System

Abstract

An object of a system according to an embodiment is to efficiently obtain information desired by a user.SOLUTION: A system includes an information collection part, a summary generation part, and a customization generation part. The information collection unit collects information based on a question of a user. The summary generation unit summarizes the information collected by the information collection unit. The customization generating unit generates a user-dedicated website based on the information summarized by the summary generating 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] With conventional technology, it was difficult to efficiently obtain the desired information through web searches, and it took a long time.

[0005] The system according to the embodiment aims to enable users to efficiently obtain desired information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a summary generation unit, and a customization generation unit. The information collection unit collects information based on a user's question. The summary generation unit summarizes the information collected by the information collection unit. The customization generation unit generates a user-specific website based on the information summarized by the summary generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to efficiently obtain desired information. [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 browser according to the embodiment of the present invention is a system in which a generation AI aggregates information based on questions entered by the user and automatically creates a website for the user. This allows working people who do not have time to search or people who are not familiar with web searches to obtain information efficiently and easily.

[0029] A browser according to an embodiment includes an information collection unit, a summary generation unit, and a customization generation unit. The information collection unit collects information based on a user's question. For example, the information collection unit analyzes the question entered by the user and collects information from multiple related websites. The information collection unit can also collect related information using a search engine. For example, if a user searches for "reviews of the latest smartphones," the information collection unit collects information from multiple review sites. The summary generation unit summarizes the information collected by the information collection unit. For example, the summary generation unit summarizes the collected information using a generation AI. The summary generation unit can also concisely summarize information using a text generation AI (e.g., LLM). For example, the summary generation unit summarizes the collected review information and compiles it into a single document. The customization generation unit generates a user-specific website based on the information summarized by the summary generation unit. For example, the customization generation unit uses a generation AI to generate HTML and CSS according to the user's preferences and device. The customization generation unit can also automatically apply responsive design to generate a web page that displays properly on both PCs and smartphones. For example, if a user searches for "healthy meal recipes," the customization generation unit collects information from multiple recipe sites, summarizes it, and compiles it into a single document, displaying it in an easy-to-read layout. This allows the browser according to the embodiment to efficiently provide information by collecting and summarizing information based on the user's question and generating a website dedicated to the user. For example, the browser allows working professionals who are short on time for searches and people unfamiliar with web searches to efficiently and easily obtain information.

[0030] The information collection unit can compare the collected information with the user's past search history and browsing history and optimize it based on the user's individual interests and concerns. For example, the information collection unit compares the information collected by the generation AI with the user's past search history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to content previously searched. The information collection unit also compares the information collected by the generation AI with the user's browsing history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to content of sites previously viewed. The information collection unit also compares the information collected by the generation AI with the user's search history and browsing history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to keywords previously searched. In this way, by optimizing information based on the user's past search history and browsing history, it is possible to provide information that matches the user's interests and concerns.

[0031] The information collection unit dynamically updates the collected information based on the user's real-time behavioral data, thereby providing the most relevant information to the user. For example, the information collection unit dynamically updates the information collected by the generation AI based on the user's mouse movements and click patterns, thereby providing the most relevant information. For example, if the user clicks on a specific link, information related to that link is preferentially displayed. The information collection unit also dynamically updates the information collected by the generation AI based on the user's real-time behavioral data, thereby providing the most relevant information. For example, if the user moves the mouse over a specific area, information related to that area is preferentially displayed. The information collection unit also dynamically updates the information collected by the generation AI based on the user's click patterns, thereby providing the most relevant information. For example, if the user clicks a specific button, information related to that button is preferentially displayed. This allows information to be dynamically updated based on the user's real-time behavioral data, thereby providing the most relevant information.

[0032] The summary generation unit can convert the collected information into different media formats and provide it in a format that suits the user's preferences. For example, the summary generation unit converts the information collected by the generation AI into a video format and provides it in a format that suits the user's preferences. For example, text information can be converted into a video to make it easier to understand visually. The summary generation unit also converts the information collected by the generation AI into an audio format and provides it in a format that suits the user's preferences. For example, text information can be converted into audio to make it easier to understand auditorily. The summary generation unit also converts the information collected by the generation AI into an image format and provides it in a format that suits the user's preferences. For example, text information can be converted into an image to make it easier to understand visually. This helps the user understand the information by converting it into a different media format and providing it in a format that suits the user's preferences.

[0033] The summary generation unit can automatically translate the collected information into different languages, making it possible to accommodate international users. The summary generation unit, for example, automatically translates information collected by the generation AI into different languages, making it possible to accommodate international users. For example, English information is translated into Japanese and provided to a Japanese user. The summary generation unit also automatically translates information collected by the generation AI into multiple languages, making it possible to accommodate international users. For example, English information is translated into French and Spanish and provided to users in each country. The summary generation unit also automatically translates information collected by the generation AI into different languages ​​in real time, making it possible to accommodate international users. For example, English information is translated into Japanese in real time and provided to a Japanese user. This allows information to be automatically translated into different languages, making it possible to accommodate international users.

[0034] The customization generation unit can learn the user's visual preferences and provide an individually optimized design. For example, the generation AI of the customization generation unit learns the user's visual preferences and provides an individually optimized design. For example, if the user prefers large font sizes, a design with a large font size is provided. The customization generation unit also learns the user's color preferences and provides an individually optimized design. For example, if the user prefers blue, a design with a blue base is provided. The customization generation unit also learns the user's visual preferences and provides an individually optimized design. For example, if the user prefers a simple layout, a design with a simple layout is provided. This improves user satisfaction by providing a design that meets the user's visual preferences.

[0035] The customization generation unit can analyze a user's voice input and generate customized HTML and CSS based on the voice command. In the customization generation unit, for example, a generation AI analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "increase the font size," a design with a large font size is generated. In addition, the customization generation unit analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "make the background color blue," a design with a blue background is generated. In addition, the customization generation unit analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "simple the layout," a design with a simple layout is generated. In this way, customized HTML and CSS are generated based on the user's voice command, improving user convenience.

[0036] The customization generation unit can analyze a user's gesture input and provide a customized interface based on the gesture. In the customization generation unit, for example, a generation AI analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user swipes the screen, an interface that moves to the next page is provided. In addition, the customization generation unit analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user pinches in, an interface that zooms out is provided. In addition, the customization generation unit analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user double-tap, an interface that executes a specific action is provided. In this way, by providing a customized interface based on the user's gesture input, convenience for the user is improved.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The information collection unit collects information based on a user's question. For example, the information collection unit analyzes the question entered by the user and collects information from multiple related websites. The information collection unit can also collect related information using a search engine. For example, if a user searches for "latest smartphone reviews," the information collection unit collects information from multiple review sites. The summary generation unit summarizes the information collected by the information collection unit. For example, the summary generation unit summarizes the collected information using a generation AI. The summary generation unit can also concisely summarize information using a text generation AI (e.g., LLM). For example, the summary generation unit summarizes the collected review information and compiles it into a single document. The customization generation unit generates a user-specific website based on the information summarized by the summary generation unit. For example, the customization generation unit uses a generation AI to generate HTML and CSS according to the user's preferences and device. The customization generation unit can also automatically apply responsive design to generate a web page that displays properly on both PCs and smartphones. For example, if a user searches for "healthy meal recipes," the customization generation unit collects information from multiple recipe sites, summarizes it, and compiles it into a single document, displaying it in an easy-to-read layout. This allows the browser according to the embodiment to efficiently provide information by collecting and summarizing information based on the user's question and generating a website dedicated to the user. For example, the browser allows working professionals who are short on time for searches and people unfamiliar with web searches to efficiently and easily obtain information.

[0039] The information collection unit can compare the collected information with the user's past search history and browsing history and optimize it based on the user's individual interests and concerns. For example, the information collection unit compares the information collected by the generation AI with the user's past search history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to content previously searched. The information collection unit also compares the information collected by the generation AI with the user's browsing history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to content of sites previously viewed. The information collection unit also compares the information collected by the generation AI with the user's search history and browsing history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to keywords previously searched. In this way, by optimizing information based on the user's past search history and browsing history, it is possible to provide information that matches the user's interests and concerns.

[0040] The information collection unit dynamically updates the collected information based on the user's real-time behavioral data, thereby providing the most relevant information to the user. For example, the information collection unit dynamically updates the information collected by the generation AI based on the user's mouse movements and click patterns, thereby providing the most relevant information. For example, if the user clicks on a specific link, information related to that link is preferentially displayed. The information collection unit also dynamically updates the information collected by the generation AI based on the user's real-time behavioral data, thereby providing the most relevant information. For example, if the user moves the mouse over a specific area, information related to that area is preferentially displayed. The information collection unit also dynamically updates the information collected by the generation AI based on the user's click patterns, thereby providing the most relevant information. For example, if the user clicks a specific button, information related to that button is preferentially displayed. This allows information to be dynamically updated based on the user's real-time behavioral data, thereby providing the most relevant information.

[0041] The summary generation unit can convert the collected information into different media formats and provide it in a format that suits the user's preferences. For example, the summary generation unit converts the information collected by the generation AI into a video format and provides it in a format that suits the user's preferences. For example, text information can be converted into a video to make it easier to understand visually. The summary generation unit also converts the information collected by the generation AI into an audio format and provides it in a format that suits the user's preferences. For example, text information can be converted into audio to make it easier to understand auditorily. The summary generation unit also converts the information collected by the generation AI into an image format and provides it in a format that suits the user's preferences. For example, text information can be converted into an image to make it easier to understand visually. This helps the user understand the information by converting it into a different media format and providing it in a format that suits the user's preferences.

[0042] The summary generation unit can automatically translate the collected information into different languages, making it possible to accommodate international users. The summary generation unit, for example, automatically translates information collected by the generation AI into different languages, making it possible to accommodate international users. For example, English information is translated into Japanese and provided to a Japanese user. The summary generation unit also automatically translates information collected by the generation AI into multiple languages, making it possible to accommodate international users. For example, English information is translated into French and Spanish and provided to users in each country. The summary generation unit also automatically translates information collected by the generation AI into different languages ​​in real time, making it possible to accommodate international users. For example, English information is translated into Japanese in real time and provided to a Japanese user. This allows information to be automatically translated into different languages, making it possible to accommodate international users.

[0043] The customization generation unit can learn the user's visual preferences and provide an individually optimized design. For example, the generation AI of the customization generation unit learns the user's visual preferences and provides an individually optimized design. For example, if the user prefers large font sizes, a design with a large font size is provided. The customization generation unit also learns the user's color preferences and provides an individually optimized design. For example, if the user prefers blue, a design with a blue base is provided. The customization generation unit also learns the user's visual preferences and provides an individually optimized design. For example, if the user prefers a simple layout, a design with a simple layout is provided. This improves user satisfaction by providing a design that meets the user's visual preferences.

[0044] The customization generation unit can analyze a user's voice input and generate customized HTML and CSS based on the voice command. In the customization generation unit, for example, a generation AI analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "increase the font size," a design with a large font size is generated. In addition, the customization generation unit analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "make the background color blue," a design with a blue background is generated. In addition, the customization generation unit analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "simple the layout," a design with a simple layout is generated. In this way, customized HTML and CSS are generated based on the user's voice command, improving user convenience.

[0045] The customization generation unit can analyze a user's gesture input and provide a customized interface based on the gesture. In the customization generation unit, for example, a generation AI analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user swipes the screen, an interface that moves to the next page is provided. In addition, the customization generation unit analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user pinches in, an interface that zooms out is provided. In addition, the customization generation unit analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user double-tap, an interface that executes a specific action is provided. In this way, by providing a customized interface based on the user's gesture input, convenience for the user is improved.

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

[0047] Step 1: The information gathering unit collects information based on the user's question. For example, it analyzes the question entered by the user and gathers information from multiple related websites. It can also gather related information using a search engine. For example, if a user searches for "reviews of the latest smartphones," it gathers information from multiple review sites. Step 2: The summary generator summarizes the information collected by the information collector. For example, the collected information is summarized using a generation AI. Alternatively, a text generation AI (e.g., LLM) can be used to concisely summarize the information. For example, the collected review information is summarized and compiled into a single document. Step 3: The customization generation unit generates a website specifically for the user based on the information summarized by the summary generation unit. For example, it uses generation AI to generate HTML and CSS according to the user's preferences and device. It can also automatically apply responsive design to generate web pages that display properly on both PCs and smartphones. For example, if a user searches for "healthy meal recipes," information is collected from multiple recipe sites, summarized, and compiled into a single document, which is then displayed in an easy-to-read layout.

[0048] (Example 2) The browser according to the embodiment of the present invention is a system in which a generation AI aggregates information based on questions entered by the user and automatically creates a website for the user. This allows working people who do not have time to search or people who are not familiar with web searches to obtain information efficiently and easily.

[0049] A browser according to an embodiment includes an information collection unit, a summary generation unit, and a customization generation unit. The information collection unit collects information based on a user's question. For example, the information collection unit analyzes the question entered by the user and collects information from multiple related websites. The information collection unit can also collect related information using a search engine. For example, if a user searches for "reviews of the latest smartphones," the information collection unit collects information from multiple review sites. The summary generation unit summarizes the information collected by the information collection unit. For example, the summary generation unit summarizes the collected information using a generation AI. The summary generation unit can also concisely summarize information using a text generation AI (e.g., LLM). For example, the summary generation unit summarizes the collected review information and compiles it into a single document. The customization generation unit generates a user-specific website based on the information summarized by the summary generation unit. For example, the customization generation unit uses a generation AI to generate HTML and CSS according to the user's preferences and device. The customization generation unit can also automatically apply responsive design to generate a web page that displays properly on both PCs and smartphones. For example, if a user searches for "healthy meal recipes," the customization generation unit collects information from multiple recipe sites, summarizes it, and compiles it into a single document, displaying it in an easy-to-read layout. This allows the browser according to the embodiment to efficiently provide information by collecting and summarizing information based on the user's question and generating a website dedicated to the user. For example, the browser allows working professionals who are short on time for searches and people unfamiliar with web searches to efficiently and easily obtain information.

[0050] The information collection unit can use an emotion estimation function to filter the collected information based on the user's emotions and prioritize displaying information that elicits positive emotions. For example, the information collection unit performs emotion analysis on the information collected by the generation AI and prioritizes displaying information that elicits positive emotions. For example, among information collected from review sites, it prioritizes displaying information that has received high user ratings. The information collection unit also uses the emotion estimation function to analyze the emotional response to the user's search content in real time and prioritizes displaying information that elicits positive emotions. For example, if a user searches for "healthy meal recipes," it prioritizes displaying recipes with many positive ratings. The information collection unit also filters the information collected by the generation AI based on the user's emotional state and prioritizes displaying information that elicits positive emotions. For example, if a user searches for "reviews of the latest smartphones," it prioritizes displaying reviews with many positive ratings. In this way, by filtering information based on the user's emotions and prioritize displaying information that elicits positive emotions, user satisfaction is improved.

[0051] The information collection unit can compare the collected information with the user's past search history and browsing history and optimize it based on the user's individual interests and concerns. For example, the information collection unit compares the information collected by the generation AI with the user's past search history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to content previously searched. The information collection unit also compares the information collected by the generation AI with the user's browsing history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to content of sites previously viewed. The information collection unit also compares the information collected by the generation AI with the user's search history and browsing history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to keywords previously searched. In this way, by optimizing information based on the user's past search history and browsing history, it is possible to provide information that matches the user's interests and concerns.

[0052] The information collection unit dynamically updates the collected information based on the user's real-time behavioral data, thereby providing the most relevant information to the user. For example, the information collection unit dynamically updates the information collected by the generation AI based on the user's mouse movements and click patterns, thereby providing the most relevant information. For example, if the user clicks on a specific link, information related to that link is preferentially displayed. The information collection unit also dynamically updates the information collected by the generation AI based on the user's real-time behavioral data, thereby providing the most relevant information. For example, if the user moves the mouse over a specific area, information related to that area is preferentially displayed. The information collection unit also dynamically updates the information collected by the generation AI based on the user's click patterns, thereby providing the most relevant information. For example, if the user clicks a specific button, information related to that button is preferentially displayed. This allows information to be dynamically updated based on the user's real-time behavioral data, thereby providing the most relevant information.

[0053] The summary generation unit can convert the collected information into different media formats and provide it in a format that suits the user's preferences. For example, the summary generation unit converts the information collected by the generation AI into a video format and provides it in a format that suits the user's preferences. For example, text information can be converted into a video to make it easier to understand visually. The summary generation unit also converts the information collected by the generation AI into an audio format and provides it in a format that suits the user's preferences. For example, text information can be converted into audio to make it easier to understand auditorily. The summary generation unit also converts the information collected by the generation AI into an image format and provides it in a format that suits the user's preferences. For example, text information can be converted into an image to make it easier to understand visually. This helps the user understand the information by converting it into a different media format and providing it in a format that suits the user's preferences.

[0054] The summary generation unit can automatically translate the collected information into different languages, making it possible to accommodate international users. The summary generation unit, for example, automatically translates information collected by the generation AI into different languages, making it possible to accommodate international users. For example, English information is translated into Japanese and provided to a Japanese user. The summary generation unit also automatically translates information collected by the generation AI into multiple languages, making it possible to accommodate international users. For example, English information is translated into French and Spanish and provided to users in each country. The summary generation unit also automatically translates information collected by the generation AI into different languages ​​in real time, making it possible to accommodate international users. For example, English information is translated into Japanese in real time and provided to a Japanese user. This allows information to be automatically translated into different languages, making it possible to accommodate international users.

[0055] The summary generation unit can use the emotion estimation function to analyze the emotional response to the content searched by the user in real time and prioritize displaying information that elicits positive emotions. For example, the summary generation unit uses the emotion estimation function to analyze the emotional response to the content searched by the user in real time and prioritize displaying information that elicits positive emotions. For example, if a user searches for "latest smartphone reviews," reviews with many positive ratings are prioritized to be displayed. The summary generation unit also uses the emotion estimation function to analyze the emotional response to the content searched by the user in real time and prioritize displaying information that elicits positive emotions. For example, if a user searches for "healthy meal recipes," recipes with many positive ratings are prioritized to be displayed. The summary generation unit also uses the emotion estimation function to analyze the emotional response to the content searched by the user in real time and prioritize displaying information that elicits positive emotions. For example, if a user searches for "recommended travel spots," spots with many positive ratings are prioritized to be displayed. In this way, by analyzing the user's emotional response in real time and prioritize displaying information that elicits positive emotions, user satisfaction is improved.

[0056] The customization generation unit can estimate the emotional state of the user and generate a customized design according to the emotion. In the customization generation unit, for example, the generation AI estimates the emotional state of the user and generates a customized design according to the emotion. For example, if the user is relaxed, a design with calm colors is generated. In addition, the customization generation unit estimates the emotional state of the user and generates a customized layout according to the emotion. For example, if the user is concentrating, a simple and intuitive layout is generated. In addition, the customization generation unit estimates the emotional state of the user and generates a customized design according to the emotion. For example, if the user is excited, a design with vivid colors is generated. In this way, by generating a customized design according to the user's emotional state, user satisfaction is improved.

[0057] The customization generation unit can learn the user's visual preferences and provide an individually optimized design. For example, the generation AI of the customization generation unit learns the user's visual preferences and provides an individually optimized design. For example, if the user prefers large font sizes, a design with a large font size is provided. The customization generation unit also learns the user's color preferences and provides an individually optimized design. For example, if the user prefers blue, a design with a blue base is provided. The customization generation unit also learns the user's visual preferences and provides an individually optimized design. For example, if the user prefers a simple layout, a design with a simple layout is provided. This improves user satisfaction by providing a design that meets the user's visual preferences.

[0058] The customization generation unit can analyze a user's voice input and generate customized HTML and CSS based on the voice command. In the customization generation unit, for example, a generation AI analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "increase the font size," a design with a large font size is generated. In addition, the customization generation unit analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "make the background color blue," a design with a blue background is generated. In addition, the customization generation unit analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "simple the layout," a design with a simple layout is generated. In this way, customized HTML and CSS are generated based on the user's voice command, improving user convenience.

[0059] The customization generation unit can analyze a user's gesture input and provide a customized interface based on the gesture. In the customization generation unit, for example, a generation AI analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user swipes the screen, an interface that moves to the next page is provided. In addition, the customization generation unit analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user pinches in, an interface that zooms out is provided. In addition, the customization generation unit analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user double-tap, an interface that executes a specific action is provided. In this way, by providing a customized interface based on the user's gesture input, convenience for the user is improved.

[0060] The customization generation unit uses the emotion estimation function to generate a customized design in real time according to the user's emotional state, thereby providing an optimal interface according to the user's emotions. The customization generation unit, for example, uses the emotion estimation function to generate a customized design in real time according to the user's emotional state. For example, if the user is feeling stressed, a design with relaxing colors is provided. The customization generation unit also uses the emotion estimation function to provide a customized interface in real time according to the user's emotional state. For example, if the user is concentrating, a simple and intuitive interface is provided. The customization generation unit also uses the emotion estimation function to generate a customized design in real time according to the user's emotional state, thereby providing an optimal interface according to the user's emotions. For example, if the user is excited, a design with vivid colors is provided. In this way, a customized design in real time according to the user's emotional state is generated and an optimal interface is provided, thereby improving user satisfaction.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The information collection unit collects information based on a user's question. For example, the information collection unit analyzes the question entered by the user and collects information from multiple related websites. The information collection unit can also collect related information using a search engine. For example, if a user searches for "latest smartphone reviews," the information collection unit collects information from multiple review sites. The summary generation unit summarizes the information collected by the information collection unit. For example, the summary generation unit summarizes the collected information using a generation AI. The summary generation unit can also concisely summarize information using a text generation AI (e.g., LLM). For example, the summary generation unit summarizes the collected review information and compiles it into a single document. The customization generation unit generates a user-specific website based on the information summarized by the summary generation unit. For example, the customization generation unit uses a generation AI to generate HTML and CSS according to the user's preferences and device. The customization generation unit can also automatically apply responsive design to generate a web page that displays properly on both PCs and smartphones. For example, if a user searches for "healthy meal recipes," the customization generation unit collects information from multiple recipe sites, summarizes it, and compiles it into a single document, displaying it in an easy-to-read layout. This allows the browser according to the embodiment to efficiently provide information by collecting and summarizing information based on the user's question and generating a website dedicated to the user. For example, the browser allows working professionals who are short on time for searches and people unfamiliar with web searches to efficiently and easily obtain information.

[0063] The information collection unit can use an emotion estimation function to filter the collected information based on the user's emotions and prioritize displaying information that elicits positive emotions. For example, the information collection unit performs emotion analysis on the information collected by the generation AI and prioritizes displaying information that elicits positive emotions. For example, among information collected from review sites, it prioritizes displaying information that has received high user ratings. The information collection unit also uses the emotion estimation function to analyze the emotional response to the user's search content in real time and prioritizes displaying information that elicits positive emotions. For example, if a user searches for "healthy meal recipes," it prioritizes displaying recipes with many positive ratings. The information collection unit also filters the information collected by the generation AI based on the user's emotional state and prioritizes displaying information that elicits positive emotions. For example, if a user searches for "reviews of the latest smartphones," it prioritizes displaying reviews with many positive ratings. In this way, by filtering information based on the user's emotions and prioritize displaying information that elicits positive emotions, user satisfaction is improved.

[0064] The information collection unit can compare the collected information with the user's past search history and browsing history and optimize it based on the user's individual interests and concerns. For example, the information collection unit compares the information collected by the generation AI with the user's past search history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to content previously searched. The information collection unit also compares the information collected by the generation AI with the user's browsing history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to content of sites previously viewed. The information collection unit also compares the information collected by the generation AI with the user's search history and browsing history and optimizes it based on the user's individual interests and concerns. For example, it prioritizes displaying information related to keywords previously searched. In this way, by optimizing information based on the user's past search history and browsing history, it is possible to provide information that matches the user's interests and concerns.

[0065] The information collection unit dynamically updates the collected information based on the user's real-time behavioral data, thereby providing the most relevant information to the user. For example, the information collection unit dynamically updates the information collected by the generation AI based on the user's mouse movements and click patterns, thereby providing the most relevant information. For example, if the user clicks on a specific link, information related to that link is preferentially displayed. The information collection unit also dynamically updates the information collected by the generation AI based on the user's real-time behavioral data, thereby providing the most relevant information. For example, if the user moves the mouse over a specific area, information related to that area is preferentially displayed. The information collection unit also dynamically updates the information collected by the generation AI based on the user's click patterns, thereby providing the most relevant information. For example, if the user clicks a specific button, information related to that button is preferentially displayed. This allows information to be dynamically updated based on the user's real-time behavioral data, thereby providing the most relevant information.

[0066] The summary generation unit can convert the collected information into different media formats and provide it in a format that suits the user's preferences. For example, the summary generation unit converts the information collected by the generation AI into a video format and provides it in a format that suits the user's preferences. For example, text information can be converted into a video to make it easier to understand visually. The summary generation unit also converts the information collected by the generation AI into an audio format and provides it in a format that suits the user's preferences. For example, text information can be converted into audio to make it easier to understand auditorily. The summary generation unit also converts the information collected by the generation AI into an image format and provides it in a format that suits the user's preferences. For example, text information can be converted into an image to make it easier to understand visually. This helps the user understand the information by converting it into a different media format and providing it in a format that suits the user's preferences.

[0067] The summary generation unit can automatically translate the collected information into different languages, making it possible to accommodate international users. The summary generation unit, for example, automatically translates information collected by the generation AI into different languages, making it possible to accommodate international users. For example, English information is translated into Japanese and provided to a Japanese user. The summary generation unit also automatically translates information collected by the generation AI into multiple languages, making it possible to accommodate international users. For example, English information is translated into French and Spanish and provided to users in each country. The summary generation unit also automatically translates information collected by the generation AI into different languages ​​in real time, making it possible to accommodate international users. For example, English information is translated into Japanese in real time and provided to a Japanese user. This allows information to be automatically translated into different languages, making it possible to accommodate international users.

[0068] The summary generation unit can use the emotion estimation function to analyze the emotional response to the content searched by the user in real time and prioritize displaying information that elicits positive emotions. For example, the summary generation unit uses the emotion estimation function to analyze the emotional response to the content searched by the user in real time and prioritize displaying information that elicits positive emotions. For example, if a user searches for "latest smartphone reviews," reviews with many positive ratings are prioritized to be displayed. The summary generation unit also uses the emotion estimation function to analyze the emotional response to the content searched by the user in real time and prioritize displaying information that elicits positive emotions. For example, if a user searches for "healthy meal recipes," recipes with many positive ratings are prioritized to be displayed. The summary generation unit also uses the emotion estimation function to analyze the emotional response to the content searched by the user in real time and prioritize displaying information that elicits positive emotions. For example, if a user searches for "recommended travel spots," spots with many positive ratings are prioritized to be displayed. In this way, by analyzing the user's emotional response in real time and prioritize displaying information that elicits positive emotions, user satisfaction is improved.

[0069] The customization generation unit can estimate the emotional state of the user and generate a customized design according to the emotion. In the customization generation unit, for example, the generation AI estimates the emotional state of the user and generates a customized design according to the emotion. For example, if the user is relaxed, a design with calm colors is generated. In addition, the customization generation unit estimates the emotional state of the user and generates a customized layout according to the emotion. For example, if the user is concentrating, a simple and intuitive layout is generated. In addition, the customization generation unit estimates the emotional state of the user and generates a customized design according to the emotion. For example, if the user is excited, a design with vivid colors is generated. In this way, by generating a customized design according to the user's emotional state, user satisfaction is improved.

[0070] The customization generation unit can learn the user's visual preferences and provide an individually optimized design. For example, the generation AI of the customization generation unit learns the user's visual preferences and provides an individually optimized design. For example, if the user prefers large font sizes, a design with a large font size is provided. The customization generation unit also learns the user's color preferences and provides an individually optimized design. For example, if the user prefers blue, a design with a blue base is provided. The customization generation unit also learns the user's visual preferences and provides an individually optimized design. For example, if the user prefers a simple layout, a design with a simple layout is provided. This improves user satisfaction by providing a design that meets the user's visual preferences.

[0071] The customization generation unit can analyze a user's voice input and generate customized HTML and CSS based on the voice command. In the customization generation unit, for example, a generation AI analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "increase the font size," a design with a large font size is generated. In addition, the customization generation unit analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "make the background color blue," a design with a blue background is generated. In addition, the customization generation unit analyzes a user's voice input and generates customized HTML and CSS based on the voice command. For example, if a user voice-inputs "simple the layout," a design with a simple layout is generated. In this way, customized HTML and CSS are generated based on the user's voice command, improving user convenience.

[0072] The customization generation unit can analyze a user's gesture input and provide a customized interface based on the gesture. In the customization generation unit, for example, a generation AI analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user swipes the screen, an interface that moves to the next page is provided. In addition, the customization generation unit analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user pinches in, an interface that zooms out is provided. In addition, the customization generation unit analyzes a user's gesture input and provides a customized interface based on the gesture. For example, when a user double-tap, an interface that executes a specific action is provided. In this way, by providing a customized interface based on the user's gesture input, convenience for the user is improved.

[0073] The customization generation unit uses the emotion estimation function to generate a customized design in real time according to the user's emotional state, thereby providing an optimal interface according to the user's emotions. The customization generation unit, for example, uses the emotion estimation function to generate a customized design in real time according to the user's emotional state. For example, if the user is feeling stressed, a design with relaxing colors is provided. The customization generation unit also uses the emotion estimation function to provide a customized interface in real time according to the user's emotional state. For example, if the user is concentrating, a simple and intuitive interface is provided. The customization generation unit also uses the emotion estimation function to generate a customized design in real time according to the user's emotional state, thereby providing an optimal interface according to the user's emotions. For example, if the user is excited, a design with vivid colors is provided. In this way, a customized design in real time according to the user's emotional state is generated and an optimal interface is provided, thereby improving user satisfaction.

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

[0075] Step 1: The information gathering unit collects information based on the user's question. For example, it analyzes the question entered by the user and gathers information from multiple related websites. It can also gather related information using a search engine. For example, if a user searches for "reviews of the latest smartphones," it gathers information from multiple review sites. Step 2: The summary generator summarizes the information collected by the information collector. For example, the collected information is summarized using a generation AI. Alternatively, a text generation AI (e.g., LLM) can be used to concisely summarize the information. For example, the collected review information is summarized and compiled into a single document. Step 3: The customization generation unit generates a website specifically for the user based on the information summarized by the summary generation unit. For example, it uses generation AI to generate HTML and CSS according to the user's preferences and device. It can also automatically apply responsive design to generate web pages that display properly on both PCs and smartphones. For example, if a user searches for "healthy meal recipes," information is collected from multiple recipe sites, summarized, and compiled into a single document, which is then displayed in an easy-to-read layout.

[0076] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0078] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0082] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0083] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0084] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0085] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0086] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0087] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0088] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0089] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0090] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0097] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0104] 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.

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

[0106] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0108] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0110] 7, 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.

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

[0112] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0116] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0117] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0120] 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.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0126] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0127] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0128] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0129] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0130] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0131] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0132] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0133] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0134] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0135] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0136] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0137] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0138] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0139] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0140] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0141] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0142] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects information based on a user's question; a summary generation unit that summarizes the information collected by the information collection unit; a customization generation unit that generates a user-specific website based on the information summarized by the summary generation unit. A system characterized by:

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

2. The system of claim 1.

3. The information collecting unit Matching the collected information with the user's past search and browsing history to optimize based on the user's individual interests.

2. The system of claim 1.

4. The information collecting unit Dynamically update the collected information based on the user's real-time behavioral data to provide the user with the most relevant information.

2. The system of claim 1.

5. The summary generation unit Converting collected information into different media formats and providing it in a format that meets the user's preferences 2. The system of claim 1.

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