system

Abstract

An object of a system according to an embodiment is to generate a highly credible news article based on real-time information from an SNS and to predict a future event.SOLUTION: A system includes an information collection part, a news generation part, a credibility evaluation part, and a future prediction part. The information collection unit collects real-time information from the SNS. The news generation unit generates a news article based on the information collected by the information collection unit. The credibility evaluation unit evaluates credibility of the news article generated by the news generation unit. The future prediction unit predicts a future event based on the information evaluated by the credibility evaluation 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] Previous technology had the problem of not being able to efficiently collect real-time information from social media, evaluate its credibility, and then predict future events.

[0005] The system according to the embodiment aims to generate highly credible news articles based on real-time information from SNS and to predict future events. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a news generation unit, a credibility evaluation unit, and a future forecast unit. The information collection unit collects real-time information from social networking sites. The news generation unit generates news articles based on the information collected by the information collection unit. The credibility evaluation unit evaluates the credibility of the news articles generated by the news generation unit. The future forecast unit predicts future events based on the information evaluated by the credibility evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate highly credible news articles based on real-time information from social media and predict future events. [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) A news delivery system according to an embodiment of the present invention collects real-time information from social media and provides news automatically generated by a generation AI. Furthermore, the system evaluates credibility based on the number of posts and the source of the information, and is equipped with a future forecasting function to predict events that may occur in the future. This allows the news delivery system to provide reliable news in real time and predict future events.

[0029] A news provision system according to an embodiment includes an information collection unit, a news generation unit, a credibility evaluation unit, and a future forecast unit. The information collection unit collects real-time information from social media. For example, the generation AI collects posts on social media in real time based on specific keywords or hashtags. The information collection unit also inputs prompts including the keywords or hashtags to be collected to the generation AI, and the generation AI collects information based on the prompts. The news generation unit generates news articles based on the information collected by the information collection unit. For example, the generation AI analyzes the collected posts, extracts important information, and creates news articles. The generation AI also generates news articles based on the collected posting data and provides them to users in real time. The credibility evaluation unit evaluates the credibility of the news articles generated by the news generation unit. For example, the generation AI determines credibility based on the number of posts and the information source. If the same information is posted by multiple highly reliable information sources, the generation AI evaluates the credibility of the information as high. The future forecast unit predicts future events based on the information evaluated by the credibility evaluation unit. For example, if there is a sudden increase in posts about a particular topic, the generation AI predicts that an event related to that topic is likely to occur in the near future. The generation AI also makes future forecasts based on collected post data and past data. As a result, the news delivery system according to the embodiment can provide reliable news in real time and predict future events. For example, users can receive disaster information in real time and respond quickly. They can also understand event information in advance and make plans.

[0030] The information collection unit can also collect information from online forums and blogs other than SNS. For example, the information collection unit uses generation AI to build a system that collects information not only from SNS but also from online forums and blogs. For example, it automatically collects forum posts and blog articles related to a specific topic. The information collection unit also analyzes online forum and blog posts to collect highly relevant information. For example, the generation AI collects forum posts and blog articles related to specific keywords or topics and uses them to generate news articles. This broadens the scope of information collection, making it possible to collect more diverse information.

[0031] The information collection unit can also analyze the content of images and videos and collect it in combination with text information. For example, the information collection unit uses generative AI to build a system that analyzes the content of images and videos on social media and collects it in combination with text information. For example, image recognition technology is used to analyze text and objects within images. The information collection unit also uses video analysis algorithms to analyze the content of videos and collect it in combination with text information. For example, generative AI analyzes the content of images and videos, extracts important information, and integrates it with text information. This allows for the collection of more diverse information by also analyzing the content of images and videos.

[0032] The information collection unit can automatically translate social media posts in different languages ​​and collect information from an international perspective. The information collection unit, for example, uses generation AI to automatically translate social media posts in different languages ​​and build a system to collect information from an international perspective. For example, it automatically translates posts in English, French, Chinese, etc. The information collection unit also uses machine translation technology to translate and collect posts in different languages ​​in real time. For example, the generation AI uses neural network translation to perform highly accurate automatic translation. This makes it possible to provide information from an international perspective by collecting information in different languages.

[0033] When generating news articles, the news generation unit can refer to past news data and compare it with similar events. For example, the news generation unit uses generation AI to build a system that references past news data when generating news articles and compares it with similar events. For example, it compares past disasters or incidents with current events. The news generation unit also evaluates the relevance of current events based on past news data. For example, the generation AI analyzes past news data and compares it with similar events. This allows the content of news articles to be enriched by referring to past data.

[0034] The credibility evaluation unit can refer to past reliability data and take long-term reliability into consideration when evaluating the reliability of an information source. For example, the credibility evaluation unit uses a generation AI to build a system that refers to past reliability data when evaluating the reliability of an information source. For example, the evaluation is based on past posting history and reliability score. The credibility evaluation unit also evaluates the reliability of an information source taking long-term reliability into consideration. For example, the generation AI analyzes fluctuations in reliability over a certain period of time and evaluates long-term performance. This allows the reliability of an information source to be evaluated more accurately by referring to past reliability data.

[0035] The credibility evaluation unit can evaluate credibility by analyzing not only the number of posts but also the degree of similarity in the content and writing style of the posts. For example, the credibility evaluation unit uses generation AI to analyze not only the number of posts but also the degree of similarity in the content and writing style of the posts, and builds a system to evaluate credibility. For example, it checks whether posts with the same content are sent from multiple information sources. The credibility evaluation unit also evaluates credibility based on the degree of similarity in the content and writing style of the posts. For example, the generation AI uses text analysis technology to evaluate the degree of similarity in the content and writing style of the posts. This allows for a more accurate evaluation of credibility by analyzing the degree of similarity in the content and writing style of the posts.

[0036] The future forecasting unit can take into account not only past data but also current trend and event information when making future forecasts. For example, the future forecasting unit uses a generation AI to build a system that takes into account not only past data but also current trend and event information when making future forecasts. For example, the future forecasting unit makes future forecasts based on current social media trends and news. The future forecasting unit also improves the accuracy of future forecasts based on current trend and event information. For example, the generation AI analyzes current trend data and event information and reflects it in the future forecast. In this way, the accuracy of future forecasts can be improved by taking into account current trend and event information.

[0037] The future forecasting unit can use a combination of different predictive models to improve the accuracy of future forecasts. The future forecasting unit, for example, uses a generation AI to build a system that uses a combination of different predictive models to improve the accuracy of future forecasts. For example, a machine learning model and a statistical model are combined. The future forecasting unit also makes future forecasts based on different predictive models. For example, the generation AI combines a machine learning model and a statistical model to improve the accuracy of future forecasts. In this way, the accuracy of future forecasts can be improved by combining different predictive models.

[0038] The future forecasting unit can make future forecasts from a global perspective using data from different regions and cultural spheres. The future forecasting unit, for example, uses generation AI to build a system that makes future forecasts from a global perspective using data from different regions and cultural spheres. For example, future forecasts are made based on social media data and news from each country. The future forecasting unit also makes future forecasts based on data from different regions and cultural spheres. For example, the generation AI analyzes data from different regions and cultural spheres and makes future forecasts from a global perspective. This makes it possible to make future forecasts from a global perspective by using data from different regions and cultural spheres.

[0039] The future forecast unit can convert the results of the future forecast into visual notes or mind maps to make them easier to understand visually. The future forecast unit, for example, uses a generation AI to build a system that converts the results of the future forecast into visual notes or mind maps. For example, the results of the future forecast are shown using diagrams or icons. The future forecast unit also uses visual notes or mind maps to make the results of the future forecast easier to understand visually. For example, the generation AI visually organizes the results of the future forecast and provides them to the user. As a result, by converting them into visual notes or mind maps, the results of the future forecast become easier to understand visually.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The news delivery system can further include a personalization unit that customizes news based on the user's interests. For example, the system can analyze the user's past browsing history and search history to provide news related to topics of interest to the user. The personalization unit can also provide local news based on the user's location information. Furthermore, the system can select appropriate news by taking into account the user's attribute information, such as age and occupation. This allows the system to provide news that is more relevant to the user.

[0042] The news delivery system further includes a voice recognition unit, which can provide news based on a user's voice command. For example, if a user issues a voice command such as "Tell me the latest sports news," the voice recognition unit analyzes the command and displays related news. The voice recognition unit can also analyze the user's voice tone and speed to estimate their emotional state. This allows for intuitive news delivery using voice commands.

[0043] The news delivery system can further include a feedback collection unit that collects user feedback and improves the quality of news. For example, users can leave ratings and comments on news articles, and the content and presentation of the news can be improved based on that feedback. The feedback collection unit can also analyze user feedback to identify common problems and areas for improvement. This makes it possible to provide news that reflects user opinions.

[0044] The news providing system may further include a health monitoring unit that monitors the user's health status and provides health-related news. For example, the health monitoring unit may analyze the user's heart rate and sleep data and provide health advice and news. The health monitoring unit may also provide news about appropriate exercise and diet according to the user's health status. This makes it possible to provide news that supports the user's health.

[0045] The news delivery system may further include a social analysis unit that analyzes the user's social network and provides news that the user's friends and followers are interested in. For example, news that the user's friends have shared or commented on may be displayed preferentially. The social analysis unit may also analyze topics that are trending within the user's social network and provide related news. This allows news to be delivered based on the user's social network.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The information collection unit collects real-time information from social media. For example, the generation AI collects posts on social media in real time based on specific keywords or hashtags. The information collection unit also inputs prompts containing the keywords or hashtags to be collected into the generation AI, and the generation AI collects information based on the prompts. Step 2: The news generation unit generates news articles based on the information collected by the information collection unit. For example, the generation AI analyzes the collected posts, extracts important information, and creates news articles. The generation AI also generates news articles based on the collected post data and provides them to users in real time. Step 3: The credibility evaluation unit evaluates the credibility of the news articles generated by the news generation unit. For example, the generation AI determines credibility based on the number of posts and the source of the information. In addition, if the same information is posted by multiple reliable sources, the generation AI evaluates the information as being highly credible. Step 4: The future prediction unit predicts future events based on the information evaluated by the credibility evaluation unit. For example, if there is a sudden increase in posts on a particular topic, the generation AI predicts that an event related to that topic is likely to occur in the near future. The generation AI also makes future predictions based on the collected post data and past data.

[0048] (Example 2) A news delivery system according to an embodiment of the present invention collects real-time information from social media and provides news automatically generated by a generation AI. Furthermore, the system evaluates credibility based on the number of posts and the source of the information, and is equipped with a future forecasting function to predict events that may occur in the future. This allows the news delivery system to provide reliable news in real time and predict future events.

[0049] A news provision system according to an embodiment includes an information collection unit, a news generation unit, a credibility evaluation unit, and a future forecast unit. The information collection unit collects real-time information from social media. For example, the generation AI collects posts on social media in real time based on specific keywords or hashtags. The information collection unit also inputs prompts including the keywords or hashtags to be collected to the generation AI, and the generation AI collects information based on the prompts. The news generation unit generates news articles based on the information collected by the information collection unit. For example, the generation AI analyzes the collected posts, extracts important information, and creates news articles. The generation AI also generates news articles based on the collected posting data and provides them to users in real time. The credibility evaluation unit evaluates the credibility of the news articles generated by the news generation unit. For example, the generation AI determines credibility based on the number of posts and the information source. If the same information is posted by multiple highly reliable information sources, the generation AI evaluates the credibility of the information as high. The future forecast unit predicts future events based on the information evaluated by the credibility evaluation unit. For example, if there is a sudden increase in posts about a particular topic, the generation AI predicts that an event related to that topic is likely to occur in the near future. The generation AI also makes future forecasts based on collected post data and past data. As a result, the news delivery system according to the embodiment can provide reliable news in real time and predict future events. For example, users can receive disaster information in real time and respond quickly. They can also understand event information in advance and make plans.

[0050] The information collection unit can analyze the emotions of posts, not just specific keywords and hashtags, and filter information based on the intensity and type of emotion. For example, the information collection unit uses a generation AI to analyze the emotions contained in posts on social media and quantify the intensity and type of emotion. For example, emotions such as joy, anger, and sadness are scored, and posts with high specific emotion scores are preferentially collected. The information collection unit also filters highly relevant information based on the results of the emotion analysis. For example, the generation AI uses an emotion dictionary to analyze the emotions of posts and score the intensity of the emotion. This allows more relevant information to be collected using emotion analysis.

[0051] The information collection unit can also collect information from online forums and blogs other than SNS. For example, the information collection unit uses generation AI to build a system that collects information not only from SNS but also from online forums and blogs. For example, it automatically collects forum posts and blog articles related to a specific topic. The information collection unit also analyzes online forum and blog posts to collect highly relevant information. For example, the generation AI collects forum posts and blog articles related to specific keywords or topics and uses them to generate news articles. This broadens the scope of information collection, making it possible to collect more diverse information.

[0052] The information collection unit can also analyze the content of images and videos and collect it in combination with text information. For example, the information collection unit uses generative AI to build a system that analyzes the content of images and videos on social media and collects it in combination with text information. For example, image recognition technology is used to analyze text and objects within images. The information collection unit also uses video analysis algorithms to analyze the content of videos and collect it in combination with text information. For example, generative AI analyzes the content of images and videos, extracts important information, and integrates it with text information. This allows for the collection of more diverse information by also analyzing the content of images and videos.

[0053] The information collection unit can automatically translate social media posts in different languages ​​and collect information from an international perspective. The information collection unit, for example, uses generation AI to automatically translate social media posts in different languages ​​and build a system to collect information from an international perspective. For example, it automatically translates posts in English, French, Chinese, etc. The information collection unit also uses machine translation technology to translate and collect posts in different languages ​​in real time. For example, the generation AI uses neural network translation to perform highly accurate automatic translation. This makes it possible to provide information from an international perspective by collecting information in different languages.

[0054] The news generation unit performs sentiment analysis when generating news articles and can use expressions that are easy to empathize with emotionally. The news generation unit, for example, uses generation AI to perform sentiment analysis when generating news articles and builds a system that uses expressions that are easy to empathize with emotionally. For example, expressions that evoke positive emotions are used preferentially. The news generation unit also selects expressions that are easy to empathize with emotionally based on the results of the sentiment analysis. For example, the generation AI uses an emotion dictionary to analyze the emotions of a news article and selects expressions that are easy to empathize with. In this way, using expressions that are easy to empathize with emotionally makes it easier to attract user attention.

[0055] When generating news articles, the news generation unit can refer to past news data and compare it with similar events. For example, the news generation unit uses generation AI to build a system that references past news data when generating news articles and compares it with similar events. For example, it compares past disasters or incidents with current events. The news generation unit also evaluates the relevance of current events based on past news data. For example, the generation AI analyzes past news data and compares it with similar events. This allows the content of news articles to be enriched by referring to past data.

[0056] The credibility evaluation unit can refer to past reliability data and take long-term reliability into consideration when evaluating the reliability of an information source. For example, the credibility evaluation unit uses a generation AI to build a system that refers to past reliability data when evaluating the reliability of an information source. For example, the evaluation is based on past posting history and reliability score. The credibility evaluation unit also evaluates the reliability of an information source taking long-term reliability into consideration. For example, the generation AI analyzes fluctuations in reliability over a certain period of time and evaluates long-term performance. This allows the reliability of an information source to be evaluated more accurately by referring to past reliability data.

[0057] The credibility evaluation unit can evaluate credibility by analyzing not only the number of posts but also the degree of similarity in the content and writing style of the posts. For example, the credibility evaluation unit uses generation AI to analyze not only the number of posts but also the degree of similarity in the content and writing style of the posts, and builds a system to evaluate credibility. For example, it checks whether posts with the same content are sent from multiple information sources. The credibility evaluation unit also evaluates credibility based on the degree of similarity in the content and writing style of the posts. For example, the generation AI uses text analysis technology to evaluate the degree of similarity in the content and writing style of the posts. This allows for a more accurate evaluation of credibility by analyzing the degree of similarity in the content and writing style of the posts.

[0058] The future forecasting unit can take into account not only past data but also current trend and event information when making future forecasts. For example, the future forecasting unit uses a generation AI to build a system that takes into account not only past data but also current trend and event information when making future forecasts. For example, the future forecasting unit makes future forecasts based on current social media trends and news. The future forecasting unit also improves the accuracy of future forecasts based on current trend and event information. For example, the generation AI analyzes current trend data and event information and reflects it in the future forecast. In this way, the accuracy of future forecasts can be improved by taking into account current trend and event information.

[0059] The future forecasting unit can use a combination of different predictive models to improve the accuracy of future forecasts. The future forecasting unit, for example, uses a generation AI to build a system that uses a combination of different predictive models to improve the accuracy of future forecasts. For example, a machine learning model and a statistical model are combined. The future forecasting unit also makes future forecasts based on different predictive models. For example, the generation AI combines a machine learning model and a statistical model to improve the accuracy of future forecasts. In this way, the accuracy of future forecasts can be improved by combining different predictive models.

[0060] The future forecasting unit can make future forecasts from a global perspective using data from different regions and cultural spheres. The future forecasting unit, for example, uses generation AI to build a system that makes future forecasts from a global perspective using data from different regions and cultural spheres. For example, future forecasts are made based on social media data and news from each country. The future forecasting unit also makes future forecasts based on data from different regions and cultural spheres. For example, the generation AI analyzes data from different regions and cultural spheres and makes future forecasts from a global perspective. This makes it possible to make future forecasts from a global perspective by using data from different regions and cultural spheres.

[0061] The future forecast unit can convert the results of the future forecast into visual notes or mind maps to make them easier to understand visually. The future forecast unit, for example, uses a generation AI to build a system that converts the results of the future forecast into visual notes or mind maps. For example, the results of the future forecast are shown using diagrams or icons. The future forecast unit also uses visual notes or mind maps to make the results of the future forecast easier to understand visually. For example, the generation AI visually organizes the results of the future forecast and provides them to the user. As a result, by converting them into visual notes or mind maps, the results of the future forecast become easier to understand visually.

[0062] The future forecast unit uses the emotion estimation function to monitor the user's emotional response to the future forecast in real time, and is able to continuously provide the most appropriate forecast. The future forecast unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to the future forecast in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The future forecast unit also continuously provides the most appropriate forecast based on the user's emotional response. For example, the generation AI analyzes the user's emotional data and adjusts the content of the forecast. In this way, the most appropriate future forecast can be provided by monitoring the user's emotional response in real time.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The news delivery system can further include a personalization unit that customizes news based on the user's interests. For example, the system can analyze the user's past browsing history and search history to provide news related to topics of interest to the user. The personalization unit can also provide local news based on the user's location information. Furthermore, the system can select appropriate news by taking into account the user's attribute information, such as age and occupation. This allows the system to provide news that is more relevant to the user.

[0065] The news delivery system can estimate the user's emotions and adjust the order in which news is displayed based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize positive news that will help the user relax. Also, if the user is excited, it can provide calming news to help the user regain their composure. Furthermore, it can adjust the tone and expression of the news depending on the user's emotions. This allows for optimal news delivery according to the user's emotional state.

[0066] The news delivery system further includes a voice recognition unit, which can provide news based on a user's voice command. For example, if a user issues a voice command such as "Tell me the latest sports news," the voice recognition unit analyzes the command and displays related news. The voice recognition unit can also analyze the user's voice tone and speed to estimate their emotional state. This allows for intuitive news delivery using voice commands.

[0067] The news delivery system can estimate a user's emotions and customize the news content based on the estimated emotions. For example, if a user is sad, it can provide news that gives encouragement and hope. If a user is happy, it can also provide positive news that will further enhance that joy. It can also change the news headline and thumbnail image according to the user's emotions. This allows the delivery of news that is in tune with the user's emotions.

[0068] The news delivery system can further include a feedback collection unit that collects user feedback and improves the quality of news. For example, users can leave ratings and comments on news articles, and the content and presentation of the news can be improved based on that feedback. The feedback collection unit can also analyze user feedback to identify common problems and areas for improvement. This makes it possible to provide news that reflects user opinions.

[0069] The news delivery system can estimate a user's emotions and adjust the timing of news delivery based on the estimated emotions. For example, it can deliver relaxing news when the user is relaxing. It can also deliver short, concise news when the user is busy. It can also change the news notification method depending on the user's emotional state. This allows news delivery to be tailored to the user's lifestyle.

[0070] The news providing system may further include a health monitoring unit that monitors the user's health status and provides health-related news. For example, the health monitoring unit may analyze the user's heart rate and sleep data and provide health advice and news. The health monitoring unit may also provide news about appropriate exercise and diet according to the user's health status. This makes it possible to provide news that supports the user's health.

[0071] The news delivery system can estimate a user's emotions and change the news format based on the estimated emotions. For example, if the user is tired, it can provide short, summarized news. If the user is relaxed, it can provide detailed articles and interactive content. It can also change the way news is displayed depending on the user's emotions. This allows news delivery tailored to the user's emotional state.

[0072] The news delivery system may further include a social analysis unit that analyzes the user's social network and provides news that the user's friends and followers are interested in. For example, news that the user's friends have shared or commented on may be displayed preferentially. The social analysis unit may also analyze topics that are trending within the user's social network and provide related news. This allows news to be delivered based on the user's social network.

[0073] The news delivery system can estimate the user's emotions and adjust the news reading function based on the estimated emotions. For example, if the user is relaxed, the news can be read in a calm tone. On the other hand, if the user is excited, the news can be read in a more subdued tone. Furthermore, it is possible to change the news reading speed and intonation depending on the user's emotions. This allows for audio news delivery that matches the user's emotional state.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The information collection unit collects real-time information from social media. For example, the generation AI collects posts on social media in real time based on specific keywords or hashtags. The information collection unit also inputs prompts containing the keywords or hashtags to be collected into the generation AI, and the generation AI collects information based on the prompts. Step 2: The news generation unit generates news articles based on the information collected by the information collection unit. For example, the generation AI analyzes the collected posts, extracts important information, and creates news articles. The generation AI also generates news articles based on the collected post data and provides them to users in real time. Step 3: The credibility evaluation unit evaluates the credibility of the news articles generated by the news generation unit. For example, the generation AI determines credibility based on the number of posts and the source of the information. In addition, if the same information is posted by multiple reliable sources, the generation AI evaluates the information as being highly credible. Step 4: The future prediction unit predicts future events based on the information evaluated by the credibility evaluation unit. For example, if there is a sudden increase in posts on a particular topic, the generation AI predicts that an event related to that topic is likely to occur in the near future. The generation AI also makes future predictions based on the collected post data and past data.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The 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.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 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.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0108] The data processing system 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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. 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 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 real-time information from SNS, a news generation unit that generates news articles based on the information collected by the information collection unit; a credibility evaluation unit that evaluates the credibility of the news article generated by the news generation unit; a future forecasting unit that predicts future events based on the information evaluated by the credibility evaluation unit. system.

2. The information collecting unit Sentiment analysis of posts, rather than just specific keywords or hashtags, to filter information based on the intensity and type of sentiment The system of claim 1 .

3. The information collecting unit Analyze the content of images and videos and collect them in combination with text information The system of claim 1 .

4. The news generation unit When generating news articles, reference past news data and compare it with similar events. The system of claim 1 .

5. The credibility evaluation unit We evaluate the credibility of posts by analyzing not only the number of posts but also the content and writing style of the posts. The system of claim 1 .

6. The future prediction unit When making future forecasts, consider not only past data but also current trends and event information. The system of claim 1 .

7. The future prediction unit Using emotion estimation, the system monitors users' emotional reactions to future forecasts in real time and continuously provides optimal forecasts. The system of claim 1 .

Images