System

Abstract

An object of a system according to an embodiment is to efficiently collect, analyze, and provide useful data from an enormous amount of information on the Internet.SOLUTION: A system includes an information collection unit, an information analysis unit, and an information provision unit. The information collecting unit collects data from various information sources on the Internet. The information analysis unit analyzes the data collected by the information collection unit. The information providing unit provides the user with the information analyzed by the information analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently collect, analyze, and provide useful data from the vast amount of information on the Internet, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect, analyze, and provide useful data from the vast amount of information on the Internet. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information analysis unit, and an information provision unit. The information collection unit collects data from various information sources on the Internet. The information analysis unit analyzes the data collected by the information collection unit. The information provision unit provides the user with the information analyzed by the information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect, analyze, and provide useful data from the vast amount of information on the Internet. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information acquisition system according to the embodiment of the present invention is a system that collects, analyzes, and provides information, allowing users to acquire the information they need in a timely manner. As a result, the information acquisition system allows users to acquire the information they need quickly and efficiently.

[0029] An information acquisition system according to an embodiment includes an information collection unit, an information analysis unit, and an information provision unit. The information collection unit collects data from various sources on the Internet. For example, the information collection unit collects information from news sites, blogs, social networking sites, specialized databases, and the like. The information collection unit can also collect data using web scraping technology. For example, the information collection unit can automatically extract data from specific websites. The information collection unit can also collect data using an API. For example, the information collection unit can obtain the latest news data using a news API. The information analysis unit analyzes the data collected by the information collection unit. For example, the generation AI can analyze the collected data using text analysis technology. The generation AI can also analyze data trends using statistical analysis technology. The generation AI can also analyze data using machine learning algorithms. For example, the generation AI can analyze text data using natural language processing technology to extract important information. Statistical analysis technology is used to analyze data distributions and trends. Machine learning algorithms are used to learn large amounts of data and find patterns. The information provision unit provides the information analyzed by the information analysis unit to a user. For example, the information provision unit provides the information in text format. The information providing unit can also provide information in a graph format. The information providing unit can also provide information in an audio format. For example, the information providing unit displays the analysis results as text. The graph format is used to provide a visual representation of data. The audio format is used to allow the user to acquire information audibly. This allows the information acquisition system according to the embodiment to quickly and efficiently acquire the information the user needs. For example, in business situations, the latest market trends and competitive information can be quickly grasped, which can be useful for strategic decision-making. Furthermore, in the fields of personal learning and hobbies, acquiring the latest information can help users gain deeper knowledge.

[0030] The information collection unit can collect information from news sites, blogs, social networking sites, and specialized databases. For example, the information collection unit collects the latest news from news sites. For example, it obtains the latest news data using a news API. The information collection unit also collects information from blogs. For example, it automatically extracts data from specific blog sites. The information collection unit also collects information from social networking sites. For example, it obtains posted data using the social networking site's API. The information collection unit also collects information from specialized databases. For example, it obtains the latest research results from an academic paper database. This makes it possible to collect information from a variety of information sources.

[0031] The information providing unit can provide information in text format, graph format, or audio format. For example, the information providing unit provides information in text format. For example, the analysis results are displayed as text. The information providing unit also provides information in graph format. For example, a graph is generated to provide a visual representation of data. The information providing unit also provides information in audio format. For example, the analysis results are played back as audio. This allows information to be provided in a format optimal for the user.

[0032] The information providing unit may have a function for sharing knowledge and information acquired by the user on social media or blogs. The information providing unit provides, for example, a function for sharing knowledge and information acquired by the user on social media. For example, a share button may be provided to allow the user to easily share information on social media. The information providing unit may also provide a function for sharing knowledge and information acquired by the user on blogs. For example, a blog posting function may be provided to allow the user to post information on a blog. This allows the user to share the information acquired with other users.

[0033] The information collection unit can add personalized information sources based on the user's past search history and browsing history. For example, the information collection unit analyzes the user's past search history and automatically selects relevant information sources. For example, it prioritizes the collection of news sites and blogs related to topics the user frequently searches for. The information collection unit also identifies information sources that the user may be interested in based on the user's browsing history, and the generation AI collects that information. For example, it obtains information from specialized databases and forums in specific fields. The information collection unit also integrates the user's past search history and browsing history to collect personalized information. For example, it collects the latest related research papers and technical reports based on keywords the user has searched in the past. This allows the user to be provided with the most suitable information sources.

[0034] The information collection unit can include local news and event information based on the user's real-time location information. For example, the generation AI of the information collection unit acquires the user's location information and collects local news and event information related to that area. For example, the information collection unit provides the latest news and event information for the city where the user is currently located. The information collection unit also collects nearby event and activity information based on the user's location information, and the generation AI provides this information. For example, it collects information on tourist spots and restaurants in the area the user is visiting. The information collection unit also utilizes the location information to collect local news and event information that the user may be interested in. For example, it provides information on local workshops and seminars that the user can attend. This makes it possible to provide information based on the user's location.

[0035] The information collection unit can collect multimedia information including audio data and video data. For example, the information collection unit uses a generation AI to collect audio data and provide it to the user. For example, it collects podcasts and audio news and makes them available to the user. The information collection unit also collects video data and provides it to the user. For example, it collects YouTube videos and recordings of online lectures and makes them available to the user. The information collection unit also integrates audio data and video data to collect multimedia information. For example, it collects audio interviews and video tutorials and provides them to the user. This allows multimedia information including audio and video to be collected.

[0036] The information collection unit can collect data from sources in different languages ​​and provide information from an international perspective. For example, the generation AI collects information from news sites and blogs in different languages ​​and provides it to users. For example, data is collected from sources such as English, French, and Chinese. The information collection unit also collects information from specialized databases and forums in different languages ​​and provides it to users. For example, it collects international academic papers and technical reports. The information collection unit also collects information in multiple languages ​​and provides information from an international perspective. For example, it collects social media posts and comments in different languages ​​and provides them to users. This makes it possible to provide information from an international perspective.

[0037] The information analysis unit reflects the user's past feedback and can provide more accurate analysis results. For example, the information analysis unit uses a generation AI to analyze the user's past feedback and improve the accuracy of the information analysis based on the results. For example, it prioritizes analysis of information sources that the user has given a high rating. The information analysis unit also uses a generation AI to evaluate the importance and reliability of information based on the user's past feedback and reflect this in the analysis results. For example, it places emphasis on data from information sources that the user trusts. The information analysis unit also uses a generation AI to learn the user's past feedback and improve the accuracy of the analysis results. For example, it adjusts the analysis algorithm based on feedback provided by the user in the past. This allows the user's feedback to be reflected and the analysis accuracy to be improved.

[0038] The information analysis unit can enhance specialized knowledge, including related patent data and academic papers. For example, the information analysis unit has the generative AI analyze related patent data to enhance specialized knowledge. For example, it collects patent information in a specific technical field and reflects this in the analysis results. The information analysis unit also collects information from an academic paper database, and the generative AI analyzes it to enhance specialized knowledge. For example, it includes the latest research results and technical reports in the analysis results. The information analysis unit also integrates patent data and academic papers, and the generative AI analyzes it to enhance specialized knowledge. For example, it reflects the latest research trends in a specific technical field in the analysis results. This enhances specialized knowledge.

[0039] The information analysis unit can integrate data from different industries and fields to provide crossover insights. For example, the information analysis unit provides crossover insights by having the generative AI analyze data from different industries and integrate it. For example, data from the technology and marketing fields can be integrated to discover new business opportunities. The information analysis unit also analyzes data from different fields and the generative AI integrates it to provide insights from a broader perspective. For example, data from the medical and entertainment fields can be integrated to propose new services. The information analysis unit also analyzes data from different industries and fields and the generative AI integrates it to provide crossover insights. For example, data from the education and technology fields can be integrated to propose new educational programs. This makes it possible to provide insights by integrating data from different industries and fields.

[0040] The information analysis unit can convert the analysis results into visual notes or mind maps, making them easier to understand visually. For example, the information analysis unit uses a generation AI to convert the analysis results into visual notes and provide them to the user. For example, it can show important points using diagrams or icons. The information analysis unit can also convert the analysis results into mind map format and visually organize related keywords and concepts. This allows users to intuitively understand the information. The information analysis unit can also develop tools that automatically generate visual notes and mind maps, allowing users to easily display the analysis results visually. For example, it can provide a function to visualize the analysis results using drag and drop. This makes it easier to understand the analysis results visually.

[0041] The information providing unit can provide information in a format optimized for the user's device and environment. For example, the information providing unit optimizes the information provided by the generation AI for a smartwatch and provides it to the user. For example, the information may be displayed in short text or simple graph format. The information providing unit may also optimize the information for an AR device and provide it to the user. For example, the information may be displayed in real time through AR glasses. The information providing unit may also build a system that automatically adjusts the format of the information depending on the user's device and environment. For example, a detailed report may be displayed on a desktop PC, and a summary may be displayed on a smartphone. This allows information to be provided in a format optimized for the user's device and environment.

[0042] The information provision unit can integrate the user's schedule and task management information and provide timely reminders. For example, the generation AI analyzes the user's schedule information and provides information related to important events and tasks. For example, it provides relevant materials and news before a meeting. The information provision unit also integrates the user's task management information and the generation AI provides timely reminders. For example, it provides relevant information and resources before a deadline. The information provision unit also builds a system in which the generation AI provides information to the user at the optimal time based on the schedule and task management information. For example, it notifies the user of relevant information before an important task. This makes it possible to provide timely reminders based on the user's schedule and task management information.

[0043] The information providing unit can link with the user's social network to promote sharing and collaboration. For example, the information providing unit links the information provided by the generation AI with the user's social network to make it easy to share. For example, it adds a share button on SNS. The information providing unit also collects feedback on the information provided by the generation AI through the social network to promote collaboration. For example, it allows users to share information with friends and colleagues and jointly brainstorm ideas. The information providing unit also links the information provided by the generation AI with the social network to build a system that makes it easier for users to share information with other users. For example, it promotes information sharing in group chats and forums. This makes it possible to link with the user's social network to promote sharing and collaboration.

[0044] The information providing unit can seamlessly synchronize between different devices, allowing users to access the same information on any device. For example, the information providing unit stores information provided by the generation AI in the cloud and builds a system that synchronizes information seamlessly between different devices. For example, it synchronizes information between a smartphone, tablet, and PC. The information providing unit also automatically synchronizes information so that users can access the same information on different devices. For example, it allows information viewed on a smartphone to be viewed on a PC as well. The information providing unit also synchronizes information provided by the generation AI in real time, allowing users to access the latest information on any device. For example, information edited on a tablet is instantly reflected on a smartphone. This allows information to be synchronized seamlessly between different devices.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The information collection unit can add personalized information sources based on the user's past search history and browsing history. For example, it can analyze the user's past search history and automatically select relevant information sources. It can prioritize collection of news sites and blogs related to topics the user frequently searches for. It can also identify information sources that the user may be interested in based on the user's browsing history, and the generation AI can collect that information. For example, it can obtain information from specialized databases and forums in specific fields. It can also integrate the user's past search history and browsing history to perform personalized information collection. For example, it can collect related latest research papers and technical reports based on keywords the user has searched in the past. This allows it to provide the user with the most suitable information sources.

[0047] The information providing unit can provide information in a format optimized for the user's device and environment. For example, the information provided by the generating AI can be optimized for a smartwatch and provided to the user. The information can be displayed in short text or simple graph format. The information can also be optimized for an AR device and provided to the user. For example, the information can be displayed in real time through AR glasses. Furthermore, a system can be built that automatically adjusts the information format depending on the user's device and environment. For example, a detailed report can be displayed on a desktop PC, while a summary can be displayed on a smartphone. This allows information to be provided in a format optimized for the user's device and environment.

[0048] The information analysis unit can reflect the user's past feedback and provide more accurate analysis results. For example, the generation AI can analyze the user's past feedback and improve the accuracy of the information analysis based on the results. Information sources that the user has given a high rating can be analyzed preferentially. The generation AI can also evaluate the importance and reliability of information based on the user's past feedback and reflect this in the analysis results. For example, it can place emphasis on data from information sources that the user trusts. Furthermore, the generation AI can learn the user's past feedback and improve the accuracy of the analysis results. For example, it can adjust the analysis algorithm based on feedback provided by the user in the past. This makes it possible to reflect the user's feedback and improve the accuracy of the analysis.

[0049] The information collection unit can collect multimedia information, including audio data and video data. For example, the generation AI can collect audio data and provide it to the user. Podcasts and audio news can be collected and made available to the user. Video data can also be collected and provided to the user. For example, YouTube videos and recordings of online lectures can be collected and made available to the user. Furthermore, audio data and video data can be integrated to collect multimedia information. For example, audio interviews and video tutorials can be collected and provided to the user. This allows multimedia information, including audio and video, to be collected.

[0050] The information provision unit can integrate the user's schedule and task management information and provide timely reminders. For example, the generation AI can analyze the user's schedule information and provide information related to important events and tasks. Relevant materials and news can be provided before a meeting. The generation AI can also integrate the user's task management information and provide timely reminders. For example, relevant information and resources can be provided before a deadline. Furthermore, a system can be built in which the generation AI provides information to the user at the optimal time based on the schedule and task management information. For example, relevant information can be notified before an important task. This makes it possible to provide timely reminders based on the user's schedule and task management information.

[0051] The information analysis unit can integrate data from different industries and fields to provide crossover insights. For example, the generative AI can analyze data from different industries and integrate it to provide crossover insights. Data from the technology and marketing fields can be integrated to discover new business opportunities. Data from different fields can also be analyzed and integrated by the generative AI to provide insights from a broader perspective. For example, data from the medical and entertainment fields can be integrated to propose new services. Furthermore, data from different industries and fields can be analyzed and integrated by the generative AI to provide crossover insights. For example, data from the education and technology fields can be integrated to propose new educational programs. This allows insights to be provided by integrating data from different industries and fields.

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

[0053] Step 1: The information collection unit collects data from various sources on the Internet. For example, it collects information from news sites, blogs, social media, specialized databases, etc. The information collection unit can also collect data using web scraping technology. For example, it can automatically extract data from specific websites. The information collection unit can also collect data using APIs. For example, it can use a news API to obtain the latest news data. Step 2: The information analysis unit analyzes the data collected by the information collection unit. For example, the generation AI analyzes the collected data using text analysis technology. The generation AI can also analyze data trends using statistical analysis technology. The generation AI can also analyze data using machine learning algorithms. For example, the generation AI analyzes text data using natural language processing technology to extract important information. Statistical analysis technology is used to analyze data distributions and trends. Machine learning algorithms are used to learn large amounts of data and find patterns. Step 3: The information providing unit provides the information analyzed by the information analysis unit to the user. For example, the information providing unit provides the information in text format. The information providing unit can also provide the information in graph format. The information providing unit can also provide the information in audio format. For example, the information providing unit displays the analysis results as text. The graph format is used to provide a visual representation of data. The audio format is used to allow the user to obtain information audibly.

[0054] (Example 2) The information acquisition system according to the embodiment of the present invention is a system that collects, analyzes, and provides information, allowing users to acquire the information they need in a timely manner. As a result, the information acquisition system allows users to acquire the information they need quickly and efficiently.

[0055] An information acquisition system according to an embodiment includes an information collection unit, an information analysis unit, and an information provision unit. The information collection unit collects data from various sources on the Internet. For example, the information collection unit collects information from news sites, blogs, social networking sites, specialized databases, and the like. The information collection unit can also collect data using web scraping technology. For example, the information collection unit can automatically extract data from specific websites. The information collection unit can also collect data using an API. For example, the information collection unit can obtain the latest news data using a news API. The information analysis unit analyzes the data collected by the information collection unit. For example, the generation AI can analyze the collected data using text analysis technology. The generation AI can also analyze data trends using statistical analysis technology. The generation AI can also analyze data using machine learning algorithms. For example, the generation AI can analyze text data using natural language processing technology to extract important information. Statistical analysis technology is used to analyze data distributions and trends. Machine learning algorithms are used to learn large amounts of data and find patterns. The information provision unit provides the information analyzed by the information analysis unit to a user. For example, the information provision unit provides the information in text format. The information providing unit can also provide information in a graph format. The information providing unit can also provide information in an audio format. For example, the information providing unit displays the analysis results as text. The graph format is used to provide a visual representation of data. The audio format is used to allow the user to acquire information audibly. This allows the information acquisition system according to the embodiment to quickly and efficiently acquire the information the user needs. For example, in business situations, the latest market trends and competitive information can be quickly grasped, which can be useful for strategic decision-making. Furthermore, in the fields of personal learning and hobbies, acquiring the latest information can help users gain deeper knowledge.

[0056] The information collection unit can collect information from news sites, blogs, social networking sites, and specialized databases. For example, the information collection unit collects the latest news from news sites. For example, it obtains the latest news data using a news API. The information collection unit also collects information from blogs. For example, it automatically extracts data from specific blog sites. The information collection unit also collects information from social networking sites. For example, it obtains posted data using the social networking site's API. The information collection unit also collects information from specialized databases. For example, it obtains the latest research results from an academic paper database. This makes it possible to collect information from a variety of information sources.

[0057] The information providing unit can provide information in text format, graph format, or audio format. For example, the information providing unit provides information in text format. For example, the analysis results are displayed as text. The information providing unit also provides information in graph format. For example, a graph is generated to provide a visual representation of data. The information providing unit also provides information in audio format. For example, the analysis results are played back as audio. This allows information to be provided in a format optimal for the user.

[0058] The information providing unit may have a function for sharing knowledge and information acquired by the user on social media or blogs. The information providing unit provides, for example, a function for sharing knowledge and information acquired by the user on social media. For example, a share button may be provided to allow the user to easily share information on social media. The information providing unit may also provide a function for sharing knowledge and information acquired by the user on blogs. For example, a blog posting function may be provided to allow the user to post information on a blog. This allows the user to share the information acquired with other users.

[0059] The information collection unit can add personalized information sources based on the user's past search history and browsing history. For example, the information collection unit analyzes the user's past search history and automatically selects relevant information sources. For example, it prioritizes the collection of news sites and blogs related to topics the user frequently searches for. The information collection unit also identifies information sources that the user may be interested in based on the user's browsing history, and the generation AI collects that information. For example, it obtains information from specialized databases and forums in specific fields. The information collection unit also integrates the user's past search history and browsing history to collect personalized information. For example, it collects the latest related research papers and technical reports based on keywords the user has searched in the past. This allows the user to be provided with the most suitable information sources.

[0060] The information collection unit can include local news and event information based on the user's real-time location information. For example, the generation AI of the information collection unit acquires the user's location information and collects local news and event information related to that area. For example, the information collection unit provides the latest news and event information for the city where the user is currently located. The information collection unit also collects nearby event and activity information based on the user's location information, and the generation AI provides this information. For example, it collects information on tourist spots and restaurants in the area the user is visiting. The information collection unit also utilizes the location information to collect local news and event information that the user may be interested in. For example, it provides information on local workshops and seminars that the user can attend. This makes it possible to provide information based on the user's location.

[0061] The information collection unit can use the emotion estimation function to preferentially collect information related to topics in which the user is currently interested. For example, the information collection unit uses the emotion estimation function to analyze the emotion of a prompt entered by the user and collect information related to topics in which the user is interested. For example, the information collection unit preferentially provides the latest news on topics that the user is excited about. The information collection unit also analyzes the user's emotional state in real time and collects information related to topics in which the user is interested. For example, the information collection unit collects blog articles and forum posts on topics in which the user expresses positive emotions. The information collection unit also uses the emotion estimation function to identify topics in which the user is currently interested and preferentially collect information related to those topics. For example, the information collection unit provides the latest research papers in technical fields in which the user is interested. This allows information based on the user's interests to be preferentially collected.

[0062] The information collection unit can collect multimedia information including audio data and video data. For example, the information collection unit uses a generation AI to collect audio data and provide it to the user. For example, it collects podcasts and audio news and makes them available to the user. The information collection unit also collects video data and provides it to the user. For example, it collects YouTube videos and recordings of online lectures and makes them available to the user. The information collection unit also integrates audio data and video data to collect multimedia information. For example, it collects audio interviews and video tutorials and provides them to the user. This allows multimedia information including audio and video to be collected.

[0063] The information collection unit can collect data from sources in different languages ​​and provide information from an international perspective. For example, the generation AI collects information from news sites and blogs in different languages ​​and provides it to users. For example, data is collected from sources such as English, French, and Chinese. The information collection unit also collects information from specialized databases and forums in different languages ​​and provides it to users. For example, it collects international academic papers and technical reports. The information collection unit also collects information in multiple languages ​​and provides information from an international perspective. For example, it collects social media posts and comments in different languages ​​and provides them to users. This makes it possible to provide information from an international perspective.

[0064] The information collection unit can use the emotion estimation function to analyze the emotional state of the user when collecting information and suggest information sources that elicit positive emotions. For example, the information collection unit uses the emotion estimation function to analyze the emotional state of the user when collecting information in real time and suggest information sources that elicit positive emotions. For example, relaxing music or videos are suggested when the user is relaxing. The information collection unit also identifies information sources that elicit positive emotions based on the user's emotional state, and the generation AI suggests them. For example, news or articles that will increase excitement when the user is excited. The information collection unit also uses the emotion estimation function to analyze the emotional state of the user when collecting information and provide information sources that elicit positive emotions. For example, content that will cheer up the user when they are feeling down is suggested. This makes it possible to suggest positive information sources based on the user's emotions.

[0065] The information analysis unit reflects the user's past feedback and can provide more accurate analysis results. For example, the information analysis unit uses a generation AI to analyze the user's past feedback and improve the accuracy of the information analysis based on the results. For example, it prioritizes analysis of information sources that the user has given a high rating. The information analysis unit also uses a generation AI to evaluate the importance and reliability of information based on the user's past feedback and reflect this in the analysis results. For example, it places emphasis on data from information sources that the user trusts. The information analysis unit also uses a generation AI to learn the user's past feedback and improve the accuracy of the analysis results. For example, it adjusts the analysis algorithm based on feedback provided by the user in the past. This allows the user's feedback to be reflected and the analysis accuracy to be improved.

[0066] The information analysis unit can enhance specialized knowledge, including related patent data and academic papers. For example, the information analysis unit has the generative AI analyze related patent data to enhance specialized knowledge. For example, it collects patent information in a specific technical field and reflects this in the analysis results. The information analysis unit also collects information from an academic paper database, and the generative AI analyzes it to enhance specialized knowledge. For example, it includes the latest research results and technical reports in the analysis results. The information analysis unit also integrates patent data and academic papers, and the generative AI analyzes it to enhance specialized knowledge. For example, it reflects the latest research trends in a specific technical field in the analysis results. This enhances specialized knowledge.

[0067] The information analysis unit can use the emotion estimation function to evaluate the user's emotional response to the analysis results and make suggestions to improve negative elements. The information analysis unit, for example, uses the emotion estimation function to evaluate the user's emotional response to the analysis results in real time and identify negative elements. For example, it analyzes the parts where the user expressed dissatisfaction. The information analysis unit also uses the generation AI to automatically make suggestions to improve negative elements based on the user's emotional response data. For example, it provides specific suggestions to improve information where the user expressed dissatisfaction. The information analysis unit also uses the emotion estimation function to evaluate the user's emotional response to the analysis results and provides feedback to improve negative elements. For example, it makes suggestions to correct the parts where the user expressed dissatisfaction. This makes it possible to improve the analysis results based on the user's emotional response.

[0068] The information analysis unit can integrate data from different industries and fields to provide crossover insights. For example, the information analysis unit provides crossover insights by having the generative AI analyze data from different industries and integrate it. For example, data from the technology and marketing fields can be integrated to discover new business opportunities. The information analysis unit also analyzes data from different fields and the generative AI integrates it to provide insights from a broader perspective. For example, data from the medical and entertainment fields can be integrated to propose new services. The information analysis unit also analyzes data from different industries and fields and the generative AI integrates it to provide crossover insights. For example, data from the education and technology fields can be integrated to propose new educational programs. This makes it possible to provide insights by integrating data from different industries and fields.

[0069] The information analysis unit can convert the analysis results into visual notes or mind maps, making them easier to understand visually. For example, the information analysis unit uses a generation AI to convert the analysis results into visual notes and provide them to the user. For example, it can show important points using diagrams or icons. The information analysis unit can also convert the analysis results into mind map format and visually organize related keywords and concepts. This allows users to intuitively understand the information. The information analysis unit can also develop tools that automatically generate visual notes and mind maps, allowing users to easily display the analysis results visually. For example, it can provide a function to visualize the analysis results using drag and drop. This makes it easier to understand the analysis results visually.

[0070] The information analysis unit can use the emotion estimation function to collect users' emotional reactions to the analysis results and improve the accuracy of the analysis algorithm. For example, the information analysis unit uses the emotion estimation function to collect users' emotional reactions to the analysis results in real time and improve the accuracy of the analysis algorithm based on that data. For example, it prioritizes the adoption of analysis results with a high number of positive reactions. The information analysis unit also builds a system that improves the accuracy of the analysis algorithm based on the user's emotional reaction data. For example, it reanalyzes analysis results with a high number of negative reactions. The information analysis unit also identifies areas for improvement in the analysis algorithm based on the emotion estimation data and improves its accuracy. For example, it makes suggestions to correct parts with low emotion scores. This allows the accuracy of the analysis algorithm to be improved based on the user's emotional reactions.

[0071] The information providing unit can provide information in a format optimized for the user's device and environment. For example, the information providing unit optimizes the information provided by the generation AI for a smartwatch and provides it to the user. For example, the information may be displayed in short text or simple graph format. The information providing unit may also optimize the information for an AR device and provide it to the user. For example, the information may be displayed in real time through AR glasses. The information providing unit may also build a system that automatically adjusts the format of the information depending on the user's device and environment. For example, a detailed report may be displayed on a desktop PC, and a summary may be displayed on a smartphone. This allows information to be provided in a format optimized for the user's device and environment.

[0072] The information provision unit can integrate the user's schedule and task management information and provide timely reminders. For example, the generation AI analyzes the user's schedule information and provides information related to important events and tasks. For example, it provides relevant materials and news before a meeting. The information provision unit also integrates the user's task management information and the generation AI provides timely reminders. For example, it provides relevant information and resources before a deadline. The information provision unit also builds a system in which the generation AI provides information to the user at the optimal time based on the schedule and task management information. For example, it notifies the user of relevant information before an important task. This makes it possible to provide timely reminders based on the user's schedule and task management information.

[0073] The information providing unit can use the emotion estimation function to analyze the emotional state of the user when receiving information and provide information in a format that elicits positive emotions. For example, the information providing unit uses the emotion estimation function to analyze the emotional state of the user when receiving information in real time and provide information in a format that elicits positive emotions. For example, when the user is relaxed, it provides relaxing music or videos. The information providing unit also provides information in a format that elicits positive emotions based on the user's emotional state. For example, when the user is excited, it provides news or articles that increase excitement. The information providing unit also uses the emotion estimation function to analyze the emotional state of the user when receiving information and provide information in a format that elicits positive emotions. For example, when the user is feeling down, it provides content that cheers up the user. In this way, it is possible to provide information in a positive format based on the user's emotions.

[0074] The information providing unit can link with the user's social network to promote sharing and collaboration. For example, the information providing unit links the information provided by the generation AI with the user's social network to make it easy to share. For example, it adds a share button on SNS. The information providing unit also collects feedback on the information provided by the generation AI through the social network to promote collaboration. For example, it allows users to share information with friends and colleagues and jointly brainstorm ideas. The information providing unit also links the information provided by the generation AI with the social network to build a system that makes it easier for users to share information with other users. For example, it promotes information sharing in group chats and forums. This makes it possible to link with the user's social network to promote sharing and collaboration.

[0075] The information providing unit can seamlessly synchronize between different devices, allowing users to access the same information on any device. For example, the information providing unit stores information provided by the generation AI in the cloud and builds a system that synchronizes information seamlessly between different devices. For example, it synchronizes information between a smartphone, tablet, and PC. The information providing unit also automatically synchronizes information so that users can access the same information on different devices. For example, it allows information viewed on a smartphone to be viewed on a PC as well. The information providing unit also synchronizes information provided by the generation AI in real time, allowing users to access the latest information on any device. For example, information edited on a tablet is instantly reflected on a smartphone. This allows information to be synchronized seamlessly between different devices.

[0076] The information provision unit can use the emotion estimation function to monitor the emotional reactions of the user when receiving information in real time and continuously adjust the optimal delivery method. The information provision unit, for example, uses the emotion estimation function to monitor the emotional reactions of the user when receiving information in real time and adjust the information delivery method based on the data. For example, it changes the format of the information if the user expresses dissatisfaction. The information provision unit also builds a system in which the generation AI continuously adjusts the information delivery method based on the user's emotional reaction data. For example, it provides information in a format that encourages a positive emotional reaction. The information provision unit also uses the emotion estimation function to monitor the emotional reactions of the user when receiving information in real time and continuously adjust the optimal delivery method. For example, it dynamically changes the information delivery method in response to changes in the user's emotions. This makes it possible to continuously adjust the information delivery method based on the user's emotional reaction.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The information collection unit can add personalized information sources based on the user's past search history and browsing history. For example, it can analyze the user's past search history and automatically select relevant information sources. It can prioritize collection of news sites and blogs related to topics the user frequently searches for. It can also identify information sources that the user may be interested in based on the user's browsing history, and the generation AI can collect that information. For example, it can obtain information from specialized databases and forums in specific fields. It can also integrate the user's past search history and browsing history to perform personalized information collection. For example, it can collect related latest research papers and technical reports based on keywords the user has searched in the past. This allows it to provide the user with the most suitable information sources.

[0079] The information providing unit can provide information in a format optimized for the user's device and environment. For example, the information provided by the generating AI can be optimized for a smartwatch and provided to the user. The information can be displayed in short text or simple graph format. The information can also be optimized for an AR device and provided to the user. For example, the information can be displayed in real time through AR glasses. Furthermore, a system can be built that automatically adjusts the information format depending on the user's device and environment. For example, a detailed report can be displayed on a desktop PC, while a summary can be displayed on a smartphone. This allows information to be provided in a format optimized for the user's device and environment.

[0080] The information collection unit can use the emotion estimation function to prioritize collection of information related to topics in which the user is currently interested. For example, the emotion estimation function can be used to analyze the emotion of a prompt entered by the user and collect information related to topics in which the user is interested. The latest news on topics that the user is excited about can be provided preferentially. The user's emotional state can also be analyzed in real time to collect information related to topics in which the user is interested. For example, blog articles and forum posts on topics in which the user expresses positive emotions can be collected. Furthermore, the emotion estimation function can be used to identify topics in which the user is currently interested and prioritize collection of information related to those topics. For example, the latest research papers in technical fields in which the user is interested can be provided. This makes it possible to prioritize collection of information based on the user's interests.

[0081] The information analysis unit can reflect the user's past feedback and provide more accurate analysis results. For example, the generation AI can analyze the user's past feedback and improve the accuracy of the information analysis based on the results. Information sources that the user has given a high rating can be analyzed preferentially. The generation AI can also evaluate the importance and reliability of information based on the user's past feedback and reflect this in the analysis results. For example, it can place emphasis on data from information sources that the user trusts. Furthermore, the generation AI can learn the user's past feedback and improve the accuracy of the analysis results. For example, it can adjust the analysis algorithm based on feedback provided by the user in the past. This makes it possible to reflect the user's feedback and improve the accuracy of the analysis.

[0082] The information providing unit can use the emotion estimation function to analyze the emotional state of the user when receiving information and provide information in a format that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotional state of the user when receiving information in real time and provide information in a format that elicits positive emotions. When the user is relaxed, relaxing music or videos can be provided. Information can also be provided in a format that elicits positive emotions based on the user's emotional state. For example, when the user is excited, news or articles that increase excitement can be provided. Furthermore, the emotion estimation function can be used to analyze the emotional state of the user when receiving information and provide information in a format that elicits positive emotions. For example, when the user is feeling down, content that cheers up can be provided. This makes it possible to provide information in a positive format based on the user's emotions.

[0083] The information collection unit can collect multimedia information, including audio data and video data. For example, the generation AI can collect audio data and provide it to the user. Podcasts and audio news can be collected and made available to the user. Video data can also be collected and provided to the user. For example, YouTube videos and recordings of online lectures can be collected and made available to the user. Furthermore, audio data and video data can be integrated to collect multimedia information. For example, audio interviews and video tutorials can be collected and provided to the user. This allows multimedia information, including audio and video, to be collected.

[0084] The information analysis unit can use the emotion estimation function to evaluate the user's emotional response to the analysis results and make suggestions to improve negative elements. For example, the emotion estimation function can be used to evaluate the user's emotional response to the analysis results in real time and identify negative elements. The parts where the user expressed dissatisfaction can be analyzed. In addition, the generation AI can automatically make suggestions to improve negative elements based on the user's emotional response data. For example, specific suggestions can be provided to improve information about which the user expressed dissatisfaction. Furthermore, the emotion estimation function can be used to evaluate the user's emotional response to the analysis results and provide feedback to improve negative elements. For example, suggestions can be made to correct parts where the user expressed dissatisfaction. This allows the analysis results to be improved based on the user's emotional response.

[0085] The information provision unit can integrate the user's schedule and task management information and provide timely reminders. For example, the generation AI can analyze the user's schedule information and provide information related to important events and tasks. Relevant materials and news can be provided before a meeting. The generation AI can also integrate the user's task management information and provide timely reminders. For example, relevant information and resources can be provided before a deadline. Furthermore, a system can be built in which the generation AI provides information to the user at the optimal time based on the schedule and task management information. For example, relevant information can be notified before an important task. This makes it possible to provide timely reminders based on the user's schedule and task management information.

[0086] The information provision unit can use the emotion estimation function to monitor the emotional response of the user when receiving information in real time and continuously adjust the optimal delivery method. For example, the emotion estimation function can be used to monitor the emotional response of the user when receiving information in real time and adjust the information delivery method based on that data. The format of the information can be changed if the user expresses dissatisfaction. It is also possible to build a system in which the generation AI continuously adjusts the information delivery method based on the user's emotional response data. For example, information can be provided in a format that encourages a positive emotional response. Furthermore, the emotion estimation function can be used to monitor the emotional response of the user when receiving information in real time and continuously adjust the optimal delivery method. For example, the information delivery method can be dynamically changed in response to changes in the user's emotions. This makes it possible to continuously adjust the information delivery method based on the user's emotional response.

[0087] The information analysis unit can integrate data from different industries and fields to provide crossover insights. For example, the generative AI can analyze data from different industries and integrate it to provide crossover insights. Data from the technology and marketing fields can be integrated to discover new business opportunities. Data from different fields can also be analyzed and integrated by the generative AI to provide insights from a broader perspective. For example, data from the medical and entertainment fields can be integrated to propose new services. Furthermore, data from different industries and fields can be analyzed and integrated by the generative AI to provide crossover insights. For example, data from the education and technology fields can be integrated to propose new educational programs. This allows insights to be provided by integrating data from different industries and fields.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The information collection unit collects data from various sources on the Internet. For example, it collects information from news sites, blogs, social media, specialized databases, etc. The information collection unit can also collect data using web scraping technology. For example, it can automatically extract data from specific websites. The information collection unit can also collect data using APIs. For example, it can use a news API to obtain the latest news data. Step 2: The information analysis unit analyzes the data collected by the information collection unit. For example, the generation AI analyzes the collected data using text analysis technology. The generation AI can also analyze data trends using statistical analysis technology. The generation AI can also analyze data using machine learning algorithms. For example, the generation AI analyzes text data using natural language processing technology to extract important information. Statistical analysis technology is used to analyze data distributions and trends. Machine learning algorithms are used to learn large amounts of data and find patterns. Step 3: The information providing unit provides the information analyzed by the information analysis unit to the user. For example, the information providing unit provides the information in text format. The information providing unit can also provide the information in graph format. The information providing unit can also provide the information in audio format. For example, the information providing unit displays the analysis results as text. The graph format is used to provide a visual representation of data. The audio format is used to allow the user to obtain information audibly.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information gathering unit that gathers data from various sources on the Internet; an information analysis unit that analyzes the data collected by the information collection unit; an information providing unit that provides the information analyzed by the information analyzing unit to a user; A system characterized by:

2. The information collecting unit Gather information from news sites, blogs, social media, and specialized databases 2. The system of claim 1.

3. The information providing unit Provide information in text, graphics, and audio formats 2. The system of claim 1.

4. The information providing unit The user has the ability to share the knowledge and information they have acquired on social media and blogs.

2. The system of claim 1.

5. The information collecting unit Adding personalized information sources based on the user's past search and browsing history 2. The system of claim 1.

6. The information collecting unit Include local news and event information based on the user's real-time location 2. The system of claim 1.

7. The information collecting unit Prioritizing collection of information related to topics of current interest to the user 2. The system of claim 1.

8. The information collecting unit Collect multimedia information, including audio and video data 2. The system of claim 1.

Images