System

Abstract

An object of the system according to the embodiment is to easily recognize a specific target in an image captured by a camera of a smartphone and to quickly perform a WEB search.SOLUTION: A system includes an image acquisition unit, an object recognition unit, a search execution unit, and a search result display unit. The image acquisition unit acquires an image captured by a camera of a smartphone. The object recognition unit recognizes a specific object in the image acquired by the image acquisition unit by touching the object with a finger. The search execution unit executes a web search based on the target object recognized by the target object recognition unit. The search result display unit displays the search result acquired by the search execution 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 the drawback of making it difficult to easily recognize specific objects in images taken with a smartphone camera and quickly perform web searches.

[0005] The system according to the embodiment aims to easily recognize specific objects in images captured by a smartphone camera and to perform quick web searches. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an object recognition unit, a search execution unit, and a search result display unit. The image acquisition unit acquires an image captured by a smartphone camera. The object recognition unit recognizes a specific object in the image acquired by the image acquisition unit by touching the object with a finger. The search execution unit executes a web search based on the object recognized by the object recognition unit. The search result display unit displays the search results acquired by the search execution unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily recognize specific objects in images captured by a smartphone camera and quickly perform web searches. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The image recognition search system according to an embodiment of the present invention is a system in which a generation AI recognizes a specific object in an image taken with a smartphone camera by touching the object with a finger and performs a web search. This allows the image recognition search system to easily recognize any object in an image taken by a user with a smartphone camera and obtain information about it through a web search.

[0029] An image recognition search system according to an embodiment includes an image acquisition unit, an object recognition unit, a search execution unit, and a search result display unit. The image acquisition unit acquires images captured by a smartphone camera. For example, the image acquisition unit can acquire images in JPEG or PNG format. The image acquisition unit can also acquire images with different resolutions. The object recognition unit recognizes a specific object in an image acquired by the image acquisition unit by touching the object with a finger. For example, when a user touches a flower in an image, the object recognition unit recognizes the flower. The object recognition unit can also detect the pressure and speed of the touch and determine the priority and importance of the object. The search execution unit performs a web search based on the object recognized by the object recognition unit. For example, the search execution unit performs a web search for information about the type of flower recognized by the generation AI. The search execution unit can perform a search using a search engine such as Google or Bing. The search result display unit displays the search results acquired by the search execution unit. For example, the search results can be displayed in the form of text, images, links, or the like. As a result, the image recognition search system according to the embodiment can easily recognize any object in an image taken by a user with a smartphone camera and obtain information about that object through a web search.

[0030] The object recognition unit can detect the pressure or speed of a finger and determine the priority or importance of an object. For example, when a user touches an object in an image, the touchscreen of the smartphone detects the finger pressure and determines the priority of the object based on the pressure. For example, the object recognition unit preferentially recognizes objects that are touched strongly. The object recognition unit also detects the finger speed when the user touches an object in an image and determines the importance of the object based on the speed. For example, an object that is touched quickly is recognized as being important. The object recognition unit also simultaneously detects both the finger pressure and speed and combines these data to comprehensively determine the priority or importance of the object. For example, an object that is touched strongly and quickly is deemed to be the most important. This allows the priority or importance of an object to be determined based on the user's touch operation, improving recognition accuracy.

[0031] The object recognition unit analyzes context information around the touched area to improve the accuracy of identifying the object. For example, when a user touches an object in an image, the object recognition unit analyzes background information around the area to improve the accuracy of identifying the object. For example, if the background is a natural environment, plants and animals are preferentially recognized. The object recognition unit also analyzes other objects around the touched area and identifies the object based on their relationships. For example, if there are leaves around a flower, the flower is recognized as a plant. The object recognition unit also uses surrounding context information to develop an algorithm that improves the accuracy of identifying the touched object. For example, the object recognition unit analyzes signs and labels around a building to identify the name and purpose of the building. In this way, the accuracy of identifying the object can be improved by analyzing surrounding context information.

[0032] The object recognition unit can combine touch operations with voice commands or gesture recognition to enable the user to specify an object from multiple interfaces. For example, when a user touches an object in an image, the object recognition unit uses a voice command to specify the object. For example, the user can touch a flower while saying, "Tell me about this flower." The object recognition unit can also use gesture recognition technology to enable the user to specify an object by performing a specific gesture. For example, the user can use pinch-in or pinch-out gestures to zoom in and out to specify the object. The object recognition unit can also combine voice commands and gesture recognition to build a system that allows the user to specify an object from multiple interfaces. For example, the user can say, "This building," and point to the building with their finger. This allows the user to specify an object from multiple interfaces, improving user convenience.

[0033] The object recognition unit may be equipped with a multi-user support function that allows multiple users to touch different objects simultaneously and collaboratively collect information. The object recognition unit adds, for example, a function that allows multiple users to touch different objects simultaneously and collect information about each object. For example, a user may simultaneously research plants or buildings seen on a trip with family or friends. The object recognition unit also uses the multi-user support function to build a system in which multiple users collaboratively collect and share information. For example, this can be used for group research or learning activities. The object recognition unit also adds a function that integrates information about objects touched simultaneously by multiple users and provides comprehensive information. For example, information from different perspectives can be combined to provide more detailed information. This allows multiple users to collaborate in collecting information, thereby promoting collaborative work and learning activities.

[0034] The object recognition unit can improve the accuracy of recognition when the generation AI recognizes an object by referring to past recognition history or the user's search history. For example, when the generation AI recognizes an object, the object recognition unit refers to past recognition history to improve the accuracy when recognizing the same object. For example, when recognizing a type of flower that was previously recognized, past data is used. The object recognition unit also refers to the user's search history to improve the accuracy of the generation AI when recognizing an object. For example, based on the type of flower that the user previously searched for, the accuracy of recognizing the same type of flower is improved. The object recognition unit also combines past recognition history and the user's search history to comprehensively improve the accuracy of the generation AI when recognizing an object. For example, the object recognition algorithm is optimized based on past data and search history. This allows the accuracy of object recognition to be improved by referring to past recognition history and the user's search history.

[0035] The object recognition unit can improve recognition accuracy by combining multiple generative AI models and performing ensemble learning. The object recognition unit, for example, combines multiple generative AI models and performs ensemble learning to improve object recognition accuracy. For example, different AI models recognize objects by leveraging their respective strengths. The object recognition unit also uses ensemble learning to integrate the recognition results of multiple generative AI models and provide the most reliable recognition result. For example, the recognition results of each model are weighted and integrated. The object recognition unit also develops an algorithm that improves object recognition accuracy by combining multiple generative AI models. For example, the recognition results of different models are compared and the most appropriate result is selected. As a result, object recognition accuracy can be improved by combining multiple generative AI models and performing ensemble learning.

[0036] The object recognition unit can simultaneously analyze voice or text data when the generation AI recognizes an object, enabling multimodal recognition. For example, when the generation AI recognizes an object, the object recognition unit simultaneously analyzes not only image data but also voice data, improving recognition accuracy. For example, a user touches a flower while asking, "What flower is this?" The object recognition unit also analyzes text data and uses it as auxiliary information when the generation AI recognizes objects. For example, it analyzes text information within an image and identifies the object based on that information. The object recognition unit also combines voice and text data to develop algorithms that improve the accuracy of the generation AI's object recognition. For example, it integrates voice commands and image data to improve recognition accuracy. This allows for simultaneous analysis of voice and text data, improving object recognition accuracy.

[0037] The object recognition unit can compare object recognition results using different generative AI models and select the most reliable result. The object recognition unit, for example, builds a system that compares object recognition results using different generative AI models and selects the most reliable result. For example, it evaluates the recognition results of each model and selects the optimal result. The object recognition unit also develops an algorithm that compares the recognition results of multiple generative AI models and selects the most reliable result. For example, it weights and integrates the results based on the recognition accuracy of each model. The object recognition unit also builds a system that compares the recognition results of different generative AI models in real time and provides the most reliable result. For example, it instantly evaluates the recognition results of each model and selects the optimal result. This makes it possible to improve recognition accuracy by comparing recognition results using different generative AI models and selecting the most reliable result.

[0038] The search execution unit automatically optimizes the search query when the generation AI performs a web search, enabling it to obtain more relevant information. For example, when the generation AI performs a web search, the search execution unit analyzes and automatically optimizes the search query entered by the user. For example, it converts an ambiguous query into specific keywords. The search execution unit also develops a search query optimization algorithm to enable the generation AI to obtain more relevant information. For example, it strengthens the search query by adding synonyms and related keywords. The search execution unit also allows the generation AI to refer to past search history and optimize the search query based on the user's search patterns. For example, it adjusts the query based on keywords the user has searched for in the past. This automatically optimizes the search query, enabling it to obtain more relevant information.

[0039] The search execution unit can simultaneously use multiple search engines and compare and integrate the results to evaluate the reliability of search results. For example, the search execution unit builds a system in which a generation AI simultaneously uses multiple search engines and compares and integrates search results obtained from each engine. For example, it integrates search results from Google, Bing, and Yahoo. The search execution unit also develops an algorithm to evaluate the reliability of search results and provides the most reliable information based on the results obtained from multiple search engines. For example, it weights and integrates the results from each engine. The search execution unit also builds a system that compares the results from multiple search engines in real time and provides the most reliable information to users. For example, it instantly evaluates the results from each engine and selects the most reliable result. This allows the reliability of search results to be improved by comparing and integrating results using multiple search engines.

[0040] When the generation AI performs a web search, the search execution unit can take into account the user's location information or time of day and provide information appropriate to the context. For example, when the generation AI performs a web search, the search execution unit takes into account the user's location information and provides information appropriate to the context. For example, if the user is in a specific area, information related to that area is displayed preferentially. The search execution unit also takes into account the user's time of day and allows the generation AI to customize search results. For example, if a search is performed at night, information related to nighttime is displayed preferentially. The search execution unit also considers both location information and time of day to build a system in which the generation AI provides information appropriate to the context. For example, event information related to a specific area and time of day is displayed. This makes it possible to provide information appropriate to the context by taking into account the user's location information and time of day.

[0041] The search execution unit updates search results in real time, allowing the generation AI to always provide the latest information. The search execution unit, for example, builds a system in which the generation AI updates search results in real time, allowing the generation AI to always provide the latest information. For example, it instantly reflects the latest information in news articles and blogs. The search execution unit also increases the frequency with which search results are updated, allowing users to always obtain the latest information. For example, it periodically re-acquires search results and updates them with the latest information. The search execution unit also develops an algorithm that updates search results in real time, allowing the generation AI to always provide the latest information. For example, it uses a cache of search results to instantly reflect the latest information. This makes it possible to always provide the latest information by updating search results in real time.

[0042] When displaying search results, the search result display unit can customize the display order or layout based on the user's browsing history or preferences. The search result display unit, for example, analyzes the user's browsing history and builds a system that customizes the display order of search results. For example, search results related to information previously viewed are preferentially displayed. The search result display unit also customizes the layout of search results based on the user's preferences. For example, information formats preferred by the user (articles, images, videos, etc.) are preferentially displayed. The search result display unit also develops a system that combines browsing history and preference data to comprehensively customize the display order and layout of search results. For example, an optimal display method is provided based on the user's past behavioral data. This improves the user experience by customizing the display order and layout based on the user's browsing history and preferences.

[0043] The search result display unit can collect user feedback on search results and continuously improve the display content based on that. The search result display unit, for example, collects user feedback on search results and builds a system that continuously improves the display content based on that data. For example, it adjusts the display order based on search results rated by users. The search result display unit also analyzes user feedback in real time and immediately improves the display content of search results. For example, it excludes search results that the user has expressed dissatisfaction with. The search result display unit also accumulates feedback data and develops a system that continuously optimizes the search result display algorithm based on that data. For example, it learns from user evaluation data and improves the display content. In this way, the user experience is improved by collecting user feedback and continuously improving the display content based on that.

[0044] The search result display unit can use AR technology to overlay information on the real world when displaying search results. The search result display unit, for example, builds a system that uses AR technology to overlay information on the real world when displaying search results. For example, the search results are overlaid on a landscape seen through a smartphone camera. The search result display unit also uses AR technology to overlay the search results on the real world, allowing the user to intuitively understand the information. For example, when standing in front of a building, information about the building is displayed. The search result display unit also develops a system that displays the search results using AR technology, allowing the user to integrate the real world and digital information. For example, information about famous places seen at tourist spots is displayed using AR. In this way, the information is overlaid on the real world using AR technology, allowing the user to intuitively understand the information.

[0045] The search result display unit can add a function to read search results aloud, making it possible to accommodate visually impaired people or users who are driving. The search result display unit can, for example, add a function to read search results aloud, building a system that can accommodate visually impaired people and users who are driving. For example, the search results can be read aloud using a smartphone's voice assistant. The search result display unit can also use the voice read function to provide information even in situations where the search results cannot be visually confirmed. For example, information about a destination can be provided by voice while driving. The search result display unit can also customize the voice read function of the search results to develop a system that provides information in a voice that suits the user's preferences. For example, the tone and speed of the voice can be adjusted when reading. This makes it possible to accommodate visually impaired people and users who are driving by reading search results aloud.

[0046] When displaying search results, the search result display unit can customize the display order or layout based on the user's browsing history or preferences. The search result display unit, for example, analyzes the user's browsing history and builds a system that customizes the display order of search results. For example, search results related to information previously viewed are preferentially displayed. The search result display unit also customizes the layout of search results based on the user's preferences. For example, information formats preferred by the user (articles, images, videos, etc.) are preferentially displayed. The search result display unit also develops a system that combines browsing history and preference data to comprehensively customize the display order and layout of search results. For example, an optimal display method is provided based on the user's past behavioral data. This improves the user experience by customizing the display order and layout based on the user's browsing history and preferences.

[0047] The search result display unit can collect user feedback on search results and continuously improve the display content based on that. The search result display unit, for example, collects user feedback on search results and builds a system that continuously improves the display content based on that data. For example, it adjusts the display order based on search results rated by users. The search result display unit also analyzes user feedback in real time and immediately improves the display content of search results. For example, it excludes search results that the user has expressed dissatisfaction with. The search result display unit also accumulates feedback data and develops a system that continuously optimizes the search result display algorithm based on that data. For example, it learns from user evaluation data and improves the display content. In this way, the user experience is improved by collecting user feedback and continuously improving the display content based on that.

[0048] The search result display unit can use AR technology to overlay information on the real world when displaying search results. The search result display unit, for example, builds a system that uses AR technology to overlay information on the real world when displaying search results. For example, the search results are overlaid on a landscape seen through a smartphone camera. The search result display unit also uses AR technology to overlay the search results on the real world, allowing the user to intuitively understand the information. For example, when standing in front of a building, information about the building is displayed. The search result display unit also develops a system that displays the search results using AR technology, allowing the user to integrate the real world and digital information. For example, information about famous places seen at tourist spots is displayed using AR. In this way, the information is overlaid on the real world using AR technology, allowing the user to intuitively understand the information.

[0049] The search result display unit can add a function to read search results aloud, making it possible to accommodate visually impaired people or users who are driving. The search result display unit can, for example, add a function to read search results aloud, building a system that can accommodate visually impaired people and users who are driving. For example, the search results can be read aloud using a smartphone's voice assistant. The search result display unit can also use the voice read function to provide information even in situations where the search results cannot be visually confirmed. For example, information about a destination can be provided by voice while driving. The search result display unit can also customize the voice read function of the search results to develop a system that provides information in a voice that suits the user's preferences. For example, the tone and speed of the voice can be adjusted when reading. This makes it possible to accommodate visually impaired people and users who are driving by reading search results aloud.

[0050] When displaying search results, the search result display unit can customize the display order or layout based on the user's browsing history or preferences. The search result display unit, for example, analyzes the user's browsing history and builds a system that customizes the display order of search results. For example, search results related to information previously viewed are preferentially displayed. The search result display unit also customizes the layout of search results based on the user's preferences. For example, information formats preferred by the user (articles, images, videos, etc.) are preferentially displayed. The search result display unit also develops a system that combines browsing history and preference data to comprehensively customize the display order and layout of search results. For example, an optimal display method is provided based on the user's past behavioral data. This improves the user experience by customizing the display order and layout based on the user's browsing history and preferences.

[0051] The search result display unit can collect user feedback on search results and continuously improve the display content based on that. The search result display unit, for example, collects user feedback on search results and builds a system that continuously improves the display content based on that data. For example, it adjusts the display order based on search results rated by users. The search result display unit also analyzes user feedback in real time and immediately improves the display content of search results. For example, it excludes search results that the user has expressed dissatisfaction with. The search result display unit also accumulates feedback data and develops a system that continuously optimizes the search result display algorithm based on that data. For example, it learns from user evaluation data and improves the display content. In this way, the user experience is improved by collecting user feedback and continuously improving the display content based on that.

[0052] The search result display unit can use AR technology to overlay information on the real world when displaying search results. The search result display unit, for example, builds a system that uses AR technology to overlay information on the real world when displaying search results. For example, the search results are overlaid on a landscape seen through a smartphone camera. The search result display unit also uses AR technology to overlay the search results on the real world, allowing the user to intuitively understand the information. For example, when standing in front of a building, information about the building is displayed. The search result display unit also develops a system that displays the search results using AR technology, allowing the user to integrate the real world and digital information. For example, information about famous places seen at tourist spots is displayed using AR. In this way, the information is overlaid on the real world using AR technology, allowing the user to intuitively understand the information.

[0053] The search result display unit can add a function to read search results aloud, making it possible to accommodate visually impaired people or users who are driving. The search result display unit can, for example, add a function to read search results aloud, building a system that can accommodate visually impaired people and users who are driving. For example, the search results can be read aloud using a smartphone's voice assistant. The search result display unit can also use the voice read function to provide information even in situations where the search results cannot be visually confirmed. For example, information about a destination can be provided by voice while driving. The search result display unit can also customize the voice read function of the search results to develop a system that provides information in a voice that suits the user's preferences. For example, the tone and speed of the voice can be adjusted when reading. This makes it possible to accommodate visually impaired people and users who are driving by reading search results aloud.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The image acquisition unit acquires images captured by the smartphone camera. For example, images in JPEG or PNG format can be acquired. The image acquisition unit can also acquire images with different resolutions. The object recognition unit recognizes a specific object in an image acquired by the image acquisition unit by touching the object with a finger. For example, when a user touches a flower in the image, the object recognition unit recognizes the flower. The object recognition unit can also detect the pressure and speed of the touch and determine the priority and importance of the object. The search execution unit performs a web search based on the object recognized by the object recognition unit. For example, the search execution unit performs a web search for information about the type of flower recognized by the generation AI. The search execution unit can perform a search using a search engine such as Google or Bing. The search result display unit displays the search results acquired by the search execution unit. For example, the search results can be displayed in the form of text, images, links, or the like. As a result, the image recognition search system according to the embodiment allows a user to easily recognize any object in an image captured by a smartphone camera and acquire information about the object through a web search.

[0056] The object recognition unit can detect the pressure or speed of a finger and determine the priority or importance of an object. For example, when a user touches an object in an image, the smartphone's touchscreen detects the finger pressure and determines the priority of the object based on that pressure. For example, objects that are touched strongly are preferentially recognized. The object recognition unit can also detect the finger speed when the user touches an object in an image and determine the importance of the object based on that speed. For example, objects that are touched quickly are recognized as being important. The object recognition unit can also simultaneously detect both the finger pressure and speed and combine these data to comprehensively determine the priority or importance of an object. For example, an object that is touched strongly and quickly is deemed to be the most important. This allows the priority or importance of objects to be determined based on the user's touch operation, improving recognition accuracy.

[0057] The object recognition unit analyzes context information around the touched area to improve the accuracy of identifying the object. For example, when a user touches an object in an image, the unit analyzes background information around the area to improve the accuracy of identifying the object. For example, if the background is a natural environment, plants and animals are preferentially recognized. The object recognition unit also analyzes other objects around the touched area and identifies the object based on their relationships. For example, if there are leaves around a flower, the flower is recognized as a plant. The object recognition unit also uses surrounding context information to develop an algorithm that improves the accuracy of identifying the touched object. For example, the unit analyzes signs and labels around a building to identify the name and purpose of the building. In this way, the accuracy of identifying the object can be improved by analyzing surrounding context information.

[0058] The object recognition unit can combine touch operations with voice commands or gesture recognition to specify objects from multiple interfaces. For example, when a user touches an object in an image, the user can specify the object by using a voice command in addition. For example, the user can touch a flower while saying, "Tell me about this flower." The object recognition unit can also use gesture recognition technology to allow the user to specify an object by performing a specific gesture. For example, the user can zoom in and out to specify an object by pinching in and out. The object recognition unit can also combine voice commands and gesture recognition to build a system that allows the user to specify objects from multiple interfaces. For example, the user can say, "This building," and point to the building with their finger. This allows the user to specify objects from multiple interfaces, improving user convenience.

[0059] The object recognition unit can be equipped with a multi-user support function that allows multiple users to touch different objects simultaneously and collaboratively collect information. For example, a function can be added that allows multiple users to touch different objects simultaneously and collect information about each object. For example, a user can simultaneously research plants or buildings seen on a trip with family or friends. The object recognition unit can also use the multi-user support function to build a system in which multiple users collaboratively collect and share information. For example, this can be used for group research or learning activities. The object recognition unit can also add a function that integrates information about objects touched simultaneously by multiple users and provides comprehensive information. For example, information from different perspectives can be combined to provide more detailed information. This allows multiple users to collaborate in collecting information, promoting collaborative work and learning activities.

[0060] When the generation AI recognizes an object, the object recognition unit can refer to past recognition history or the user's search history to improve recognition accuracy. For example, when the generation AI recognizes an object, it refers to past recognition history to improve accuracy when recognizing the same object. For example, when recognizing a type of flower that was previously recognized, it uses past data. The object recognition unit also refers to the user's search history to improve the accuracy when the generation AI recognizes objects. For example, it improves the accuracy when recognizing the same type of flower based on the type of flower that the user previously searched for. The object recognition unit also combines past recognition history and the user's search history to comprehensively improve the accuracy when the generation AI recognizes objects. For example, it optimizes the object recognition algorithm based on past data and search history. This allows the accuracy of object recognition to be improved by referring to past recognition history and the user's search history.

[0061] The object recognition unit can improve recognition accuracy by combining multiple generative AI models and performing ensemble learning. For example, multiple generative AI models can be combined to perform ensemble learning to improve object recognition accuracy. For example, different AI models can recognize objects by leveraging their respective strengths. The object recognition unit also uses ensemble learning to integrate the recognition results of multiple generative AI models to provide the most reliable recognition result. For example, the recognition results of each model can be weighted and integrated. The object recognition unit also develops an algorithm that improves object recognition accuracy by combining multiple generative AI models. For example, the recognition results of different models can be compared and the most appropriate result selected. As a result, object recognition accuracy can be improved by combining multiple generative AI models and performing ensemble learning.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The image acquisition unit acquires an image taken with the smartphone camera. For example, images in JPEG or PNG format can be acquired, and images with different resolutions can also be acquired. Step 2: The object recognition unit recognizes a specific object in the image acquired by the image acquisition unit by touching it with a finger. For example, if the user touches a flower in the image, the object recognition unit recognizes the flower. It can also detect the pressure and speed of the touch to determine the priority and importance of the object. Step 3: The search execution unit performs a web search based on the object recognized by the object recognition unit. For example, the search execution unit searches the web for information about the recognized flower type. The search execution unit can perform the search using a search engine such as Google or Bing. Step 4: The search result display unit displays the search results obtained by the search execution unit. For example, the search results can be displayed in the form of text, images, links, etc.

[0064] (Example 2) The image recognition search system according to an embodiment of the present invention is a system in which a generation AI recognizes a specific object in an image taken with a smartphone camera by touching the object with a finger and performs a web search. This allows the image recognition search system to easily recognize any object in an image taken by a user with a smartphone camera and obtain information about it through a web search.

[0065] An image recognition search system according to an embodiment includes an image acquisition unit, an object recognition unit, a search execution unit, and a search result display unit. The image acquisition unit acquires images captured by a smartphone camera. For example, the image acquisition unit can acquire images in JPEG or PNG format. The image acquisition unit can also acquire images with different resolutions. The object recognition unit recognizes a specific object in an image acquired by the image acquisition unit by touching the object with a finger. For example, when a user touches a flower in an image, the object recognition unit recognizes the flower. The object recognition unit can also detect the pressure and speed of the touch and determine the priority and importance of the object. The search execution unit performs a web search based on the object recognized by the object recognition unit. For example, the search execution unit performs a web search for information about the type of flower recognized by the generation AI. The search execution unit can perform a search using a search engine such as Google or Bing. The search result display unit displays the search results acquired by the search execution unit. For example, the search results can be displayed in the form of text, images, links, or the like. As a result, the image recognition search system according to the embodiment can easily recognize any object in an image taken by a user with a smartphone camera and obtain information about that object through a web search.

[0066] The object recognition unit can detect the pressure or speed of a finger and determine the priority or importance of an object. For example, when a user touches an object in an image, the touchscreen of the smartphone detects the finger pressure and determines the priority of the object based on the pressure. For example, the object recognition unit preferentially recognizes objects that are touched strongly. The object recognition unit also detects the finger speed when the user touches an object in an image and determines the importance of the object based on the speed. For example, an object that is touched quickly is recognized as being important. The object recognition unit also simultaneously detects both the finger pressure and speed and combines these data to comprehensively determine the priority or importance of the object. For example, an object that is touched strongly and quickly is deemed to be the most important. This allows the priority or importance of an object to be determined based on the user's touch operation, improving recognition accuracy.

[0067] The object recognition unit analyzes context information around the touched area to improve the accuracy of identifying the object. For example, when a user touches an object in an image, the object recognition unit analyzes background information around the area to improve the accuracy of identifying the object. For example, if the background is a natural environment, plants and animals are preferentially recognized. The object recognition unit also analyzes other objects around the touched area and identifies the object based on their relationships. For example, if there are leaves around a flower, the flower is recognized as a plant. The object recognition unit also uses surrounding context information to develop an algorithm that improves the accuracy of identifying the touched object. For example, the object recognition unit analyzes signs and labels around a building to identify the name and purpose of the building. In this way, the accuracy of identifying the object can be improved by analyzing surrounding context information.

[0068] The object recognition unit uses an emotion estimation function to analyze the user's emotion when touching an object and adjust the object recognition accuracy according to the user's level of interest. The object recognition unit, for example, analyzes the user's facial expression and voice when touching an object in an image to estimate the emotion. For example, if the user's facial expression is excited or the tone of their voice is high, it determines that the user has a high level of interest in the object. The object recognition unit also uses the emotion estimation function to analyze the user's emotion when touching in real time and adjust the object recognition accuracy based on the emotion. For example, if the user has a strong positive emotion, the recognition accuracy is increased. The object recognition unit also accumulates user emotion data and continuously improves the object recognition algorithm based on the data. For example, it learns past emotion data and provides recognition accuracy according to the user's interest. This allows the object recognition accuracy to be adjusted based on the user's emotion and provide information according to the user's level of interest.

[0069] The object recognition unit can combine touch operations with voice commands or gesture recognition to enable the user to specify an object from multiple interfaces. For example, when a user touches an object in an image, the object recognition unit uses a voice command to specify the object. For example, the user can touch a flower while saying, "Tell me about this flower." The object recognition unit can also use gesture recognition technology to enable the user to specify an object by performing a specific gesture. For example, the user can use pinch-in or pinch-out gestures to zoom in and out to specify the object. The object recognition unit can also combine voice commands and gesture recognition to build a system that allows the user to specify an object from multiple interfaces. For example, the user can say, "This building," and point to the building with their finger. This allows the user to specify an object from multiple interfaces, improving user convenience.

[0070] The object recognition unit may be equipped with a multi-user support function that allows multiple users to touch different objects simultaneously and collaboratively collect information. The object recognition unit adds, for example, a function that allows multiple users to touch different objects simultaneously and collect information about each object. For example, a user may simultaneously research plants or buildings seen on a trip with family or friends. The object recognition unit also uses the multi-user support function to build a system in which multiple users collaboratively collect and share information. For example, this can be used for group research or learning activities. The object recognition unit also adds a function that integrates information about objects touched simultaneously by multiple users and provides comprehensive information. For example, information from different perspectives can be combined to provide more detailed information. This allows multiple users to collaborate in collecting information, thereby promoting collaborative work and learning activities.

[0071] The object recognition unit uses the emotion estimation function to display the emotion of the user when touching in real time, thereby providing an interaction that elicits positive emotions. For example, the object recognition unit analyzes the emotion of the user when touching an object in an image in real time and displays that emotion on the screen. For example, if the user is excited, an icon indicating that emotion is displayed. The object recognition unit also uses the emotion estimation function to analyze the emotion of the user when touching and provides an interaction that elicits positive emotions. For example, an encouraging message is displayed for an object in which the user shows interest. The object recognition unit also provides feedback on touch operations based on the user's emotion data, thereby building a system that elicits positive emotions. For example, positive comments or information is displayed for the object touched by the user. This improves the user experience by displaying the user's emotion in real time and providing an interaction that elicits positive emotions.

[0072] The object recognition unit can improve the accuracy of recognition when the generation AI recognizes an object by referring to past recognition history or the user's search history. For example, when the generation AI recognizes an object, the object recognition unit refers to past recognition history to improve the accuracy when recognizing the same object. For example, when recognizing a type of flower that was previously recognized, past data is used. The object recognition unit also refers to the user's search history to improve the accuracy of the generation AI when recognizing an object. For example, based on the type of flower that the user previously searched for, the accuracy of recognizing the same type of flower is improved. The object recognition unit also combines past recognition history and the user's search history to comprehensively improve the accuracy of the generation AI when recognizing an object. For example, the object recognition algorithm is optimized based on past data and search history. This allows the accuracy of object recognition to be improved by referring to past recognition history and the user's search history.

[0073] The object recognition unit can improve recognition accuracy by combining multiple generative AI models and performing ensemble learning. The object recognition unit, for example, combines multiple generative AI models and performs ensemble learning to improve object recognition accuracy. For example, different AI models recognize objects by leveraging their respective strengths. The object recognition unit also uses ensemble learning to integrate the recognition results of multiple generative AI models and provide the most reliable recognition result. For example, the recognition results of each model are weighted and integrated. The object recognition unit also develops an algorithm that improves object recognition accuracy by combining multiple generative AI models. For example, the recognition results of different models are compared and the most appropriate result is selected. As a result, object recognition accuracy can be improved by combining multiple generative AI models and performing ensemble learning.

[0074] The object recognition unit can use the emotion estimation function to filter the recognition results based on the user's emotion and provide information that is likely to interest the user preferentially. The object recognition unit, for example, uses the emotion estimation function to filter the recognition results based on the user's emotion. For example, if the user is excited, information related to that emotion is provided preferentially. The object recognition unit also customizes the recognition results based on the user's emotion data and provides information that is likely to interest the user. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The object recognition unit also uses the emotion estimation function to filter the recognition results based on the user's emotion and build a system that provides information that matches the user's interests and concerns. For example, the recognition results are adjusted based on the user's emotion score. This improves the user experience by filtering the recognition results based on the user's emotion and providing information that is likely to interest the user preferentially.

[0075] The object recognition unit can simultaneously analyze voice or text data when the generation AI recognizes an object, enabling multimodal recognition. For example, when the generation AI recognizes an object, the object recognition unit simultaneously analyzes not only image data but also voice data, improving recognition accuracy. For example, a user touches a flower while asking, "What flower is this?" The object recognition unit also analyzes text data and uses it as auxiliary information when the generation AI recognizes objects. For example, it analyzes text information within an image and identifies the object based on that information. The object recognition unit also combines voice and text data to develop algorithms that improve the accuracy of the generation AI's object recognition. For example, it integrates voice commands and image data to improve recognition accuracy. This allows for simultaneous analysis of voice and text data, improving object recognition accuracy.

[0076] The object recognition unit can compare object recognition results using different generative AI models and select the most reliable result. The object recognition unit, for example, builds a system that compares object recognition results using different generative AI models and selects the most reliable result. For example, it evaluates the recognition results of each model and selects the optimal result. The object recognition unit also develops an algorithm that compares the recognition results of multiple generative AI models and selects the most reliable result. For example, it weights and integrates the results based on the recognition accuracy of each model. The object recognition unit also builds a system that compares the recognition results of different generative AI models in real time and provides the most reliable result. For example, it instantly evaluates the recognition results of each model and selects the optimal result. This makes it possible to improve recognition accuracy by comparing recognition results using different generative AI models and selecting the most reliable result.

[0077] The object recognition unit uses the emotion estimation function to customize the recognition results based on the user's emotion and provide information that matches the user's interests and concerns. The object recognition unit, for example, uses the emotion estimation function to build a system that customizes the recognition results based on the user's emotion. For example, if the user is excited, information related to that emotion is preferentially provided. The object recognition unit also customizes the recognition results based on the user's emotion data and provides information that matches the user's interests and concerns. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The object recognition unit also uses the emotion estimation function to filter the recognition results based on the user's emotion and build a system that provides information that matches the user's interests and concerns. For example, the recognition results are adjusted based on the user's emotion score. This improves the user experience by customizing the recognition results based on the user's emotion and providing information that matches the user's interests and concerns.

[0078] The search execution unit automatically optimizes the search query when the generation AI performs a web search, enabling it to obtain more relevant information. For example, when the generation AI performs a web search, the search execution unit analyzes and automatically optimizes the search query entered by the user. For example, it converts an ambiguous query into specific keywords. The search execution unit also develops a search query optimization algorithm to enable the generation AI to obtain more relevant information. For example, it strengthens the search query by adding synonyms and related keywords. The search execution unit also allows the generation AI to refer to past search history and optimize the search query based on the user's search patterns. For example, it adjusts the query based on keywords the user has searched for in the past. This automatically optimizes the search query, enabling it to obtain more relevant information.

[0079] The search execution unit can simultaneously use multiple search engines and compare and integrate the results to evaluate the reliability of search results. For example, the search execution unit builds a system in which a generation AI simultaneously uses multiple search engines and compares and integrates search results obtained from each engine. For example, it integrates search results from Google, Bing, and Yahoo. The search execution unit also develops an algorithm to evaluate the reliability of search results and provides the most reliable information based on the results obtained from multiple search engines. For example, it weights and integrates the results from each engine. The search execution unit also builds a system that compares the results from multiple search engines in real time and provides the most reliable information to users. For example, it instantly evaluates the results from each engine and selects the most reliable result. This allows the reliability of search results to be improved by comparing and integrating results using multiple search engines.

[0080] The search execution unit can use the emotion estimation function to filter search results based on the user's emotion and prioritize displaying information that elicits positive emotions. The search execution unit, for example, uses the emotion estimation function to build a system that filters search results based on the user's emotion. For example, if the user is excited, information related to that emotion is prioritized for display. The search execution unit also customizes search results based on the user's emotion data and provides information that elicits positive emotions. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The search execution unit also uses the emotion estimation function to build a system that filters search results based on the user's emotion and provides information that matches the user's interests and concerns. For example, the search results are adjusted based on the user's emotion score. This improves the user experience by filtering search results based on the user's emotion and prioritize displaying information that elicits positive emotions.

[0081] When the generation AI performs a web search, the search execution unit can take into account the user's location information or time of day and provide information appropriate to the context. For example, when the generation AI performs a web search, the search execution unit takes into account the user's location information and provides information appropriate to the context. For example, if the user is in a specific area, information related to that area is displayed preferentially. The search execution unit also takes into account the user's time of day and allows the generation AI to customize search results. For example, if a search is performed at night, information related to nighttime is displayed preferentially. The search execution unit also considers both location information and time of day to build a system in which the generation AI provides information appropriate to the context. For example, event information related to a specific area and time of day is displayed. This makes it possible to provide information appropriate to the context by taking into account the user's location information and time of day.

[0082] The search execution unit updates search results in real time, allowing the generation AI to always provide the latest information. The search execution unit, for example, builds a system in which the generation AI updates search results in real time, allowing the generation AI to always provide the latest information. For example, it instantly reflects the latest information in news articles and blogs. The search execution unit also increases the frequency with which search results are updated, allowing users to always obtain the latest information. For example, it periodically re-acquires search results and updates them with the latest information. The search execution unit also develops an algorithm that updates search results in real time, allowing the generation AI to always provide the latest information. For example, it uses a cache of search results to instantly reflect the latest information. This makes it possible to always provide the latest information by updating search results in real time.

[0083] The search execution unit uses the emotion estimation function to customize search results based on the user's emotions and provide information that matches the user's interests and concerns. The search execution unit, for example, uses the emotion estimation function to build a system that customizes search results based on the user's emotions. For example, if the user is excited, information related to that emotion is preferentially provided. The search execution unit also customizes search results based on the user's emotion data and provides information that matches the user's interests and concerns. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The search execution unit also uses the emotion estimation function to build a system that filters search results based on the user's emotions and provides information that matches the user's interests and concerns. For example, the search results are adjusted based on the user's emotion score. This improves the user experience by customizing search results based on the user's emotions and providing information that matches the user's interests and concerns.

[0084] When displaying search results, the search result display unit can customize the display order or layout based on the user's browsing history or preferences. The search result display unit, for example, analyzes the user's browsing history and builds a system that customizes the display order of search results. For example, search results related to information previously viewed are preferentially displayed. The search result display unit also customizes the layout of search results based on the user's preferences. For example, information formats preferred by the user (articles, images, videos, etc.) are preferentially displayed. The search result display unit also develops a system that combines browsing history and preference data to comprehensively customize the display order and layout of search results. For example, an optimal display method is provided based on the user's past behavioral data. This improves the user experience by customizing the display order and layout based on the user's browsing history and preferences.

[0085] The search result display unit can collect user feedback on search results and continuously improve the display content based on that. The search result display unit, for example, collects user feedback on search results and builds a system that continuously improves the display content based on that data. For example, it adjusts the display order based on search results rated by users. The search result display unit also analyzes user feedback in real time and immediately improves the display content of search results. For example, it excludes search results that the user has expressed dissatisfaction with. The search result display unit also accumulates feedback data and develops a system that continuously optimizes the search result display algorithm based on that data. For example, it learns from user evaluation data and improves the display content. In this way, the user experience is improved by collecting user feedback and continuously improving the display content based on that.

[0086] The search result display unit can use the emotion estimation function to adjust the display content based on the user's emotion and prioritize displaying information that elicits positive emotions. The search result display unit, for example, uses the emotion estimation function to build a system that adjusts the display content of search results based on the user's emotion. For example, if the user is excited, information related to that emotion is prioritized for display. The search result display unit also customizes the display content of search results based on the user's emotion data and provides information that elicits positive emotions. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The search result display unit also uses the emotion estimation function to build a system that filters search results based on the user's emotion and provides information that matches the user's interests and concerns. For example, the search results are adjusted based on the user's emotion score. This improves the user experience by adjusting the display content based on the user's emotion and prioritize displaying information that elicits positive emotions.

[0087] The search result display unit can use AR technology to overlay information on the real world when displaying search results. The search result display unit, for example, builds a system that uses AR technology to overlay information on the real world when displaying search results. For example, the search results are overlaid on a landscape seen through a smartphone camera. The search result display unit also uses AR technology to overlay the search results on the real world, allowing the user to intuitively understand the information. For example, when standing in front of a building, information about the building is displayed. The search result display unit also develops a system that displays the search results using AR technology, allowing the user to integrate the real world and digital information. For example, information about famous places seen at tourist spots is displayed using AR. In this way, the information is overlaid on the real world using AR technology, allowing the user to intuitively understand the information.

[0088] The search result display unit can add a function to read search results aloud, making it possible to accommodate visually impaired people or users who are driving. The search result display unit can, for example, add a function to read search results aloud, building a system that can accommodate visually impaired people and users who are driving. For example, the search results can be read aloud using a smartphone's voice assistant. The search result display unit can also use the voice read function to provide information even in situations where the search results cannot be visually confirmed. For example, information about a destination can be provided by voice while driving. The search result display unit can also customize the voice read function of the search results to develop a system that provides information in a voice that suits the user's preferences. For example, the tone and speed of the voice can be adjusted when reading. This makes it possible to accommodate visually impaired people and users who are driving by reading search results aloud.

[0089] The search result display unit uses the emotion estimation function to customize the display content based on the user's emotion and provide information that matches the user's interests and concerns. The search result display unit, for example, uses the emotion estimation function to build a system that customizes the display content of search results based on the user's emotion. For example, if the user is excited, information related to that emotion is preferentially displayed. The search result display unit also customizes the display content of search results based on the user's emotion data and provides information that matches the user's interests and concerns. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The search result display unit also uses the emotion estimation function to build a system that filters search results based on the user's emotion and provides information that matches the user's interests and concerns. For example, the search results are adjusted based on the user's emotion score. This improves the user experience by customizing the display content based on the user's emotion and providing information that matches the user's interests and concerns.

[0090] When displaying search results, the search result display unit can customize the display order or layout based on the user's browsing history or preferences. The search result display unit, for example, analyzes the user's browsing history and builds a system that customizes the display order of search results. For example, search results related to information previously viewed are preferentially displayed. The search result display unit also customizes the layout of search results based on the user's preferences. For example, information formats preferred by the user (articles, images, videos, etc.) are preferentially displayed. The search result display unit also develops a system that combines browsing history and preference data to comprehensively customize the display order and layout of search results. For example, an optimal display method is provided based on the user's past behavioral data. This improves the user experience by customizing the display order and layout based on the user's browsing history and preferences.

[0091] The search result display unit can collect user feedback on search results and continuously improve the display content based on that. The search result display unit, for example, collects user feedback on search results and builds a system that continuously improves the display content based on that data. For example, it adjusts the display order based on search results rated by users. The search result display unit also analyzes user feedback in real time and immediately improves the display content of search results. For example, it excludes search results that the user has expressed dissatisfaction with. The search result display unit also accumulates feedback data and develops a system that continuously optimizes the search result display algorithm based on that data. For example, it learns from user evaluation data and improves the display content. In this way, the user experience is improved by collecting user feedback and continuously improving the display content based on that.

[0092] The search result display unit can use the emotion estimation function to adjust the display content based on the user's emotion and prioritize displaying information that elicits positive emotions. The search result display unit, for example, uses the emotion estimation function to build a system that adjusts the display content of search results based on the user's emotion. For example, if the user is excited, information related to that emotion is prioritized for display. The search result display unit also customizes the display content of search results based on the user's emotion data and provides information that elicits positive emotions. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The search result display unit also uses the emotion estimation function to build a system that filters search results based on the user's emotion and provides information that matches the user's interests and concerns. For example, the search results are adjusted based on the user's emotion score. This improves the user experience by adjusting the display content based on the user's emotion and prioritize displaying information that elicits positive emotions.

[0093] The search result display unit can use AR technology to overlay information on the real world when displaying search results. The search result display unit, for example, builds a system that uses AR technology to overlay information on the real world when displaying search results. For example, the search results are overlaid on a landscape seen through a smartphone camera. The search result display unit also uses AR technology to overlay the search results on the real world, allowing the user to intuitively understand the information. For example, when standing in front of a building, information about the building is displayed. The search result display unit also develops a system that displays the search results using AR technology, allowing the user to integrate the real world and digital information. For example, information about famous places seen at tourist spots is displayed using AR. In this way, the information is overlaid on the real world using AR technology, allowing the user to intuitively understand the information.

[0094] The search result display unit can add a function to read search results aloud, making it possible to accommodate visually impaired people or users who are driving. The search result display unit can, for example, add a function to read search results aloud, building a system that can accommodate visually impaired people and users who are driving. For example, the search results can be read aloud using a smartphone's voice assistant. The search result display unit can also use the voice read function to provide information even in situations where the search results cannot be visually confirmed. For example, information about a destination can be provided by voice while driving. The search result display unit can also customize the voice read function of the search results to develop a system that provides information in a voice that suits the user's preferences. For example, the tone and speed of the voice can be adjusted when reading. This makes it possible to accommodate visually impaired people and users who are driving by reading search results aloud.

[0095] The search result display unit uses the emotion estimation function to customize the display content based on the user's emotion and provide information that matches the user's interests and concerns. The search result display unit, for example, uses the emotion estimation function to build a system that customizes the display content of search results based on the user's emotion. For example, if the user is excited, information related to that emotion is preferentially displayed. The search result display unit also customizes the display content of search results based on the user's emotion data and provides information that matches the user's interests and concerns. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The search result display unit also uses the emotion estimation function to build a system that filters search results based on the user's emotion and provides information that matches the user's interests and concerns. For example, the search results are adjusted based on the user's emotion score. This improves the user experience by customizing the display content based on the user's emotion and providing information that matches the user's interests and concerns.

[0096] When displaying search results, the search result display unit can customize the display order or layout based on the user's browsing history or preferences. The search result display unit, for example, analyzes the user's browsing history and builds a system that customizes the display order of search results. For example, search results related to information previously viewed are preferentially displayed. The search result display unit also customizes the layout of search results based on the user's preferences. For example, information formats preferred by the user (articles, images, videos, etc.) are preferentially displayed. The search result display unit also develops a system that combines browsing history and preference data to comprehensively customize the display order and layout of search results. For example, an optimal display method is provided based on the user's past behavioral data. This improves the user experience by customizing the display order and layout based on the user's browsing history and preferences.

[0097] The search result display unit can collect user feedback on search results and continuously improve the display content based on that. The search result display unit, for example, collects user feedback on search results and builds a system that continuously improves the display content based on that data. For example, it adjusts the display order based on search results rated by users. The search result display unit also analyzes user feedback in real time and immediately improves the display content of search results. For example, it excludes search results that the user has expressed dissatisfaction with. The search result display unit also accumulates feedback data and develops a system that continuously optimizes the search result display algorithm based on that data. For example, it learns from user evaluation data and improves the display content. In this way, the user experience is improved by collecting user feedback and continuously improving the display content based on that.

[0098] The search result display unit can use the emotion estimation function to adjust the display content based on the user's emotion and prioritize displaying information that elicits positive emotions. The search result display unit, for example, uses the emotion estimation function to build a system that adjusts the display content of search results based on the user's emotion. For example, if the user is excited, information related to that emotion is prioritized for display. The search result display unit also customizes the display content of search results based on the user's emotion data and provides information that elicits positive emotions. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The search result display unit also uses the emotion estimation function to build a system that filters search results based on the user's emotion and provides information that matches the user's interests and concerns. For example, the search results are adjusted based on the user's emotion score. This improves the user experience by adjusting the display content based on the user's emotion and prioritize displaying information that elicits positive emotions.

[0099] The search result display unit can use AR technology to overlay information on the real world when displaying search results. The search result display unit, for example, builds a system that uses AR technology to overlay information on the real world when displaying search results. For example, the search results are overlaid on a landscape seen through a smartphone camera. The search result display unit also uses AR technology to overlay the search results on the real world, allowing the user to intuitively understand the information. For example, when standing in front of a building, information about the building is displayed. The search result display unit also develops a system that displays the search results using AR technology, allowing the user to integrate the real world and digital information. For example, information about famous places seen at tourist spots is displayed using AR. In this way, the information is overlaid on the real world using AR technology, allowing the user to intuitively understand the information.

[0100] The search result display unit can add a function to read search results aloud, making it possible to accommodate visually impaired people or users who are driving. The search result display unit can, for example, add a function to read search results aloud, building a system that can accommodate visually impaired people and users who are driving. For example, the search results can be read aloud using a smartphone's voice assistant. The search result display unit can also use the voice read function to provide information even in situations where the search results cannot be visually confirmed. For example, information about a destination can be provided by voice while driving. The search result display unit can also customize the voice read function of the search results to develop a system that provides information in a voice that suits the user's preferences. For example, the tone and speed of the voice can be adjusted when reading. This makes it possible to accommodate visually impaired people and users who are driving by reading search results aloud.

[0101] The search result display unit uses the emotion estimation function to customize the display content based on the user's emotion and provide information that matches the user's interests and concerns. The search result display unit, for example, uses the emotion estimation function to build a system that customizes the display content of search results based on the user's emotion. For example, if the user is excited, information related to that emotion is preferentially displayed. The search result display unit also customizes the display content of search results based on the user's emotion data and provides information that matches the user's interests and concerns. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The search result display unit also uses the emotion estimation function to build a system that filters search results based on the user's emotion and provides information that matches the user's interests and concerns. For example, the search results are adjusted based on the user's emotion score. This improves the user experience by customizing the display content based on the user's emotion and providing information that matches the user's interests and concerns.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The image acquisition unit acquires images captured by the smartphone camera. For example, images in JPEG or PNG format can be acquired. The image acquisition unit can also acquire images with different resolutions. The object recognition unit recognizes a specific object in an image acquired by the image acquisition unit by touching the object with a finger. For example, when a user touches a flower in the image, the object recognition unit recognizes the flower. The object recognition unit can also detect the pressure and speed of the touch and determine the priority and importance of the object. The search execution unit performs a web search based on the object recognized by the object recognition unit. For example, the search execution unit performs a web search for information about the type of flower recognized by the generation AI. The search execution unit can perform a search using a search engine such as Google or Bing. The search result display unit displays the search results acquired by the search execution unit. For example, the search results can be displayed in the form of text, images, links, or the like. As a result, the image recognition search system according to the embodiment allows a user to easily recognize any object in an image captured by a smartphone camera and acquire information about the object through a web search.

[0104] The object recognition unit can detect the pressure or speed of a finger and determine the priority or importance of an object. For example, when a user touches an object in an image, the smartphone's touchscreen detects the finger pressure and determines the priority of the object based on that pressure. For example, objects that are touched strongly are preferentially recognized. The object recognition unit can also detect the finger speed when the user touches an object in an image and determine the importance of the object based on that speed. For example, objects that are touched quickly are recognized as being important. The object recognition unit can also simultaneously detect both the finger pressure and speed and combine these data to comprehensively determine the priority or importance of an object. For example, an object that is touched strongly and quickly is deemed to be the most important. This allows the priority or importance of objects to be determined based on the user's touch operation, improving recognition accuracy.

[0105] The object recognition unit analyzes context information around the touched area to improve the accuracy of identifying the object. For example, when a user touches an object in an image, the unit analyzes background information around the area to improve the accuracy of identifying the object. For example, if the background is a natural environment, plants and animals are preferentially recognized. The object recognition unit also analyzes other objects around the touched area and identifies the object based on their relationships. For example, if there are leaves around a flower, the flower is recognized as a plant. The object recognition unit also uses surrounding context information to develop an algorithm that improves the accuracy of identifying the touched object. For example, the unit analyzes signs and labels around a building to identify the name and purpose of the building. In this way, the accuracy of identifying the object can be improved by analyzing surrounding context information.

[0106] The object recognition unit uses the emotion estimation function to analyze the emotion of the user when touching an object and adjust the object recognition accuracy according to the level of interest or concern. For example, the emotion is estimated by analyzing the facial expression and voice of the user when touching an object in an image. For example, if the user's facial expression is excited or the tone of their voice is high, it is determined that the user has a high level of interest in the object. The object recognition unit also uses the emotion estimation function to analyze the emotion of the user when touching in real time and adjust the object recognition accuracy based on the emotion. For example, if the emotion is strong, the recognition accuracy is increased. The object recognition unit also accumulates user emotion data and continuously improves the object recognition algorithm based on that data. For example, it learns past emotion data and provides recognition accuracy according to the user's interest or concern. This makes it possible to adjust the object recognition accuracy based on the user's emotion and provide information according to the level of interest or concern.

[0107] The object recognition unit can combine touch operations with voice commands or gesture recognition to specify objects from multiple interfaces. For example, when a user touches an object in an image, the user can specify the object by using a voice command in addition. For example, the user can touch a flower while saying, "Tell me about this flower." The object recognition unit can also use gesture recognition technology to allow the user to specify an object by performing a specific gesture. For example, the user can zoom in and out to specify an object by pinching in and out. The object recognition unit can also combine voice commands and gesture recognition to build a system that allows the user to specify objects from multiple interfaces. For example, the user can say, "This building," and point to the building with their finger. This allows the user to specify objects from multiple interfaces, improving user convenience.

[0108] The object recognition unit can be equipped with a multi-user support function that allows multiple users to touch different objects simultaneously and collaboratively collect information. For example, a function can be added that allows multiple users to touch different objects simultaneously and collect information about each object. For example, a user can simultaneously research plants or buildings seen on a trip with family or friends. The object recognition unit can also use the multi-user support function to build a system in which multiple users collaboratively collect and share information. For example, this can be used for group research or learning activities. The object recognition unit can also add a function that integrates information about objects touched simultaneously by multiple users and provides comprehensive information. For example, information from different perspectives can be combined to provide more detailed information. This allows multiple users to collaborate in collecting information, promoting collaborative work and learning activities.

[0109] The object recognition unit can use the emotion estimation function to display the emotion of the user when touching in real time, providing an interaction that elicits positive emotions. For example, the emotion of the user when touching an object in an image can be analyzed in real time, and the emotion can be displayed on the screen. For example, if the user is excited, an icon indicating that emotion can be displayed. The object recognition unit can also use the emotion estimation function to analyze the emotion of the user when touching, and provide an interaction that elicits positive emotions. For example, an encouraging message can be displayed for an object in which the user shows interest. The object recognition unit can also provide feedback on touch operations based on the user's emotion data, building a system that elicits positive emotions. For example, positive comments or information can be displayed for an object touched by the user. This improves the user experience by displaying the user's emotion in real time and providing an interaction that elicits positive emotions.

[0110] When the generation AI recognizes an object, the object recognition unit can refer to past recognition history or the user's search history to improve recognition accuracy. For example, when the generation AI recognizes an object, it refers to past recognition history to improve accuracy when recognizing the same object. For example, when recognizing a type of flower that was previously recognized, it uses past data. The object recognition unit also refers to the user's search history to improve the accuracy when the generation AI recognizes objects. For example, it improves the accuracy when recognizing the same type of flower based on the type of flower that the user previously searched for. The object recognition unit also combines past recognition history and the user's search history to comprehensively improve the accuracy when the generation AI recognizes objects. For example, it optimizes the object recognition algorithm based on past data and search history. This allows the accuracy of object recognition to be improved by referring to past recognition history and the user's search history.

[0111] The object recognition unit can improve recognition accuracy by combining multiple generative AI models and performing ensemble learning. For example, multiple generative AI models can be combined to perform ensemble learning to improve object recognition accuracy. For example, different AI models can recognize objects by leveraging their respective strengths. The object recognition unit also uses ensemble learning to integrate the recognition results of multiple generative AI models to provide the most reliable recognition result. For example, the recognition results of each model can be weighted and integrated. The object recognition unit also develops an algorithm that improves object recognition accuracy by combining multiple generative AI models. For example, the recognition results of different models can be compared and the most appropriate result selected. As a result, object recognition accuracy can be improved by combining multiple generative AI models and performing ensemble learning.

[0112] The object recognition unit can use the emotion estimation function to filter the recognition results based on the user's emotion and provide information that is likely to interest the user preferentially. For example, the emotion estimation function is used to filter the recognition results based on the user's emotion. For example, if the user is excited, information related to that emotion is provided preferentially. The object recognition unit also customizes the recognition results based on the user's emotion data and provides information that is likely to interest the user. For example, if the user has a strong positive emotion, information related to that emotion is displayed. The object recognition unit also uses the emotion estimation function to filter the recognition results based on the user's emotion and build a system that provides information that matches the user's interests and concerns. For example, the recognition results are adjusted based on the user's emotion score. This improves the user experience by filtering the recognition results based on the user's emotion and providing information that is likely to interest the user preferentially.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The image acquisition unit acquires an image taken with the smartphone camera. For example, images in JPEG or PNG format can be acquired, and images with different resolutions can also be acquired. Step 2: The object recognition unit recognizes a specific object in the image acquired by the image acquisition unit by touching it with a finger. For example, if the user touches a flower in the image, the object recognition unit recognizes the flower. It can also detect the pressure and speed of the touch to determine the priority and importance of the object. Step 3: The search execution unit performs a web search based on the object recognized by the object recognition unit. For example, the search execution unit searches the web for information about the recognized flower type. The search execution unit can perform the search using a search engine such as Google or Bing. Step 4: The search result display unit displays the search results obtained by the search execution unit. For example, the search results can be displayed in the form of text, images, links, etc.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image acquisition unit that acquires an image taken by a smartphone camera; an object recognition unit that recognizes a specific object in an image acquired by the image acquisition unit by touching the object with a finger; a search execution unit that executes a web search based on the object recognized by the object recognition unit; a search result display unit that displays the search results obtained by the search execution unit. A system characterized by:

2. The object recognition unit Detecting the pressure or speed of the finger and determining the priority or importance of the object 2. The system of claim 1.

3. The object recognition unit Analyzing context information around the touched area to improve the accuracy of identifying the object.

2. The system of claim 1.

4. The object recognition unit The user's feelings when touching are analyzed, and the recognition accuracy of the object is adjusted according to the degree of interest or concern.

2. The system of claim 1.

5. The object recognition unit In addition to touch operations, voice commands or gesture recognition can be combined to specify the object from multiple interfaces.

2. The system of claim 1.

6. The object recognition unit A multi-user function is provided in which multiple users simultaneously touch different objects and cooperate to collect information.

2. The system of claim 1.

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