System

Abstract

An object of a system according to an embodiment is to efficiently collect information desired by a user and automatically edit the information in accordance with a designated time frame.SOLUTION: A system includes an information collection unit and an editing unit. The information collection unit includes a generation AI and collects information based on an instruction of a user. The editing unit automatically edits the collected information in accordance with the time frame designated by the user.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 techniques have had the problem of making it difficult for users to efficiently collect the information they want to obtain and edit it to fit a specified time frame.

[0005] The system according to the embodiment aims to efficiently collect information that a user wants to obtain and automatically compile it to fit a specified time frame. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit and an editing unit. The information collection unit is equipped with a generation AI and collects information based on user instructions. The editing unit automatically edits the collected information to fit a time frame specified by the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect information that a user wants to obtain and automatically compile it to fit a specified time frame. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The summary tool system according to an embodiment of the present invention is a system that collects only the information that a user wants to obtain and automatically edits it at the time that the user wants to view it. In this system, the chat GTP, which is a generation AI, collects information based on the user's instructions and automatically edits the information to fit the time frame specified by the user. This allows the summary tool system to efficiently collect the information that the user needs and view it at the time that the user wants to view it.

[0029] The summary tool system according to the embodiment includes an information collection unit and an editing unit. The information collection unit is equipped with a generation AI and collects information based on user instructions. For example, if a user instructs the generation AI to "collect information about the latest technology," the generation AI collects related information from online news articles, blogs, papers, etc. If the user instructs the generation AI to "only include information from reliable sources," the generation AI can filter out information from less reliable sources and include only information from reliable sources. Furthermore, the generation AI can provide customized information based on the user's preferences and past search history. For example, if a user has frequently searched for information related to technology in the past, the generation AI can prioritize providing the latest technology information. The editing unit automatically edits the information collected by the information collection unit to fit a user-specified time frame. For example, if a user instructs the generation AI to "edit the information so that it can be read in five minutes," the generation AI summarizes the collected information and edits it so that it can be read in five minutes. The generation AI can also filter the collected information to include only the information the user truly needs. For example, a generative AI can receive a prompt like "Please summarize the main points of this passage" and extract the key points of the answer to create a summary. This allows the summary tool system to collect information based on the user's instructions and automatically compile it to fit a specified time frame.

[0030] The information collection unit can refer to the information source's past reliability data and preferentially collect information from highly reliable information sources. For example, the generation AI of the information collection unit refers to the information source's past reliability data and preferentially collects information from highly reliable information sources. For example, it gives priority to information sources that have provided accurate information in the past. The information collection unit also analyzes past data to evaluate the reliability of the information source and calculates a reliability score. For example, it preferentially collects information from information sources with a high reliability score. The information collection unit also builds a system in which the generation AI automatically selects highly reliable information based on the information source's reliability data. For example, it excludes information from less reliable sources. This allows information from highly reliable sources to be preferentially collected.

[0031] The information gathering unit can collect information from sources with different perspectives or opinions in a balanced manner. For example, the information gathering unit allows the generation AI to collect information from sources with different perspectives or opinions in a balanced manner. For example, it collects information evenly from sources with different positions or backgrounds. Furthermore, to ensure diversity of information, the information gathering unit regularly updates the sources collected by the generation AI to incorporate new perspectives and opinions. For example, it adds newly emerged sources. Furthermore, the information gathering unit builds a system in which the generation AI evaluates the diversity of information and collects information in a balanced manner. For example, it selects information so as not to be biased toward a particular perspective. This allows for balanced collection of information from sources with different perspectives and opinions.

[0032] The information collection unit can provide information that is easy to understand visually by including multimedia content such as images or videos in the information it collects. For example, the information collection unit can provide information that is easy to understand visually by including images and videos in the information collected by the generation AI. For example, the information collection unit collects images and videos related to news articles. In addition, the information collection unit allows the generation AI to utilize image recognition and video analysis technology to collect information including multimedia content. For example, the content of images and videos can be analyzed to extract related information. In addition, the information collection unit develops an interface to visually display the information collected by the generation AI. For example, the collected information can be displayed together with images and videos. This makes it possible to provide information that is easy to understand visually.

[0033] The information collection unit collects information in different languages ​​and can provide information from an international perspective. For example, the generation AI collects information in different languages ​​and provides information from an international perspective. For example, it collects information in multiple languages, such as English, French, and Chinese. The information collection unit also builds a system that automatically translates information collected in different languages ​​and provides it to users. For example, it translates collected information in real time. The information collection unit also develops a multilingual information collection algorithm for the generation AI to provide information from an international perspective. For example, it collects information sources in different languages ​​evenly. This makes it possible to provide information from an international perspective.

[0034] The editing department can refer to past editing history and perform editing that suits the user's preferences. For example, the generation AI refers to past editing history and performs editing that suits the user's preferences. For example, editing is performed based on editing styles that users have previously given high ratings to. The editing department also builds a system that analyzes the user's past editing history and performs editing that suits the preferences. For example, editing that reflects the user's preferences is automatically performed. The editing department also develops an algorithm that allows the generation AI to perform editing that suits the user's preferences based on the past editing history. For example, it learns the user's preferences and reflects them in the editing. This makes it possible to perform editing that suits the user's preferences.

[0035] The editorial department can optimize the structure of the information, allowing users to understand it efficiently and in a short amount of time. For example, the editorial department optimizes the structure of the information edited by the generation AI, allowing users to understand it efficiently and in a short amount of time. For example, by emphasizing important points and organizing the information. To optimize the structure of the information, the editorial department has the generation AI automatically organize the information and edit it into a format that is easy for users to understand. For example, by adding bullet points and headings. The editorial department also builds a system that allows the generation AI to optimize the structure of the information, allowing users to understand it efficiently and in a short amount of time. For example, by extracting the key points of the information and summarizing them concisely. This allows users to understand the information efficiently and in a short amount of time.

[0036] The editorial department can provide information in different formats to provide information in a format that suits the user's preferences. For example, the editorial department can provide information edited by the generation AI in different formats to provide information in a format that suits the user's preferences. For example, information can be provided in text, audio, or video format. The editorial department can also build a system in which the generation AI automatically converts information into different formats to provide information in a format that suits the user's preferences. For example, text information can be converted into audio. The editorial department can also provide information edited by the generation AI in different formats to allow the user to select. For example, information can be displayed in text, audio, or video format. This allows information to be provided in a format that suits the user's preferences.

[0037] The editorial department can optimize information for different devices, allowing users to comfortably view information on any device. For example, the editorial department can optimize information edited by the generation AI for different devices, allowing users to comfortably view information on any device. For example, it can be compatible with smartphones, tablets, and PCs. The editorial department can also build a system in which the generation AI automatically adjusts the layout of information to optimize it for different devices. For example, it can display information to fit the screen size of the device. The editorial department can also optimize information edited by the generation AI for different devices, allowing users to select. For example, it can provide layouts compatible with smartphones, tablets, and PCs. This allows users to comfortably view information on any device.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The information collection unit can also filter information based on the user's health condition. For example, if the user has a specific health problem, it can prioritize collecting information about the latest research and treatments related to that problem. The information collection unit can also identify information that has a positive impact on health based on the user's health data and prioritize collecting that information. For example, it can collect information about healthy eating and exercise. Furthermore, the information collection unit can build a system that monitors the user's health condition and provides health information as needed. For example, if the user's health condition worsens, it can provide information suggesting appropriate measures.

[0040] The information collecting unit can also filter information based on the user's learning style. For example, if the user is a visual learner, it can prioritize collecting information that includes many diagrams and graphs. The information collecting unit can also identify information about effective learning methods based on the user's learning history and prioritize collecting that information. For example, it can collect information about learning methods that have been effective in the past. Furthermore, the information collecting unit can build a system that monitors the user's learning style and provides learning information as needed. For example, it can provide information that suggests appropriate learning methods according to the user's learning progress.

[0041] The information collection unit can also filter information based on the user's hobbies and interests. For example, if the user has a particular hobby, it will prioritize collecting the latest information and information about events related to that hobby. The information collection unit can also identify information that will interest the user based on the user's interest data and prioritize collecting that information. For example, it can collect information about a new hobby. Furthermore, the information collection unit can build a system that monitors the user's hobbies and interests and provides related information as needed. For example, if the user's interests change, it can provide information about the new hobby.

[0042] The information collection unit can also filter information based on the user's occupation. For example, if the user is engaged in a specific occupation, it can prioritize collecting information about the latest research and industry news related to that occupation. The information collection unit can also identify information related to the occupation based on the user's occupation data and prioritize collecting that information. For example, it can collect information about skill development related to the occupation. Furthermore, the information collection unit can build a system that monitors the user's occupation and provides information about the occupation as needed. For example, if the user changes occupation, it can provide information about the new occupation.

[0043] The editorial department can also summarize information based on the user's learning progress. For example, if the user has a specific learning goal, it can summarize and provide information related to that goal. The editorial department can also build a system that summarizes information according to the user's learning progress based on the user's learning data. For example, it can summarize information in stages according to the user's learning progress. Furthermore, the editorial department can develop a system that monitors the user's learning progress and summarizes and provides information related to learning as needed. For example, it can provide appropriate summaries according to the user's learning progress.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The information gathering unit is equipped with a generation AI and collects information based on user instructions. For example, if the user instructs the generation AI to "collect information about the latest technology," the AI ​​will collect relevant information from online news articles, blogs, papers, etc. If the user instructs the generation AI to "only include information from highly reliable sources," the AI ​​can filter out information from less reliable sources and include only information from highly reliable sources. Furthermore, the generation AI can provide customized information based on the user's preferences and past search history. For example, if the user has searched frequently for information about technology in the past, the AI ​​will prioritize providing the latest technology information. Step 2: The editing department automatically edits the information collected by the information collection department to fit the time frame specified by the user. For example, if the user instructs the AI ​​to "edit it so that it can be read in five minutes," the AI ​​will summarize the collected information and edit it so that it can be read in five minutes. The AI ​​can also filter the collected information, leaving only the information that the user truly needs. For example, if the AI ​​receives a prompt such as "Summarize the main points of this passage," it will extract the main points of the answer and create a summary.

[0046] (Example 2) The summary tool system according to an embodiment of the present invention is a system that collects only the information that a user wants to obtain and automatically edits it at the time that the user wants to view it. In this system, the chat GTP, which is a generation AI, collects information based on the user's instructions and automatically edits the information to fit the time frame specified by the user. This allows the summary tool system to efficiently collect the information that the user needs and view it at the time that the user wants to view it.

[0047] The summary tool system according to the embodiment includes an information collection unit and an editing unit. The information collection unit is equipped with a generation AI and collects information based on user instructions. For example, if a user instructs the generation AI to "collect information about the latest technology," the generation AI collects related information from online news articles, blogs, papers, etc. If the user instructs the generation AI to "only include information from reliable sources," the generation AI can filter out information from less reliable sources and include only information from reliable sources. Furthermore, the generation AI can provide customized information based on the user's preferences and past search history. For example, if a user has frequently searched for information related to technology in the past, the generation AI can prioritize providing the latest technology information. The editing unit automatically edits the information collected by the information collection unit to fit a user-specified time frame. For example, if a user instructs the generation AI to "edit the information so that it can be read in five minutes," the generation AI summarizes the collected information and edits it so that it can be read in five minutes. The generation AI can also filter the collected information to include only the information the user truly needs. For example, a generative AI can receive a prompt like "Please summarize the main points of this passage" and extract the key points of the answer to create a summary. This allows the summary tool system to collect information based on the user's instructions and automatically compile it to fit a specified time frame.

[0048] The information collection unit can use the emotion estimation function to filter based on the user's emotions and prioritize collecting information that elicits positive emotions. For example, the information collection unit performs emotion analysis on the information collected by the generation AI and prioritizes filtering of information that elicits positive emotions. For example, it prioritizes collecting information that makes the user feel joy or excitement. The information collection unit also identifies information that elicits positive emotions based on the user's past emotion data and prioritizes collecting that information. For example, it filters based on information that the user has previously rated highly. The information collection unit also uses the emotion estimation function to automatically select information that is likely to elicit positive emotions from the collected information. For example, it prioritizes collecting information with a high emotion score. This allows information that elicits positive emotions to be prioritized.

[0049] The information collection unit can refer to the information source's past reliability data and preferentially collect information from highly reliable information sources. For example, the generation AI of the information collection unit refers to the information source's past reliability data and preferentially collects information from highly reliable information sources. For example, it gives priority to information sources that have provided accurate information in the past. The information collection unit also analyzes past data to evaluate the reliability of the information source and calculates a reliability score. For example, it preferentially collects information from information sources with a high reliability score. The information collection unit also builds a system in which the generation AI automatically selects highly reliable information based on the information source's reliability data. For example, it excludes information from less reliable sources. This allows information from highly reliable sources to be preferentially collected.

[0050] The information gathering unit can collect information from sources with different perspectives or opinions in a balanced manner. For example, the information gathering unit allows the generation AI to collect information from sources with different perspectives or opinions in a balanced manner. For example, it collects information evenly from sources with different positions or backgrounds. Furthermore, to ensure diversity of information, the information gathering unit regularly updates the sources collected by the generation AI to incorporate new perspectives and opinions. For example, it adds newly emerged sources. Furthermore, the information gathering unit builds a system in which the generation AI evaluates the diversity of information and collects information in a balanced manner. For example, it selects information so as not to be biased toward a particular perspective. This allows for balanced collection of information from sources with different perspectives and opinions.

[0051] The information collection unit can provide information that is easy to understand visually by including multimedia content such as images or videos in the information it collects. For example, the information collection unit can provide information that is easy to understand visually by including images and videos in the information collected by the generation AI. For example, the information collection unit collects images and videos related to news articles. In addition, the information collection unit allows the generation AI to utilize image recognition and video analysis technology to collect information including multimedia content. For example, the content of images and videos can be analyzed to extract related information. In addition, the information collection unit develops an interface to visually display the information collected by the generation AI. For example, the collected information can be displayed together with images and videos. This makes it possible to provide information that is easy to understand visually.

[0052] The information collection unit collects information in different languages ​​and can provide information from an international perspective. For example, the generation AI collects information in different languages ​​and provides information from an international perspective. For example, it collects information in multiple languages, such as English, French, and Chinese. The information collection unit also builds a system that automatically translates information collected in different languages ​​and provides it to users. For example, it translates collected information in real time. The information collection unit also develops a multilingual information collection algorithm for the generation AI to provide information from an international perspective. For example, it collects information sources in different languages ​​evenly. This makes it possible to provide information from an international perspective.

[0053] The information collection unit uses the emotion estimation function to monitor the user's emotional response to the collected information in real time, and can provide information according to the user's emotions. The information collection unit, for example, uses the emotion estimation function to monitor the user's emotional response to the collected information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information collection unit also builds a system that provides information according to the user's emotions based on the user's emotional response data. For example, it provides information that elicits positive emotions preferentially. The information collection unit also collects emotion estimation data in real time and develops a system that dynamically provides information according to the user's emotions. For example, it adjusts the information according to changes in the user's emotions. This makes it possible to provide information according to the user's emotions.

[0054] The editorial department can use the emotion estimation function to create summaries based on the user's emotions and edit the content to elicit positive emotions. For example, when editing information collected by the generation AI, the editorial department uses the emotion estimation function to create summaries based on the user's emotions. For example, editing the content to elicit positive emotions. The editorial department also builds a system that creates summaries according to emotions based on the user's emotion data. For example, it prioritizes generating summaries that elicit positive emotions. The editorial department also uses the emotion estimation function to summarize the collected information and edit the content to elicit positive emotions. For example, it prioritizes summarizing information with a high emotion score. This allows editing the content to elicit positive emotions.

[0055] The editing department can refer to past editing history and perform editing that suits the user's preferences. For example, the generation AI refers to past editing history and performs editing that suits the user's preferences. For example, editing is performed based on editing styles that users have previously given high ratings to. The editing department also builds a system that analyzes the user's past editing history and performs editing that suits the preferences. For example, editing that reflects the user's preferences is automatically performed. The editing department also develops an algorithm that allows the generation AI to perform editing that suits the user's preferences based on the past editing history. For example, it learns the user's preferences and reflects them in the editing. This makes it possible to perform editing that suits the user's preferences.

[0056] The editorial department can optimize the structure of the information, allowing users to understand it efficiently and in a short amount of time. For example, the editorial department optimizes the structure of the information edited by the generation AI, allowing users to understand it efficiently and in a short amount of time. For example, by emphasizing important points and organizing the information. To optimize the structure of the information, the editorial department has the generation AI automatically organize the information and edit it into a format that is easy for users to understand. For example, by adding bullet points and headings. The editorial department also builds a system that allows the generation AI to optimize the structure of the information, allowing users to understand it efficiently and in a short amount of time. For example, by extracting the key points of the information and summarizing them concisely. This allows users to understand the information efficiently and in a short amount of time.

[0057] The editorial department can provide information in different formats to provide information in a format that suits the user's preferences. For example, the editorial department can provide information edited by the generation AI in different formats to provide information in a format that suits the user's preferences. For example, information can be provided in text, audio, or video format. The editorial department can also build a system in which the generation AI automatically converts information into different formats to provide information in a format that suits the user's preferences. For example, text information can be converted into audio. The editorial department can also provide information edited by the generation AI in different formats to allow the user to select. For example, information can be displayed in text, audio, or video format. This allows information to be provided in a format that suits the user's preferences.

[0058] The editorial department can optimize information for different devices, allowing users to comfortably view information on any device. For example, the editorial department can optimize information edited by the generation AI for different devices, allowing users to comfortably view information on any device. For example, it can be compatible with smartphones, tablets, and PCs. The editorial department can also build a system in which the generation AI automatically adjusts the layout of information to optimize it for different devices. For example, it can display information to fit the screen size of the device. The editorial department can also optimize information edited by the generation AI for different devices, allowing users to select. For example, it can provide layouts compatible with smartphones, tablets, and PCs. This allows users to comfortably view information on any device.

[0059] The editorial department uses the emotion estimation function to monitor the emotional reactions of users to edited information in real time, and can continuously edit according to the user's emotions. The editorial department, for example, uses the emotion estimation function to monitor the emotional reactions of users to edited information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The editorial department also builds a system that continuously edits according to the user's emotions based on the user's emotional response data. For example, it prioritizes editing that elicits positive emotions. The editorial department also collects emotion estimation data in real time and develops a system that dynamically edits according to the user's emotions. For example, it adjusts the editing content according to changes in the user's emotions. This makes it possible to continuously edit according to the user's emotions.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The information collection unit can also filter information based on the user's health condition. For example, if the user has a specific health problem, it can prioritize collecting information about the latest research and treatments related to that problem. The information collection unit can also identify information that has a positive impact on health based on the user's health data and prioritize collecting that information. For example, it can collect information about healthy eating and exercise. Furthermore, the information collection unit can build a system that monitors the user's health condition and provides health information as needed. For example, if the user's health condition worsens, it can provide information suggesting appropriate measures.

[0062] The information collection unit can prioritize collecting information that reduces stress based on the user's emotions. For example, if the user is feeling stressed, it collects information about relaxation methods and stress management. The information collection unit can also identify information that is effective in reducing stress based on the user's emotional data and prioritize collecting that information. For example, it can collect information about meditation and yoga. Furthermore, the information collection unit can use an emotion estimation function to automatically select information that is useful for reducing stress from the collected information. For example, it can prioritize collecting relaxation methods with high emotion scores.

[0063] The information collecting unit can also filter information based on the user's learning style. For example, if the user is a visual learner, it can prioritize collecting information that includes many diagrams and graphs. The information collecting unit can also identify information about effective learning methods based on the user's learning history and prioritize collecting that information. For example, it can collect information about learning methods that have been effective in the past. Furthermore, the information collecting unit can build a system that monitors the user's learning style and provides learning information as needed. For example, it can provide information that suggests appropriate learning methods according to the user's learning progress.

[0064] The information collection unit can prioritize collecting information that will increase motivation based on the user's emotions. For example, if the user is not feeling motivated, it can collect information about success stories and words of encouragement to increase motivation. The information collection unit can also identify information that is effective in increasing motivation based on the user's emotional data and prioritize collecting that information. For example, it can collect information about self-development. Furthermore, the information collection unit can use an emotion estimation function to automatically select information that will increase motivation from the collected information. For example, it can prioritize collecting success stories with high emotional scores.

[0065] The information collection unit can also filter information based on the user's hobbies and interests. For example, if the user has a particular hobby, it will prioritize collecting the latest information and information about events related to that hobby. The information collection unit can also identify information that will interest the user based on the user's interest data and prioritize collecting that information. For example, it can collect information about a new hobby. Furthermore, the information collection unit can build a system that monitors the user's hobbies and interests and provides related information as needed. For example, if the user's interests change, it can provide information about the new hobby.

[0066] The information collecting unit can prioritize collecting information that has a relaxing effect based on the user's emotions. For example, if the user is tense, it collects information about relaxing music and natural scenery. The information collecting unit can also identify information that has a relaxing effect based on the user's emotional data and collect that information preferentially. For example, it can collect information about relaxation music and meditation. Furthermore, the information collecting unit can use an emotion estimation function to automatically select information that has a relaxing effect from the collected information. For example, it can prioritize collecting relaxation music with a high emotion score.

[0067] The information collection unit can also filter information based on the user's occupation. For example, if the user is engaged in a specific occupation, it can prioritize collecting information about the latest research and industry news related to that occupation. The information collection unit can also identify information related to the occupation based on the user's occupation data and prioritize collecting that information. For example, it can collect information about skill development related to the occupation. Furthermore, the information collection unit can build a system that monitors the user's occupation and provides information about the occupation as needed. For example, if the user changes occupation, it can provide information about the new occupation.

[0068] The editorial department can add visual elements that evoke positive emotions based on the user's emotions. For example, they can incorporate images and videos that make the user feel happy into the edited content. The editorial department can also identify visual elements that evoke positive emotions based on the user's emotion data and add those elements to the edited content. For example, they can prioritize the use of images and videos with high emotion scores. Furthermore, the editorial department can use emotion estimation functions to build a system that automatically adds visual elements that evoke positive emotions to the edited content. For example, they can dynamically adjust visual elements according to the user's emotions.

[0069] The editorial department can also summarize information based on the user's learning progress. For example, if the user has a specific learning goal, it can summarize and provide information related to that goal. The editorial department can also build a system that summarizes information according to the user's learning progress based on the user's learning data. For example, it can summarize information in stages according to the user's learning progress. Furthermore, the editorial department can develop a system that monitors the user's learning progress and summarizes and provides information related to learning as needed. For example, it can provide appropriate summaries according to the user's learning progress.

[0070] The editorial department can add audio elements that evoke positive emotions based on the user's emotions. For example, they can incorporate music or narration that makes the user feel happy into the edited content. The editorial department can also identify audio elements that evoke positive emotions based on the user's emotional data and add those elements to the edited content. For example, they can prioritize using music or narration with a high emotional score. Furthermore, the editorial department can use the emotion estimation function to build a system that automatically adds audio elements that evoke positive emotions to the edited content. For example, they can dynamically adjust audio elements according to the user's emotions.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The information gathering unit is equipped with a generation AI and collects information based on user instructions. For example, if the user instructs the generation AI to "collect information about the latest technology," the AI ​​will collect relevant information from online news articles, blogs, papers, etc. If the user instructs the generation AI to "only include information from highly reliable sources," the AI ​​can filter out information from less reliable sources and include only information from highly reliable sources. Furthermore, the generation AI can provide customized information based on the user's preferences and past search history. For example, if the user has searched frequently for information about technology in the past, the AI ​​will prioritize providing the latest technology information. Step 2: The editing department automatically edits the information collected by the information collection department to fit the time frame specified by the user. For example, if the user instructs the AI ​​to "edit it so that it can be read in five minutes," the AI ​​will summarize the collected information and edit it so that it can be read in five minutes. The AI ​​can also filter the collected information, leaving only the information that the user truly needs. For example, if the AI ​​receives a prompt such as "Summarize the main points of this passage," it will extract the main points of the answer and create a summary.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 equipped with a generative AI, an editing unit that edits the information collected by the information collecting unit, The information collecting unit Collect information based on your instructions, The editorial department Automatically compile the collected information to fit a time frame specified by the user. A system characterized by:

2. The information collecting unit Filtering is performed based on the user's emotions, and information that elicits positive emotions is preferentially collected.

2. The system of claim 1.

3. The information collecting unit Prioritize information gathering from reliable sources by referring to the past reliability data of the source.

2. The system of claim 1.

4. The information collecting unit Include multimedia content such as images or videos in the information collected to provide information that is easy to understand visually.

2. The system of claim 1.

5. The editorial department A summary is made based on the user's emotions and edited to elicit positive emotions.

2. The system of claim 1.

6. The editorial department Optimizing the structure of the information to enable the user to understand the information efficiently in a short time 2. The system of claim 1.

7. The editorial department Providing the information in different formats and providing the information in a format that is responsive to the user's preferences 2. The system of claim 1.

8. The editorial department The user's emotional response to the edited information is monitored in real time, and editing is continuously performed according to the user's emotions.

2. The system of claim 1.

Images