System

Abstract

An object of the system according to the embodiment is to acquire real-time information from an image posted by a user and reflect the information on a search engine.SOLUTION: A system according to an embodiment includes an image posting unit, a generation AI analysis unit, and a point assignment unit. The image posting unit receives an image posted by a user. The generated AI analysis unit analyzes the image received by the image posting unit. The point awarding unit awards points based on the information analyzed by the generated AI analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to obtain real-time information from images posted by users, which can lead to delays in updating information on search engines.

[0005] The system according to the embodiment aims to obtain real-time information from images posted by users and reflect the information in a search engine. [Means for solving the problem]

[0006] The system according to the embodiment includes an image posting unit, a generation AI analysis unit, and a point granting unit. The image posting unit accepts images posted by users. The generation AI analysis unit analyzes the images accepted by the image posting unit. The point granting unit grants points based on information analyzed by the generation AI analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can obtain real-time information from images posted by users and reflect it in a search engine. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The search engine system according to the embodiment of the present invention is a system that reflects information inferred from images posted by users in real time and awards points. This allows the search engine system to provide real-time information from images posted by users and to increase users' motivation to post by awarding points.

[0029] A search engine system according to an embodiment includes an image posting unit, a generation AI analysis unit, and a point assignment unit. The image posting unit accepts images posted by users. For example, the image posting unit can accept image formats such as JPEG, PNG, and GIF. The image posting unit can also store images uploaded by users on a server. The generation AI analysis unit analyzes the images accepted by the image posting unit. For example, the generation AI analysis unit can detect objects in the image using an image recognition algorithm. The generation AI analysis unit can also analyze the content of the image using a feature extraction method. The generation AI analysis unit can also reflect information obtained from the image in search results in real time. For example, the generation AI analysis unit can analyze weather and traffic conditions in the image and reflect that information in the search results. The point assignment unit assigns points based on the information analyzed by the generation AI analysis unit. For example, the point assignment unit assigns points using a scoring method based on the analysis results. The point assignment unit can also set point units and assign points to users as rewards. The point-granting unit can also set the point-granting unit price in a ladder system according to the posting frequency and ratings from other users. This allows the search engine system to provide real-time information from users' image posts and increase their motivation to post by granting points. For example, if a user posts an image showing the current weather or traffic conditions, the generation AI analysis unit analyzes that information and reflects it in the search results. Also, if a user posts an image showing the congestion situation at a tourist destination, that information is also reflected in real time. This allows other users to obtain the latest information, improving the convenience of the search engine.

[0030] The generative AI analysis unit can track changes in objects and scenes within an image and reflect changes in information over time in real time. For example, the generative AI analysis unit can compare consecutively posted images to detect changes in objects and scenes. For example, if images of the same location are posted at different times, the generative AI analysis unit can analyze the changes and update the real-time information. The generative AI analysis unit can also use an object detection algorithm to track changes in the position and shape of objects within an image. The generative AI analysis unit can also analyze changes in scenes within an image using scene analysis technology. This allows changes in information over time to be reflected in real time. For example, the generative AI analysis unit can analyze changes in traffic conditions within an image and reflect them in search results as real-time traffic information. The generative AI analysis unit can also analyze changes in weather within an image and reflect them in search results as real-time weather information.

[0031] The generative AI analysis unit can compare information obtained from an image with images posted by other users and provide mutually complementary information. For example, the generative AI analysis unit can compare images posted by multiple users, extract common information, and mutually complement each other. For example, it can analyze images of the same location from different angles to grasp the overall picture. The generative AI analysis unit can also integrate information within an image to provide more detailed information. The generative AI analysis unit can also compare images posted by other users to evaluate the reliability of the information. This allows it to provide mutually complementary information by comparing with images posted by other users. For example, the generative AI analysis unit can analyze images posted by multiple users and evaluate reliability based on common information. The generative AI analysis unit can also integrate information from different users to provide more accurate information.

[0032] The generative AI analysis unit can provide more diverse real-time information by expanding the types of images and including audio or video as its analysis targets. For example, the generative AI analysis unit can analyze not only images but also audio data, thereby reflecting information obtained from audio in real time. For example, it can analyze audio at a site to reflect noise levels and the content of people's conversations. The generative AI analysis unit can also analyze video data to reflect information obtained from video in real time. For example, it can analyze the movement of objects in a video and reflect that information in real time. The generative AI analysis unit can also use voice recognition technology to convert audio data into text data and reflect that information in real time. By including audio and video as its analysis targets, the generative AI analysis unit can provide more diverse real-time information. For example, the generative AI analysis unit can analyze audio data to understand the situation at a site and reflect that information in search results. The generative AI analysis unit can also analyze video data to track the movement of objects and reflect that information in real time.

[0033] The generative AI analysis unit can link the analyzed information with different search engines or social media platforms to promote the sharing and dissemination of information. The generative AI analysis unit, for example, links the analyzed information with other search engines to reflect it in search results. For example, it links with Google or Bing to provide real-time information widely. The generative AI analysis unit can also link the analyzed information with social media platforms to promote the sharing and dissemination of information. For example, it links with Twitter or Facebook to share real-time information. The generative AI analysis unit can also share data with other search engines or social media platforms using API integration. This can promote the sharing and dissemination of information. For example, the generative AI analysis unit can link the analyzed information with other search engines to provide real-time information to more users. The generative AI analysis unit can also promote the sharing of information by linking with social media platforms to spread the information.

[0034] The point assigning unit can implement an algorithm that evaluates the diversity and novelty of the posted content and assign additional points to highly original posts. The point assigning unit, for example, implements an algorithm that evaluates the diversity of the posted content and assigns additional points to posts that are based on different themes or perspectives. For example, additional points are assigned to images of the same location from different angles. The point assigning unit can also implement an algorithm that evaluates novelty and assign additional points to highly original posts. The point assigning unit can also provide special rewards for highly original posts using an algorithm that scores the uniqueness of the posted content. In this way, additional points can be assigned to highly original posts. For example, the point assigning unit can increase users' motivation to post by assigning additional points to posts that are based on different themes or perspectives. The point assigning unit can also improve user satisfaction by providing special rewards for highly original posts.

[0035] The point granting unit can link the point granting system with other online services or applications to expand the range of use of points. For example, the point granting unit can link the point granting system with an electronic payment service to enable points to be used for online shopping or to pay for services. For example, PayPay points can be used to purchase products. The point granting unit can also link with other online services or applications to expand the range of use of points. For example, points can be used in affiliated services. The point granting unit can also set a point exchange rate and enable points to be exchanged between different services. This can expand the range of use of points. For example, the point granting unit can link with an electronic payment service to enable points to be used for online shopping or to pay for services. The point granting unit can also link with other online services or applications to expand the range of use of points, thereby improving user convenience.

[0036] The point assigning unit can add an option to the point assigning system that allows contributors to use their points for donations or social contribution activities. The point assigning unit, for example, adds an option to the point assigning system that allows users to donate points. For example, the points are donated to a specific charity or a social contribution project. The point assigning unit can also provide an option that allows points to be used for social contribution activities. For example, points can be used to participate in volunteer activities. The point assigning unit can also set criteria for selecting donation recipients so that users can donate points to trustworthy organizations. This allows points to be used for donations or social contribution activities. For example, the point assigning unit provides an option that allows users to donate points to a specific charity. The point assigning unit can also increase users' motivation to contribute to society by providing an option that allows points to be used for social contribution activities.

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

[0038] The search engine system may further include a location information acquisition unit that acquires user location information. When a user posts an image, the location information acquisition unit acquires the location information and provides it to the generation AI analysis unit. For example, the location information acquisition unit can identify the user's current location using GPS data and analyze that information along with the image. The location information acquisition unit can also store a history of places the user has previously visited, allowing the generation AI analysis unit to analyze information based on that history. This makes it possible to utilize the user's location information to provide more accurate real-time information. For example, if a user posts an image taken at a tourist spot, the congestion situation at the tourist spot can be analyzed based on the location information and provided to other users. Also, if a user posts an image of traffic congestion, traffic congestion information can be updated in real time based on the location information.

[0039] The search engine system may further include an interest analysis unit that analyzes a user's interests. The interest analysis unit analyzes a user's interests based on keywords the user has previously searched for and pages the user has viewed. For example, the interest analysis unit can extract keywords that the user frequently searches for and provide that information to the generation AI analysis unit. The interest analysis unit can also analyze the content of pages the user has viewed and identify the user's areas of interest. This allows for more personalized search results to be provided based on the user's interests. For example, if a user frequently searches for information about travel, the interest analysis unit can prioritize the display of the latest travel-related information based on that information. Also, if a user is interested in a particular sport, the interest analysis unit can provide the latest news and event information related to that sport.

[0040] The generative AI analysis unit can compare information obtained from an image with images posted by other users and provide mutually complementary information. For example, the generative AI analysis unit can compare images posted by multiple users, extract common information, and mutually complement each other. For example, the generative AI analysis unit can analyze images of the same location from different angles to grasp the overall picture. The generative AI analysis unit can also integrate information within an image to provide more detailed information. The generative AI analysis unit can also compare images posted by other users to evaluate the reliability of the information. This allows it to provide mutually complementary information by comparing with images posted by other users. For example, the generative AI analysis unit can analyze images posted by multiple users and evaluate reliability based on common information. The generative AI analysis unit can also integrate information from different users to provide more accurate information.

[0041] The generative AI analysis unit can provide more diverse real-time information by expanding the types of images and including audio or video in its analysis targets. For example, the generative AI analysis unit can analyze not only images but also audio data, thereby reflecting information obtained from audio in real time. For example, it can analyze audio at a site to reflect noise levels and the content of people's conversations. The generative AI analysis unit can also analyze video data to reflect information obtained from video in real time. For example, it can analyze the movement of objects in a video and reflect that information in real time. The generative AI analysis unit can also use voice recognition technology to convert audio data into text data and reflect that information in real time. This allows the generative AI analysis unit to provide more diverse real-time information by including audio and video in its analysis targets. For example, the generative AI analysis unit can analyze audio data to understand the situation at a site and reflect that information in search results. The generative AI analysis unit can also analyze video data to track the movement of objects and reflect that information in real time.

[0042] The point assigning unit can implement an algorithm that evaluates the diversity and novelty of the posted content and assign additional points to highly original posts. For example, the point assigning unit can implement an algorithm that evaluates the diversity of the posted content and assign additional points to posts that are based on different themes or perspectives. For example, additional points are assigned to images of the same location from different angles. The point assigning unit can also implement an algorithm that evaluates novelty and assign additional points to highly original posts. The point assigning unit can also provide special rewards to highly original posts using an algorithm that scores the uniqueness of the posted content. In this way, additional points can be assigned to highly original posts. For example, the point assigning unit can increase users' motivation to post by assigning additional points to posts that are based on different themes or perspectives. The point assigning unit can also improve user satisfaction by providing special rewards to highly original posts.

[0043] The search engine system can further include a purchase history analysis unit that analyzes a user's purchasing history. The purchase history analysis unit analyzes a user's purchasing trends based on the user's history of past purchases of products and services. For example, the purchase history analysis unit can identify product categories that the user frequently purchases and provide that information to the generation AI analysis unit. The purchase history analysis unit can also analyze reviews and ratings of products purchased by the user to understand the user's purchasing trends. This makes it possible to provide more personalized search results based on the user's purchasing history. For example, if a user frequently purchases products from a specific brand, the purchase history analysis unit can prioritize displaying new products and related products from the same brand based on that information. Also, if a user highly rates products in a specific category, products from that category can be prioritized.

[0044] The point granting unit can link the point granting system with other online services or applications to expand the range of use of points. For example, the point granting unit can link the point granting system with an electronic payment service to enable points to be used for online shopping or to pay for services. For example, PayPay points can be used to purchase products. The point granting unit can also link with other online services or applications to expand the range of use of points. For example, points can be used in affiliated services. The point granting unit can also set a point exchange rate and enable points to be exchanged between different services. This expands the range of use of points. For example, the point granting unit can link with an electronic payment service to enable points to be used for online shopping or to pay for services. The point granting unit can also link with other online services or applications to expand the range of use of points, thereby improving user convenience.

[0045] The point assigning unit can add an option to the point assigning system that allows contributors to use their points for donations or social contribution activities. For example, the point assigning unit adds an option to the point assigning system that allows users to donate points. For example, the points are donated to a specific charity or a social contribution project. The point assigning unit can also provide an option that allows users to use their points for social contribution activities. For example, the points can be used to participate in volunteer activities. The point assigning unit can also set selection criteria for donation recipients so that users can donate points to trustworthy organizations. This allows points to be used for donations or social contribution activities. For example, the point assigning unit provides an option that allows users to donate points to a specific charity. The point assigning unit can also increase users' motivation to contribute to society by providing an option that allows users to use their points for social contribution activities.

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

[0047] Step 1: The image posting unit accepts images posted by users. For example, the image posting unit can accept image formats such as JPEG, PNG, and GIF. The image posting unit can also save images uploaded by users on the server. Step 2: The generative AI analysis unit analyzes the image received by the image submission unit. For example, the generative AI analysis unit detects objects in the image using an image recognition algorithm. The generative AI analysis unit can also analyze the content of the image using a feature extraction method. The generative AI analysis unit can also reflect information obtained from the image in search results in real time. For example, the generative AI analysis unit analyzes the weather and traffic conditions in the image and reflects that information in the search results. Step 3: The point assigning unit assigns points based on the information analyzed by the generation AI analysis unit. For example, the point assigning unit assigns points using a scoring method based on the analysis results. The point assigning unit can also set point units and assign points to users as rewards. The point assigning unit can also set the point award unit price in a ladder system depending on the posting frequency and evaluations from other users.

[0048] (Example 2) The search engine system according to the embodiment of the present invention is a system that reflects information inferred from images posted by users in real time and awards points. This allows the search engine system to provide real-time information from images posted by users and to increase users' motivation to post by awarding points.

[0049] A search engine system according to an embodiment includes an image posting unit, a generation AI analysis unit, and a point assignment unit. The image posting unit accepts images posted by users. For example, the image posting unit can accept image formats such as JPEG, PNG, and GIF. The image posting unit can also store images uploaded by users on a server. The generation AI analysis unit analyzes the images accepted by the image posting unit. For example, the generation AI analysis unit can detect objects in the image using an image recognition algorithm. The generation AI analysis unit can also analyze the content of the image using a feature extraction method. The generation AI analysis unit can also reflect information obtained from the image in search results in real time. For example, the generation AI analysis unit can analyze weather and traffic conditions in the image and reflect that information in the search results. The point assignment unit assigns points based on the information analyzed by the generation AI analysis unit. For example, the point assignment unit assigns points using a scoring method based on the analysis results. The point assignment unit can also set point units and assign points to users as rewards. The point-granting unit can also set the point-granting unit price in a ladder system according to the posting frequency and ratings from other users. This allows the search engine system to provide real-time information from users' image posts and increase their motivation to post by granting points. For example, if a user posts an image showing the current weather or traffic conditions, the generation AI analysis unit analyzes that information and reflects it in the search results. Also, if a user posts an image showing the congestion situation at a tourist destination, that information is also reflected in real time. This allows other users to obtain the latest information, improving the convenience of the search engine.

[0050] The generative AI analysis unit can infer the poster's emotions based on information inferred from the image and evaluate the reliability of the information based on those emotions. When analyzing an image, the generative AI analysis unit, for example, analyzes the poster's facial expressions and comments made at the time of posting to infer emotions. For example, a post containing a smiling image or a positive comment is evaluated as being highly reliable. The generative AI analysis unit can also use an emotion estimation algorithm to quantify the poster's emotions and evaluate the reliability of the information based on that numerical value. The generative AI analysis unit can also calculate an information reliability score based on the emotion estimation results and evaluate the reliability of the information based on that score. This allows the reliability of information to be evaluated based on the poster's emotions. For example, a post with a positive emotion is evaluated as being highly reliable, and a post with a negative emotion is evaluated as being low reliability.

[0051] The generative AI analysis unit can track changes in objects and scenes within an image and reflect changes in information over time in real time. For example, the generative AI analysis unit can compare consecutively posted images to detect changes in objects and scenes. For example, if images of the same location are posted at different times, the generative AI analysis unit can analyze the changes and update the real-time information. The generative AI analysis unit can also use an object detection algorithm to track changes in the position and shape of objects within an image. The generative AI analysis unit can also analyze changes in scenes within an image using scene analysis technology. This allows changes in information over time to be reflected in real time. For example, the generative AI analysis unit can analyze changes in traffic conditions within an image and reflect them in search results as real-time traffic information. The generative AI analysis unit can also analyze changes in weather within an image and reflect them in search results as real-time weather information.

[0052] The generative AI analysis unit can compare information obtained from an image with images posted by other users and provide mutually complementary information. For example, the generative AI analysis unit can compare images posted by multiple users, extract common information, and mutually complement each other. For example, it can analyze images of the same location from different angles to grasp the overall picture. The generative AI analysis unit can also integrate information within an image to provide more detailed information. The generative AI analysis unit can also compare images posted by other users to evaluate the reliability of the information. This allows it to provide mutually complementary information by comparing with images posted by other users. For example, the generative AI analysis unit can analyze images posted by multiple users and evaluate reliability based on common information. The generative AI analysis unit can also integrate information from different users to provide more accurate information.

[0053] The generative AI analysis unit can provide more diverse real-time information by expanding the types of images and including audio or video as its analysis targets. For example, the generative AI analysis unit can analyze not only images but also audio data, thereby reflecting information obtained from audio in real time. For example, it can analyze audio at a site to reflect noise levels and the content of people's conversations. The generative AI analysis unit can also analyze video data to reflect information obtained from video in real time. For example, it can analyze the movement of objects in a video and reflect that information in real time. The generative AI analysis unit can also use voice recognition technology to convert audio data into text data and reflect that information in real time. By including audio and video as its analysis targets, the generative AI analysis unit can provide more diverse real-time information. For example, the generative AI analysis unit can analyze audio data to understand the situation at a site and reflect that information in search results. The generative AI analysis unit can also analyze video data to track the movement of objects and reflect that information in real time.

[0054] The generative AI analysis unit can link the analyzed information with different search engines or social media platforms to promote the sharing and dissemination of information. The generative AI analysis unit, for example, links the analyzed information with other search engines to reflect it in search results. For example, it links with Google or Bing to provide real-time information widely. The generative AI analysis unit can also link the analyzed information with social media platforms to promote the sharing and dissemination of information. For example, it links with Twitter or Facebook to share real-time information. The generative AI analysis unit can also share data with other search engines or social media platforms using API integration. This can promote the sharing and dissemination of information. For example, the generative AI analysis unit can link the analyzed information with other search engines to provide real-time information to more users. The generative AI analysis unit can also promote the sharing of information by linking with social media platforms to spread the information.

[0055] The generative AI analysis unit can estimate the poster's emotions when analyzing images and prioritize posts with positive emotions. For example, when analyzing images, the generative AI analysis unit can estimate the poster's emotions based on their facial expressions and comments and prioritize posts with positive emotions. For example, posts containing smiling images and positive comments can be prioritized. The generative AI analysis unit can also use an emotion estimation algorithm to quantify the poster's emotions and determine the display order based on that numerical value. The generative AI analysis unit can also display posts with positive emotions higher in search results based on the emotion estimation results. This allows posts with positive emotions to be prioritized. For example, the generative AI analysis unit can provide positive information to users by preferentially displaying posts with smiling images and positive comments. The generative AI analysis unit can also improve user satisfaction by displaying posts with positive emotions higher in search results based on the emotion estimation results.

[0056] The point assigning unit can estimate the poster's emotions when assigning points, and assign bonus points to posts with positive emotions. For example, the point assigning unit analyzes the poster's facial expressions and comments when assigning points, and assigns bonus points to posts with positive emotions. For example, the point assigning unit assigns additional points to posts that include a smiling image or a positive comment. The point assigning unit can also use an emotion estimation algorithm to quantify the poster's emotions and assign bonus points based on the numerical value. The point assigning unit can also provide special rewards to posts with positive emotions based on the emotion estimation result. In this way, bonus points can be assigned to posts with positive emotions. For example, the point assigning unit can assign additional points to posts that include a smiling image or a positive comment, thereby increasing the user's motivation to post. The point assigning unit can also improve user satisfaction by providing special rewards to posts with positive emotions based on the emotion estimation result.

[0057] The point assigning unit can implement an algorithm that evaluates the diversity and novelty of the posted content and assign additional points to highly original posts. The point assigning unit, for example, implements an algorithm that evaluates the diversity of the posted content and assigns additional points to posts that are based on different themes or perspectives. For example, additional points are assigned to images of the same location from different angles. The point assigning unit can also implement an algorithm that evaluates novelty and assign additional points to highly original posts. The point assigning unit can also provide special rewards for highly original posts using an algorithm that scores the uniqueness of the posted content. In this way, additional points can be assigned to highly original posts. For example, the point assigning unit can increase users' motivation to post by assigning additional points to posts that are based on different themes or perspectives. The point assigning unit can also improve user satisfaction by providing special rewards for highly original posts.

[0058] The point granting unit can link the point granting system with other online services or applications to expand the range of use of points. For example, the point granting unit can link the point granting system with an electronic payment service to enable points to be used for online shopping or to pay for services. For example, PayPay points can be used to purchase products. The point granting unit can also link with other online services or applications to expand the range of use of points. For example, points can be used in affiliated services. The point granting unit can also set a point exchange rate and enable points to be exchanged between different services. This can expand the range of use of points. For example, the point granting unit can link with an electronic payment service to enable points to be used for online shopping or to pay for services. The point granting unit can also link with other online services or applications to expand the range of use of points, thereby improving user convenience.

[0059] The point assigning unit can add an option to the point assigning system that allows contributors to use their points for donations or social contribution activities. The point assigning unit, for example, adds an option to the point assigning system that allows users to donate points. For example, the points are donated to a specific charity or a social contribution project. The point assigning unit can also provide an option that allows points to be used for social contribution activities. For example, points can be used to participate in volunteer activities. The point assigning unit can also set criteria for selecting donation recipients so that users can donate points to trustworthy organizations. This allows points to be used for donations or social contribution activities. For example, the point assigning unit provides an option that allows users to donate points to a specific charity. The point assigning unit can also increase users' motivation to contribute to society by providing an option that allows points to be used for social contribution activities.

[0060] The point assigning unit can estimate the emotions of a poster and provide special rewards or benefits for posts that reflect positive emotions. The point assigning unit, for example, adds an emotion estimation function and provides special rewards for posts that reflect positive emotions. For example, special points are awarded for posts that include smiling images or positive comments. The point assigning unit can also use an emotion estimation algorithm to quantify the emotions of a poster and provide special rewards based on the quantified values. The point assigning unit can also set types of special rewards or benefits and provide users with options. This makes it possible to provide special rewards or benefits for posts that reflect positive emotions. For example, the point assigning unit can increase users' motivation to post by awarding special points to posts that include smiling images or positive comments. The point assigning unit can also improve user satisfaction by providing special rewards or benefits based on the emotion estimation results.

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

[0062] The search engine system may further include a location information acquisition unit that acquires user location information. When a user posts an image, the location information acquisition unit acquires the location information and provides it to the generation AI analysis unit. For example, the location information acquisition unit can identify the user's current location using GPS data and analyze that information along with the image. The location information acquisition unit can also store a history of places the user has previously visited, allowing the generation AI analysis unit to analyze information based on that history. This makes it possible to utilize the user's location information to provide more accurate real-time information. For example, if a user posts an image taken at a tourist spot, the congestion situation at the tourist spot can be analyzed based on the location information and provided to other users. Also, if a user posts an image of traffic congestion, traffic congestion information can be updated in real time based on the location information.

[0063] The generative AI analysis unit can infer the poster's emotions based on information inferred from the image and evaluate the reliability of the information based on those emotions. For example, when analyzing an image, the generative AI analysis unit analyzes the poster's facial expressions and comments made at the time of posting to infer emotions. For example, posts that include smiling images or positive comments are evaluated as highly reliable. The generative AI analysis unit can also use an emotion estimation algorithm to quantify the poster's emotions and evaluate the reliability of the information based on that numerical value. The generative AI analysis unit can also calculate an information reliability score based on the emotion estimation results and evaluate the reliability of the information based on that score. This allows the reliability of information to be evaluated based on the poster's emotions. For example, posts with positive emotions are evaluated as highly reliable, and posts with negative emotions are evaluated as less reliable.

[0064] The search engine system may further include an interest analysis unit that analyzes a user's interests. The interest analysis unit analyzes a user's interests based on keywords the user has previously searched for and pages the user has viewed. For example, the interest analysis unit can extract keywords that the user frequently searches for and provide that information to the generation AI analysis unit. The interest analysis unit can also analyze the content of pages the user has viewed and identify the user's areas of interest. This allows for more personalized search results to be provided based on the user's interests. For example, if a user frequently searches for information about travel, the interest analysis unit can prioritize the display of the latest travel-related information based on that information. Also, if a user is interested in a particular sport, the interest analysis unit can provide the latest news and event information related to that sport.

[0065] The generative AI analysis unit can compare information obtained from an image with images posted by other users and provide mutually complementary information. For example, the generative AI analysis unit can compare images posted by multiple users, extract common information, and mutually complement each other. For example, the generative AI analysis unit can analyze images of the same location from different angles to grasp the overall picture. The generative AI analysis unit can also integrate information within an image to provide more detailed information. The generative AI analysis unit can also compare images posted by other users to evaluate the reliability of the information. This allows it to provide mutually complementary information by comparing with images posted by other users. For example, the generative AI analysis unit can analyze images posted by multiple users and evaluate reliability based on common information. The generative AI analysis unit can also integrate information from different users to provide more accurate information.

[0066] The generative AI analysis unit can provide more diverse real-time information by expanding the types of images and including audio or video in its analysis targets. For example, the generative AI analysis unit can analyze not only images but also audio data, thereby reflecting information obtained from audio in real time. For example, it can analyze audio at a site to reflect noise levels and the content of people's conversations. The generative AI analysis unit can also analyze video data to reflect information obtained from video in real time. For example, it can analyze the movement of objects in a video and reflect that information in real time. The generative AI analysis unit can also use voice recognition technology to convert audio data into text data and reflect that information in real time. This allows the generative AI analysis unit to provide more diverse real-time information by including audio and video in its analysis targets. For example, the generative AI analysis unit can analyze audio data to understand the situation at a site and reflect that information in search results. The generative AI analysis unit can also analyze video data to track the movement of objects and reflect that information in real time.

[0067] The search engine system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit estimates the user's health condition from images and comments posted by the user and provides the estimated health condition to the generation AI analysis unit. For example, the health monitoring unit may analyze comments about the user's complexion and physical condition to evaluate the user's health condition. The health monitoring unit may also analyze images posted by the user about diet and exercise to monitor the user's health condition. This makes it possible to provide appropriate advice and information based on the user's health condition. For example, if a user posts an image of a tired face, the health monitoring unit may use that information to provide advice on how to rest and relax. Also, if a user posts an image of a healthy meal, the health monitoring unit may use that information to provide information on maintaining health.

[0068] The generative AI analysis unit can estimate the poster's emotions when analyzing images and prioritize posts with positive emotions. For example, when analyzing images, the generative AI analysis unit estimates emotions based on the poster's facial expressions and comments and prioritizes posts with positive emotions. For example, posts containing smiling images and positive comments are prioritized. The generative AI analysis unit can also use an emotion estimation algorithm to quantify the poster's emotions and determine the display order based on that numerical value. The generative AI analysis unit can also display posts with positive emotions higher in search results based on the emotion estimation results. This allows posts with positive emotions to be prioritized. For example, the generative AI analysis unit provides positive information to users by preferentially displaying posts with smiling images and positive comments. The generative AI analysis unit can also improve user satisfaction by displaying posts with positive emotions higher in search results based on the emotion estimation results.

[0069] The point assigning unit can estimate the poster's emotions when assigning points, and assign bonus points to posts with positive emotions. For example, the point assigning unit analyzes the poster's facial expressions and comments when assigning points, and assigns bonus points to posts with positive emotions. For example, the point assigning unit assigns additional points to posts that include a smiling image or a positive comment. The point assigning unit can also use an emotion estimation algorithm to quantify the poster's emotions and assign bonus points based on the numerical value. The point assigning unit can also provide special rewards to posts with positive emotions based on the emotion estimation result. In this way, bonus points can be assigned to posts with positive emotions. For example, the point assigning unit can increase the user's motivation to post by assigning additional points to posts that include a smiling image or a positive comment. The point assigning unit can also improve user satisfaction by providing special rewards to posts with positive emotions based on the emotion estimation result.

[0070] The point assigning unit can implement an algorithm that evaluates the diversity and novelty of the posted content and assign additional points to highly original posts. For example, the point assigning unit can implement an algorithm that evaluates the diversity of the posted content and assign additional points to posts that are based on different themes or perspectives. For example, additional points are assigned to images of the same location from different angles. The point assigning unit can also implement an algorithm that evaluates novelty and assign additional points to highly original posts. The point assigning unit can also provide special rewards to highly original posts using an algorithm that scores the uniqueness of the posted content. In this way, additional points can be assigned to highly original posts. For example, the point assigning unit can increase users' motivation to post by assigning additional points to posts that are based on different themes or perspectives. The point assigning unit can also improve user satisfaction by providing special rewards to highly original posts.

[0071] The search engine system can further include a purchase history analysis unit that analyzes a user's purchasing history. The purchase history analysis unit analyzes a user's purchasing trends based on the user's history of past purchases of products and services. For example, the purchase history analysis unit can identify product categories that the user frequently purchases and provide that information to the generation AI analysis unit. The purchase history analysis unit can also analyze reviews and ratings of products purchased by the user to understand the user's purchasing trends. This makes it possible to provide more personalized search results based on the user's purchasing history. For example, if a user frequently purchases products from a specific brand, the purchase history analysis unit can prioritize displaying new products and related products from the same brand based on that information. Also, if a user highly rates products in a specific category, products from that category can be prioritized.

[0072] The point granting unit can link the point granting system with other online services or applications to expand the range of use of points. For example, the point granting unit can link the point granting system with an electronic payment service to enable points to be used for online shopping or to pay for services. For example, PayPay points can be used to purchase products. The point granting unit can also link with other online services or applications to expand the range of use of points. For example, points can be used in affiliated services. The point granting unit can also set a point exchange rate and enable points to be exchanged between different services. This expands the range of use of points. For example, the point granting unit can link with an electronic payment service to enable points to be used for online shopping or to pay for services. The point granting unit can also link with other online services or applications to expand the range of use of points, thereby improving user convenience.

[0073] The point assigning unit can add an option to the point assigning system that allows contributors to use their points for donations or social contribution activities. For example, the point assigning unit adds an option to the point assigning system that allows users to donate points. For example, the points are donated to a specific charity or a social contribution project. The point assigning unit can also provide an option that allows users to use their points for social contribution activities. For example, the points can be used to participate in volunteer activities. The point assigning unit can also set selection criteria for donation recipients so that users can donate points to trustworthy organizations. This allows points to be used for donations or social contribution activities. For example, the point assigning unit provides an option that allows users to donate points to a specific charity. The point assigning unit can also increase users' motivation to contribute to society by providing an option that allows users to use their points for social contribution activities.

[0074] The point assigning unit can estimate the emotions of a poster and provide special rewards or benefits for posts that have positive emotions. For example, the point assigning unit adds an emotion estimation function and provides special rewards for posts that have positive emotions. For example, special points are assigned to posts that include smiling images or positive comments. The point assigning unit can also use an emotion estimation algorithm to quantify the emotions of a poster and provide special rewards based on the quantified values. The point assigning unit can also set types of special rewards or benefits and provide users with options. This makes it possible to provide special rewards or benefits for posts that have positive emotions. For example, the point assigning unit can increase users' motivation to post by assigning special points to posts that include smiling images or positive comments. The point assigning unit can also improve user satisfaction by providing special rewards or benefits based on the emotion estimation results.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The image posting unit accepts images posted by users. For example, the image posting unit can accept image formats such as JPEG, PNG, and GIF. The image posting unit can also save images uploaded by users on the server. Step 2: The generative AI analysis unit analyzes the image received by the image submission unit. For example, the generative AI analysis unit detects objects in the image using an image recognition algorithm. The generative AI analysis unit can also analyze the content of the image using a feature extraction method. The generative AI analysis unit can also reflect information obtained from the image in search results in real time. For example, the generative AI analysis unit analyzes the weather and traffic conditions in the image and reflects that information in the search results. Step 3: The point assigning unit assigns points based on the information analyzed by the generation AI analysis unit. For example, the point assigning unit assigns points using a scoring method based on the analysis results. The point assigning unit can also set point units and assign points to users as rewards. The point assigning unit can also set the point award unit price in a ladder system depending on the posting frequency and evaluations from other users.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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).

[0086] 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.

[0087] 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.

[0088] 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.

[0089] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0090] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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."

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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]

[0144] 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 posting unit that accepts images posted by users; a generation AI analysis unit that analyzes the image accepted by the image posting unit; and a point granting unit that grants points based on the information analyzed by the generation AI analysis unit. A system characterized by:

2. The generation AI analysis unit Tracks changes in objects and scenes within the image and reflects changes in information over time in real time.

2. The system of claim 1.

3. The generation AI analysis unit Expanding the types of images to include audio and video analysis will provide more diverse real-time information.

2. The system of claim 1.

4. The point giving unit When awarding points, the poster's emotions are estimated, and bonus points are awarded to posts with positive emotions.

2. The system of claim 1.

5. The point giving unit Linking the points system with other online services or applications to expand the scope of use of said points 2. The system of claim 1.

6. The generation AI analysis unit The poster's feelings are estimated based on information inferred from the image, and the reliability of the information is evaluated based on the feelings.

2. The system of claim 1.

Images