System

Abstract

An object of a system according to an embodiment is to enable a user to efficiently reach information that the user really needs.SOLUTION: A system includes a query reception part, a generation AI, a query decomposition part, a non-synchronous query part, a summary generation part, and a result page creation part. The query receiving unit receives a search query of a user. The generation AI interprets the query received by the query receiver. The query decomposition unit decomposes the query interpreted by the generation AI and generates a supplementary query. The asynchronous query unit asynchronously queries a search engine about the plurality of queries generated by the query decomposition unit. The summary generation unit interprets the search result acquired by the asynchronous inquiry unit and generates a summary. The result page creation unit creates a result page based on the summary created by the summary generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to find the information you really need from the vast number of websites available.

[0005] The system according to the embodiment aims to enable users to efficiently find the information they really need. [Means for solving the problem]

[0006] The system according to the embodiment includes a query receiving unit, a generation AI, a query decomposition unit, an asynchronous query unit, a summary generation unit, and a result page creation unit. The query receiving unit receives a user's search query. The generation AI interprets the query received by the query receiving unit. The query decomposition unit decomposes the query interpreted by the generation AI and generates a supplementary query. The asynchronous query unit asynchronously queries a search engine for multiple queries generated by the query decomposition unit. The summary generation unit interprets the search results obtained by the asynchronous query unit and creates a summary. The result page creation unit creates a result page based on the summary created by the summary generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to efficiently reach the information that he or she really needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A search system according to an embodiment of the present invention automatically interprets a user's search query, generates a supplementary query using a generation AI, asynchronously queries a search engine, and summarizes and displays the results. This allows the search system to interpret the user's search query and provide higher quality search results.

[0029] A search system according to an embodiment includes a query receiving unit, a generation AI, a query decomposition unit, an asynchronous query unit, a summary generation unit, and a result page creation unit. The query receiving unit receives a user's search query. For example, the query receiving unit can receive a query in text format. The query receiving unit can also receive a query in voice format. The query receiving unit can also receive a query in image format. The generation AI interprets the query received by the query receiving unit. For example, the generation AI interprets the query using a text generation AI (e.g., LLM). The generation AI can also interpret the query using a multimodal generation AI. The generation AI can also interpret the query using natural language processing technology. The query decomposition unit decomposes the query interpreted by the generation AI and generates a supplemental query. For example, the query decomposition unit decomposes the query using keyword extraction technology. The query decomposition unit can also decompose the query using grammar analysis technology. The query decomposition unit can also decompose the query using topic modeling technology. The asynchronous query unit asynchronously queries the search engine with the multiple queries generated by the query decomposition unit. For example, the asynchronous query unit queries the search engine using an asynchronous communication protocol. The asynchronous query unit can also query the search engine using parallel processing technology. The asynchronous query unit can also query the search engine using real-time monitoring technology. The summary generation unit interprets the search results obtained by the asynchronous query unit and creates summaries. For example, the summary generation unit creates summaries using a summarization algorithm. The summary generation unit can also create summaries using information extraction technology. The summary generation unit can also create summaries using natural language generation technology. The result page creation unit creates a result page based on the summaries created by the summary generation unit. For example, the result page creation unit creates a result page using page layout technology. The result page creation unit can also create a result page using information display technology.The result page creation unit can also create a result page using user interface design technology. This allows the search system according to the embodiment to interpret a user's search query and provide higher quality search results. For example, the search system may interpret a user's search query, generate a supplemental query, asynchronously query a search engine, and summarize and display the results. This allows the user to obtain more diversified and higher quality information.

[0030] The generation AI can refer to a user's past search history or browsing history to provide a more personalized interpretation. For example, if a user types, "I want to know reviews of the latest smartphones," the generation AI will refer to the user's past search history and prioritize reviews of specific brands and models, taking into account the brands and models of smartphones the user has previously searched for. The generation AI can also refer to the user's past browsing history to provide a more appropriate interpretation based on the information the user has previously viewed. For example, the generation AI will interpret reviews of the latest smartphones based on reviews of smartphones the user has previously viewed. This allows the generation AI to provide more appropriate search results based on the user's past behavior.

[0031] The generation AI can prioritize region-specific information based on the user's current geographic location information. For example, if a user types "nearby cafe," the generation AI obtains the user's current geographic location information and prioritizes nearby cafes based on that location. For example, if the user is in Tokyo, it will provide information about cafes in Tokyo. The generation AI can also prioritize region-specific events and services based on the user's geographic location information. For example, if the user is in Osaka, it will provide information about events held in Osaka. This allows the generation AI to provide more appropriate search results based on the user's current location.

[0032] The query receiving unit can adopt an input method using gestures or eye tracking in addition to voice input. For example, when a user inputs a search query, the query receiving unit adopts gesture input in addition to voice input. For example, the user can input a specific command by waving his or her hand. The query receiving unit can also adopt an input method using eye tracking. For example, the user can input a search query by fixing his or her gaze at a specific position. This allows for the adoption of a variety of input methods, thereby improving user convenience.

[0033] Generative AI can understand a broader context based on the content of a user's social media posts. For example, if a user types in "recommended movies," generative AI can refer to the social media posts and interpret more personalized movies based on the user's previously posted movie preferences and ratings. Generative AI can also understand a user's interests based on the content of the user's social media posts. For example, it can interpret recommended music based on the user's previously posted music preferences. This allows the system to provide more relevant search results by referring to the user's social media posts.

[0034] The query decomposition unit can use either specialized or general terms, taking into account the user's level of expertise. For example, if a user inputs "latest AI technology," the generation AI will consider the user's level of expertise and generate a query using specialized terms and a query using general terms. For example, it will generate "latest deep learning technology" and "latest AI technology." The query decomposition unit can also use appropriate terms based on the user's level of expertise. For example, if the user is a beginner, it will generate a query using general terms, and if the user is an expert, it will generate a query using specialized terms. This makes it possible to provide appropriate search results according to the user's level of expertise.

[0035] The query decomposition unit can automatically extract related topics or subtopics and generate more detailed supplemental queries. For example, if a user enters "latest smartphone reviews," the generation AI automatically extracts related topics and subtopics and generates supplemental queries such as "2023 smartphone reviews" or "comparison of popular smartphones." The query decomposition unit can also extract related topics and subtopics using topic modeling technology. For example, the generation AI uses topic modeling technology to extract related keywords and phrases and generate supplemental queries based on them. This allows for the automatic extraction of related topics and subtopics, thereby providing more detailed search results.

[0036] The query decomposition unit can obtain information from an international perspective by generating supplemental queries in different languages. For example, if a user inputs "reviews of the latest smartphones," the generation AI generates supplemental queries in different languages, such as "reviews of smartphones in 2023 (English)" or "reviews of the latest smartphones (Chinese)." The query decomposition unit can also generate supplemental queries in different languages ​​using machine translation technology. For example, the generation AI uses machine translation technology to translate a user's query into multiple languages ​​and generates supplemental queries based on the translated queries. In this way, by generating supplemental queries in different languages, information from an international perspective can be provided.

[0037] The query decomposition unit can suggest related topics based on the user's interests. For example, if a user enters "latest smartphone reviews," the generation AI will take the user's interests into consideration and suggest related topics such as "smartphone camera performance" and "battery life." The query decomposition unit can also suggest related topics based on the user's past search history and social media posts. For example, the generation AI can suggest related topics based on the user's past searches and posts. This allows the system to provide more appropriate search results based on the user's interests.

[0038] The asynchronous query unit can monitor the response speed of the search engine in real time and send queries at the optimal timing. In the asynchronous query unit, for example, the generation AI monitors the response speed of the search engine in real time and sends queries when the response speed is fast. For example, queries are sent at times when the server load is low. The asynchronous query unit can also monitor the response speed of the search engine using real-time monitoring technology. For example, the generation AI uses real-time monitoring technology to evaluate the response speed of the search engine and send queries at the optimal timing. This allows queries to be sent at the optimal timing based on the response speed of the search engine.

[0039] The asynchronous query unit can sequentially analyze asynchronous query results and notify the user when important information is obtained. For example, if a user searches for "reviews of the latest smartphones," the asynchronous query unit will notify the user when an important review is found. The asynchronous query unit can also identify important information based on the query results and notify the user. For example, the generation AI will analyze the query results, evaluate important information, and notify the user. This enables a quick response by notifying the user when important information is obtained.

[0040] The asynchronous query unit can use multiple search engines simultaneously to obtain more diverse information. In the asynchronous query unit, for example, the generation AI uses multiple search engines simultaneously to make asynchronous queries. For example, search engines such as Google, Bing, and Yahoo are used simultaneously to obtain information. The asynchronous query unit can also use multiple search engines simultaneously using parallel search technology. For example, the generation AI uses parallel search technology to send queries to multiple search engines simultaneously to obtain information. In this way, by using multiple search engines, more diverse information can be provided.

[0041] The asynchronous query unit can automatically categorize asynchronous query results and display them by category according to the user's needs. For example, the generation AI automatically categorizes asynchronous query results and displays them by category according to the user's needs. For example, if a user searches for "latest smartphone reviews," the results are categorized into reviews, comparisons, expert opinions, etc. The asynchronous query unit can also display information by category according to the user's needs based on the query results. For example, the generation AI analyzes the query results and displays information by category according to the user's needs. This makes it possible to provide information by category according to the user's needs.

[0042] When creating a summary of search results, the summary generation unit can refer to the user's past search history to provide a more personalized summary. For example, when the generation AI creates a summary of search results, the summary generation unit can refer to the user's past search history to provide a personalized summary based on the user's interests. For example, a summary can be created based on the brands and models of smartphones that the user has searched for in the past. The summary generation unit can also provide an appropriate summary based on the user's past search history. For example, the generation AI can provide a personalized summary based on the content that the user has searched for in the past. This makes it possible to provide a more appropriate summary based on the user's past search history.

[0043] The summary generation unit can automatically extract related images and videos when creating a summary, and provide a summary that is visually easy to understand. For example, when the generation AI creates a summary of search results, the summary generation unit automatically extracts related images and videos, and provides a summary that is visually easy to understand. For example, images and videos related to smartphone reviews can be included in the summary. The summary generation unit can also provide a summary that is visually easy to understand based on images and videos. For example, the generation AI analyzes images and videos, extracts visually easy-to-understand information, and creates a summary based on that information. This makes it possible to provide a summary that is visually easy to understand.

[0044] The summary generation unit can provide information from an international perspective by automatically translating the summaries of search results into different languages. For example, the generation AI automatically translates the summaries of search results into different languages ​​to provide information from an international perspective. For example, translation into multiple languages ​​such as English, French, and Chinese. The summary generation unit can also provide summaries in different languages ​​using machine translation technology. For example, the generation AI uses machine translation technology to translate the summaries of search results into multiple languages ​​and provides information based on that. In this way, by automatically translating into different languages, information from an international perspective can be provided.

[0045] When creating a summary, the summary generation unit can suggest related topics based on the user's interests. For example, when the generation AI creates a summary of search results, the summary generation unit takes the user's interests into consideration and suggests related topics such as "smartphone camera performance" and "battery life." The summary generation unit can also suggest related topics based on the user's past search history and social media posts. For example, the generation AI can suggest related topics based on the user's past searches and posts. This allows the system to provide a more appropriate summary based on the user's interests.

[0046] When creating a result page, the result page creation unit can refer to the user's past browsing history and provide a more personalized result page. For example, when the generation AI creates a result page, the result page creation unit can refer to the user's past browsing history and provide a personalized result page based on the user's interests and concerns. For example, the result page can be created based on the brand and model of smartphones that the user has previously viewed. The result page creation unit can also provide an appropriate result page based on the user's past browsing history. For example, the generation AI can provide a personalized result page based on the content that the user has previously viewed. This makes it possible to provide a more appropriate result page based on the user's past browsing history.

[0047] The result page creation unit can automatically extract related images and videos when creating a result page, and provide a result page that is visually easy to understand. For example, when the generation AI creates a result page, the result page creation unit can automatically extract related images and videos, and provide a result page that is visually easy to understand. For example, images and videos related to smartphone reviews can be included in the result page. The result page creation unit can also provide a result page that is visually easy to understand based on images and videos. For example, the generation AI analyzes images and videos, extracts visually easy to understand information, and creates a result page based on that information. This makes it possible to provide a result page that is visually easy to understand.

[0048] The result page creation unit can provide information from an international perspective by automatically translating the result page into different languages. For example, the generation AI automatically translates the result page into different languages ​​to provide information from an international perspective. For example, translation into multiple languages ​​such as English, French, and Chinese. The result page creation unit can also provide result pages in different languages ​​using machine translation technology. For example, the generation AI uses machine translation technology to translate the result page into multiple languages ​​and provides information based on that. In this way, by automatically translating into different languages, information from an international perspective can be provided.

[0049] When creating a result page, the result page creation unit can suggest related topics based on the user's interests. For example, when the generation AI creates a result page, the result page creation unit takes the user's interests into consideration and suggests related topics such as "smartphone camera performance" and "battery life." The result page creation unit can also suggest related topics based on the user's past search history and social media posts. For example, the generation AI can suggest related topics based on the user's past searches and posts. This makes it possible to provide a more appropriate result page based on the user's interests.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The query receiving unit receives a user's search query. For example, the query receiving unit can receive a query in text format. The query receiving unit can also receive a query in voice format. The query receiving unit can also receive a query in image format. The generation AI interprets the query received by the query receiving unit. For example, the generation AI can interpret the query using a text generation AI (e.g., LLM). The generation AI can also interpret the query using a multimodal generation AI. The generation AI can also interpret the query using natural language processing technology. The query decomposition unit decomposes the query interpreted by the generation AI and generates a supplemental query. For example, the query decomposition unit decomposes the query using keyword extraction technology. The query decomposition unit can also decompose the query using grammatical analysis technology. The query decomposition unit can also decompose the query using topic modeling technology. The asynchronous query unit asynchronously queries a search engine for multiple queries generated by the query decomposition unit. For example, the asynchronous query unit queries the search engine using an asynchronous communication protocol. The asynchronous query unit can also query the search engine using parallel processing technology. The asynchronous query unit can also query the search engine using real-time monitoring technology. The summary generation unit interprets the search results obtained by the asynchronous query unit and creates summaries. For example, the summary generation unit creates summaries using a summarization algorithm. The summary generation unit can also create summaries using information extraction technology. The summary generation unit can also create summaries using natural language generation technology. The result page creation unit creates result pages based on the summaries created by the summary generation unit. For example, the result page creation unit creates result pages using page layout technology. The result page creation unit can also create result pages using information display technology. The result page creation unit can also create result pages using user interface design technology. This allows the search system according to the embodiment to interpret a user's search query and provide higher quality search results.For example, a search system can interpret a user's search query, generate supplemental queries, asynchronously query a search engine, and summarize and display the results, thereby providing users with more diversified and higher-quality information.

[0052] The generation AI can refer to a user's past search history or browsing history to provide a more personalized interpretation. For example, if a user types, "I want to know reviews of the latest smartphones," the generation AI will refer to the user's past search history and prioritize reviews of specific brands and models, taking into account the brands and models of smartphones the user has previously searched for. The generation AI can also refer to the user's past browsing history to provide a more appropriate interpretation based on the information the user has previously viewed. For example, the generation AI will interpret reviews of the latest smartphones based on reviews of smartphones the user has previously viewed. This allows the generation AI to provide more appropriate search results based on the user's past behavior.

[0053] The generation AI can prioritize region-specific information based on the user's current geographic location information. For example, if a user types "nearby cafe," the generation AI obtains the user's current geographic location information and prioritizes nearby cafes based on that location. For example, if the user is in Tokyo, it will provide information about cafes in Tokyo. The generation AI can also prioritize region-specific events and services based on the user's geographic location information. For example, if the user is in Osaka, it will provide information about events held in Osaka. This allows the generation AI to provide more appropriate search results based on the user's current location.

[0054] The query receiving unit can adopt an input method using gestures or eye tracking in addition to voice input. For example, when a user inputs a search query, the query receiving unit adopts gesture input in addition to voice input. For example, the user can input a specific command by waving his or her hand. The query receiving unit can also adopt an input method using eye tracking. For example, the user can input a search query by fixing his or her gaze at a specific position. This allows for the adoption of a variety of input methods, thereby improving user convenience.

[0055] Generative AI can understand a broader context based on the content of a user's social media posts. For example, if a user types in "recommended movies," generative AI can refer to the social media posts and interpret more personalized movies based on the user's previously posted movie preferences and ratings. Generative AI can also understand a user's interests based on the content of the user's social media posts. For example, it can interpret recommended music based on the user's previously posted music preferences. This allows the system to provide more relevant search results by referring to the user's social media posts.

[0056] The query decomposition unit can use either specialized or general terms, taking into account the user's level of expertise. For example, if a user inputs "latest AI technology," the generation AI will consider the user's level of expertise and generate a query using specialized terms and a query using general terms. For example, it will generate "latest deep learning technology" and "latest AI technology." The query decomposition unit can also use appropriate terms based on the user's level of expertise. For example, if the user is a beginner, it will generate a query using general terms, and if the user is an expert, it will generate a query using specialized terms. This makes it possible to provide appropriate search results according to the user's level of expertise.

[0057] The query decomposition unit can automatically extract related topics or subtopics and generate more detailed supplemental queries. For example, if a user enters "latest smartphone reviews," the generation AI automatically extracts related topics and subtopics and generates supplemental queries such as "2023 smartphone reviews" or "comparison of popular smartphones." The query decomposition unit can also extract related topics and subtopics using topic modeling technology. For example, the generation AI uses topic modeling technology to extract related keywords and phrases and generate supplemental queries based on them. This allows for the automatic extraction of related topics and subtopics, thereby providing more detailed search results.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The query receiving unit receives a user's search query. For example, the query receiving unit can receive the query in text format, voice format, or image format. Step 2: The generation AI interprets the query received by the query receiving unit. For example, the generation AI interprets the query using text generation AI (e.g., LLM), multimodal generation AI, or natural language processing technology. Step 3: The query decomposition unit decomposes the query interpreted by the generation AI and generates a supplementary query. For example, the query decomposition unit decomposes the query using keyword extraction technology, grammar analysis technology, and topic modeling technology. Step 4: The asynchronous query unit asynchronously queries the search engine with the multiple queries generated by the query decomposition unit. For example, the asynchronous query unit queries the search engine using an asynchronous communication protocol, a parallel processing technique, and a real-time monitoring technique. Step 5: The summary generator interprets the search results obtained by the asynchronous query module and creates summaries. For example, the summary generator may use summarization algorithms, information extraction techniques, and natural language generation techniques to create summaries. Step 6: The result page creation unit creates a result page based on the summary created by the summary generation unit. For example, the result page creation unit creates the result page using page layout technology, information display technology, and user interface design technology.

[0060] (Example 2) A search system according to an embodiment of the present invention automatically interprets a user's search query, generates a supplementary query using a generation AI, asynchronously queries a search engine, and summarizes and displays the results. This allows the search system to interpret the user's search query and provide higher quality search results.

[0061] A search system according to an embodiment includes a query receiving unit, a generation AI, a query decomposition unit, an asynchronous query unit, a summary generation unit, and a result page creation unit. The query receiving unit receives a user's search query. For example, the query receiving unit can receive a query in text format. The query receiving unit can also receive a query in voice format. The query receiving unit can also receive a query in image format. The generation AI interprets the query received by the query receiving unit. For example, the generation AI interprets the query using a text generation AI (e.g., LLM). The generation AI can also interpret the query using a multimodal generation AI. The generation AI can also interpret the query using natural language processing technology. The query decomposition unit decomposes the query interpreted by the generation AI and generates a supplemental query. For example, the query decomposition unit decomposes the query using keyword extraction technology. The query decomposition unit can also decompose the query using grammar analysis technology. The query decomposition unit can also decompose the query using topic modeling technology. The asynchronous query unit asynchronously queries the search engine with the multiple queries generated by the query decomposition unit. For example, the asynchronous query unit queries the search engine using an asynchronous communication protocol. The asynchronous query unit can also query the search engine using parallel processing technology. The asynchronous query unit can also query the search engine using real-time monitoring technology. The summary generation unit interprets the search results obtained by the asynchronous query unit and creates summaries. For example, the summary generation unit creates summaries using a summarization algorithm. The summary generation unit can also create summaries using information extraction technology. The summary generation unit can also create summaries using natural language generation technology. The result page creation unit creates a result page based on the summaries created by the summary generation unit. For example, the result page creation unit creates a result page using page layout technology. The result page creation unit can also create a result page using information display technology.The result page creation unit can also create a result page using user interface design technology. This allows the search system according to the embodiment to interpret a user's search query and provide higher quality search results. For example, the search system may interpret a user's search query, generate a supplemental query, asynchronously query a search engine, and summarize and display the results. This allows the user to obtain more diversified and higher quality information.

[0062] The generation AI can refer to a user's past search history or browsing history to provide a more personalized interpretation. For example, if a user types, "I want to know reviews of the latest smartphones," the generation AI will refer to the user's past search history and prioritize reviews of specific brands and models, taking into account the brands and models of smartphones the user has previously searched for. The generation AI can also refer to the user's past browsing history to provide a more appropriate interpretation based on the information the user has previously viewed. For example, the generation AI will interpret reviews of the latest smartphones based on reviews of smartphones the user has previously viewed. This allows the generation AI to provide more appropriate search results based on the user's past behavior.

[0063] The generation AI can prioritize region-specific information based on the user's current geographic location information. For example, if a user types "nearby cafe," the generation AI obtains the user's current geographic location information and prioritizes nearby cafes based on that location. For example, if the user is in Tokyo, it will provide information about cafes in Tokyo. The generation AI can also prioritize region-specific events and services based on the user's geographic location information. For example, if the user is in Osaka, it will provide information about events held in Osaka. This allows the generation AI to provide more appropriate search results based on the user's current location.

[0064] The generation AI can use the emotion estimation function to analyze the emotion of the user when inputting a query and interpret the query according to that emotion. For example, if the user inputs "methods for relieving stress," the generation AI will use the emotion estimation function to analyze the user's emotion, and if it determines that the user is highly stressed, it will prioritize interpretations of relaxation and refreshment methods. The generation AI can also interpret queries according to the user's emotion based on the user's emotion. For example, if the user inputs "fun activities," it will use the emotion estimation function to analyze the user's emotion and prioritize interpretations of activities that elicit positive emotions. This allows the generation AI to provide more appropriate search results based on the user's emotion.

[0065] The query receiving unit can adopt an input method using gestures or eye tracking in addition to voice input. For example, when a user inputs a search query, the query receiving unit adopts gesture input in addition to voice input. For example, the user can input a specific command by waving his or her hand. The query receiving unit can also adopt an input method using eye tracking. For example, the user can input a search query by fixing his or her gaze at a specific position. This allows for the adoption of a variety of input methods, thereby improving user convenience.

[0066] Generative AI can understand a broader context based on the content of a user's social media posts. For example, if a user types in "recommended movies," generative AI can refer to the social media posts and interpret more personalized movies based on the user's previously posted movie preferences and ratings. Generative AI can also understand a user's interests based on the content of the user's social media posts. For example, it can interpret recommended music based on the user's previously posted music preferences. This allows the system to provide more relevant search results by referring to the user's social media posts.

[0067] The generative AI can use its emotion estimation function to analyze the emotions of users in real time when they enter a search query and provide suggestions that elicit positive emotions. For example, when a user enters "stress relief methods," the generative AI can use its emotion estimation function to analyze the user's emotions in real time and suggest relaxation methods or fun activities that will elicit positive emotions. The generative AI can also make suggestions based on the user's emotions. For example, when a user enters "fun activities," the generative AI can use its emotion estimation function to analyze the user's emotions in real time and suggest activities that will elicit positive emotions. This allows the system to provide more appropriate search results based on the user's emotions.

[0068] The query decomposition unit can use either specialized or general terms, taking into account the user's level of expertise. For example, if a user inputs "latest AI technology," the generation AI will consider the user's level of expertise and generate a query using specialized terms and a query using general terms. For example, it will generate "latest deep learning technology" and "latest AI technology." The query decomposition unit can also use appropriate terms based on the user's level of expertise. For example, if the user is a beginner, it will generate a query using general terms, and if the user is an expert, it will generate a query using specialized terms. This makes it possible to provide appropriate search results according to the user's level of expertise.

[0069] The query decomposition unit can automatically extract related topics or subtopics and generate more detailed supplemental queries. For example, if a user enters "latest smartphone reviews," the generation AI automatically extracts related topics and subtopics and generates supplemental queries such as "2023 smartphone reviews" or "comparison of popular smartphones." The query decomposition unit can also extract related topics and subtopics using topic modeling technology. For example, the generation AI uses topic modeling technology to extract related keywords and phrases and generate supplemental queries based on them. This allows for the automatic extraction of related topics and subtopics, thereby providing more detailed search results.

[0070] The query decomposition unit uses the emotion estimation function to generate supplemental queries based on the user's emotions, allowing the provision of information that is likely to resonate emotionally. For example, if a user inputs "stress relief methods," the query decomposition unit uses the emotion estimation function to analyze the user's emotions and generate supplemental queries to elicit positive emotions. For example, it generates "ways to relax" or "fun activities." The query decomposition unit can also generate supplemental queries that correspond to the user's emotions based on the user's emotions. For example, if a user inputs "fun activities," it uses the emotion estimation function to analyze the user's emotions and generate supplemental queries to elicit positive emotions. This allows the provision of more appropriate search results based on the user's emotions.

[0071] The query decomposition unit can obtain information from an international perspective by generating supplemental queries in different languages. For example, if a user inputs "reviews of the latest smartphones," the generation AI generates supplemental queries in different languages, such as "reviews of smartphones in 2023 (English)" or "reviews of the latest smartphones (Chinese)." The query decomposition unit can also generate supplemental queries in different languages ​​using machine translation technology. For example, the generation AI uses machine translation technology to translate a user's query into multiple languages ​​and generates supplemental queries based on the translated queries. In this way, by generating supplemental queries in different languages, information from an international perspective can be provided.

[0072] The query decomposition unit can suggest related topics based on the user's interests. For example, if a user enters "latest smartphone reviews," the generation AI will take the user's interests into consideration and suggest related topics such as "smartphone camera performance" and "battery life." The query decomposition unit can also suggest related topics based on the user's past search history and social media posts. For example, the generation AI can suggest related topics based on the user's past searches and posts. This allows the system to provide more appropriate search results based on the user's interests.

[0073] The query decomposition unit uses the emotion estimation function to analyze the user's emotions in real time when generating a supplemental query, and can suggest a supplemental query that elicits positive emotions. For example, when a user inputs "stress relief methods," the query decomposition unit uses the emotion estimation function to analyze the user's emotions in real time and generate a supplemental query that elicits positive emotions. For example, it generates "ways to relax" or "fun activities." The query decomposition unit can also generate a supplemental query that corresponds to the user's emotions based on the user's emotions. For example, when a user inputs "fun activities," it uses the emotion estimation function to analyze the user's emotions in real time and generate a supplemental query that elicits positive emotions. This makes it possible to provide more appropriate search results based on the user's emotions.

[0074] The asynchronous query unit can monitor the response speed of the search engine in real time and send queries at the optimal timing. In the asynchronous query unit, for example, the generation AI monitors the response speed of the search engine in real time and sends queries when the response speed is fast. For example, queries are sent at times when the server load is low. The asynchronous query unit can also monitor the response speed of the search engine using real-time monitoring technology. For example, the generation AI uses real-time monitoring technology to evaluate the response speed of the search engine and send queries at the optimal timing. This allows queries to be sent at the optimal timing based on the response speed of the search engine.

[0075] The asynchronous query unit can sequentially analyze asynchronous query results and notify the user when important information is obtained. For example, if a user searches for "reviews of the latest smartphones," the asynchronous query unit will notify the user when an important review is found. The asynchronous query unit can also identify important information based on the query results and notify the user. For example, the generation AI will analyze the query results, evaluate important information, and notify the user. This enables a quick response by notifying the user when important information is obtained.

[0076] The asynchronous query unit can use the emotion estimation function to prioritize displaying search results that correspond to the user's emotions. For example, if a user searches for "stress relief methods," the generation AI uses the emotion estimation function to analyze the user's emotions and prioritize displaying search results that elicit positive emotions. For example, it displays relaxation methods and fun activities. The asynchronous query unit can also prioritize displaying search results that correspond to the user's emotions based on the user's emotions. For example, if a user searches for "fun activities," it uses the emotion estimation function to analyze the user's emotions and prioritize displaying search results that elicit positive emotions. This makes it possible to provide more appropriate search results based on the user's emotions.

[0077] The asynchronous query unit can use multiple search engines simultaneously to obtain more diverse information. In the asynchronous query unit, for example, the generation AI uses multiple search engines simultaneously to make asynchronous queries. For example, search engines such as Google, Bing, and Yahoo are used simultaneously to obtain information. The asynchronous query unit can also use multiple search engines simultaneously using parallel search technology. For example, the generation AI uses parallel search technology to send queries to multiple search engines simultaneously to obtain information. In this way, by using multiple search engines, more diverse information can be provided.

[0078] The asynchronous query unit can automatically categorize asynchronous query results and display them by category according to the user's needs. For example, the generation AI automatically categorizes asynchronous query results and displays them by category according to the user's needs. For example, if a user searches for "latest smartphone reviews," the results are categorized into reviews, comparisons, expert opinions, etc. The asynchronous query unit can also display information by category according to the user's needs based on the query results. For example, the generation AI analyzes the query results and displays information by category according to the user's needs. This makes it possible to provide information by category according to the user's needs.

[0079] The asynchronous query unit can use the emotion estimation function to analyze the user's emotional response to asynchronous query results in real time and display optimal results. For example, the asynchronous query unit uses a generation AI to analyze the user's emotional response to asynchronous query results in real time and display optimal results that will elicit positive emotions. For example, if a user searches for "stress relief methods," relaxation methods and fun activities will be displayed. The asynchronous query unit can also display optimal results based on the user's emotional response. For example, if a user searches for "fun activities," the emotion estimation function will analyze the user's emotional response and display optimal results that will elicit positive emotions. This makes it possible to provide more appropriate search results based on the user's emotions.

[0080] When creating a summary of search results, the summary generation unit can refer to the user's past search history to provide a more personalized summary. For example, when the generation AI creates a summary of search results, the summary generation unit can refer to the user's past search history to provide a personalized summary based on the user's interests. For example, a summary can be created based on the brands and models of smartphones that the user has searched for in the past. The summary generation unit can also provide an appropriate summary based on the user's past search history. For example, the generation AI can provide a personalized summary based on the content that the user has searched for in the past. This makes it possible to provide a more appropriate summary based on the user's past search history.

[0081] The summary generation unit can automatically extract related images and videos when creating a summary, and provide a summary that is visually easy to understand. For example, when the generation AI creates a summary of search results, the summary generation unit automatically extracts related images and videos, and provides a summary that is visually easy to understand. For example, images and videos related to smartphone reviews can be included in the summary. The summary generation unit can also provide a summary that is visually easy to understand based on images and videos. For example, the generation AI analyzes images and videos, extracts visually easy-to-understand information, and creates a summary based on that information. This makes it possible to provide a summary that is visually easy to understand.

[0082] The summary generation unit uses the emotion estimation function to create summaries based on the user's emotions, and can provide information that is likely to resonate emotionally. For example, when the generation AI creates summaries of search results, the summary generation unit uses the emotion estimation function to analyze the user's emotions and provide summaries that elicit positive emotions. For example, relaxation methods and fun activities can be included in the summary. The summary generation unit can also provide summaries that correspond to the user's emotions based on the user's emotions. For example, if a user searches for "fun activities," the emotion estimation function can be used to analyze the user's emotions and provide summaries that elicit positive emotions. This makes it possible to provide more appropriate summaries based on the user's emotions.

[0083] The summary generation unit can provide information from an international perspective by automatically translating the summaries of search results into different languages. For example, the generation AI automatically translates the summaries of search results into different languages ​​to provide information from an international perspective. For example, translation into multiple languages ​​such as English, French, and Chinese. The summary generation unit can also provide summaries in different languages ​​using machine translation technology. For example, the generation AI uses machine translation technology to translate the summaries of search results into multiple languages ​​and provides information based on that. In this way, by automatically translating into different languages, information from an international perspective can be provided.

[0084] When creating a summary, the summary generation unit can suggest related topics based on the user's interests. For example, when the generation AI creates a summary of search results, the summary generation unit takes the user's interests into consideration and suggests related topics such as "smartphone camera performance" and "battery life." The summary generation unit can also suggest related topics based on the user's past search history and social media posts. For example, the generation AI can suggest related topics based on the user's past searches and posts. This allows the system to provide a more appropriate summary based on the user's interests.

[0085] The summary generation unit uses the emotion estimation function to analyze the user's emotions in real time when creating a summary, and can provide a summary that elicits positive emotions. For example, when the generation AI creates a summary of search results, the summary generation unit uses the emotion estimation function to analyze the user's emotions in real time and provide a summary that elicits positive emotions. For example, relaxation methods and fun activities can be included in the summary. The summary generation unit can also provide a summary that corresponds to the user's emotions based on the user's emotions. For example, if a user searches for "fun activities," the emotion estimation function can analyze the user's emotions in real time and provide a summary that elicits positive emotions. This makes it possible to provide a more appropriate summary based on the user's emotions.

[0086] When creating a result page, the result page creation unit can refer to the user's past browsing history and provide a more personalized result page. For example, when the generation AI creates a result page, the result page creation unit can refer to the user's past browsing history and provide a personalized result page based on the user's interests and concerns. For example, the result page can be created based on the brand and model of smartphones that the user has previously viewed. The result page creation unit can also provide an appropriate result page based on the user's past browsing history. For example, the generation AI can provide a personalized result page based on the content that the user has previously viewed. This makes it possible to provide a more appropriate result page based on the user's past browsing history.

[0087] The result page creation unit can automatically extract related images and videos when creating a result page, and provide a result page that is visually easy to understand. For example, when the generation AI creates a result page, the result page creation unit can automatically extract related images and videos, and provide a result page that is visually easy to understand. For example, images and videos related to smartphone reviews can be included in the result page. The result page creation unit can also provide a result page that is visually easy to understand based on images and videos. For example, the generation AI analyzes images and videos, extracts visually easy to understand information, and creates a result page based on that information. This makes it possible to provide a result page that is visually easy to understand.

[0088] The result page creation unit uses the emotion estimation function to create a result page based on the user's emotions, and can provide information that is likely to resonate emotionally. For example, when the generation AI creates a result page, the result page creation unit uses the emotion estimation function to analyze the user's emotions and provide a result page that elicits positive emotions. For example, relaxation methods and fun activities are included in the result page. The result page creation unit can also provide a result page that corresponds to the user's emotions based on the user's emotions. For example, if a user searches for "fun activities," the emotion estimation function is used to analyze the user's emotions and provide a result page that elicits positive emotions. This makes it possible to provide a more appropriate result page based on the user's emotions.

[0089] The result page creation unit can provide information from an international perspective by automatically translating the result page into different languages. For example, the generation AI automatically translates the result page into different languages ​​to provide information from an international perspective. For example, translation into multiple languages ​​such as English, French, and Chinese. The result page creation unit can also provide result pages in different languages ​​using machine translation technology. For example, the generation AI uses machine translation technology to translate the result page into multiple languages ​​and provides information based on that. In this way, by automatically translating into different languages, information from an international perspective can be provided.

[0090] When creating a result page, the result page creation unit can suggest related topics based on the user's interests. For example, when the generation AI creates a result page, the result page creation unit takes the user's interests into consideration and suggests related topics such as "smartphone camera performance" and "battery life." The result page creation unit can also suggest related topics based on the user's past search history and social media posts. For example, the generation AI can suggest related topics based on the user's past searches and posts. This makes it possible to provide a more appropriate result page based on the user's interests.

[0091] The result page creation unit can use the emotion estimation function to analyze the user's emotions in real time when creating a result page, and provide a result page that elicits positive emotions. For example, when the generation AI creates a result page, the result page creation unit can use the emotion estimation function to analyze the user's emotions in real time, and provide a result page that elicits positive emotions. For example, relaxation methods and fun activities can be included in the result page. The result page creation unit can also provide a result page that corresponds to the user's emotions based on the user's emotions. For example, if a user searches for "fun activities," the emotion estimation function can be used to analyze the user's emotions in real time, and provide a result page that elicits positive emotions. This makes it possible to provide a more appropriate result page based on the user's emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The query receiving unit receives a user's search query. For example, the query receiving unit can receive a query in text format. The query receiving unit can also receive a query in voice format. The query receiving unit can also receive a query in image format. The generation AI interprets the query received by the query receiving unit. For example, the generation AI can interpret the query using a text generation AI (e.g., LLM). The generation AI can also interpret the query using a multimodal generation AI. The generation AI can also interpret the query using natural language processing technology. The query decomposition unit decomposes the query interpreted by the generation AI and generates a supplemental query. For example, the query decomposition unit decomposes the query using keyword extraction technology. The query decomposition unit can also decompose the query using grammatical analysis technology. The query decomposition unit can also decompose the query using topic modeling technology. The asynchronous query unit asynchronously queries a search engine for multiple queries generated by the query decomposition unit. For example, the asynchronous query unit queries the search engine using an asynchronous communication protocol. The asynchronous query unit can also query the search engine using parallel processing technology. The asynchronous query unit can also query the search engine using real-time monitoring technology. The summary generation unit interprets the search results obtained by the asynchronous query unit and creates summaries. For example, the summary generation unit creates summaries using a summarization algorithm. The summary generation unit can also create summaries using information extraction technology. The summary generation unit can also create summaries using natural language generation technology. The result page creation unit creates result pages based on the summaries created by the summary generation unit. For example, the result page creation unit creates result pages using page layout technology. The result page creation unit can also create result pages using information display technology. The result page creation unit can also create result pages using user interface design technology. This allows the search system according to the embodiment to interpret a user's search query and provide higher quality search results.For example, a search system can interpret a user's search query, generate supplemental queries, asynchronously query a search engine, and summarize and display the results, thereby providing users with more diversified and higher-quality information.

[0094] The generation AI can refer to a user's past search history or browsing history to provide a more personalized interpretation. For example, if a user types, "I want to know reviews of the latest smartphones," the generation AI will refer to the user's past search history and prioritize reviews of specific brands and models, taking into account the brands and models of smartphones the user has previously searched for. The generation AI can also refer to the user's past browsing history to provide a more appropriate interpretation based on the information the user has previously viewed. For example, the generation AI will interpret reviews of the latest smartphones based on reviews of smartphones the user has previously viewed. This allows the generation AI to provide more appropriate search results based on the user's past behavior.

[0095] The generation AI can prioritize region-specific information based on the user's current geographic location information. For example, if a user types "nearby cafe," the generation AI obtains the user's current geographic location information and prioritizes nearby cafes based on that location. For example, if the user is in Tokyo, it will provide information about cafes in Tokyo. The generation AI can also prioritize region-specific events and services based on the user's geographic location information. For example, if the user is in Osaka, it will provide information about events held in Osaka. This allows the generation AI to provide more appropriate search results based on the user's current location.

[0096] The generation AI can use the emotion estimation function to analyze the emotion of the user when inputting a query and interpret the query according to that emotion. For example, if the user inputs "methods for relieving stress," the generation AI will use the emotion estimation function to analyze the user's emotion, and if it determines that the user is highly stressed, it will prioritize interpretations of relaxation and refreshment methods. The generation AI can also interpret queries according to the user's emotion based on the user's emotion. For example, if the user inputs "fun activities," it will use the emotion estimation function to analyze the user's emotion and prioritize interpretations of activities that elicit positive emotions. This allows the generation AI to provide more appropriate search results based on the user's emotion.

[0097] The query receiving unit can adopt an input method using gestures or eye tracking in addition to voice input. For example, when a user inputs a search query, the query receiving unit adopts gesture input in addition to voice input. For example, the user can input a specific command by waving his or her hand. The query receiving unit can also adopt an input method using eye tracking. For example, the user can input a search query by fixing his or her gaze at a specific position. This allows for the adoption of a variety of input methods, thereby improving user convenience.

[0098] Generative AI can understand a broader context based on the content of a user's social media posts. For example, if a user types in "recommended movies," generative AI can refer to the social media posts and interpret more personalized movies based on the user's previously posted movie preferences and ratings. Generative AI can also understand a user's interests based on the content of the user's social media posts. For example, it can interpret recommended music based on the user's previously posted music preferences. This allows the system to provide more relevant search results by referring to the user's social media posts.

[0099] The generative AI can use its emotion estimation function to analyze the emotions of users in real time when they enter a search query and provide suggestions that elicit positive emotions. For example, when a user enters "stress relief methods," the generative AI can use its emotion estimation function to analyze the user's emotions in real time and suggest relaxation methods or fun activities that will elicit positive emotions. The generative AI can also make suggestions based on the user's emotions. For example, when a user enters "fun activities," the generative AI can use its emotion estimation function to analyze the user's emotions in real time and suggest activities that will elicit positive emotions. This allows the system to provide more appropriate search results based on the user's emotions.

[0100] The query decomposition unit can use either specialized or general terms, taking into account the user's level of expertise. For example, if a user inputs "latest AI technology," the generation AI will consider the user's level of expertise and generate a query using specialized terms and a query using general terms. For example, it will generate "latest deep learning technology" and "latest AI technology." The query decomposition unit can also use appropriate terms based on the user's level of expertise. For example, if the user is a beginner, it will generate a query using general terms, and if the user is an expert, it will generate a query using specialized terms. This makes it possible to provide appropriate search results according to the user's level of expertise.

[0101] The query decomposition unit can automatically extract related topics or subtopics and generate more detailed supplemental queries. For example, if a user enters "latest smartphone reviews," the generation AI automatically extracts related topics and subtopics and generates supplemental queries such as "2023 smartphone reviews" or "comparison of popular smartphones." The query decomposition unit can also extract related topics and subtopics using topic modeling technology. For example, the generation AI uses topic modeling technology to extract related keywords and phrases and generate supplemental queries based on them. This allows for the automatic extraction of related topics and subtopics, thereby providing more detailed search results.

[0102] The query decomposition unit uses the emotion estimation function to generate supplemental queries based on the user's emotions, allowing the provision of information that is likely to resonate emotionally. For example, if a user inputs "stress relief methods," the query decomposition unit uses the emotion estimation function to analyze the user's emotions and generate supplemental queries to elicit positive emotions. For example, it generates "ways to relax" or "fun activities." The query decomposition unit can also generate supplemental queries that correspond to the user's emotions based on the user's emotions. For example, if a user inputs "fun activities," it uses the emotion estimation function to analyze the user's emotions and generate supplemental queries to elicit positive emotions. This allows the provision of more appropriate search results based on the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The query receiving unit receives a user's search query. For example, the query receiving unit can receive the query in text format, voice format, or image format. Step 2: The generation AI interprets the query received by the query receiving unit. For example, the generation AI interprets the query using text generation AI (e.g., LLM), multimodal generation AI, or natural language processing technology. Step 3: The query decomposition unit decomposes the query interpreted by the generation AI and generates a supplementary query. For example, the query decomposition unit decomposes the query using keyword extraction technology, grammar analysis technology, and topic modeling technology. Step 4: The asynchronous query unit asynchronously queries the search engine with the multiple queries generated by the query decomposition unit. For example, the asynchronous query unit queries the search engine using an asynchronous communication protocol, a parallel processing technique, and a real-time monitoring technique. Step 5: The summary generator interprets the search results obtained by the asynchronous query module and creates summaries. For example, the summary generator may use summarization algorithms, information extraction techniques, and natural language generation techniques to create summaries. Step 6: The result page creation unit creates a result page based on the summary created by the summary generation unit. For example, the result page creation unit creates the result page using page layout technology, information display technology, and user interface design technology.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 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.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 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. a unit for receiving a user's search query; a generation AI that interprets the query received by the query receiving unit; a query decomposition unit that decomposes the query interpreted by the generation AI and generates a supplemental query; an asynchronous query unit that asynchronously queries a search engine with the multiple queries generated by the query decomposition unit; a summary generator that interprets the search results obtained by the asynchronous query unit and creates a summary; a result page creation unit that creates a result page based on the summary created by the summary creation unit. A system characterized by:

2. The query receiving unit Introduce gesture or eye-tracking input methods in addition to voice input 2. The system of claim 1.

3. The generated AI is Understand the broader context based on the user's social media posts 2. The system of claim 1.

4. The asynchronous inquiry unit Monitoring the response speed of the search engine in real time and sending the query at the optimal timing.

2. The system of claim 1.

5. The summary generation unit When creating the summary of the search results, the user's past search history is referenced to provide a more personalized summary.

2. The system of claim 1.

6. The result page creation unit Using an emotion estimation function, the result page is created based on the user's emotions, and information that is likely to resonate with the user emotionally is provided.

2. The system of claim 1.

7. The generated AI is Using emotion estimation function, the emotion of the user is analyzed at the time of input, and the query is interpreted according to the emotion.

2. The system of claim 1.

8. The query decomposition unit Using an emotion estimation function, a supplementary query based on the user's emotion is generated, and information that is likely to be emotionally relatable is provided.

2. The system of claim 1.

Images