System

Abstract

An object of a system according to an embodiment is to set an advertisement area using generated AI and to efficiently perform image analysis and grasp of commodity information.SOLUTION: A system includes an advertisement area setting part, an image analysis part, a meta-information generation part, a commodity information grasping part, an eye tracking device, and a purchase guide line proposal part. The advertisement area setting unit sets an advertisement area using the generation AI. The image analysis unit analyzes the image based on the advertisement area set by the advertisement area setting unit. The meta information generation unit generates meta information on the basis of the image analyzed by the image analysis unit. The product information grasping unit grasps the product information based on the meta information generated by the meta information generating unit. The eye tracking device collects eye gaze data. The purchase lead line proposal section proposes a purchase lead line based on the line-of-sight data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently set advertising areas, analyze images, or grasp product information, leaving room for improvement.

[0005] The system of the embodiment aims to set advertising areas using generation AI and efficiently perform image analysis and grasp product information. [Means for solving the problem]

[0006] The system according to the embodiment includes an advertising area setting unit, an image analysis unit, a meta information generation unit, a product information grasping unit, an eye tracking device, and a purchase path suggestion unit. The advertising area setting unit sets an advertising area using a generation AI. The image analysis unit analyzes an image based on the advertising area set by the advertising area setting unit. The meta information generation unit generates meta information based on the image analyzed by the image analysis unit. The product information grasping unit grasps product information based on the meta information generated by the meta information generation unit. The eye tracking device collects gaze data. The purchase path suggestion unit suggests a purchase path based on the gaze data. [Effects of the Invention]

[0007] The system according to the embodiment can set advertising areas using generation AI, and can efficiently perform image analysis and grasp product information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The advertising area setting system according to an embodiment of the present invention is a system that uses generative AI to innovatively set advertising areas, perform image analysis, create meta information, grasp product information, provide an eye-tracking device, and occupy purchase paths. As a result, the advertising area setting system can efficiently set advertising areas, grasp product information, and occupy purchase paths.

[0029] An advertising area setting system according to an embodiment includes an advertising area setting unit, an image analysis unit, a meta information generation unit, a product information acquisition unit, an eye tracking device, and a purchase path suggestion unit. The advertising area setting unit sets an advertising area using a generation AI. For example, the generation AI analyzes viewer gaze data and identifies the most visually effective advertising area. The image analysis unit analyzes an image based on the advertising area set by the advertising area setting unit. For example, the generation AI analyzes objects and scenes in the image and extracts visually important information. The meta information generation unit generates meta information based on the image analyzed by the image analysis unit. For example, the generation AI generates meta information related to objects and scenes in the image. The product information acquisition unit acquires product information based on the meta information generated by the meta information generation unit. For example, the generation AI identifies products in a video and collects and analyzes their information. The eye tracking device collects gaze data. For example, it analyzes how viewers view a television screen and provides the data to the generation AI. The purchase path suggestion unit suggests a purchase path based on the gaze data. For example, the generation AI analyzes viewer gaze data and proposes the optimal purchase path, allowing the advertising area setting system to efficiently set advertising areas, grasp product information, and occupy purchase paths.

[0030] The advertising area setting unit can analyze the viewer's past viewing history and dynamically set the optimal advertising area for each individual viewer. For example, the generation AI analyzes the viewer's past viewing history and identifies content and scenes that are likely to interest the viewer. Based on that data, the advertising area setting unit dynamically sets the optimal advertising area for the viewer. Furthermore, based on the viewing history, the generation AI analyzes the viewer's preferences and interests and places advertisements in areas that the viewer is most interested in. For example, advertisements are displayed in scenes related to a particular genre or theme. Furthermore, the generation AI analyzes the viewer's viewing history and sets advertising areas based on scenes and content that the viewer has previously rated highly. This makes it possible to place advertisements that are likely to attract the viewer's attention.

[0031] The advertising area setting unit analyzes viewer gaze data in real time and can instantly display advertisements in areas where viewer gazes are concentrated. In the advertising area setting unit, for example, the generation AI collects viewer gaze data in real time and analyzes the degree of gaze concentration. By instantly displaying advertisements in areas where viewer gazes are concentrated, it becomes easier to attract viewer attention. In addition, the generation AI analyzes viewer gaze data in real time and identifies areas where gazes are concentrated. The generation AI instantly displays advertisements in those areas, effectively attracting viewer gaze. Furthermore, the generation AI analyzes viewer gaze data in real time and dynamically places advertisements in areas where gaze concentration is high. This makes it possible to effectively guide the viewer's gaze. This makes it possible to place advertisements that effectively attract the viewer's gaze.

[0032] The advertising area setting unit can simultaneously analyze the viewer's gaze data and audio data to set an advertising area that is effective for both the visual and auditory senses. For example, the generation AI simultaneously collects the viewer's gaze data and audio data to identify an advertising area that is effective for both the visual and auditory senses. For example, the generation AI analyzes audio data related to scenes that the viewer is paying attention to. Furthermore, based on the viewer's gaze data and audio data, the generation AI dynamically sets the area that the viewer is most interested in. Placing ads that are effective for both the visual and auditory senses makes it easier to attract the viewer's attention. Furthermore, the generation AI analyzes the viewer's gaze data and audio data to identify the area where the viewer feels the most positive emotions. Placing ads in that area makes it easier to attract the viewer's attention. This makes it possible to place ads that are effective for both the visual and auditory senses.

[0033] The advertising area setting unit can integrate viewing data from different devices and set an advertising area compatible with multiple devices. For example, the generation AI collects and integrates viewing data from different devices (smartphones, tablets, etc.) in the advertising area setting unit. The optimal advertising area is set regardless of the device the viewer is viewing on. Furthermore, based on the viewing data from different devices, the generation AI analyzes the viewer's gaze data and dynamically sets an advertising area compatible with multiple devices. This makes it possible to place ads that are more likely to attract the viewer's attention. Furthermore, the generation AI integrates viewing data from different devices and analyzes the gaze data regardless of the device the viewer is viewing on. Setting the optimal advertising area makes it possible to place ads that are more likely to attract the viewer's attention. This makes it possible to integrate viewing data from different devices and place ads compatible with multiple devices.

[0034] The image analysis unit can analyze a scene in an image and automatically generate background information and related historical data for the scene. For example, the image analysis unit uses a generation AI to analyze a scene in an image and automatically generate background information for that scene. For example, it provides information about historical buildings and scenery. The image analysis unit also analyzes a scene in an image and automatically generates related historical data. For example, it provides information related to a specific era or culture. The generation AI also analyzes a scene in an image and automatically generates background information and related historical data for that scene. For example, it provides information about the people and places that appear in the scene. This makes it possible to automatically generate background information and related historical data for a scene.

[0035] The image analysis unit can analyze the movement of objects in an image and generate meta information based on the movement. In the image analysis unit, for example, a generation AI analyzes the movement of objects in an image and generates meta information based on that movement. For example, sports play analysis and motion analysis are performed. In addition, the movement of objects in an image is analyzed and the generation AI generates meta information based on that movement. For example, motion analysis of dance or performance is performed. In addition, the generation AI analyzes the movement of objects in an image and generates meta information based on that movement. For example, sports play analysis and motion analysis are performed. This makes it possible to generate meta information based on movement.

[0036] The image analysis unit can simultaneously analyze objects in an image and audio data to generate meta information related to both vision and hearing. In the image analysis unit, for example, a generation AI simultaneously analyzes objects in an image and audio data to generate meta information related to both vision and hearing. For example, it provides synchronization information between video and audio. Furthermore, based on objects in an image and audio data, the generation AI generates meta information related to both vision and hearing. For example, it provides synchronization information between video and audio. Furthermore, the generation AI simultaneously analyzes objects in an image and audio data to generate meta information related to both vision and hearing. For example, it provides synchronization information between video and audio. This makes it possible to generate meta information related to both vision and hearing.

[0037] The image analysis unit can integrate image data from different camera angles and generate meta information from multiple viewpoints. In the image analysis unit, for example, the generation AI integrates image data from different camera angles and generates meta information from multiple viewpoints. For example, it provides analytical information from multiple camera angles of a sporting event. Furthermore, the generation AI generates meta information from multiple viewpoints based on image data from different camera angles. For example, it performs scene analysis of a movie or drama. Furthermore, the generation AI integrates image data from different camera angles and generates meta information from multiple viewpoints. For example, it provides analytical information from multiple camera angles of a sporting event. This makes it possible to generate meta information from multiple viewpoints.

[0038] The product information grasping unit can identify products in videos and automatically collect and analyze past reviews and ratings of those products. In the product information grasping unit, for example, the generation AI identifies products in videos and automatically collects and analyzes past reviews and ratings of those products. For example, it collects data from online review sites and social media. In addition, the product in the video is analyzed, and the generation AI automatically collects and analyzes past reviews and ratings of those products. For example, it provides product information based on user ratings and comments. Furthermore, the generation AI identifies products in videos and automatically collects and analyzes past reviews and ratings of those products. For example, it collects data from online review sites and social media. This makes it possible to automatically collect and analyze past reviews and ratings of products.

[0039] The product information grasping unit can identify products in the video and automatically generate tutorial videos on how to use the product and related products. In the product information grasping unit, for example, a generation AI identifies products in the video and automatically generates tutorial videos on how to use the product and related products. For example, a video explaining how to use and maintain the product is provided. In addition, the generation AI analyzes the products in the video and automatically generates tutorial videos on how to use the product and related products. For example, a video explaining how to use and maintain the product is provided. In addition, the generation AI identifies products in the video and automatically generates tutorial videos on how to use the product and related products. For example, a video explaining how to use and maintain the product is provided. This makes it possible to automatically generate tutorial videos on how to use the product and related products.

[0040] The product information grasping unit can simultaneously analyze audio data related to products in a video and provide product information related to both visual and auditory senses. For example, the generation AI can simultaneously analyze audio data related to products in a video and provide product information related to both visual and auditory senses. For example, it can analyze audio descriptions of products and audio reviews. Furthermore, based on the product and audio data in the video, the generation AI can provide product information related to both visual and auditory senses. For example, it can analyze audio descriptions of products and audio reviews. Furthermore, the generation AI can simultaneously analyze audio data related to products in a video and provide product information related to both visual and auditory senses. For example, it can analyze audio descriptions of products and audio reviews. This makes it possible to provide product information related to both visual and auditory senses.

[0041] The product information grasping unit can integrate product information across different programs and episodes to provide consistent product information to viewers. In the product information grasping unit, for example, the generation AI integrates product information across different programs and episodes to provide consistent product information to viewers. For example, if the same product is introduced in multiple programs, that information is integrated. Furthermore, the generation AI provides consistent product information to viewers based on product information across different programs and episodes. For example, if the same product is introduced in multiple programs, that information is integrated. Furthermore, the generation AI integrates product information across different programs and episodes to provide consistent product information to viewers. For example, if the same product is introduced in multiple programs, that information is integrated. This makes it possible to provide consistent product information across different programs and episodes.

[0042] The eye-tracking device can provide the collected gaze data to the generation AI and analyze the viewer's gaze patterns. For example, the eye-tracking device can provide the collected gaze data to the generation AI and analyze the viewer's gaze patterns. For example, it can identify which part the viewer is focusing on. Furthermore, the generation AI can analyze the viewer's gaze patterns based on the gaze data. For example, it can identify which part the viewer is focusing on. Furthermore, the eye-tracking device can provide the collected gaze data to the generation AI and analyze the viewer's gaze patterns. For example, it can identify which part the viewer is focusing on. This makes it possible to analyze the viewer's gaze patterns.

[0043] An eye-tracking device can visualize the gaze movements of viewers in real time using collected gaze data. An eye-tracking device, for example, uses collected gaze data to visualize the gaze movements of viewers in real time. For example, it displays which parts the viewers are focusing on in a graph or heat map. Furthermore, based on the gaze data, the gaze movements of viewers can be visualized in real time. For example, it displays which parts the viewers are focusing on in a graph or heat map. Furthermore, an eye-tracking device uses collected gaze data to visualize the gaze movements of viewers in real time. For example, it displays which parts the viewers are focusing on in a graph or heat map. This makes it possible to visualize the gaze movements of viewers in real time.

[0044] The eye tracking device can use the collected gaze data to provide interactive advertisements based on the viewer's gaze movements. The eye tracking device, for example, uses the collected gaze data to provide interactive advertisements based on the viewer's gaze movements. For example, an advertisement is displayed in a portion where the viewer is paying attention. Furthermore, the eye tracking device can use the collected gaze data to provide interactive advertisements based on the viewer's gaze movements. For example, an advertisement is displayed in a portion where the viewer is paying attention. Furthermore, the eye tracking device can use the collected gaze data to provide interactive advertisements based on the viewer's gaze movements. For example, an advertisement is displayed in a portion where the viewer is paying attention. This makes it possible to provide interactive advertisements based on the viewer's gaze movements.

[0045] The eye tracking device can be made compatible with different devices and collect gaze data across multiple devices. The eye tracking device can be made compatible with different devices (smartphones, tablets, etc.) and collect gaze data across multiple devices. For example, gaze data can be collected regardless of which device the viewer is using to watch. Furthermore, the eye tracking device can collect gaze data of the viewer based on gaze data from different devices. For example, gaze data can be collected regardless of which device the viewer is using to watch. Furthermore, the eye tracking device can be made compatible with different devices (smartphones, tablets, etc.) and collect gaze data across multiple devices. For example, gaze data can be collected regardless of which device the viewer is using to watch. This makes it possible to collect gaze data across multiple devices.

[0046] An eye-tracking device can simultaneously collect viewer gaze data and audio data to provide data related to both vision and hearing. For example, an eye-tracking device can simultaneously collect viewer gaze data and audio data to provide data related to both vision and hearing. For example, audio data related to a portion on which the viewer is focusing is collected. Furthermore, based on the gaze data and audio data, the eye-tracking device can provide data related to both vision and hearing. For example, audio data related to a portion on which the viewer is focusing is collected. Furthermore, an eye-tracking device can simultaneously collect viewer gaze data and audio data to provide data related to both vision and hearing. For example, audio data related to a portion on which the viewer is focusing is collected. This makes it possible to provide data related to both vision and hearing.

[0047] The purchase path suggestion unit can analyze the viewer's past purchase history and dynamically set the optimal purchase path for each viewer. In the purchase path suggestion unit, for example, the generation AI analyzes the viewer's past purchase history and dynamically sets the optimal purchase path for products that the viewer is likely to be interested in. For example, it suggests products related to products that the viewer has previously purchased. Furthermore, based on the purchase history, the generation AI analyzes the viewer's preferences and interests and provides purchase links for products that the viewer is most interested in. For example, it suggests products related to a specific genre or theme. Furthermore, the generation AI analyzes the viewer's purchase history and sets a purchase path based on products that the viewer has given high ratings in the past. This provides purchase links that are likely to attract the viewer's interest. This makes it possible to dynamically set the optimal purchase path based on the viewer's past purchase history.

[0048] The purchase path suggestion unit can analyze viewer gaze data in real time and instantly provide purchase links for products that the viewer is interested in. For example, the generation AI in the purchase path suggestion unit collects viewer gaze data in real time and instantly provides purchase links for products that the viewer is interested in. For example, it displays purchase links for products that the viewer is paying attention to. It also analyzes viewer gaze data in real time and instantly provides purchase links for products that the viewer is interested in. This allows viewers to purchase products smoothly. It also analyzes viewer gaze data in real time and instantly provides purchase links for products that the viewer is interested in. For example, it displays purchase links for products that the viewer is paying attention to. This makes it possible to instantly provide purchase links for products that the viewer is interested in.

[0049] The purchase lead suggestion unit can simultaneously analyze the viewer's gaze data and voice data to propose purchase leads related to both vision and hearing. For example, the generation AI simultaneously collects the viewer's gaze data and voice data to propose purchase leads related to both vision and hearing. For example, it analyzes voice data related to products that the viewer is interested in. Furthermore, based on the gaze data and voice data, the generation AI provides purchase links for products that the viewer is most interested in. For example, it analyzes voice data related to products that the viewer is interested in. Furthermore, the generation AI simultaneously analyzes the viewer's gaze data and voice data to propose purchase leads related to both vision and hearing. For example, it analyzes voice data related to products that the viewer is interested in. This makes it possible to propose purchase leads related to both vision and hearing.

[0050] The purchase path proposal unit can integrate viewing data from different devices and propose a purchase path that is compatible with multiple devices. In the purchase path proposal unit, for example, the generation AI collects and integrates viewing data from different devices (smartphones, tablets, etc.). It proposes the optimal purchase path regardless of the device the viewer is watching on. Furthermore, based on the viewing data from different devices, the generation AI analyzes the viewer's gaze data and dynamically sets a purchase path that is compatible with multiple devices. This provides a purchase link that is likely to attract the viewer's attention. Furthermore, the generation AI integrates viewing data from different devices and analyzes gaze data regardless of the device the viewer is watching on. By proposing the optimal purchase path, it provides a purchase link that is likely to attract the viewer's attention. This makes it possible to propose a purchase path that is compatible with multiple devices.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The advertising area setting unit can simultaneously analyze the viewer's gaze data and audio data to set an advertising area that is effective for both the visual and auditory senses. For example, the generation AI simultaneously collects the viewer's gaze data and audio data to identify an advertising area that is effective for both the visual and auditory senses. By analyzing audio data related to scenes that the viewer is paying attention to, it is possible to place ads that are effective for both the visual and auditory senses. Furthermore, based on the viewer's gaze data and audio data, the generation AI dynamically sets the area that the viewer is most interested in. Placing ads that are effective for both the visual and auditory senses makes it easier to attract the viewer's attention. Furthermore, the generation AI analyzes the viewer's gaze data and audio data to identify the area where the viewer feels the most positive emotions. Placing ads in that area makes it easier to attract the viewer's attention. This makes it possible to place ads that are effective for both the visual and auditory senses.

[0053] The advertising area setting unit can analyze a viewer's past viewing history and dynamically set the optimal advertising area for each individual viewer. For example, the generation AI analyzes a viewer's past viewing history and identifies content and scenes that are likely to interest the viewer. Based on that data, the optimal advertising area for the viewer is dynamically set. Furthermore, based on the viewing history, the generation AI analyzes the viewer's preferences and interests and places advertisements in areas that the viewer is most interested in. For example, advertisements are displayed in scenes related to a particular genre or theme. Furthermore, the generation AI analyzes the viewer's viewing history and sets advertising areas based on scenes and content that the viewer has previously rated highly. This makes it possible to place advertisements that are more likely to attract the viewer's attention.

[0054] The advertising area setting unit analyzes viewer gaze data in real time and can instantly display advertisements in areas where viewer gazes are concentrated. For example, the generation AI collects viewer gaze data in real time and analyzes the degree of gaze concentration. By instantly displaying advertisements in areas where viewer gazes are concentrated, it becomes easier to attract viewer attention. In addition, the generation AI analyzes viewer gaze data in real time and identifies areas where gazes are concentrated. The generation AI instantly displays advertisements in those areas, effectively attracting viewer gaze. Furthermore, the generation AI analyzes viewer gaze data in real time and dynamically places advertisements in areas where gaze concentration is high. This allows for effective guidance of viewer gaze. This makes it possible to place advertisements that effectively attract viewer gaze.

[0055] The advertising area setting unit can integrate viewing data from different devices and set an advertising area compatible with multiple devices. For example, the generation AI collects and integrates viewing data from different devices (smartphones, tablets, etc.). The optimal advertising area is set regardless of the device the viewer is viewing on. Furthermore, based on the viewing data from different devices, the generation AI analyzes the viewer's gaze data and dynamically sets an advertising area compatible with multiple devices. This makes it possible to place ads that are more likely to attract the viewer's attention. Furthermore, the generation AI integrates viewing data from different devices and analyzes the gaze data regardless of the device the viewer is viewing on. Setting the optimal advertising area makes it possible to place ads that are more likely to attract the viewer's attention. This makes it possible to integrate viewing data from different devices and place ads compatible with multiple devices.

[0056] The image analysis unit can analyze scenes in an image and automatically generate background information and related historical data for the scene. For example, the generation AI analyzes a scene in an image and automatically generates background information for that scene. For example, it provides information about historical buildings and scenery. The generation AI also analyzes a scene in an image and automatically generates related historical data. For example, it provides information related to a specific era or culture. The generation AI also analyzes a scene in an image and automatically generates background information and related historical data for that scene. For example, it provides information about the people and places that appear in the scene. This makes it possible to automatically generate background information and related historical data for a scene.

[0057] The image analysis unit can analyze the movement of objects in an image and generate meta information based on that movement. For example, the generation AI analyzes the movement of objects in an image and generates meta information based on that movement. For example, sports play analysis and motion analysis are performed. The generation AI can also analyze the movement of objects in an image and generate meta information based on that movement. For example, dance or performance motion analysis is performed. The generation AI can also analyze the movement of objects in an image and generate meta information based on that movement. For example, sports play analysis and motion analysis are performed. This makes it possible to generate meta information based on movement.

[0058] The image analysis unit can integrate image data from different camera angles and generate meta information from multiple viewpoints. For example, the generation AI can integrate image data from different camera angles and generate meta information from multiple viewpoints. For example, it can provide analytical information from multiple camera angles of a sporting event. Furthermore, the generation AI can generate meta information from multiple viewpoints based on image data from different camera angles. For example, it can analyze scenes in movies and dramas. Furthermore, the generation AI can integrate image data from different camera angles and generate meta information from multiple viewpoints. For example, it can provide analytical information from multiple camera angles of a sporting event. This makes it possible to generate meta information from multiple viewpoints.

[0059] The product information acquisition unit can identify products in videos and automatically collect and analyze past reviews and ratings of those products. For example, the generation AI can identify products in videos and automatically collect and analyze past reviews and ratings of those products. For example, it can collect data from online review sites and social media. The generation AI can also analyze products in videos and automatically collect and analyze past reviews and ratings of those products. For example, it can provide product information based on user ratings and comments. The generation AI can also identify products in videos and automatically collect and analyze past reviews and ratings of those products. For example, it can collect data from online review sites and social media. This makes it possible to automatically collect and analyze past reviews and ratings of products.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The advertising area setting unit uses the generation AI to set the advertising area. For example, the generation AI analyzes viewer gaze data and identifies the most visually effective advertising area. Step 2: The image analysis unit analyzes the image based on the advertising area set by the advertising area setting unit. For example, the generation AI analyzes objects and scenes in the image and extracts visually important information. Step 3: The meta information generator generates meta information based on the image analyzed by the image analyzer. For example, the generation AI generates meta information related to objects and scenes in the image. Step 4: The product information grasping unit grasps product information based on the meta information generated by the meta information generating unit. For example, the generating AI identifies products in the video and collects and analyzes that information. Step 5: The eye-tracking device collects gaze data, for example, analyzing how viewers look at the TV screen, and provides that data to the generation AI. Step 6: The purchase path suggestion unit proposes a purchase path based on the gaze data. For example, the generation AI analyzes the viewer's gaze data and proposes the optimal purchase path.

[0062] (Example 2) The advertising area setting system according to an embodiment of the present invention is a system that uses generative AI to innovatively set advertising areas, perform image analysis, create meta information, grasp product information, provide an eye-tracking device, and occupy purchase paths. As a result, the advertising area setting system can efficiently set advertising areas, grasp product information, and occupy purchase paths.

[0063] An advertising area setting system according to an embodiment includes an advertising area setting unit, an image analysis unit, a meta information generation unit, a product information acquisition unit, an eye tracking device, and a purchase path suggestion unit. The advertising area setting unit sets an advertising area using a generation AI. For example, the generation AI analyzes viewer gaze data and identifies the most visually effective advertising area. The image analysis unit analyzes an image based on the advertising area set by the advertising area setting unit. For example, the generation AI analyzes objects and scenes in the image and extracts visually important information. The meta information generation unit generates meta information based on the image analyzed by the image analysis unit. For example, the generation AI generates meta information related to objects and scenes in the image. The product information acquisition unit acquires product information based on the meta information generated by the meta information generation unit. For example, the generation AI identifies products in a video and collects and analyzes their information. The eye tracking device collects gaze data. For example, it analyzes how viewers view a television screen and provides the data to the generation AI. The purchase path suggestion unit suggests a purchase path based on the gaze data. For example, the generation AI analyzes viewer gaze data and proposes the optimal purchase path, allowing the advertising area setting system to efficiently set advertising areas, grasp product information, and occupy purchase paths.

[0064] The advertising area setting unit combines viewer gaze data and emotion estimation functions to identify areas where viewers are most interested and place advertisements in those areas. For example, the advertising area setting unit uses a generation AI to collect viewer gaze data in real time and analyze the degree of gaze concentration. Furthermore, it uses an emotion estimation function to measure viewers' emotional responses and combines the gaze and emotion data to identify the optimal advertising area. Furthermore, based on the viewer's gaze data and emotion data, the generation AI dynamically sets areas where viewers are most interested. For example, it places advertisements in scenes where the viewer smiles or appears surprised. Furthermore, the generation AI analyzes the viewer's gaze data and emotion data to identify areas where viewers feel the most positive emotions. Placing advertisements in those areas makes it easier to attract viewers' attention. This makes it possible to place advertisements that are more likely to attract viewers' interest.

[0065] The advertising area setting unit can analyze the viewer's past viewing history and dynamically set the optimal advertising area for each individual viewer. For example, the generation AI analyzes the viewer's past viewing history and identifies content and scenes that are likely to interest the viewer. Based on that data, the advertising area setting unit dynamically sets the optimal advertising area for the viewer. Furthermore, based on the viewing history, the generation AI analyzes the viewer's preferences and interests and places advertisements in areas that the viewer is most interested in. For example, advertisements are displayed in scenes related to a particular genre or theme. Furthermore, the generation AI analyzes the viewer's viewing history and sets advertising areas based on scenes and content that the viewer has previously rated highly. This makes it possible to place advertisements that are likely to attract the viewer's attention.

[0066] The advertising area setting unit analyzes viewer gaze data in real time and can instantly display advertisements in areas where viewer gazes are concentrated. In the advertising area setting unit, for example, the generation AI collects viewer gaze data in real time and analyzes the degree of gaze concentration. By instantly displaying advertisements in areas where viewer gazes are concentrated, it becomes easier to attract viewer attention. In addition, the generation AI analyzes viewer gaze data in real time and identifies areas where gazes are concentrated. The generation AI instantly displays advertisements in those areas, effectively attracting viewer gaze. Furthermore, the generation AI analyzes viewer gaze data in real time and dynamically places advertisements in areas where gaze concentration is high. This makes it possible to effectively guide the viewer's gaze. This makes it possible to place advertisements that effectively attract the viewer's gaze.

[0067] The advertising area setting unit can simultaneously analyze the viewer's gaze data and audio data to set an advertising area that is effective for both the visual and auditory senses. For example, the generation AI simultaneously collects the viewer's gaze data and audio data to identify an advertising area that is effective for both the visual and auditory senses. For example, the generation AI analyzes audio data related to scenes that the viewer is paying attention to. Furthermore, based on the viewer's gaze data and audio data, the generation AI dynamically sets the area that the viewer is most interested in. Placing ads that are effective for both the visual and auditory senses makes it easier to attract the viewer's attention. Furthermore, the generation AI analyzes the viewer's gaze data and audio data to identify the area where the viewer feels the most positive emotions. Placing ads in that area makes it easier to attract the viewer's attention. This makes it possible to place ads that are effective for both the visual and auditory senses.

[0068] The advertising area setting unit can integrate viewing data from different devices and set an advertising area compatible with multiple devices. For example, the generation AI collects and integrates viewing data from different devices (smartphones, tablets, etc.) in the advertising area setting unit. The optimal advertising area is set regardless of the device the viewer is viewing on. Furthermore, based on the viewing data from different devices, the generation AI analyzes the viewer's gaze data and dynamically sets an advertising area compatible with multiple devices. This makes it possible to place ads that are more likely to attract the viewer's attention. Furthermore, the generation AI integrates viewing data from different devices and analyzes the gaze data regardless of the device the viewer is viewing on. Setting the optimal advertising area makes it possible to place ads that are more likely to attract the viewer's attention. This makes it possible to integrate viewing data from different devices and place ads compatible with multiple devices.

[0069] The advertising area setting unit can use the emotion estimation function to identify areas where viewers feel the most positive emotions and place advertisements in those areas. For example, the advertising area setting unit uses the generation AI to analyze viewer emotion data and identify areas where viewers feel the most positive emotions. Placing advertisements in those areas makes it easier to attract viewers' attention. Furthermore, based on the viewer emotion data, the generation AI dynamically sets areas where viewers feel the most positive emotions. For example, advertisements are placed in scenes where the viewer smiles or is surprised. Furthermore, the generation AI analyzes the viewer emotion data and identifies areas where viewers feel the most positive emotions. Placing advertisements in those areas makes it easier to attract viewers' attention. This makes it possible to place advertisements that draw out positive emotions from viewers.

[0070] The image analysis unit can analyze objects in an image and generate meta information based on the viewer's emotions using an emotion estimation function. In the image analysis unit, for example, a generation AI analyzes objects in an image and generates meta information based on the viewer's emotional data. For example, information related to objects that the viewer feels positive about is provided. The image analysis unit also analyzes objects in an image and measures the viewer's emotional response using an emotion estimation function. The generation AI uses that data to generate meta information based on the viewer's emotions. The generation AI further analyzes objects in an image and generates meta information based on the viewer's emotional data. For example, information related to objects that are likely to interest the viewer is provided. This makes it possible to generate meta information based on the viewer's emotions.

[0071] The image analysis unit can analyze a scene in an image and automatically generate background information and related historical data for the scene. For example, the image analysis unit uses a generation AI to analyze a scene in an image and automatically generate background information for that scene. For example, it provides information about historical buildings and scenery. The image analysis unit also analyzes a scene in an image and automatically generates related historical data. For example, it provides information related to a specific era or culture. The generation AI also analyzes a scene in an image and automatically generates background information and related historical data for that scene. For example, it provides information about the people and places that appear in the scene. This makes it possible to automatically generate background information and related historical data for a scene.

[0072] The image analysis unit can analyze the movement of objects in an image and generate meta information based on the movement. In the image analysis unit, for example, a generation AI analyzes the movement of objects in an image and generates meta information based on that movement. For example, sports play analysis and motion analysis are performed. In addition, the movement of objects in an image is analyzed and the generation AI generates meta information based on that movement. For example, motion analysis of dance or performance is performed. In addition, the generation AI analyzes the movement of objects in an image and generates meta information based on that movement. For example, sports play analysis and motion analysis are performed. This makes it possible to generate meta information based on movement.

[0073] The image analysis unit can simultaneously analyze objects in an image and audio data to generate meta information related to both vision and hearing. In the image analysis unit, for example, a generation AI simultaneously analyzes objects in an image and audio data to generate meta information related to both vision and hearing. For example, it provides synchronization information between video and audio. Furthermore, based on objects in an image and audio data, the generation AI generates meta information related to both vision and hearing. For example, it provides synchronization information between video and audio. Furthermore, the generation AI simultaneously analyzes objects in an image and audio data to generate meta information related to both vision and hearing. For example, it provides synchronization information between video and audio. This makes it possible to generate meta information related to both vision and hearing.

[0074] The image analysis unit can integrate image data from different camera angles and generate meta information from multiple viewpoints. In the image analysis unit, for example, the generation AI integrates image data from different camera angles and generates meta information from multiple viewpoints. For example, it provides analytical information from multiple camera angles of a sporting event. Furthermore, the generation AI generates meta information from multiple viewpoints based on image data from different camera angles. For example, it performs scene analysis of a movie or drama. Furthermore, the generation AI integrates image data from different camera angles and generates meta information from multiple viewpoints. For example, it provides analytical information from multiple camera angles of a sporting event. This makes it possible to generate meta information from multiple viewpoints.

[0075] The image analysis unit can use the emotion estimation function to generate meta information related to scenes that viewers find most interesting. In the image analysis unit, for example, the generation AI analyzes the viewer's emotion data and generates meta information related to scenes that viewers find most interesting. For example, it provides information related to scenes that viewers find positive. Furthermore, based on the viewer's emotion data, the generation AI generates meta information related to scenes that viewers find most interesting. For example, it provides information related to scenes that viewers are likely to find interesting. Furthermore, the generation AI analyzes the viewer's emotion data and generates meta information related to scenes that viewers find most interesting. For example, it provides information related to scenes that viewers find positive. This makes it possible to generate meta information related to scenes that are likely to attract viewers' interest.

[0076] The product information grasping unit can identify products in the video and provide product information based on the viewer's emotions using the emotion estimation function. In the product information grasping unit, for example, the generation AI identifies products in the video and provides product information based on the viewer's emotional data. For example, it provides information related to products that the viewer feels positive about. It also analyzes products in the video and measures the viewer's emotional response using the emotion estimation function. The generation AI uses that data to provide product information based on the viewer's emotions. Furthermore, the generation AI identifies products in the video and provides product information based on the viewer's emotional data. For example, it provides information related to products that the viewer is likely to be interested in. This makes it possible to provide product information based on the viewer's emotions.

[0077] The product information grasping unit can identify products in videos and automatically collect and analyze past reviews and ratings of those products. In the product information grasping unit, for example, the generation AI identifies products in videos and automatically collects and analyzes past reviews and ratings of those products. For example, it collects data from online review sites and social media. In addition, the product in the video is analyzed, and the generation AI automatically collects and analyzes past reviews and ratings of those products. For example, it provides product information based on user ratings and comments. Furthermore, the generation AI identifies products in videos and automatically collects and analyzes past reviews and ratings of those products. For example, it collects data from online review sites and social media. This makes it possible to automatically collect and analyze past reviews and ratings of products.

[0078] The product information grasping unit can identify products in the video and automatically generate tutorial videos on how to use the product and related products. In the product information grasping unit, for example, a generation AI identifies products in the video and automatically generates tutorial videos on how to use the product and related products. For example, a video explaining how to use and maintain the product is provided. In addition, the generation AI analyzes the products in the video and automatically generates tutorial videos on how to use the product and related products. For example, a video explaining how to use and maintain the product is provided. In addition, the generation AI identifies products in the video and automatically generates tutorial videos on how to use the product and related products. For example, a video explaining how to use and maintain the product is provided. This makes it possible to automatically generate tutorial videos on how to use the product and related products.

[0079] The product information grasping unit can simultaneously analyze audio data related to products in a video and provide product information related to both visual and auditory senses. For example, the generation AI can simultaneously analyze audio data related to products in a video and provide product information related to both visual and auditory senses. For example, it can analyze audio descriptions of products and audio reviews. Furthermore, based on the product and audio data in the video, the generation AI can provide product information related to both visual and auditory senses. For example, it can analyze audio descriptions of products and audio reviews. Furthermore, the generation AI can simultaneously analyze audio data related to products in a video and provide product information related to both visual and auditory senses. For example, it can analyze audio descriptions of products and audio reviews. This makes it possible to provide product information related to both visual and auditory senses.

[0080] The product information grasping unit can integrate product information across different programs and episodes to provide consistent product information to viewers. In the product information grasping unit, for example, the generation AI integrates product information across different programs and episodes to provide consistent product information to viewers. For example, if the same product is introduced in multiple programs, that information is integrated. Furthermore, the generation AI provides consistent product information to viewers based on product information across different programs and episodes. For example, if the same product is introduced in multiple programs, that information is integrated. Furthermore, the generation AI integrates product information across different programs and episodes to provide consistent product information to viewers. For example, if the same product is introduced in multiple programs, that information is integrated. This makes it possible to provide consistent product information across different programs and episodes.

[0081] The product information grasping unit can use the emotion estimation function to provide product information that viewers are most interested in in real time. In the product information grasping unit, for example, the generation AI analyzes the viewer's emotion data and provides product information that viewers are most interested in in real time. For example, it provides information related to products that viewers feel positive about. Furthermore, based on the viewer's emotion data, the generation AI provides product information that viewers are most interested in in real time. For example, it provides information related to products that viewers are likely to be interested in. Furthermore, the generation AI analyzes the viewer's emotion data and provides product information that viewers are most interested in in real time. For example, it provides information related to products that viewers feel positive about. This makes it possible to provide product information that is likely to attract viewers' interest in real time.

[0082] The eye-tracking device can provide the collected gaze data to the generation AI and analyze the viewer's gaze patterns. For example, the eye-tracking device can provide the collected gaze data to the generation AI and analyze the viewer's gaze patterns. For example, it can identify which part the viewer is focusing on. Furthermore, the generation AI can analyze the viewer's gaze patterns based on the gaze data. For example, it can identify which part the viewer is focusing on. Furthermore, the eye-tracking device can provide the collected gaze data to the generation AI and analyze the viewer's gaze patterns. For example, it can identify which part the viewer is focusing on. This makes it possible to analyze the viewer's gaze patterns.

[0083] An eye-tracking device can visualize the gaze movements of viewers in real time using collected gaze data. An eye-tracking device, for example, uses collected gaze data to visualize the gaze movements of viewers in real time. For example, it displays which parts the viewers are focusing on in a graph or heat map. Furthermore, based on the gaze data, the gaze movements of viewers can be visualized in real time. For example, it displays which parts the viewers are focusing on in a graph or heat map. Furthermore, an eye-tracking device uses collected gaze data to visualize the gaze movements of viewers in real time. For example, it displays which parts the viewers are focusing on in a graph or heat map. This makes it possible to visualize the gaze movements of viewers in real time.

[0084] The eye tracking device can use the collected gaze data to provide interactive advertisements based on the viewer's gaze movements. The eye tracking device, for example, uses the collected gaze data to provide interactive advertisements based on the viewer's gaze movements. For example, an advertisement is displayed in a portion where the viewer is paying attention. Furthermore, the eye tracking device can use the collected gaze data to provide interactive advertisements based on the viewer's gaze movements. For example, an advertisement is displayed in a portion where the viewer is paying attention. Furthermore, the eye tracking device can use the collected gaze data to provide interactive advertisements based on the viewer's gaze movements. For example, an advertisement is displayed in a portion where the viewer is paying attention. This makes it possible to provide interactive advertisements based on the viewer's gaze movements.

[0085] The eye tracking device can be made compatible with different devices and collect gaze data across multiple devices. The eye tracking device can be made compatible with different devices (smartphones, tablets, etc.) and collect gaze data across multiple devices. For example, gaze data can be collected regardless of which device the viewer is using to watch. Furthermore, the eye tracking device can collect gaze data of the viewer based on gaze data from different devices. For example, gaze data can be collected regardless of which device the viewer is using to watch. Furthermore, the eye tracking device can be made compatible with different devices (smartphones, tablets, etc.) and collect gaze data across multiple devices. For example, gaze data can be collected regardless of which device the viewer is using to watch. This makes it possible to collect gaze data across multiple devices.

[0086] An eye-tracking device can simultaneously collect viewer gaze data and audio data to provide data related to both vision and hearing. For example, an eye-tracking device can simultaneously collect viewer gaze data and audio data to provide data related to both vision and hearing. For example, audio data related to a portion on which the viewer is focusing is collected. Furthermore, based on the gaze data and audio data, the eye-tracking device can provide data related to both vision and hearing. For example, audio data related to a portion on which the viewer is focusing is collected. Furthermore, an eye-tracking device can simultaneously collect viewer gaze data and audio data to provide data related to both vision and hearing. For example, audio data related to a portion on which the viewer is focusing is collected. This makes it possible to provide data related to both vision and hearing.

[0087] An eye-tracking device can analyze viewers' emotional responses using an emotion estimation function based on collected gaze data, and use the data to set up advertising areas and grasp product information. For example, an eye-tracking device can analyze viewers' emotional responses using an emotion estimation function based on collected gaze data. For example, it can calculate an emotion score for the part the viewer is paying attention to. Furthermore, a generation AI can analyze viewers' emotional responses based on gaze data and emotion data, and use the data to set up advertising areas. For example, it can place advertisements in areas where viewers feel positive emotions. Furthermore, an eye-tracking device can analyze viewers' emotional responses using an emotion estimation function based on collected gaze data, and use the data to grasp product information. For example, it can provide information related to products that are likely to interest viewers. This makes it possible to analyze viewers' emotional responses and use the data to set up advertising areas and grasp product information.

[0088] The purchase path suggestion unit can use gaze data and emotion estimation functions to suggest a purchase path that will evoke the most positive emotions in the viewer. For example, the generation AI analyzes the viewer's gaze data and emotion data to suggest a purchase path that will evoke the most positive emotions in the viewer. For example, it provides purchase links for products that are likely to interest the viewer. Furthermore, based on the gaze data and emotion data, the generation AI dynamically sets a purchase path that will evoke the most positive emotions in the viewer. For example, it places purchase links in scenes where the viewer smiles or is surprised. Furthermore, the generation AI analyzes the viewer's gaze data and emotion data to suggest a purchase path that will evoke the most positive emotions in the viewer. For example, it provides purchase links for products that are likely to interest the viewer. This makes it possible to suggest a purchase path that will evoke the most positive emotions in the viewer.

[0089] The purchase path suggestion unit can analyze the viewer's past purchase history and dynamically set the optimal purchase path for each viewer. In the purchase path suggestion unit, for example, the generation AI analyzes the viewer's past purchase history and dynamically sets the optimal purchase path for products that the viewer is likely to be interested in. For example, it suggests products related to products that the viewer has previously purchased. Furthermore, based on the purchase history, the generation AI analyzes the viewer's preferences and interests and provides purchase links for products that the viewer is most interested in. For example, it suggests products related to a specific genre or theme. Furthermore, the generation AI analyzes the viewer's purchase history and sets a purchase path based on products that the viewer has given high ratings in the past. This provides purchase links that are likely to attract the viewer's interest. This makes it possible to dynamically set the optimal purchase path based on the viewer's past purchase history.

[0090] The purchase path suggestion unit can analyze viewer gaze data in real time and instantly provide purchase links for products that the viewer is interested in. For example, the generation AI in the purchase path suggestion unit collects viewer gaze data in real time and instantly provides purchase links for products that the viewer is interested in. For example, it displays purchase links for products that the viewer is paying attention to. It also analyzes viewer gaze data in real time and instantly provides purchase links for products that the viewer is interested in. This allows viewers to purchase products smoothly. It also analyzes viewer gaze data in real time and instantly provides purchase links for products that the viewer is interested in. For example, it displays purchase links for products that the viewer is paying attention to. This makes it possible to instantly provide purchase links for products that the viewer is interested in.

[0091] The purchase lead suggestion unit can simultaneously analyze the viewer's gaze data and voice data to propose purchase leads related to both vision and hearing. For example, the generation AI simultaneously collects the viewer's gaze data and voice data to propose purchase leads related to both vision and hearing. For example, it analyzes voice data related to products that the viewer is interested in. Furthermore, based on the gaze data and voice data, the generation AI provides purchase links for products that the viewer is most interested in. For example, it analyzes voice data related to products that the viewer is interested in. Furthermore, the generation AI simultaneously analyzes the viewer's gaze data and voice data to propose purchase leads related to both vision and hearing. For example, it analyzes voice data related to products that the viewer is interested in. This makes it possible to propose purchase leads related to both vision and hearing.

[0092] The purchase path proposal unit can integrate viewing data from different devices and propose a purchase path that is compatible with multiple devices. In the purchase path proposal unit, for example, the generation AI collects and integrates viewing data from different devices (smartphones, tablets, etc.). It proposes the optimal purchase path regardless of the device the viewer is watching on. Furthermore, based on the viewing data from different devices, the generation AI analyzes the viewer's gaze data and dynamically sets a purchase path that is compatible with multiple devices. This provides a purchase link that is likely to attract the viewer's attention. Furthermore, the generation AI integrates viewing data from different devices and analyzes gaze data regardless of the device the viewer is watching on. By proposing the optimal purchase path, it provides a purchase link that is likely to attract the viewer's attention. This makes it possible to propose a purchase path that is compatible with multiple devices.

[0093] The purchase path suggestion unit can use the emotion estimation function to suggest emotion-based purchase paths for products in which the viewer is most interested. In the purchase path suggestion unit, for example, the generation AI analyzes the viewer's emotion data and suggests emotion-based purchase paths for products in which the viewer is most interested. For example, it provides purchase links for products for which the viewer has positive emotions. Furthermore, based on the viewer's emotion data, the generation AI dynamically sets emotion-based purchase paths for products in which the viewer is most interested. For example, it places purchase links in scenes in which the viewer smiles or is surprised. Furthermore, the generation AI analyzes the viewer's emotion data and suggests emotion-based purchase paths for products in which the viewer is most interested. For example, it provides purchase links for products that are likely to interest the viewer. This makes it possible to suggest emotion-based purchase paths for products in which the viewer is most interested.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The advertising area setting unit can simultaneously analyze the viewer's gaze data and audio data to set an advertising area that is effective for both the visual and auditory senses. For example, the generation AI simultaneously collects the viewer's gaze data and audio data to identify an advertising area that is effective for both the visual and auditory senses. By analyzing audio data related to scenes that the viewer is paying attention to, it is possible to place ads that are effective for both the visual and auditory senses. Furthermore, based on the viewer's gaze data and audio data, the generation AI dynamically sets the area that the viewer is most interested in. Placing ads that are effective for both the visual and auditory senses makes it easier to attract the viewer's attention. Furthermore, the generation AI analyzes the viewer's gaze data and audio data to identify the area where the viewer feels the most positive emotions. Placing ads in that area makes it easier to attract the viewer's attention. This makes it possible to place ads that are effective for both the visual and auditory senses.

[0096] The advertising area setting unit can analyze a viewer's past viewing history and dynamically set the optimal advertising area for each individual viewer. For example, the generation AI analyzes a viewer's past viewing history and identifies content and scenes that are likely to interest the viewer. Based on that data, the optimal advertising area for the viewer is dynamically set. Furthermore, based on the viewing history, the generation AI analyzes the viewer's preferences and interests and places advertisements in areas that the viewer is most interested in. For example, advertisements are displayed in scenes related to a particular genre or theme. Furthermore, the generation AI analyzes the viewer's viewing history and sets advertising areas based on scenes and content that the viewer has previously rated highly. This makes it possible to place advertisements that are more likely to attract the viewer's attention.

[0097] The advertising area setting unit analyzes viewer gaze data in real time and can instantly display advertisements in areas where viewer gazes are concentrated. For example, the generation AI collects viewer gaze data in real time and analyzes the degree of gaze concentration. By instantly displaying advertisements in areas where viewer gazes are concentrated, it becomes easier to attract viewer attention. In addition, the generation AI analyzes viewer gaze data in real time and identifies areas where gazes are concentrated. The generation AI instantly displays advertisements in those areas, effectively attracting viewer gaze. Furthermore, the generation AI analyzes viewer gaze data in real time and dynamically places advertisements in areas where gaze concentration is high. This allows for effective guidance of viewer gaze. This makes it possible to place advertisements that effectively attract viewer gaze.

[0098] The advertising area setting unit can integrate viewing data from different devices and set an advertising area compatible with multiple devices. For example, the generation AI collects and integrates viewing data from different devices (smartphones, tablets, etc.). The optimal advertising area is set regardless of the device the viewer is viewing on. Furthermore, based on the viewing data from different devices, the generation AI analyzes the viewer's gaze data and dynamically sets an advertising area compatible with multiple devices. This makes it possible to place ads that are more likely to attract the viewer's attention. Furthermore, the generation AI integrates viewing data from different devices and analyzes the gaze data regardless of the device the viewer is viewing on. Setting the optimal advertising area makes it possible to place ads that are more likely to attract the viewer's attention. This makes it possible to integrate viewing data from different devices and place ads compatible with multiple devices.

[0099] The advertising area setting unit can use the emotion estimation function to identify areas where viewers feel the most positive emotions and place advertisements in those areas. For example, the generation AI analyzes the viewer's emotional data and identifies areas where viewers feel the most positive emotions. Placing advertisements in those areas makes it easier to attract the viewer's attention. Furthermore, based on the viewer's emotional data, the generation AI dynamically sets areas where viewers feel the most positive emotions. For example, advertisements are placed in scenes where the viewer smiles or is surprised. Furthermore, the generation AI analyzes the viewer's emotional data and identifies areas where viewers feel the most positive emotions. Placing advertisements in those areas makes it easier to attract the viewer's attention. This makes it possible to place advertisements that draw out positive emotions in viewers.

[0100] The image analysis unit can analyze scenes in an image and automatically generate background information and related historical data for the scene. For example, the generation AI analyzes a scene in an image and automatically generates background information for that scene. For example, it provides information about historical buildings and scenery. The generation AI also analyzes a scene in an image and automatically generates related historical data. For example, it provides information related to a specific era or culture. The generation AI also analyzes a scene in an image and automatically generates background information and related historical data for that scene. For example, it provides information about the people and places that appear in the scene. This makes it possible to automatically generate background information and related historical data for a scene.

[0101] The image analysis unit can analyze the movement of objects in an image and generate meta information based on that movement. For example, the generation AI analyzes the movement of objects in an image and generates meta information based on that movement. For example, sports play analysis and motion analysis are performed. The generation AI can also analyze the movement of objects in an image and generate meta information based on that movement. For example, dance or performance motion analysis is performed. The generation AI can also analyze the movement of objects in an image and generate meta information based on that movement. For example, sports play analysis and motion analysis are performed. This makes it possible to generate meta information based on movement.

[0102] The image analysis unit can integrate image data from different camera angles and generate meta information from multiple viewpoints. For example, the generation AI can integrate image data from different camera angles and generate meta information from multiple viewpoints. For example, it can provide analytical information from multiple camera angles of a sporting event. Furthermore, the generation AI can generate meta information from multiple viewpoints based on image data from different camera angles. For example, it can analyze scenes in movies and dramas. Furthermore, the generation AI can integrate image data from different camera angles and generate meta information from multiple viewpoints. For example, it can provide analytical information from multiple camera angles of a sporting event. This makes it possible to generate meta information from multiple viewpoints.

[0103] The image analysis unit can use the emotion estimation function to generate meta information related to scenes that viewers find most interesting. For example, the generation AI analyzes the viewer's emotion data and generates meta information related to scenes that viewers find most interesting. For example, it provides information related to scenes that viewers find positive. Furthermore, based on the viewer's emotion data, the generation AI generates meta information related to scenes that viewers find most interesting. For example, it provides information related to scenes that viewers are likely to find interesting. Furthermore, the generation AI analyzes the viewer's emotion data and generates meta information related to scenes that viewers find most interesting. For example, it provides information related to scenes that viewers find positive. This makes it possible to generate meta information related to scenes that are likely to attract viewers' interest.

[0104] The product information acquisition unit can identify products in videos and automatically collect and analyze past reviews and ratings of those products. For example, the generation AI can identify products in videos and automatically collect and analyze past reviews and ratings of those products. For example, it can collect data from online review sites and social media. The generation AI can also analyze products in videos and automatically collect and analyze past reviews and ratings of those products. For example, it can provide product information based on user ratings and comments. The generation AI can also identify products in videos and automatically collect and analyze past reviews and ratings of those products. For example, it can collect data from online review sites and social media. This makes it possible to automatically collect and analyze past reviews and ratings of products.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The advertising area setting unit uses the generation AI to set the advertising area. For example, the generation AI analyzes viewer gaze data and identifies the most visually effective advertising area. Step 2: The image analysis unit analyzes the image based on the advertising area set by the advertising area setting unit. For example, the generation AI analyzes objects and scenes in the image and extracts visually important information. Step 3: The meta information generator generates meta information based on the image analyzed by the image analyzer. For example, the generation AI generates meta information related to objects and scenes in the image. Step 4: The product information grasping unit grasps product information based on the meta information generated by the meta information generating unit. For example, the generating AI identifies products in the video and collects and analyzes that information. Step 5: The eye-tracking device collects gaze data, for example, analyzing how viewers look at the TV screen, and provides that data to the generation AI. Step 6: The purchase path suggestion unit proposes a purchase path based on the gaze data. For example, the generation AI analyzes the viewer's gaze data and proposes the optimal purchase path.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[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 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.

[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 (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).

[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] 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.

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

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0122] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0124] The data processing system 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.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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]

[0174] 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 advertising area setting unit that sets an advertising area using a generation AI; an image analysis unit that analyzes an image based on the advertising area set by the advertising area setting unit; a meta information generating unit that generates meta information based on the image analyzed by the image analyzing unit; a product information grasping unit that grasps product information based on the meta information generated by the meta information generating unit; An eye tracking device that collects gaze data; a purchase lead suggestion unit that suggests a purchase lead based on the line of sight data. A system characterized by:

2. The advertising area setting unit By combining viewer gaze data and emotion estimation functions, the system identifies areas where the viewer is most interested and places advertisements in those areas.

2. The system of claim 1.

3. The advertising area setting unit Analyzes viewers' past viewing history and dynamically sets the optimal advertising area for each individual viewer 2. The system of claim 1.

4. The advertising area setting unit Analyzes viewer gaze data in real time and instantly displays advertisements in areas where the viewer's gaze is concentrated 2. The system of claim 1.

5. The advertising area setting unit Simultaneously analyzes viewer gaze data and audio data to set advertising areas that are effective for both visual and auditory perception.

2. The system of claim 1.

6. The advertising area setting unit Integrate viewing data from different devices and set advertising areas compatible with multiple devices 2. The system of claim 1.

7. The advertising area setting unit Identify areas where your audience has the most positive feelings and place your ads in those areas 2. The system of claim 1.

8. The image analysis unit Analyzes objects in images and generates meta information based on viewer emotions 2. The system of claim 1.

Images