For example, it collects articles on the latest industry trends and emerging companies from news sites and user reactions and trends from social media. It also collects information on new technologies and inventions from patent databases. The analysis unit uses a generation AI to analyze the information collected by the information collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze news articles and social media posts. If there is a sudden increase in mentions of a particular company or technology, it determines that the company or technology is likely to be a potential threat. The generation AI also analyzes patent information to understand how new technologies are evolving. For example, the generation AI analyzes news articles and social media posts. If there is a sudden increase in mentions of a particular company or technology, it determines that the company or technology is likely to be a potential threat. The generative AI also analyzes patent information to understand how new technologies are evolving. The alerting unit identifies potential threats based on the results of the analysis by the analysis unit and issues alerts. For example, if a specific startup company is growing rapidly, it will notify detailed information about that company. Also, if a new technology appears on the market and there is a possibility that that technology will compete, it will notify information about that technology. For example, if a specific startup company is growing rapidly, it will notify detailed information about that company. Also, if a new technology appears on the market and there is a possibility that that technology will compete, it will notify information about that technology. The notification unit notifies of alerts issued by the alerting unit. For example, the notification unit may send alerts to responsible personnel via email or push notification. The notification unit may also display alerts on a dashboard. For example, the notification unit may send alerts to responsible personnel via email or push notification. The notification unit may also display alerts on a dashboard.As a result, the notification system according to the embodiment can prompt strategic responses by identifying potential threats in a timely manner and issuing warnings. For example, by receiving a warning, a responsible person can take prompt strategic responses. In addition, the notification system can provide highly accurate analysis results because it analyzes information using generative AI. Furthermore, the notification system can provide comprehensive information because it collects information from multiple sources on the Internet.

[0052] The information gathering unit evaluates the reliability of the information to be collected and can prioritize the collection of only highly reliable information. For example, the information gathering unit calculates a reliability score for the information source in order for the generation AI to evaluate the reliability of information collected from news sites and social media. For example, it prioritizes the collection of information from highly reliable news sites and official accounts. In addition, the information gathering unit allows the generation AI to analyze the reliability of the source and author of the information in order to evaluate the reliability of the collected information. For example, it determines reliability based on past performance and evaluations. In addition, the information gathering unit analyzes the degree of agreement from multiple information sources in order for the generation AI to evaluate the reliability of the information. For example, if the same content is reported from multiple highly reliable information sources, it prioritizes the collection of that information. This prioritizes the collection of highly reliable information, improving the accuracy of the analysis results.

[0053] The information collection unit can also collect information from industry-specific forums or expert blogs. For example, the information collection unit monitors specific keywords and topics so that the generation AI can collect information from industry-specific forums. For example, it collects the latest discussions from technology and marketing forums. In addition, the information collection unit regularly checks the blogs of specific experts and influencers so that the generation AI can collect information from expert blogs. For example, it collects the opinions of industry leaders and experts. In addition, when the generation AI collects information from industry-specific forums and expert blogs, the information collection unit automatically extracts related topics and keywords and prioritizes the collection of important information. This allows the generation AI to obtain more specialized information by collecting information from industry-specific forums and expert blogs.

[0054] The information collection unit can use the emotion estimation function to prioritize the collection of information that evokes particularly strong emotional reactions from the collected information. In the information collection unit, for example, the generation AI analyzes social media posts and comments and calculates an emotion score. For example, it prioritizes the collection of posts with strong positive or negative emotions. The information collection unit also uses the emotion estimation function to analyze the content of news articles and blogs and identify information that evokes strong emotional reactions. For example, it prioritizes the collection of articles with high emotion scores. In addition, the information collection unit uses the emotion estimation function to filter information that evokes particularly strong emotional reactions from the collected information, and prioritizes the collection of important information. In this way, by prioritizing the collection of information that evokes strong emotional reactions, important information is not overlooked.

[0055] The information collection unit can also collect information from audio data or video data, and integrate it with text data for analysis. In the information collection unit, for example, the generation AI analyzes audio data and extracts important information. For example, the content of podcasts and webinars is converted into text data and used for analysis. In addition, to collect information from video data, the generation AI analyzes video subtitles and audio and extracts important information. For example, the content of YouTube videos and online seminars is analyzed. In addition, the information collection unit integrates audio data and video data with text data to perform comprehensive analysis. For example, the content of audio and video is combined with text data for analysis to identify important information. This enables more comprehensive information analysis by integrating audio data and video data with text data for analysis.

[0056] The information collection unit can automatically translate information in different languages ​​and collect information from a global perspective. For example, the generation AI automatically translates news articles and social media posts in different languages ​​to collect information from a global perspective. For example, it translates and analyzes information in English, Chinese, Spanish, etc. The information collection unit also uses the automatic translation function to collect patent information in different languages ​​and grasp global technology trends. For example, it collects information from patent databases in various countries, translates it, and analyzes it. The information collection unit also automatically translates information in different languages ​​using the generation AI to identify trends and threats from a global perspective. For example, it translates and analyzes international news and social media posts. This allows information to be collected from a global perspective by automatically translating information in different languages.

[0057] The information collection unit can estimate the user's emotions in real time when entering information and make suggestions that elicit positive emotions. For example, the information collection unit uses a generation AI to analyze the user's facial expressions and voice when entering information and estimate emotions in real time. For example, it uses a camera or microphone to analyze the user's emotions and make positive suggestions. The information collection unit also uses the emotion estimation function to provide an interface that elicits positive emotions when the user enters information. For example, it presents encouraging messages and success stories. The information collection unit also provides feedback in real time based on the emotion estimation data when the user enters information and offers advice that strengthens positive emotions. For example, it displays appropriate encouragement or praise based on the input content. In this way, the user's emotions can be estimated in real time and suggestions that elicit positive emotions can be made, thereby improving user satisfaction.

[0058] The analysis unit can identify potential threats by analyzing the frequency of specific keywords and phrases in the collected information. For example, the generation AI analyzes news articles and social media posts to calculate the frequency of specific keywords and phrases. For example, if there is a sudden increase in mentions of a startup or new technology, the analysis unit identifies that information as a potential threat. The analysis unit also analyzes patent information and analyzes the frequency of keywords related to specific technologies and inventions. For example, if a new technology is rapidly evolving, the analysis unit identifies that technology as a potential threat. The analysis unit also analyzes the frequency of specific keywords and phrases in the information collected by the generation AI to identify trends and threats. For example, if there is an increase in mentions of a specific company or technology, the analysis unit identifies that information as a potential threat. This makes it possible to identify potential threats by analyzing the frequency of specific keywords and phrases.

[0059] The analysis unit detects abnormal patterns by comparing it with past data and can identify potential threats. For example, the generation AI compares it with past news articles and social media posts to detect abnormal patterns. For example, if there is a sudden increase in mentions of a particular company or technology, the analysis unit identifies that information as a potential threat. The analysis unit also analyzes patent information and compares it with past data to detect abnormal patterns. For example, if a new technology is rapidly evolving, the analysis unit identifies that technology as a potential threat. The analysis unit also compares it with past data to detect abnormal patterns and identify potential threats. For example, if there is a sudden increase in the frequency of appearance of a particular keyword or phrase, the analysis unit identifies that information as a potential threat. This allows potential threats to be identified by detecting abnormal patterns by comparing it with past data.

[0060] The analysis unit uses the emotion estimation function to predict the user's emotional reaction based on the analysis results and identify threats that may cause a strong emotional reaction. For example, the analysis unit uses the generation AI to analyze news articles and social media posts and calculate an emotion score. For example, it prioritizes analyzing posts with strong negative emotions and identifies potential threats. The analysis unit also uses the emotion estimation function to predict the user's emotional reaction based on the analysis results. For example, if information about a specific company or technology is likely to cause a strong emotional reaction, it identifies that information as a potential threat. The analysis unit also uses the emotion estimation function to predict the user's emotional reaction based on the analysis results and identify threats that may cause a strong emotional reaction. For example, it prioritizes analyzing information with a high emotion score. This makes it possible to identify threats that may cause a strong emotional reaction by predicting the user's emotional reaction.

[0061] The analysis unit can cross-reference information from different industries and fields to identify new threats. For example, the generation AI analyzes and cross-references news articles and social media posts from different industries. For example, it combines information from the technology field and the consumer market to identify new threats. The analysis unit also analyzes patent information and cross-references technologies and inventions from different fields. For example, it combines technologies from the medical and IT fields to identify new threats. The generation AI also cross-references information from different industries and fields to identify new threats. For example, it combines trends and technologies from different industries to identify new threats. This makes it possible to identify new threats by cross-referencing information from different industries and fields.

[0062] The analysis unit can integrate different data sources to perform more comprehensive analysis. For example, the generation AI analyzes economic and market data in addition to news articles and social media posts to perform a comprehensive analysis. For example, it identifies potential threats based on economic indicators and market trends. The analysis unit also integrates economic and market data when analyzing patent information to understand technological evolution and market trends. For example, it analyzes the impact of new technologies on the market. The analysis unit also integrates different data sources to perform a comprehensive analysis. For example, it combines news articles, social media posts, economic and market data to identify potential threats. This enables more comprehensive analysis by integrating different data sources.

[0063] The analysis unit uses the emotion estimation function to monitor the user's emotional reactions to the analysis results in real time and continuously adjust the optimal analysis method. For example, the generation AI in the analysis unit monitors the user's emotional reactions to the analysis results in real time and adjusts the analysis method based on that data. For example, the analysis method is changed if there are a lot of negative reactions. The analysis unit also uses the emotion estimation function to collect the user's emotional reactions to the analysis results and continuously adjusts the optimal analysis method. For example, it prioritizes the adoption of methods that have a lot of positive reactions. The analysis unit also uses the emotion estimation function to monitor the user's emotional reactions to the analysis results in real time and dynamically adjusts the analysis method. For example, it optimizes the analysis method in response to changes in the user's emotions. In this way, the analysis accuracy is improved by monitoring the user's emotional reactions in real time and continuously adjusting the optimal analysis method.

[0064] The warning unit can automatically set the priority of warnings and give priority to the most important warnings. For example, the generation AI evaluates the importance of warnings based on the analysis results and automatically sets the priority. For example, the warning priority is determined based on the impact and urgency of potential threats. In addition, the warning unit takes into account past data and user feedback when the generation AI sets the priority of warnings. For example, it gives priority to warnings for threats that have had a high impact in the past. In addition, the warning unit builds a system in which the generation AI automatically sets the priority of warnings and gives priority to the most important warnings. For example, it immediately gives priority to warnings for threats with a high urgency. In this way, by automatically setting the priority of warnings, it is possible to give priority to the most important warnings.

[0065] The warning unit can evaluate the importance of a new threat by comparing it with past warning history. For example, the generation AI analyzes past warning history to evaluate the importance of a new threat. For example, it determines the importance by comparing it with similar threats that occurred in the past. Furthermore, when the generation AI issues a warning, the warning unit evaluates the impact of the new threat by comparing it with past warning history. For example, it determines the importance of the new threat based on the impact of past threats. Furthermore, the warning unit constructs a system in which the generation AI evaluates the importance of a new threat based on past warning history and issues a warning. For example, it sets the priority of the warning by referring to past data. This allows the importance of a new threat to be evaluated by comparing it with past warning history.

[0066] The warning unit uses the emotion estimation function to consider the emotional state of the user receiving the warning and issue the warning at the optimal timing. For example, the generation AI in the warning unit analyzes the user's emotional state in real time and issues the warning at the optimal timing. For example, the warning is delayed if the user is feeling stressed. The warning unit also uses the emotion estimation function to consider the user's emotional state and adjust the timing of the warning. For example, it issues a warning when the user is relaxed. The warning unit also uses the emotion estimation function to build a system in which the generation AI monitors the user's emotional state and issues the warning at the optimal timing. For example, it issues a warning when the user's emotion score is stable. This makes it possible to issue a warning at the optimal timing by taking the user's emotional state into consideration.

[0067] The warning unit can issue a customized warning based on the user's job title and job content. For example, the generation AI analyzes the user's job title and job content and issues a customized warning. For example, the generation AI issues strategic warnings to management and warnings including specific countermeasures to field personnel. The warning unit also customizes the content of the warning based on the user's job content. For example, the generation AI issues warnings about market trends to marketing personnel and warnings about technological threats to technical personnel. The warning unit also builds a system in which the generation AI issues customized warnings based on the user's job title and job content. For example, the content of the warning can be adjusted based on the user's profile information. This allows for customized warnings to be issued based on the user's job title and job content, enabling more appropriate responses.

[0068] The warning unit can provide notifications compatible with different devices. For example, when the generation AI issues a warning, the warning unit provides a notification compatible with the user's smartphone or wearable device. For example, the warning unit sends a push notification to the smartphone and transmits the warning by vibration to the wearable device. The warning unit also builds a warning notification system compatible with different devices, and the generation AI sends the warning to the optimal device. For example, the warning unit analyzes the user's device usage status and provides the notification to the optimal device. The warning unit also provides an interface for providing notifications compatible with different devices when the generation AI issues a warning. For example, the warning is notified through a smartphone app or an app for the wearable device. In this way, by providing notifications compatible with different devices, the user can receive the warning on the optimal device.

[0069] The warning unit can use the emotion estimation function to monitor the emotional response of the user receiving the warning in real time and adjust the content of the warning. For example, when the generation AI issues a warning, the warning unit monitors the user's emotional response in real time and adjusts the content of the warning. For example, the warning content is softened if the user is feeling stressed. The warning unit also uses the emotion estimation function to analyze the user's emotional response and adjust the content of the warning. For example, a more detailed warning is issued when the user is relaxed. The warning unit also builds a system in which the generation AI uses the emotion estimation function to monitor the user's emotional response in real time and dynamically adjust the content of the warning. For example, the level of detail of the warning is changed according to the user's emotion score. This makes it possible to provide the user with the most appropriate warning by monitoring the user's emotional response in real time and adjusting the content of the warning.

[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 notification system may further include a behavior analysis unit that analyzes a user's behavioral history. The behavior analysis unit, for example, analyzes what information the user has shown interest in in the past and prioritizes notification of information of high interest. For example, a user who is interested in a particular technology field may be prioritized in notification of the latest information related to that field. The behavior analysis unit may also analyze a user's browsing history and click history to predict the user's interests. For example, if the user frequently views information about a particular company or technology, new information about that company or technology may be prioritized in notification. The behavior analysis unit may also analyze a user's behavioral patterns and send notifications at the optimal time. For example, if a user tends to check information outside of normal business hours, notifications may be sent during those times. This allows for more personalized notifications by analyzing a user's behavioral history.

[0072] The notification system may further include an emotion customization unit that estimates the user's emotion and customizes the notification content based on the estimated emotion. For example, the emotion customization unit may refrain from sending low-urgency notifications when the user is feeling stressed. For example, detailed information may be provided only when the user is relaxed. The emotion customization unit may also adjust the tone of the notification according to the user's emotional state. For example, if the user is feeling positive, an encouraging message may be added. The emotion customization unit may also monitor changes in the user's emotion in real time and dynamically adjust the notification content. For example, if the user is feeling negative, the notification content may be simplified and information that elicits positive emotions may be provided. In this way, customizing the notification content based on the user's emotion can improve user satisfaction.

[0073] The notification system may further include a schedule adjustment unit that takes the user's schedule into consideration when sending notifications. The schedule adjustment unit, for example, acquires the user's calendar information and can avoid sending notifications before and after important meetings or events. For example, the schedule adjustment unit can prevent the user from receiving notifications during a meeting. The schedule adjustment unit can also refrain from sending notifications outside the user's working hours. For example, the schedule adjustment unit can prevent the user from receiving notifications while on vacation or late at night. The schedule adjustment unit can also set optimal notification timing based on the user's schedule. For example, the schedule adjustment unit can prevent the user from receiving notifications during normal working hours. In this way, by sending notifications taking the user's schedule into consideration, the user's work efficiency can be improved.

[0074] The notification system may further include an emotion prioritization unit that estimates the user's emotion and sets notification priorities based on the estimated emotion. For example, if the user is feeling stressed, the emotion prioritization unit may prioritize only notifications with high urgency. For example, detailed information may be provided only when the user is relaxed. The emotion prioritization unit may also adjust the frequency of notifications according to the user's emotional state. For example, if the user is feeling positive, the frequency of notifications may be increased. The emotion prioritization unit may also monitor changes in the user's emotion in real time and dynamically adjust the priority of notifications. For example, if the user is feeling negative, the frequency of notifications may be reduced and information that elicits positive emotions may be provided. In this way, by setting notification priorities based on the user's emotion, the user's stress may be reduced and satisfaction may be improved.

[0075] The notification system may further include a health monitoring unit that monitors the user's health state and sends a notification according to the health state. The health monitoring unit may, for example, monitor the user's heart rate and sleep state, and send a notification only when the user's health state is good. For example, a notification may be sent after the user has had enough sleep. The health monitoring unit may also adjust the content of the notification according to the user's health state. For example, a brief notification may be sent when the user is tired. The health monitoring unit may also monitor the user's health state in real time and adjust the timing of the notification according to the health state. For example, a notification may be sent when the user is relaxed. In this way, by sending notifications taking the user's health state into consideration, the user's health can be supported and stress reduced.

[0076] The notification system may further include an emotion personalization unit that estimates a user's emotion and personalizes the content of the notification based on the estimated emotion. For example, the emotion personalization unit can provide a notification including detailed information or additional suggestions when the user has positive emotions. For example, when the user is relaxed, it can provide relevant news or trending information. The emotion personalization unit can also adjust the tone of the notification according to the user's emotional state. For example, when the user is expressing negative emotions, it can add an encouraging message. The emotion personalization unit can also monitor changes in the user's emotion in real time and dynamically adjust the content of the notification. For example, when the user is expressing positive emotions, it can provide a notification including detailed information or suggestions. In this way, by personalizing the notification content based on the user's emotion, user satisfaction can be improved.

[0077] The notification system may further include a location information unit that takes into account the user's geographical location information when sending notifications. For example, when the user is in a specific area, the location information unit may prioritize notification of information related to that area. For example, the user may receive business news about that area while on a business trip. The location information unit may also analyze the user's movement patterns and send notifications at the optimal timing. For example, a notification may be sent while the user is commuting. The location information unit may also monitor the user's geographical location information in real time and adjust the content of the notification based on the location information. For example, when the user is at a specific event venue, information related to the event may be notified. In this way, by sending notifications taking into account the user's geographical location information, more relevant information can be provided.

[0078] The notification system may further include an emotion timing unit that estimates the user's emotion and adjusts the timing of notifications based on the estimated emotion. For example, the emotion timing unit may delay notifications when the user is feeling stressed. For example, notifications may be sent only when the user is relaxed. The emotion timing unit may also adjust the frequency of notifications according to the user's emotional state. For example, the frequency of notifications may be increased when the user is feeling positive. The emotion timing unit may also monitor changes in the user's emotion in real time and dynamically adjust the timing of notifications. For example, when the user is feeling negative, the frequency of notifications may be reduced and information that elicits positive emotions may be provided. In this way, adjusting the timing of notifications based on the user's emotion can reduce the user's stress and improve satisfaction.

[0079] The notification system may further include a behavior prediction unit that analyzes a user's past behavioral data and predicts future behavior. The behavior prediction unit, for example, analyzes what information a user has responded to in the past and predicts future behavior. For example, a user who is interested in a particular technology field may be prioritized in receiving notifications of the latest information related to that field. The behavior prediction unit may also analyze a user's past behavioral patterns and send notifications at the optimal time. For example, if a user tends to check information outside of normal business hours, notifications may be sent during those times. The behavior prediction unit may also monitor a user's behavioral data in real time, predict future behavior, and adjust notification content accordingly. For example, if a user frequently views information about a particular company or technology, new information about that company or technology may be prioritized in notifications. This enables more personalized notifications by analyzing a user's past behavioral data.

[0080] The notification system may further include an emotional content unit that estimates the user's emotions and adjusts the content of the notification based on the estimated emotions. For example, the emotional content unit may provide a notification including detailed information or additional suggestions when the user has positive emotions. For example, when the user is relaxed, the emotional content unit may provide relevant news or trending information. The emotional content unit may also adjust the tone of the notification according to the user's emotional state. For example, when the user is expressing negative emotions, the emotional content unit may include an encouraging message. The emotional content unit may also monitor changes in the user's emotions in real time and dynamically adjust the content of the notification. For example, when the user is expressing positive emotions, the notification may include detailed information or suggestions. In this way, adjusting the content of the notification based on the user's emotions can improve user satisfaction.

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

[0082] Step 1: The information gathering department collects information from multiple online sources. For example, they can gather the latest industry trends from news sites, user reactions and trends from social media, and information on new technologies and inventions from patent databases. Step 2: In the analysis section, the generation AI analyzes the information collected by the information collection section. For example, the generation AI uses text generation AI (e.g., LLM) to analyze news articles and social media posts, and if there is a sudden increase in mentions of a particular company or technology, it determines that the company or technology is likely to be a potential threat. The generation AI also analyzes patent information to understand how new technologies are evolving. Step 3: The warning unit identifies potential threats based on the results of the analysis by the analysis unit and issues a warning. For example, if a specific emerging company is growing rapidly, detailed information about that company will be notified. Also, if a new technology appears on the market and that technology may be competitive, information about that technology will be notified. Step 4: The notification unit notifies the user of the warning issued by the warning unit. For example, the notification unit may send the warning to the user by email or push notification. The notification unit may also display the warning on a dashboard.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information gathering department that collects information from multiple sources on the Internet; an analysis unit that analyzes the information collected by the information collection unit; a warning unit that identifies a potential threat based on the results of the analysis by the analysis unit and issues a warning; a notification unit that notifies the user of the warning issued by the warning unit. A system characterized by:

2. The information collecting unit Prioritize the information you collect that evokes the strongest emotional response.

2. The system of claim 1.

3. The analysis unit Analyzing the frequency of specific keywords and phrases in the collected information to identify potential threats.

2. The system of claim 1.

4. The warning unit Automatically prioritize alerts to ensure the most important ones are notified first 2. The system of claim 1.

5. The information collecting unit Estimates emotions in real time when users enter information and makes suggestions that elicit positive emotions 2. The system of claim 1.

Images