System

Abstract

An object of a system according to an embodiment is to efficiently acquire the latest information by using generated AI.SOLUTION: A system includes a search keyword generation unit, a search unit, a page content acquisition unit, and an analysis unit. A retrieval keyword generation part inputs contents to be investigated to the generation AI and generates an optimum retrieval keyword for deriving the contents. The search unit performs an Internet search using the search keyword generated by the search keyword generation unit. The page content acquisition unit acquires all character strings of the page selected by the search unit. The analysis unit analyzes the character string acquired by the page content acquisition unit and extracts a content to be examined.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 for generative AI to provide the latest information.

[0005] The system according to the embodiment aims to efficiently obtain the latest information using a generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a search keyword generation unit, a search unit, a page content acquisition unit, and an analysis unit. The search keyword generation unit inputs the content to be searched into the generation AI and generates optimal search keywords to derive that content. The search unit performs an internet search using the search keywords generated by the search keyword generation unit. The page content acquisition unit acquires all character strings of the pages selected by the search unit. The analysis unit analyzes the character strings acquired by the page content acquisition unit and extracts the content to be searched. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently acquire the latest information using a generation AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information provision system according to an embodiment of the present invention uses a generation AI to provide answers based on the latest information. This system generates search keywords, searches and selects pages, and acquires and analyzes page content. This allows the generation AI in the information provision system to provide answers based on the latest information.

[0029] An information provision system according to an embodiment includes a search keyword generation unit, a search unit, a page content acquisition unit, and an analysis unit. The search keyword generation unit inputs a search target to a generation AI and generates optimal search keywords to derive the target target. For example, in response to a question such as "What are the features of the latest smartphones?", the generation AI suggests search keywords such as "Latest smartphone features 2023." The search unit performs an internet search using the search keywords generated by the search keyword generation unit. For example, the keywords suggested by the generation AI are searched on a search engine such as Google or Bing. The page content acquisition unit acquires all character strings of pages selected by the search unit. For example, it acquires page content of news sites or specialized blogs that appear at the top of search results. The analysis unit analyzes the character strings acquired by the page content acquisition unit and extracts the target target target. For example, the generation AI analyzes the content of the page and generates an answer such as "Features of the latest smartphones include improved camera performance and extended battery life." This allows the information provision system to provide answers based on the latest information provided by the generation AI.

[0030] The search keyword generation unit can generate personalized search keywords based on the user's past search history or interests. For example, the search keyword generation unit inputs the user's past search history into the generation AI and generates personalized search keywords based on that data. For example, a user who has performed many smartphone-related searches in the past can be suggested keywords such as "latest smartphone 2023 features." This allows the user to be provided with the optimal search keywords.

[0031] The search keyword generation unit allows users to provide feedback on search keywords proposed by the generation AI, and the generation AI can readjust the keywords based on that feedback. For example, the search keyword generation unit allows users to provide feedback on search keywords proposed by the generation AI. For example, users can input feedback such as "this keyword is not appropriate," and the generation AI can readjust the keywords based on that. This makes it possible to provide search keywords that reflect user feedback.

[0032] The search keyword generation unit can simultaneously generate search keywords for related images or videos for the search keywords proposed by the generation AI. For example, the search keyword generation unit simultaneously generates search keywords for related images and videos for the search keywords proposed by the generation AI. For example, for "latest smartphone features 2023," it suggests keywords such as "latest smartphone images 2023" and "latest smartphone videos 2023." This allows for the provision of search keywords for related images and videos.

[0033] The search keyword generation unit automatically generates search keywords in different languages ​​and can obtain the latest information from international sources. For example, the search keyword generation unit uses a generation AI to automatically generate search keywords in different languages ​​and obtain the latest information from international sources. For example, the AI ​​translates "Latest smartphone features 2023" into keywords such as "Latest smartphone features 2023" or "Nuevas caracteristicas del smartphone 2023." This allows the latest information to be obtained from international sources.

[0034] When selecting search result pages, the search unit can introduce an algorithm that evaluates the reliability or authority of the page, and prioritize the selection of highly reliable information sources. For example, when selecting search result pages, the search unit introduces an algorithm that evaluates the reliability or authority of the page. For example, pages are selected based on the reliability scores of news sites and specialized blogs. This allows highly reliable information sources to be selected with priority.

[0035] When selecting search result pages, the search unit can consider the update frequency or latest update date and time of the page and prioritize pages that contain the latest information. For example, when selecting search result pages, the search unit can implement an algorithm that considers the update frequency or latest update date and time of the page. For example, frequently updated news sites and blogs can be prioritized. This allows pages that contain the latest information to be prioritized.

[0036] The search unit can take into consideration display optimization on different devices when selecting search result pages. For example, the search unit introduces an algorithm that takes into consideration display optimization on different devices when selecting search result pages. For example, pages that are optimized for display on smartphones and tablets are preferentially selected. This allows the selection of pages that take into consideration display optimization on different devices.

[0037] When selecting search result pages, the search unit can evaluate the page loading speed or user experience and select pages that can be browsed comfortably. For example, when selecting search result pages, the search unit introduces an algorithm that evaluates the page loading speed and user experience. For example, pages that load quickly and provide a high user experience are preferentially selected. This allows the selection of pages that can be browsed comfortably.

[0038] When the generation AI analyzes the page content, the analysis unit also takes into account the metadata or tag information within the page, allowing for more accurate information extraction. For example, when the generation AI analyzes the page content, the analysis unit also takes into account the metadata and tag information within the page, allowing for more accurate information extraction. For example, information is extracted based on the page title or keyword tags. This enables more accurate information extraction.

[0039] When the generation AI analyzes the page content, the analysis unit can also analyze the content of the images or videos on the page and integrate it with the text information to generate an answer. For example, when the generation AI analyzes the page content, the analysis unit can also analyze the content of the images or videos on the page and integrate it with the text information to generate an answer. For example, it can analyze image captions or video subtitles and reflect them in the answer. This makes it possible to generate highly accurate answers that include the content of the images and videos.

[0040] When the generation AI analyzes the page content, the analysis unit simultaneously analyzes the page content in different languages, and can generate answers that support multiple languages. For example, when the generation AI analyzes the page content, the analysis unit simultaneously analyzes the page content in different languages, and can generate answers that support multiple languages. For example, the analysis unit analyzes the page content in English and French and reflects that in the answer. This makes it possible to generate answers that support multiple languages.

[0041] When the generation AI analyzes the page content, the analysis unit also analyzes the linked information within the page, and can generate a comprehensive answer that includes related information. For example, when the generation AI analyzes the page content, the analysis unit also analyzes the linked information within the page, and can generate a comprehensive answer that includes related information. For example, the content of the linked page is analyzed and included in the answer. This makes it possible to generate a comprehensive answer that includes related information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The information providing system may further include a location information acquiring unit that acquires location information of the user. For example, if the user is in a specific area, the location information acquiring unit may provide the latest information related to that area preferentially. For example, if the user is traveling, the location information acquiring unit may provide information about tourist spots and restaurants in that area. This makes it possible to provide personalized information according to the user's current location.

[0044] The information provision system can further include a history analysis unit that analyzes the user's past search history. The history analysis unit suggests new related information based on, for example, keywords the user has searched for in the past or pages the user has viewed. For example, if the user has searched a lot for smartphone-related information in the past, the system can suggest reviews and comparison articles of new smartphones. This makes it possible to provide information based on the user's interests.

[0045] The information provision system may further include a social media linking unit that links with the user's social media account. The social media linking unit may, for example, analyze the user's social media posts and interests and generate search keywords based on them. For example, if a user posts on social media that they want the latest smartphone, the system may suggest keywords such as "latest smartphone 2023 features" based on that post. This allows the system to provide personalized information based on the user's social media activity.

[0046] The information providing system may further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit may suggest new related information based on products and services the user has purchased in the past. For example, if the user has previously purchased a smartphone, the purchase history analysis unit may suggest reviews of new smartphones and information on accessories. This makes it possible to provide personalized information based on the user's purchase history.

[0047] The information providing system may further include a learning history analysis unit that analyzes the user's learning history. The learning history analysis unit may suggest new related information based on, for example, the content the user has learned in the past and their interests. For example, if the user has studied a lot of programming-related information in the past, information on new programming languages ​​and tools may be suggested. This makes it possible to provide personalized information based on the user's learning history.

[0048] The information providing system may further include a device usage history analysis unit that analyzes the user's device usage history. The device usage history analysis unit may propose an optimal information display format based on, for example, which device the user has used to search for information in the past. For example, if the user frequently uses a smartphone, it may propose an information display format optimized for smartphones. This makes it possible to realize optimal information display based on the user's device usage history.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The search keyword generator inputs the search content into the AI ​​and generates the optimal search keywords to derive that content. For example, in response to a question such as "What are the features of the latest smartphones?", the AI ​​will suggest search keywords such as "Latest smartphone features 2023." Step 2: The search unit performs an internet search using the search keywords generated by the search keyword generation unit. For example, the search unit searches the keywords suggested by the generation AI on a search engine such as Google or Bing. Step 3: The page content acquisition unit acquires all the text strings of the pages selected by the search unit. For example, it acquires the page content of news sites or specialized blogs that appear at the top of the search results. Step 4: The analysis unit analyzes the text obtained by the page content acquisition unit and extracts the desired content. For example, the generation AI analyzes the content of the page and generates an answer such as, "Features of the latest smartphones are improved camera performance and extended battery life."

[0051] (Example 2) The information provision system according to an embodiment of the present invention uses a generation AI to provide answers based on the latest information. This system generates search keywords, searches and selects pages, and acquires and analyzes page content. This allows the generation AI in the information provision system to provide answers based on the latest information.

[0052] An information provision system according to an embodiment includes a search keyword generation unit, a search unit, a page content acquisition unit, and an analysis unit. The search keyword generation unit inputs a search target to a generation AI and generates optimal search keywords to derive the target target. For example, in response to a question such as "What are the features of the latest smartphones?", the generation AI suggests search keywords such as "Latest smartphone features 2023." The search unit performs an internet search using the search keywords generated by the search keyword generation unit. For example, the keywords suggested by the generation AI are searched on a search engine such as Google or Bing. The page content acquisition unit acquires all character strings of pages selected by the search unit. For example, it acquires page content of news sites or specialized blogs that appear at the top of search results. The analysis unit analyzes the character strings acquired by the page content acquisition unit and extracts the target target target. For example, the generation AI analyzes the content of the page and generates an answer such as "Features of the latest smartphones include improved camera performance and extended battery life." This allows the information provision system to provide answers based on the latest information provided by the generation AI.

[0053] The search keyword generation unit can generate personalized search keywords based on the user's past search history or interests. For example, the search keyword generation unit inputs the user's past search history into the generation AI and generates personalized search keywords based on that data. For example, a user who has performed many smartphone-related searches in the past can be suggested keywords such as "latest smartphone 2023 features." This allows the user to be provided with the optimal search keywords.

[0054] The search keyword generation unit allows users to provide feedback on search keywords proposed by the generation AI, and the generation AI can readjust the keywords based on that feedback. For example, the search keyword generation unit allows users to provide feedback on search keywords proposed by the generation AI. For example, users can input feedback such as "this keyword is not appropriate," and the generation AI can readjust the keywords based on that. This makes it possible to provide search keywords that reflect user feedback.

[0055] The search keyword generation unit can use the emotion estimation function to analyze the user's emotional state and generate search keywords that elicit positive emotions. For example, the search keyword generation unit can use the emotion estimation function to analyze the user's emotional state in real time and generate search keywords that elicit positive emotions. For example, if the user is feeling stressed, the search keyword generation unit can suggest keywords such as "latest smartphone features for relaxation 2023." This allows the search keyword generation unit to provide search keywords that correspond to the user's emotions.

[0056] The search keyword generation unit can simultaneously generate search keywords for related images or videos for the search keywords proposed by the generation AI. For example, the search keyword generation unit simultaneously generates search keywords for related images and videos for the search keywords proposed by the generation AI. For example, for "latest smartphone features 2023," it suggests keywords such as "latest smartphone images 2023" and "latest smartphone videos 2023." This allows for the provision of search keywords for related images and videos.

[0057] The search keyword generation unit automatically generates search keywords in different languages ​​and can obtain the latest information from international sources. For example, the search keyword generation unit uses a generation AI to automatically generate search keywords in different languages ​​and obtain the latest information from international sources. For example, the AI ​​translates "Latest smartphone features 2023" into keywords such as "Latest smartphone features 2023" or "Nuevas caracteristicas del smartphone 2023." This allows the latest information to be obtained from international sources.

[0058] The search keyword generation unit can use the emotion estimation function to generate more specific and emotionally relatable search keywords based on the emotional reaction to the question entered by the user. For example, the search keyword generation unit uses the emotion estimation function to analyze the emotional reaction to the question entered by the user and generate search keywords that are easy to empathize with. For example, if the user is excited, the search keyword generation unit suggests keywords such as "Amazing Latest Smartphone Features 2023." This makes it possible to provide search keywords that are easy to empathize with the user's emotions.

[0059] When selecting search result pages, the search unit can introduce an algorithm that evaluates the reliability or authority of the page, and prioritize the selection of highly reliable information sources. For example, when selecting search result pages, the search unit introduces an algorithm that evaluates the reliability or authority of the page. For example, pages are selected based on the reliability scores of news sites and specialized blogs. This allows highly reliable information sources to be selected with priority.

[0060] When selecting search result pages, the search unit can consider the update frequency or latest update date and time of the page and prioritize pages that contain the latest information. For example, when selecting search result pages, the search unit can implement an algorithm that considers the update frequency or latest update date and time of the page. For example, frequently updated news sites and blogs can be prioritized. This allows pages that contain the latest information to be prioritized.

[0061] The search unit can use the emotion estimation function to analyze the emotional response to a page selected by the user and preferentially select pages that elicit positive emotions. For example, the search unit can use the emotion estimation function to analyze the emotional response to a page selected by the user and preferentially select pages that elicit positive emotions. For example, pages that the user is happy with are preferentially displayed. This makes it possible to preferentially select pages that elicit positive emotions.

[0062] The search unit can take into consideration display optimization on different devices when selecting search result pages. For example, the search unit introduces an algorithm that takes into consideration display optimization on different devices when selecting search result pages. For example, pages that are optimized for display on smartphones and tablets are preferentially selected. This allows the selection of pages that take into consideration display optimization on different devices.

[0063] When selecting search result pages, the search unit can evaluate the page loading speed or user experience and select pages that can be browsed comfortably. For example, when selecting search result pages, the search unit introduces an algorithm that evaluates the page loading speed and user experience. For example, pages that load quickly and provide a high user experience are preferentially selected. This allows the selection of pages that can be browsed comfortably.

[0064] The search unit uses the emotion estimation function to monitor the emotional response to the page selected by the user in real time, and can continuously select the optimal page. For example, the search unit uses the emotion estimation function to monitor the emotional response to the page selected by the user in real time, and builds a system that continuously selects the optimal page. For example, pages in which the user shows positive emotions are preferentially displayed. This allows the optimal page to be continuously selected.

[0065] When the generation AI analyzes the page content, the analysis unit also takes into account the metadata or tag information within the page, allowing for more accurate information extraction. For example, when the generation AI analyzes the page content, the analysis unit also takes into account the metadata and tag information within the page, allowing for more accurate information extraction. For example, information is extracted based on the page title or keyword tags. This enables more accurate information extraction.

[0066] When the generation AI analyzes the page content, the analysis unit can also analyze the content of the images or videos on the page and integrate it with the text information to generate an answer. For example, when the generation AI analyzes the page content, the analysis unit can also analyze the content of the images or videos on the page and integrate it with the text information to generate an answer. For example, it can analyze image captions or video subtitles and reflect them in the answer. This makes it possible to generate highly accurate answers that include the content of the images and videos.

[0067] The analysis unit can use the emotion estimation function to analyze the user's emotional response to the analysis results of the page content and generate an answer that elicits positive emotions. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to the analysis results of the page content and generate an answer that elicits positive emotions. For example, the answer includes information that will make the user happy with priority. This makes it possible to generate an answer that elicits positive emotions.

[0068] When the generation AI analyzes the page content, the analysis unit simultaneously analyzes the page content in different languages, and can generate answers that support multiple languages. For example, when the generation AI analyzes the page content, the analysis unit simultaneously analyzes the page content in different languages, and can generate answers that support multiple languages. For example, the analysis unit analyzes the page content in English and French and reflects that in the answer. This makes it possible to generate answers that support multiple languages.

[0069] When the generation AI analyzes the page content, the analysis unit also analyzes the linked information within the page, and can generate a comprehensive answer that includes related information. For example, when the generation AI analyzes the page content, the analysis unit also analyzes the linked information within the page, and can generate a comprehensive answer that includes related information. For example, the content of the linked page is analyzed and included in the answer. This makes it possible to generate a comprehensive answer that includes related information.

[0070] The analysis unit can use the emotion estimation function to monitor the user's emotional response to the analysis results of the page content in real time and continuously generate optimal answers. The analysis unit, for example, uses the emotion estimation function to monitor the user's emotional response to the analysis results of the page content in real time and builds a system that continuously generates optimal answers. For example, the analysis unit adjusts answers based on the user's emotion score. This allows the optimal answers to be continuously generated.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The information provision system may further include a voice analysis unit that analyzes the user's voice input. For example, when a user inputs a question by voice, the voice analysis unit converts the voice into text and passes it to the search keyword generation unit. This allows the user to input questions by voice without using a keyboard, improving convenience. The voice analysis unit can also analyze the tone and speed of the user's voice to estimate the user's emotional state. For example, if the user is excited, search keywords that reflect that emotion can be generated. This makes it possible to provide search results that reflect the user's emotions.

[0073] The information providing system may further include a location information acquiring unit that acquires location information of the user. For example, if the user is in a specific area, the location information acquiring unit may provide the latest information related to that area preferentially. For example, if the user is traveling, the location information acquiring unit may provide information about tourist spots and restaurants in that area. This makes it possible to provide personalized information according to the user's current location.

[0074] The information provision system can further include a history analysis unit that analyzes the user's past search history. The history analysis unit suggests new related information based on, for example, keywords the user has searched for in the past or pages the user has viewed. For example, if the user has searched a lot for smartphone-related information in the past, the system can suggest reviews and comparison articles of new smartphones. This makes it possible to provide information based on the user's interests.

[0075] The information provision system may further include an emotion monitoring unit that monitors the user's emotional state in real time. The emotion monitoring unit, for example, analyzes the user's facial expressions and tone of voice while the user is browsing information to estimate the user's emotional state. This makes it possible to provide relaxing information to the user when the user is feeling stressed, and more interesting information to the user when the user is excited. This makes it possible to provide optimal information according to the user's emotions.

[0076] The information provision system may further include a social media linking unit that links with the user's social media account. The social media linking unit may, for example, analyze the user's social media posts and interests and generate search keywords based on them. For example, if a user posts on social media that they want the latest smartphone, the system may suggest keywords such as "latest smartphone 2023 features" based on that post. This allows the system to provide personalized information based on the user's social media activity.

[0077] The information provision system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and stress level in real time and generates search keywords based on that data. For example, if the user shows a high stress level, it may suggest keywords such as "latest smartphone features for relaxation 2023." This allows the system to provide optimal information according to the user's health condition.

[0078] The information providing system may further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit may suggest new related information based on products and services the user has purchased in the past. For example, if the user has previously purchased a smartphone, the purchase history analysis unit may suggest reviews of new smartphones and information on accessories. This makes it possible to provide personalized information based on the user's purchase history.

[0079] The information providing system may further include a learning history analysis unit that analyzes the user's learning history. The learning history analysis unit may suggest new related information based on, for example, the content the user has learned in the past and their interests. For example, if the user has studied a lot of programming-related information in the past, information on new programming languages ​​and tools may be suggested. This makes it possible to provide personalized information based on the user's learning history.

[0080] The information providing system may further include a display order adjustment unit that estimates the user's emotional state and adjusts the display order of information based on the estimated emotional state. For example, if the user is expressing positive emotions, the display order adjustment unit may preferentially display information that further enhances the emotions. For example, if the user is happy, entertainment-related information may be preferentially displayed. This makes it possible to realize optimal information display according to the user's emotions.

[0081] The information providing system may further include a device usage history analysis unit that analyzes the user's device usage history. The device usage history analysis unit may propose an optimal information display format based on, for example, which device the user has used to search for information in the past. For example, if the user frequently uses a smartphone, it may propose an information display format optimized for smartphones. This makes it possible to realize optimal information display based on the user's device usage history.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The search keyword generator inputs the search content into the AI ​​and generates the optimal search keywords to derive that content. For example, in response to a question such as "What are the features of the latest smartphones?", the AI ​​will suggest search keywords such as "Latest smartphone features 2023." Step 2: The search unit performs an internet search using the search keywords generated by the search keyword generation unit. For example, the search unit searches the keywords suggested by the generation AI on a search engine such as Google or Bing. Step 3: The page content acquisition unit acquires all the text strings of the pages selected by the search unit. For example, it acquires the page content of news sites or specialized blogs that appear at the top of the search results. Step 4: The analysis unit analyzes the text obtained by the page content acquisition unit and extracts the desired content. For example, the generation AI analyzes the content of the page and generates an answer such as, "Features of the latest smartphones are improved camera performance and extended battery life."

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 search keyword generation unit that inputs the content to be searched into the generation AI and generates the optimal search keywords to derive that content; a search unit that performs an internet search using the search keywords generated by the search keyword generation unit; a page content acquisition unit that acquires all character strings of the page selected by the search unit; an analysis unit that analyzes the character string acquired by the page content acquisition unit and extracts the content to be checked; A system characterized by:

2. The search keyword generation unit Generate personalized search keywords based on a user's past search history or interests 2. The system of claim 1.

3. The search keyword generation unit The AI ​​also generates search keywords for related images or videos for each search keyword it suggests.

2. The system of claim 1.

4. The search unit When selecting pages in search results, we use algorithms that evaluate the reliability or authority of those pages, prioritizing highly reliable sources of information.

2. The system of claim 1.

5. The analysis unit When the AI ​​analyzes the page content, it also takes into account the metadata or tag information within the page, allowing for more accurate information extraction.

2. The system of claim 1.

6. The search keyword generation unit Using emotion estimation functionality, we analyze the user's emotional state and generate search keywords that elicit positive emotions.

2. The system of claim 1.

7. The search unit Using an emotion estimation function, analyze the user's emotional response to the selected pages and preferentially select the pages that elicit positive emotions.

2. The system of claim 1.

8. The analysis unit Using emotion estimation, we analyze the user's emotional response to the analysis results of the page content and generate answers that elicit positive emotions.

2. The system of claim 1.

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