System

Abstract

A system is provided.SOLUTION: A system comprising: means for streaming video content; means for a user to tap a specific location on a device; means for transmitting the tapped location information and image data of the corresponding frame to a server; means for recognizing a subject using the received image data and location information; means for extracting feature information of the subject; means for searching a product database based on the extracted feature information; means for returning relevant product information to the user device; and means for displaying the returned product information.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] In recent years, viewing video content has become commonplace, and many viewers are increasingly interested in the items they see in videos. However, it takes a lot of time and effort for viewers to find detailed information about those items. Even when they find an item that interests them, they often don't know where to get it or how to purchase it. This situation not only detracts from the viewing experience, but also causes e-commerce sites to miss opportunities to increase sales. Therefore, it is necessary to provide a system that allows viewers to easily obtain detailed information about items they are interested in while watching a video and then purchase them directly. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for streaming video content, a means for a user to tap a specific location on a device, a means for transmitting the tapped location information and image data of the corresponding frame to a server, a means for recognizing a subject using the received image data and location information, a means for extracting feature information about the subject, a means for searching a product database based on the extracted feature information, a means for returning related product information to the user's device, and a means for displaying the returned product information. This allows viewers to instantly obtain detailed information about items that interest them while watching a video and be directed to an e-commerce site. The system also supports an intuitive and efficient product purchasing process for viewers by transmitting and receiving user authentication information and by including product names, descriptions, prices, stock availability, and sales URLs in the returned product information.

[0006] "Video content" refers to video data that provides information and entertainment to users visually and audibly.

[0007] "Streaming" is a technology that transmits data continuously over the Internet and makes it available for viewing in real time or on demand.

[0008] A "device" is an electronic device for viewing and operating video content, and specifically includes smartphones, tablets, and personal computers.

[0009] "Tapping" refers to a user lightly touching a device's touchscreen with a finger to perform a selection operation.

[0010] "Location information" is information that indicates the coordinate data of a tap on the touch screen of a device.

[0011] "Image data" is digital data captured as a still image of a specific frame of video.

[0012] A "server" is a computer system for receiving, analyzing, and processing data sent from devices.

[0013] A "subject" is an object that a user taps on, such as an object or person that appears in the video.

[0014] An "AI model" is an algorithm or machine learning model that uses artificial intelligence technology to analyze and recognize data.

[0015] "Feature information" is digital data that represents the attributes of a subject, and specifically includes information such as shape, color, and pattern.

[0016] A "product database" is a digital database that systematically stores product information.

[0017] "Product information" is data showing details of a specific product, including the product name, description, price, stock status, sales URL, and the like.

[0018] "User device" refers to a device owned by a user for viewing and operating video content.

[0019] "Related" means matching or similar to the subject recognized by the viewer's tap.

[0020] "Return" refers to sending data again from the server to the user device.

[0021] "Displaying" refers to visually presenting information on the screen of a user device.

[0022] "User authentication information" is digital data used to identify a user, and may include a user ID and password. [Brief explanation of the drawings]

[0023] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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, a 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), and an APU (Accelerated Processing Unit).

[0027] 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.

[0028] 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.

[0029] 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), Bluetooth (registered trademark), etc.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0035] 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.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] This system allows viewers to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented mainly through three entities: a server, a device, and a user.

[0045] System Configuration

[0046] server

[0047] The server streams the video content and uses an AI model to recognize the subject based on user input. The server then searches a product database based on this information, retrieves the relevant product information, and returns it.

[0048] Terminal

[0049] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal has the function of displaying video content, acquiring user tap information, and sending this information to the server. It also displays product information returned from the server.

[0050] User

[0051] Users watch video content and tap on items they are interested in. They obtain product information through their device and purchase the products as needed.

[0052] Program processing flow

[0053] 1. Video Streaming and Display

[0054] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0055] The terminal decodes the received stream data and displays it as a video to the user.

[0056] 2. The user taps on an item

[0057] The user becomes interested in a particular item in the video content and taps on that item.

[0058] 3. Acquiring and sending tap information

[0059] The device acquires the coordinate data of the location where the user tapped, and also captures a video frame at the moment of the tap as a screenshot.

[0060] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[0061] 4. Subject Recognition

[0062] The server inputs the received information into an AI model that recognizes the subject based on the tap location, and uses an object detection algorithm to find the nearest object to a specific coordinate.

[0063] The server extracts characteristic information (shape, color, material pattern, etc.) of the recognized subject.

[0064] 5. Searching for related information

[0065] The server searches the product database based on the extracted feature information and uses an image similarity search algorithm to identify relevant or similar products.

[0066] The server obtains detailed information about the relevant product (product name, description, price, stock status, sales URL, etc.).

[0067] 6. Return and Display of Information

[0068] The server returns the acquired product information to the terminal.

[0069] The terminal displays the returned information on the user's screen and presents related product information using a pop-up or overlay window.

[0070] Specific examples

[0071] A user sees a scene in a movie where the main character is wearing a special jacket, and becomes interested in the jacket. The user taps the location of the jacket on the device screen. The device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server.

[0072] The server inputs the received data into an AI model to extract the jacket's characteristics (design, color, material, etc.), which are then used to search a product database to retrieve product information for similar jackets.

[0073] The acquired information is sent back from the server to the device, which then displays it to the user. The user can check the information presented, and if they like it, they can click on the displayed link to go to the e-commerce site and purchase the jacket on the spot.

[0074] In this way, MediaTouch can enhance viewers' video content viewing experience and increase sales on e-commerce sites.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0078] Step 2:

[0079] The terminal decodes the received stream data and displays it as a video to the user.

[0080] Step 3:

[0081] The user finds an item of interest within the video content and taps on it.

[0082] Step 4:

[0083] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[0084] Step 5:

[0085] The device will capture a video frame at the moment of tapping as a screenshot.

[0086] Step 6:

[0087] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[0088] Step 7:

[0089] The server inputs the received image data and location information into the AI ​​model.

[0090] Step 8:

[0091] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[0092] Step 9:

[0093] The server searches the product database based on the extracted feature information.

[0094] Step 10:

[0095] The server obtains information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.).

[0096] Step 11:

[0097] The server returns the acquired product information to the terminal.

[0098] Step 12:

[0099] The terminal displays the returned product information on the user's screen using a pop-up or overlay window.

[0100] Step 13:

[0101] The user checks the displayed product information and, if interested, taps the sales URL.

[0102] Step 14:

[0103] The device will open the link in a browser and redirect the user to the corresponding product page on the e-commerce site.

[0104] Step 15:

[0105] Users check out the products on the e-commerce site and, if they like them, proceed with the purchase.

[0106] In this way, through a series of processing flows, the user can easily obtain detailed information about an item that interests them while watching the video, and can immediately purchase it.

[0107] Example 1

[0108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0109] In recent years, there has been an increasing demand for systems that allow users to instantly obtain information about and purchase items of interest while watching video content. However, current systems have difficulty accurately and quickly recognizing items in a video and presenting appropriate product information. In particular, even when a user taps on a specific item in a video, accurate subject recognition and smooth acquisition of related information are often not possible. The present invention aims to solve these problems and provide a more user-friendly system.

[0110] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0111] In this invention, the server includes means for streaming video content, means for a user to tap a specific position on a terminal device, means for transmitting the tapped position information and image data of the corresponding frame to the server, means for utilizing a generative AI model that recognizes the subject using the received image data and position information, means for extracting feature information of the subject, means for searching a database based on the extracted feature information, means for searching related information using a similarity algorithm, means for returning related product information to the user terminal, and means for displaying the returned product information. This allows a user to easily tap an item of interest while watching video content to obtain detailed information and quickly purchase it.

[0112] "Video Content" refers to viewable media files such as movies, television programs, advertisements, and live streams.

[0113] "Streaming" refers to a technology that transmits and receives data in real time over the Internet, allowing users to use the data immediately.

[0114] "User" refers to an individual or group that utilizes the system to view video content and obtain information about a particular item.

[0115] "Terminal device" refers to an electronic device, such as a smartphone, tablet, or PC, that allows a user to view and operate video content.

[0116] "Tap" refers to the act of lightly pressing a specific position on the touchscreen of a terminal device using a finger or a stylus.

[0117] "Location information" refers to coordinate data (X and Y coordinates) when a user taps on a terminal device.

[0118] "Image data" refers to still image information for a particular video frame.

[0119] A "server" is a centralized computing system that processes data and provides resources on a network.

[0120] "Generative AI model" refers to a machine learning model that uses artificial intelligence techniques and is trained to perform a specific task.

[0121] "Subject" refers to a specific object or person present in a video or image.

[0122] "Feature information" refers to identifiable attributes of a subject, such as its shape, color, and material pattern.

[0123] "Database" refers to a data management system for storing data in a systematic and structured manner.

[0124] A "similarity algorithm" refers to a calculation method for calculating the similarity of given data and searching for the most similar data.

[0125] "Product information" refers to detailed information about a product, such as the product name, description, price, stock status, and sales URL.

[0126] "Return" refers to the operation of sending data back from the server to the user terminal.

[0127] "Display" refers to the visual presentation of data or information on the screen of a user terminal.

[0128] This system allows viewers to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented mainly through three entities: a server, a device, and a user.

[0129] Server Processing

[0130] The server encodes the video content and continuously sends it to the device as streaming data. FFmpeg and other encoding tools are used. The server receives the user's tap position information, screenshots, and user authentication information sent from the device. The received data is input into a generative AI model, which recognizes the subject based on the tap position. Feature information of the recognized subject is extracted and used to search a product database. Related information is searched using a similarity algorithm to obtain detailed information about the relevant product. This information (product name, description, price, stock status, sales URL, etc.) is then sent back to the device.

[0131] Terminal handling

[0132] The device receives the streaming data from the server, decodes it, and displays it as a video to the user. When the user taps on a specific item in the video content, the device acquires the coordinate data of the tapped position and a screenshot of the corresponding frame. The tap position information, screenshot, and user authentication information are sent to the server. The product information returned from the server is displayed on the device, presenting relevant product information to the user. The display format is a pop-up or overlay window.

[0133] User operations

[0134] Users can watch video content on their devices and tap on items that interest them. By tapping on an item that interests them, detailed information about that item is immediately displayed. Users can then purchase the product based on the information presented.

[0135] Specific examples

[0136] For example, if a user sees a scene in a movie where the main character is wearing a special jacket, they become interested in the jacket and tap the location of the jacket on their device screen. The device then sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server. The server then inputs the received data into a generative AI model to extract the jacket's characteristics (design, color, material, etc.). These characteristics are then used to search a product database to obtain information on similar jackets. The server then sends the obtained information back to the device, which then displays it to the user. The user can review the information presented, and if they like it, they can click on a link to go to an e-commerce site and purchase the jacket on the spot. In this way, MediaTouch can enhance the viewer's video content viewing experience and increase e-commerce site sales.

[0137] Example prompts using generative AI models

[0138] An example of a prompt to input to a generative AI model might be, "Please extract the features of the item the user taps on in the video and provide detailed information about that item."

[0139] As described above, this system allows users to instantly obtain detailed information about items that interest them while watching video content, enabling them to quickly purchase them. This is expected to improve the viewing experience and lead to commercial success.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] Video Streaming and Display

[0143] The server encodes the video content and sends it continuously to the terminal as streaming data. The software used is an encoding tool such as FFmpeg. The input data is an unencoded video file, and the output data is streaming data that the terminal can receive. The terminal decodes the streaming data received from the server and displays it as a video to the user. Decoding is done using VLC player or HTML5 video tags. Specifically, the video is played on the terminal.

[0144] Step 2:

[0145] User taps on an item

[0146] The user taps on a specific item in the video content. The input data is the user's touch operation, and the output data is the coordinate information of the tapped location. The action here refers to the user tapping an object of interest on the device's touchscreen with a finger or stylus.

[0147] Step 3:

[0148] Acquiring and sending tap information

[0149] The device acquires the coordinate data of the location where the user tapped. Specifically, it captures the coordinate data using JavaScript or a touch event listener in the native app. It then captures the video frame at the moment of the tap as a screenshot. The input data is the coordinate information and the video frame, and the output data is the coordinate information and the screenshot image. This data is sent to the server. Specifically, the data is sent to the server.

[0150] Step 4:

[0151] Subject Recognition

[0152] The server inputs the received coordinate information and screenshot into a generative AI model and recognizes the subject based on the tapped location. The AI ​​model used is an object detection algorithm such as YOLO (You Only Look Once). The input data is the screenshot image and coordinate information, and the output data is the subject recognition result (such as a bounding box). Specifically, the AI ​​model identifies the subject in the screenshot.

[0153] Step 5:

[0154] Search for related information

[0155] The server extracts feature information (shape, color, material pattern, etc.) from the recognized object. The input data is the object recognition result, and the output data is a feature vector. Based on these features, a product database is searched and related product information is identified using a similarity algorithm. Specific operations include executing a database query and retrieving search results.

[0156] Step 6:

[0157] Returning and Displaying Information

[0158] The server returns the acquired product information (product name, description, price, stock status, sales URL, etc.) to the terminal. Specifically, data is sent from the server to the terminal. The terminal displays the returned information on the user's screen. Product information is presented to the user using a pop-up or overlay window. The input data is the information returned from the server, and the output data is the product information that is displayed to the user. Specifically, the product information is displayed on the screen.

[0159] Through the above steps, users can easily tap on items of interest while watching video content, obtain detailed information, and purchase the items.

[0160] (Application example 1)

[0161] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0162] Conventional systems that link video content viewing with e-commerce have difficulty instantly recognizing items that users are interested in and providing product information, resulting in a poor user experience and reduced purchasing motivation. Furthermore, there is a need for a highly accurate means of easily and quickly identifying specific products within video content.

[0163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0164] In this invention, the server includes means for detecting an object using the generated AI model, means for extracting image data of the tap area based on the location information, and means for identifying product information using an image similarity search algorithm, which enables a user to easily identify a product while watching video content and instantly obtain detailed information about the product.

[0165] "Video content" refers to visual media such as videos, movies, and animations that are distributed via the Internet in streaming format.

[0166] A "device" is an information processing device such as a smartphone, tablet, or personal computer that a user uses to view and operate video content.

[0167] A "tap" is an operation in which a user presses a specific position on the touch screen of a device with their finger, and location information is obtained by this operation.

[0168] A "server" is a computer system that distributes video content over the cloud or network and receives and processes user operation information.

[0169] "Location information" is the specific coordinate data of a location where a user taps on the device screen.

[0170] "Image data" is still image data captured from a specific frame of video content.

[0171] The "subject" is an object that exists at the position in the video content that the user taps.

[0172] A "generated AI model" is an artificial intelligence model that has been trained using machine learning algorithms to perform a specific task, such as object recognition.

[0173] The "tap area" is a partial area of ​​image data that includes the area around the position where the user tapped.

[0174] An "image similarity search algorithm" is a computational method for identifying similar images or objects based on image feature information.

[0175] The "product database" is a database that stores product information, including product name, description, price, stock status, sales URL, and other information.

[0176] "Product information" is detailed data about a specific product obtained from a product database.

[0177] "User authentication information" is information for identifying a user, and includes a login ID, password, and the like.

[0178] "Return data" is data such as product information that is sent from the server to the user's device.

[0179] This invention relates to a system that allows users to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented by three entities: a server, a terminal, and a user.

[0180] The server encodes the video content and distributes it in streaming format. It also receives the user's tap location information and image data for the corresponding frame, and uses the generated AI model to recognize the subject based on the tap location. Using an object detection algorithm, it extracts the recognized subject's characteristics (shape, color, material pattern, etc.), searches a product database based on these, identifies the corresponding product information, and sends it back to the device.

[0181] The terminal is a device held by the user (smartphone, tablet, PC, etc.) that receives and displays video content. It obtains the coordinate data of the location where the user taps and captures the video frame at the moment of tapping as a screenshot. It sends the tap information (X and Y coordinates), screenshot, and user authentication information to the server. It receives the product information returned from the server and displays it to the user.

[0182] Users can watch video content and tap on items they are interested in. This allows them to obtain product information through their device and purchase the product if necessary.

[0183] As a specific example, let's say a user sees a particular jacket in a movie scene and becomes interested in it. The user taps on the jacket on the device screen, and the device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server. The server inputs the received data into the generated AI model, extracts the jacket's features, and uses those features to search a product database. It then retrieves product information for similar jackets and sends it back to the device. The device displays this information to the user, and if the user likes it, they can click on the displayed link to go to an e-commerce site and purchase the jacket on the spot.

[0184] In this way, MediaTouch can enhance viewers' video content viewing experience and increase sales on e-commerce sites.

[0185] An example prompt is:

[0186] "Please implement a system that recognizes the item that the user taps on in the video frame and provides detailed information about the product.

[0187] Input the image data of the tapped coordinates into the AI ​​model

[0188] Perform product recognition and obtain relevant product information

[0189] Display detailed product information (product name, description, price, link, etc.)

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] The server encodes the video content and delivers it to the terminal in streaming format. The terminal decodes the received stream data and displays it in a format that the user can view. The input is the video content data, and the output is the terminal screen displaying the video. Specifically, the operation involves receiving the video data, decoding it, and displaying it on the screen.

[0193] Step 2:

[0194] The user watches video content and taps on an item that interests them. The device detects the user's tapping and obtains the coordinate data of the tapped location. The input is the user's tapping operation, and the output is coordinate data (X and Y coordinates). Specifically, the device detects the tapped location on the touch screen and stores the coordinates in memory.

[0195] Step 3:

[0196] The device captures the video frame at the moment of tapping as a screenshot. The input is the current frame of the video content, and the output is the screenshot image data. Specifically, the device generates a still image of the currently displayed frame, saves it, and uses it for subsequent processing.

[0197] Step 4:

[0198] The device sends tap information (X and Y coordinates), screenshot, and user authentication information to the server. The input is coordinate data, screenshot image, and user authentication information, and the output is a notification to the server that data transmission is complete. Specifically, the device generates a data packet and sends it to the server via the network.

[0199] Step 5:

[0200] The server inputs the received tap information and screenshot into the generated AI model and recognizes the subject based on the tap location. The input is the tap information and screenshot image, and the output is the subject's feature information. Specifically, the server uses an image processing algorithm to analyze the area around the tap location and identify the object.

[0201] Step 6:

[0202] The server extracts the subject's feature information and uses it to search the product database. The input is the subject's feature information, and the output is the corresponding product information. Specifically, the server executes a database query using the feature information as keywords to retrieve highly relevant product records.

[0203] Step 7:

[0204] The server returns the acquired product information to the terminal. The input is the product information, and the output is a notification of completion of data transmission to the terminal. Specifically, the product information is compiled into a data packet and sent to the terminal.

[0205] Step 8:

[0206] The terminal receives the product information returned from the server and displays it to the user. The input is the returned product information, and the output is the product details displayed on the user's screen. Specifically, it analyzes the received data and displays the information using a GUI. The user can check the presented information, and if they like it, click the displayed link to go to the e-commerce site and purchase the product on the spot.

[0207] In this way, a system is provided that enhances the viewing experience of video content through a series of processing steps, and enables users to easily obtain product information and make purchases on the spot.

[0208] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0209] This system allows viewers to tap on items of interest on their device while watching video content to obtain detailed information and easily purchase them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it also provides a function to filter or highlight product information based on the user's interests and emotional state. This system is implemented primarily through three entities: a server, a device, and a user.

[0210] System Configuration

[0211] server

[0212] The server delivers video content in streaming format and uses an AI model to recognize the subject based on user input. The server then searches a product database based on this information, retrieves the relevant product information, and returns it. Furthermore, it uses an emotion engine to analyze the user's emotions and uses the results to optimize the provision of product information.

[0213] Terminal

[0214] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal has the function of displaying video content, acquiring user tap information, and sending this information to the server. It also displays product information returned from the server and analyzes user emotions in real time through an emotion engine.

[0215] User

[0216] Users watch video content and tap on items they are interested in. They obtain product information through their device and purchase the products as needed. Furthermore, the user's emotions are analyzed through the emotion engine, and they receive customized product information based on the analysis results.

[0217] Program processing flow

[0218] Video Streaming and Display

[0219] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0220] The terminal decodes the received stream data and displays it as a video to the user.

[0221] User taps on an item

[0222] The user becomes interested in a particular item in the video content and taps on that item.

[0223] Acquiring and sending tap information

[0224] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[0225] In addition, the video frame at the moment of tapping is captured as a screenshot, and the tap information and screenshot are sent to the server.

[0226] Subject recognition and feature extraction

[0227] The server inputs the received image data and location information into the AI ​​model and recognizes the subject based on the tapped location.

[0228] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[0229] Product database search and information return

[0230] The server searches the product database based on the extracted feature information.

[0231] Information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.) is obtained and returned to the terminal.

[0232] Emotion analysis using an emotion engine

[0233] The device runs an emotion engine that analyzes the user's facial expressions, voice, operation patterns, etc. in real time.

[0234] The emotion engine analyzes the user's emotional state and sends the results to the server.

[0235] Providing customized information

[0236] Based on the results of the sentiment analysis, the server filters or emphasizes product information according to the user's interests and emotions.

[0237] The customized product information is returned to the terminal and displayed on the user's screen.

[0238] Specific examples

[0239] A user sees a scene in a movie where the main character is wearing a special jacket, and becomes interested in the jacket. The user taps the location of the jacket on the device screen. The device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server.

[0240] The server inputs the received data into an AI model to extract the jacket's characteristics (design, color, material, etc.), which are then used to search a product database to retrieve product information for similar jackets.

[0241] At the same time, the emotion engine analyzes the user's facial expressions, tone of voice, and operation patterns to determine whether the user is particularly excited. Based on the results of this emotion analysis, the server returns product information that it believes will be most appealing to the user.

[0242] The acquired information is sent back from the server to the device, which then displays it to the user. The user can check the information presented, and if they like it, they can click on the displayed link to go to the e-commerce site and purchase the jacket on the spot.

[0243] In this way, the present invention can enhance the viewer's video content viewing experience and increase sales on e-commerce sites. The introduction of an emotion engine makes it possible to provide customized product information according to the user's emotional state, further improving the user experience.

[0244] The processing flow will be explained below.

[0245] Step 1:

[0246] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0247] Step 2:

[0248] The terminal decodes the received stream data and displays it as a video to the user.

[0249] Step 3:

[0250] The user finds an item of interest within the video content and taps on it.

[0251] Step 4:

[0252] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[0253] Step 5:

[0254] The device will capture a video frame at the moment of tapping as a screenshot.

[0255] Step 6:

[0256] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[0257] Step 7:

[0258] The server inputs the received data into an AI model and recognizes the subject based on the tapped location.

[0259] Step 8:

[0260] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[0261] Step 9:

[0262] The server searches the product database based on the extracted feature information.

[0263] Step 10:

[0264] The server obtains information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.).

[0265] Step 11:

[0266] The device collects the user's facial expressions, tone of voice, and operation patterns in real time using a camera and microphone.

[0267] Step 12:

[0268] The emotion engine analyzes the collected data and determines the user's emotional state (e.g., joy, surprise, excitement, etc.).

[0269] Step 13:

[0270] The server filters or emphasizes product information based on the emotional state information obtained from the emotion engine, in accordance with the emotional state of the user.

[0271] Step 14:

[0272] The server returns the customized product information to the terminal.

[0273] Step 15:

[0274] The terminal displays the returned product information on the user's screen using a pop-up or overlay window.

[0275] Step 16:

[0276] The user checks the displayed product information and, if interested, taps the sales URL.

[0277] Step 17:

[0278] The device will open the link in a browser and redirect the user to the corresponding product page on the e-commerce site.

[0279] Step 18:

[0280] Users check out the products on the e-commerce site and, if they like them, proceed with the purchase.

[0281] This series of processing flows allows users to easily obtain detailed information about items of interest while watching a video, receive product information customized according to their emotional state, and make a purchase on the spot.

[0282] Example 2

[0283] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0284] There is a demand for a system that allows users viewing video content to instantly obtain detailed information about items they are interested in and easily purchase them. However, previous systems have had problems with not being able to quickly and accurately detect items and provide information, which does not improve the user experience. Furthermore, they are unable to analyze user emotions and provide customized information, resulting in a decrease in user satisfaction. The present invention aims to solve these problems and provide a system that simultaneously improves the viewing experience and sales on e-commerce sites.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0286] In this invention, the server includes means for streaming video content, means for extracting feature information of a subject, and means for analyzing the user's emotional state and filtering or emphasizing information based on the result. This allows the user to instantly obtain detailed information about items that interest the user while watching the video content, and provides information customized based on the user's emotions.

[0287] "Video content" refers to information that includes images and audio for viewing by a user.

[0288] "Streaming" refers to the technology of receiving data continuously and playing it instantly.

[0289] "Device" refers to an electronic device that can be operated by a user, such as a computer, smartphone, or tablet.

[0290] "Tap" refers to the act of touching a specific location on a device's touchscreen with a finger or stylus.

[0291] A "server" refers to a computer system that provides services to other computers and devices over a network.

[0292] A "subject" refers to a particular object or person in the video content that is of interest to the user.

[0293] "Feature information" refers to identifiable attribute data such as the shape, color, pattern, and material of the subject.

[0294] A "database" refers to a system that systematically manages various data and allows for efficient search and retrieval.

[0295] "Emotional state" refers to the internal psychological state of the user, which is analyzed from facial expressions, voice, operation patterns, etc.

[0296] A "generative AI model" refers to a program that generates, recognizes, and analyzes various data based on machine learning or deep learning.

[0297] A "prompt sentence" refers to an input sentence that gives instructions to a generative AI model.

[0298] This system allows viewers to tap on items of interest on their device while watching video content to obtain detailed information and easily purchase them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it also provides a function to filter or highlight product information based on the user's interests and emotional state. This system is implemented primarily through three entities: a server, a device, and a user.

[0299] System Configuration

[0300] server

[0301] The server encodes the video content in real time and transmits it continuously to the device in streaming format, allowing users to watch high-quality video without interruption. The server inputs the received tap information and image data into an AI model to recognize the subject and extract its features. The AI ​​model uses a machine learning framework such as TensorFlow. The server searches a product database based on the extracted feature information to retrieve relevant product information. It then uses an emotion engine to analyze the user's emotional state and filters or emphasizes the product information based on the results. Facial expression analysis tools such as OpenCV are used for the emotion engine's analysis.

[0302] Terminal

[0303] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal decodes the stream data received from the server and displays it as an image to the user. It also has the function of obtaining tap information from the user, capturing a screenshot of the corresponding field, and sending it to the server. JavaScript is used to obtain the coordinate data of the tap position. HTML5 functions can be used to capture screenshots. Furthermore, the terminal displays the product information returned from the server, and analyzes the user's emotions in real time through an emotion engine. The emotion data is sent to the server and used to provide customized information.

[0304] User

[0305] Users watch video content and tap on items of interest on the device screen. The device then sends information about the tap location and a screenshot of the relevant frame to the server. The user then checks the customized product information returned by the server and purchases the product if necessary. The user's facial expressions, tone of voice, and operation patterns are analyzed in real time by the emotion engine.

[0306] Specific examples

[0307] For example, suppose a user is interested in the jacket worn by the main character in a movie scene. The user taps on the location of the jacket on the screen of their device (which can be a smartphone or tablet). The device sends the coordinate data of the tapped location and a screenshot of the relevant frame to the server. The server inputs the received data into an AI model and extracts features such as the jacket's design, color, and material. The feature information is then used to search a product database and retrieve product information for similar jackets.

[0308] At the same time, the device's emotion engine analyzes the user's facial expressions, tone of voice, and emotional state based on their actions. Based on the results of this analysis, the server returns product information that it believes will be most appealing to the user, highlighting it. The user can review the customized product information displayed on their device, click on the provided link to go to the e-commerce site, and purchase the jacket on the spot.

[0309] Prompt Sentence Examples

[0310] "Please explain the system that retrieves detailed information about a specific item in video content when the user taps on it, and provides customized product information based on sentiment analysis."

[0311] In this way, the present invention can enhance the viewer's video content viewing experience and increase sales on e-commerce sites. The introduction of an emotion engine makes it possible to provide customized product information according to the user's emotional state, which is expected to further improve the user experience.

[0312] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0313] Step 1: Video Streaming and Display

[0314] server

[0315] The server encodes the video content and continuously transmits it to the terminal as streaming data. Specifically, it uses FFmpeg to compress and encode the video data in real time. The input is unencoded video data, and the output is encoded stream data.

[0316] Terminal

[0317] The terminal decodes the received stream data and plays it on the user's device. <video>Display video using tags. The input is the encoded stream data, and the output is the video being played.

[0318] Specific behavior:

[0319] The server encodes the movie video data and transmits it in streaming format. The terminal receives the stream data and displays the video on the user's device screen.

[0320] Step 2: User taps on an item

[0321] User

[0322] The user finds an item of interest in the video content and taps on it. The user's input is a tap on a specific location on the device screen.

[0323] Specific behavior:

[0324] The user becomes interested in the jacket the protagonist is wearing and taps the location of the jacket on the smartphone screen.

[0325] Step 3: Acquiring and sending tap information

[0326] Terminal

[0327] The device obtains the coordinate data (X and Y coordinates) of the user's tap position. It then captures the video frame at the moment of the tap as a screenshot and sends the tap information and screenshot to the server. The coordinate data is obtained using JavaScript, and the screenshot is taken using HTML5 functions. The input is the user's tap operation, and the output is the coordinate data and screenshot image.

[0328] Specific behavior:

[0329] The device captures the user's tap position as X and Y coordinates, captures a screenshot of that moment, and sends it to the server.

[0330] Step 4: Object recognition and feature extraction

[0331] server

[0332] The server inputs the received screenshot and location information into an AI model, recognizes the subject based on the tapped location, and extracts the subject's features (shape, color, pattern, etc.). Image processing is performed using TensorFlow and other tools. The input is the screenshot and coordinate data, and the output is the subject's feature information.

[0333] Specific behavior:

[0334] The image data received by the server is input into a TensorFlow model to extract features such as the jacket's design, color, and material.

[0335] Step 5: Search product database and return information

[0336] server

[0337] The server searches a product database based on the extracted feature information. It obtains information about the relevant product or similar products and returns that information to the terminal. This search is performed using an SQL database. The input is the feature information of the subject, and the output is product data (product name, description, price, stock status, sales URL, etc.).

[0338] Specific behavior:

[0339] The server searches the product database, acquires product information for similar jackets, and returns it to the terminal.

[0340] Step 6: Emotion analysis using the emotion engine

[0341] Terminal

[0342] The device uses an emotion engine to analyze the user's facial expressions, voice, and operation patterns in real time. Facial expression analysis is performed using OpenCV. The input is the user's facial expression and voice data, and the output is analyzed emotional state data. The analysis results are sent to the server.

[0343] Specific behavior:

[0344] The device captures the user's face with a camera, analyzes facial expressions in real time, and sends the results to a server.

[0345] Step 7: Provide customized information

[0346] server

[0347] The server filters or emphasizes product information based on the user's interests and emotions based on the results of the emotion analysis. The customized product information is sent back to the terminal and displayed to the user. The input is the emotional state data and product information, and the output is the customized product information.

[0348] Specific behavior:

[0349] The server highlights the most suitable product information for the user based on the analysis results and returns this information to the terminal, which then displays it to the user.

[0350] (Application example 2)

[0351] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0352] There is a need for a system that allows consumers to easily search for and purchase products that interest them while watching video content. Currently, it takes a great deal of time and effort to identify an item of interest, obtain detailed information about it, and then proceed to purchase it. In addition, there is a lack of product information that takes into account the viewer's emotional state, resulting in an insufficient personalization of the shopping experience. Therefore, there is a need for a system that can optimize product information based on the viewer's interests and emotions and facilitate the purchase process.

[0353] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for streaming video content, means for a user to tap a specific position on the device, means for transmitting the tapped position information and image data of the corresponding frame to the server, means for recognizing a subject using the received image data and position information, means for extracting feature information of the subject, means for searching a product database based on the extracted feature information, means for returning related product information to the user device, means for displaying the returned product information, means for recognizing the emotional state by analyzing the user's facial expressions and voice, and means for filtering or highlighting and providing product information based on the emotional state. This allows viewers to easily search for and purchase products of interest while watching video content, and makes it possible to provide personalized product information according to the viewer's emotional state.

[0354] "Video content" refers to visual and audio digital information, such as movies, dramas, music videos, advertisements, and news, that is streamed over the Internet or devices.

[0355] "Streaming" refers to a technology in which digital data (such as videos and music) is continuously distributed over the Internet, allowing users to download and play it in real time.

[0356] "Device" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to view and interact with video content.

[0357] "Tapping" refers to the operation of touching a specified position on a touch screen with a user's finger.

[0358] "Location information" refers to the coordinate data (X and Y coordinates) of the point where the user tapped on the touchscreen.

[0359] The "relevant frame" refers to a specific image of the video content at the moment when the user performs the tap operation.

[0360] "Image data" refers to digital data containing visual information for a frame.

[0361] A "server" refers to a computer system that provides data in response to requests from client devices over a network.

[0362] The term "subject" refers to an object or person that exists within the video content and is identified based on the position where the user taps.

[0363] "Feature information" refers to identifiable attribute data such as the shape, color, and pattern of the subject.

[0364] "Product database" refers to a digital database that stores information about multiple products (such as product names, prices, and descriptions).

[0365] "Product information" refers to detailed information about a specific product (product name, description, price, stock status, sales URL, etc.) obtained from a product database.

[0366] "Facial expression analysis" refers to a technology that analyzes a user's facial expressions and estimates their emotional state.

[0367] "Voice analysis" refers to technology that analyzes a user's voice and estimates their emotional state.

[0368] "Emotional state" refers to a state that represents the user's current emotion (e.g., excitement, joy, surprise).

[0369] "Filtering" refers to the process of selecting data (such as product information) based on specific conditions.

[0370] "Highlighting" refers to the process of visually highlighting particular data to make important information stand out to the user.

[0371] The present invention is a system that allows users to tap on a specific item while watching video content to obtain detailed information about that item and purchase it. The system also uses an emotion engine to provide a personalized shopping experience based on the user's emotional state.

[0372] System Configuration

[0373] server

[0374] The server is built using web server software such as Flask and has the following functions: First, it encodes the video content and sends it to the client device in streaming format. It also receives tap location information and image data for the corresponding frame, which it inputs into an AI model to recognize the subject. Next, it extracts the subject's feature information and uses that information to search a product database. It then returns related product information to the client device. During this process, it analyzes the user's facial expressions and voice in real time and filters or highlights product information based on their emotional state.

[0375] Terminal

[0376] The device can be a user's smartphone, tablet, or PC. It has the functions of displaying video content, acquiring user tap information, and sending this information to a server. It also has the function of displaying product information returned from the server and analyzing user emotions in real time through an emotion engine.

[0377] User

[0378] Users can view video content and tap on items they are interested in to obtain detailed product information. The emotion engine also analyzes the user's facial expressions and voice, and the user receives customized product information based on the analysis results.

[0379] Program processing flow

[0380] The server uses Flask to stream video content. The video content is encoded frame by frame using OpenCV and sent to the client device. When a user taps an item on the screen, the device captures the coordinate data of the tapped position and sends a screenshot of the corresponding frame to the server.

[0381] The server inputs the received image data and coordinate information into a TensorFlow AI model to recognize the object. It then extracts feature information such as the object's shape, color, and pattern and searches a product database. The product database contains information such as the product name, description, price, stock status, and sales URL, and retrieves the relevant product information.

[0382] Furthermore, an emotion recognition model using Keras is used to analyze the user's facial expressions and voice in real time. The emotion analysis results are sent to the server, and the product information is filtered or highlighted based on the results.

[0383] Specific examples

[0384] For example, suppose a user is watching a scene in a movie where the main character is wearing a special jacket. The user becomes interested in the jacket and taps on the location of the jacket on their smartphone screen. The device sends the coordinates of the tapped location and a screenshot of the relevant frame to the server. The server's AI model extracts the jacket's features and searches a product database to retrieve related product information.

[0385] At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is excited. Based on the results of this emotion analysis, the server highlights the most relevant product information and sends it back to the client device. The user can review the displayed product information and, if they like it, click the link to proceed with the purchase.

[0386] Example prompts to input to the generative AI model

[0387] We want an application that, when a user taps on a particular item (say, a jacket) in a movie scene, retrieves and displays information about that item. The application will analyze the user's facial expressions and filter or highlight relevant products based on the user's emotions. To achieve this, write the following Python code:

[0388] 1. Stream video content with a Flask server.

[0389] 2. Obtain the user's tap information and send it to the server.

[0390] 3. The AI ​​model recognizes the subject and obtains product information.

[0391] 4. Analyze user emotions with the emotion engine.

[0392] 5. Send related product information back to the client for display.

[0393] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0394] Step 1:

[0395] The server uses Flask to stream video content. It processes the video file that the user is watching using OpenCV, encodes each frame, and sends it continuously to the client. The input data is the video file, and the output data is the encoded streaming data.

[0396] Step 2:

[0397] The device receives the streamed video content and uses JavaScript to obtain user tap information. When a user taps a specific position on the device screen, the coordinate data (X and Y coordinates) of that position is obtained. The input data is the user's tap operation, and the output data is the coordinate data of the tap position.

[0398] Step 3:

[0399] The device sends the tapped location information and a screenshot of the corresponding frame to the server. The coordinate data obtained by JavaScript and the screenshot captured using a library such as html2canvas are sent to the server. The input data is the tapped location coordinate data and the screenshot, and the output data is the data sent to the server.

[0400] Step 4:

[0401] The server uses the received image data and location information to input it into a TensorFlow AI model to recognize the subject. The input data is image data and location information, and the output data is the subject recognition result. Specifically, the server identifies the object based on the tapped position in the image and extracts its features.

[0402] Step 5:

[0403] The server searches a product database based on the extracted feature information. The input data is the feature information, and the output data is product information obtained from the product database. Specifically, the server searches the product database based on identifiable attributes such as the shape, color, and pattern of the subject, and identifies related products.

[0404] Step 6:

[0405] The server returns product information to the terminal. The input data is the product information, and the output data is the data returned to the terminal. Specifically, detailed product information such as the product name, description, price, stock status, and sales URL is sent to the user device.

[0406] Step 7:

[0407] The device displays the product information returned from the server. At the same time, the device uses a camera and microphone to analyze the user's facial expressions and voice and executes an emotion engine. The input data is the user's facial expressions and voice, and the output data is the emotion analysis results.

[0408] Step 8:

[0409] The server filters or highlights product information based on the emotion analysis results. The input data are the emotion analysis results and product information, and the output data is the filtered or highlighted product information. Specifically, the server optimizes the display method of related product information according to the user's emotional state (e.g., excitement, joy).

[0410] Step 9:

[0411] The terminal displays customized product information to the user. The input data is filtered or highlighted product information, and the output data is information displayed to the user's visual perception. Specifically, the terminal displays product information in a format that corresponds to the user's emotions and provides a purchase link.

[0412] 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.

[0413] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0414] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0415] [Second embodiment]

[0416] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0417] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0418] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0419] 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.

[0420] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0421] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0422] 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.

[0423] 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.

[0424] 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 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.

[0425] 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.

[0426] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0427] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0428] This system allows viewers to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented mainly through three entities: a server, a device, and a user.

[0429] System Configuration

[0430] server

[0431] The server streams the video content and uses an AI model to recognize the subject based on user input. The server then searches a product database based on this information, retrieves the relevant product information, and returns it.

[0432] Terminal

[0433] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal has the function of displaying video content, acquiring user tap information, and sending this information to the server. It also displays product information returned from the server.

[0434] User

[0435] Users watch video content and tap on items they are interested in. They obtain product information through their device and purchase the products as needed.

[0436] Program processing flow

[0437] 1. Video Streaming and Display

[0438] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0439] The terminal decodes the received stream data and displays it as a video to the user.

[0440] 2. The user taps on an item

[0441] The user becomes interested in a particular item in the video content and taps on that item.

[0442] 3. Acquiring and sending tap information

[0443] The device acquires the coordinate data of the location where the user tapped, and also captures a video frame at the moment of the tap as a screenshot.

[0444] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[0445] 4. Subject Recognition

[0446] The server inputs the received information into an AI model that recognizes the subject based on the tap location, and uses an object detection algorithm to find the nearest object to a specific coordinate.

[0447] The server extracts characteristic information (shape, color, material pattern, etc.) of the recognized subject.

[0448] 5. Searching for related information

[0449] The server searches the product database based on the extracted feature information and uses an image similarity search algorithm to identify relevant or similar products.

[0450] The server obtains detailed information about the relevant product (product name, description, price, stock status, sales URL, etc.).

[0451] 6. Return and Display of Information

[0452] The server returns the acquired product information to the terminal.

[0453] The terminal displays the returned information on the user's screen and presents related product information using a pop-up or overlay window.

[0454] Specific examples

[0455] A user sees a scene in a movie where the main character is wearing a special jacket, and becomes interested in the jacket. The user taps the location of the jacket on the device screen. The device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server.

[0456] The server inputs the received data into an AI model to extract the jacket's characteristics (design, color, material, etc.), which are then used to search a product database to retrieve product information for similar jackets.

[0457] The acquired information is sent back from the server to the device, which then displays it to the user. The user can check the information presented, and if they like it, they can click on the displayed link to go to the e-commerce site and purchase the jacket on the spot.

[0458] In this way, MediaTouch can enhance viewers' video content viewing experience and increase sales on e-commerce sites.

[0459] The processing flow will be explained below.

[0460] Step 1:

[0461] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0462] Step 2:

[0463] The terminal decodes the received stream data and displays it as a video to the user.

[0464] Step 3:

[0465] The user finds an item of interest within the video content and taps on it.

[0466] Step 4:

[0467] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[0468] Step 5:

[0469] The device will capture a video frame at the moment of tapping as a screenshot.

[0470] Step 6:

[0471] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[0472] Step 7:

[0473] The server inputs the received image data and location information into the AI ​​model.

[0474] Step 8:

[0475] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[0476] Step 9:

[0477] The server searches the product database based on the extracted feature information.

[0478] Step 10:

[0479] The server obtains information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.).

[0480] Step 11:

[0481] The server returns the acquired product information to the terminal.

[0482] Step 12:

[0483] The terminal displays the returned product information on the user's screen using a pop-up or overlay window.

[0484] Step 13:

[0485] The user checks the displayed product information and, if interested, taps the sales URL.

[0486] Step 14:

[0487] The device will open the link in a browser and redirect the user to the corresponding product page on the e-commerce site.

[0488] Step 15:

[0489] Users check out the products on the e-commerce site and, if they like them, proceed with the purchase.

[0490] In this way, through a series of processing flows, the user can easily obtain detailed information about an item that interests them while watching the video, and can immediately purchase it.

[0491] Example 1

[0492] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0493] In recent years, there has been an increasing demand for systems that allow users to instantly obtain information about and purchase items of interest while watching video content. However, current systems have difficulty accurately and quickly recognizing items in a video and presenting appropriate product information. In particular, even when a user taps on a specific item in a video, accurate subject recognition and smooth acquisition of related information are often not possible. The present invention aims to solve these problems and provide a more user-friendly system.

[0494] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0495] In this invention, the server includes means for streaming video content, means for a user to tap a specific position on a terminal device, means for transmitting the tapped position information and image data of the corresponding frame to the server, means for utilizing a generative AI model that recognizes the subject using the received image data and position information, means for extracting feature information of the subject, means for searching a database based on the extracted feature information, means for searching related information using a similarity algorithm, means for returning related product information to the user terminal, and means for displaying the returned product information. This allows a user to easily tap an item of interest while watching video content to obtain detailed information and quickly purchase it.

[0496] "Video Content" refers to viewable media files such as movies, television programs, advertisements, and live streams.

[0497] "Streaming" refers to a technology that transmits and receives data in real time over the Internet, allowing users to use the data immediately.

[0498] "User" refers to an individual or group that utilizes the system to view video content and obtain information about a particular item.

[0499] "Terminal device" refers to an electronic device, such as a smartphone, tablet, or PC, that allows a user to view and operate video content.

[0500] "Tap" refers to the act of lightly pressing a specific position on the touchscreen of a terminal device using a finger or a stylus.

[0501] "Location information" refers to coordinate data (X and Y coordinates) when a user taps on a terminal device.

[0502] "Image data" refers to still image information for a particular video frame.

[0503] A "server" is a centralized computing system that processes data and provides resources on a network.

[0504] "Generative AI model" refers to a machine learning model that uses artificial intelligence techniques and is trained to perform a specific task.

[0505] "Subject" refers to a specific object or person present in a video or image.

[0506] "Feature information" refers to identifiable attributes of a subject, such as its shape, color, and material pattern.

[0507] "Database" refers to a data management system for storing data in a systematic and structured manner.

[0508] A "similarity algorithm" refers to a calculation method for calculating the similarity of given data and searching for the most similar data.

[0509] "Product information" refers to detailed information about a product, such as the product name, description, price, stock status, and sales URL.

[0510] "Return" refers to the operation of sending data back from the server to the user terminal.

[0511] "Display" refers to the visual presentation of data or information on the screen of a user terminal.

[0512] This system allows viewers to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented mainly through three entities: a server, a device, and a user.

[0513] Server Processing

[0514] The server encodes the video content and continuously sends it to the device as streaming data. FFmpeg and other encoding tools are used. The server receives the user's tap position information, screenshots, and user authentication information sent from the device. The received data is input into a generative AI model, which recognizes the subject based on the tap position. Feature information of the recognized subject is extracted and used to search a product database. Related information is searched using a similarity algorithm to obtain detailed information about the relevant product. This information (product name, description, price, stock status, sales URL, etc.) is then sent back to the device.

[0515] Terminal handling

[0516] The device receives the streaming data from the server, decodes it, and displays it as a video to the user. When the user taps on a specific item in the video content, the device acquires the coordinate data of the tapped position and a screenshot of the corresponding frame. The tap position information, screenshot, and user authentication information are sent to the server. The product information returned from the server is displayed on the device, presenting relevant product information to the user. The display format is a pop-up or overlay window.

[0517] User operations

[0518] Users can watch video content on their devices and tap on items that interest them. By tapping on an item that interests them, detailed information about that item is immediately displayed. Users can then purchase the product based on the information presented.

[0519] Specific examples

[0520] For example, if a user sees a scene in a movie where the main character is wearing a special jacket, they become interested in the jacket and tap the location of the jacket on their device screen. The device then sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server. The server then inputs the received data into a generative AI model to extract the jacket's characteristics (design, color, material, etc.). These characteristics are then used to search a product database to obtain information on similar jackets. The server then sends the obtained information back to the device, which then displays it to the user. The user can review the information presented, and if they like it, they can click on a link to go to an e-commerce site and purchase the jacket on the spot. In this way, MediaTouch can enhance the viewer's video content viewing experience and increase e-commerce site sales.

[0521] Example prompts using generative AI models

[0522] An example of a prompt to input to a generative AI model might be, "Please extract the features of the item the user taps on in the video and provide detailed information about that item."

[0523] As described above, this system allows users to instantly obtain detailed information about items that interest them while watching video content, enabling them to quickly purchase them. This is expected to improve the viewing experience and lead to commercial success.

[0524] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0525] Step 1:

[0526] Video Streaming and Display

[0527] The server encodes the video content and sends it continuously to the terminal as streaming data. The software used is an encoding tool such as FFmpeg. The input data is an unencoded video file, and the output data is streaming data that the terminal can receive. The terminal decodes the streaming data received from the server and displays it as a video to the user. Decoding is done using VLC player or HTML5 video tags. Specifically, the video is played on the terminal.

[0528] Step 2:

[0529] User taps on an item

[0530] The user taps on a specific item in the video content. The input data is the user's touch operation, and the output data is the coordinate information of the tapped location. The action here refers to the user tapping an object of interest on the device's touchscreen with a finger or stylus.

[0531] Step 3:

[0532] Acquiring and sending tap information

[0533] The device acquires the coordinate data of the location where the user tapped. Specifically, it captures the coordinate data using JavaScript or a touch event listener in the native app. It then captures the video frame at the moment of the tap as a screenshot. The input data is the coordinate information and the video frame, and the output data is the coordinate information and the screenshot image. This data is sent to the server. Specifically, the data is sent to the server.

[0534] Step 4:

[0535] Subject Recognition

[0536] The server inputs the received coordinate information and screenshot into a generative AI model and recognizes the subject based on the tapped location. The AI ​​model used is an object detection algorithm such as YOLO (You Only Look Once). The input data is the screenshot image and coordinate information, and the output data is the subject recognition result (such as a bounding box). Specifically, the AI ​​model identifies the subject in the screenshot.

[0537] Step 5:

[0538] Search for related information

[0539] The server extracts feature information (shape, color, material pattern, etc.) from the recognized object. The input data is the object recognition result, and the output data is a feature vector. Based on these features, a product database is searched and related product information is identified using a similarity algorithm. Specific operations include executing a database query and retrieving search results.

[0540] Step 6:

[0541] Returning and Displaying Information

[0542] The server returns the acquired product information (product name, description, price, stock status, sales URL, etc.) to the terminal. Specifically, data is sent from the server to the terminal. The terminal displays the returned information on the user's screen. Product information is presented to the user using a pop-up or overlay window. The input data is the information returned from the server, and the output data is the product information that is displayed to the user. Specifically, the product information is displayed on the screen.

[0543] Through the above steps, users can easily tap on items of interest while watching video content, obtain detailed information, and purchase the items.

[0544] (Application example 1)

[0545] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0546] Conventional systems that link video content viewing with e-commerce have difficulty instantly recognizing items that users are interested in and providing product information, resulting in a poor user experience and reduced purchasing motivation. Furthermore, there is a need for a highly accurate means of easily and quickly identifying specific products within video content.

[0547] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0548] In this invention, the server includes means for detecting an object using the generated AI model, means for extracting image data of the tap area based on the location information, and means for identifying product information using an image similarity search algorithm, which enables a user to easily identify a product while watching video content and instantly obtain detailed information about the product.

[0549] "Video content" refers to visual media such as videos, movies, and animations that are distributed via the Internet in streaming format.

[0550] A "device" is an information processing device such as a smartphone, tablet, or personal computer that a user uses to view and operate video content.

[0551] A "tap" is an operation in which a user presses a specific position on the touch screen of a device with their finger, and location information is obtained by this operation.

[0552] A "server" is a computer system that distributes video content over the cloud or network and receives and processes user operation information.

[0553] "Location information" is the specific coordinate data of a location where a user taps on the device screen.

[0554] "Image data" is still image data captured from a specific frame of video content.

[0555] The "subject" is an object that exists at the position in the video content that the user taps.

[0556] A "generated AI model" is an artificial intelligence model that has been trained using machine learning algorithms to perform a specific task, such as object recognition.

[0557] The "tap area" is a partial area of ​​image data that includes the area around the position where the user tapped.

[0558] An "image similarity search algorithm" is a computational method for identifying similar images or objects based on image feature information.

[0559] The "product database" is a database that stores product information, including product name, description, price, stock status, sales URL, and other information.

[0560] "Product information" is detailed data about a specific product obtained from a product database.

[0561] "User authentication information" is information for identifying a user, and includes a login ID, password, and the like.

[0562] "Return data" is data such as product information that is sent from the server to the user's device.

[0563] This invention relates to a system that allows users to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented by three entities: a server, a terminal, and a user.

[0564] The server encodes the video content and distributes it in streaming format. It also receives the user's tap location information and image data for the corresponding frame, and uses the generated AI model to recognize the subject based on the tap location. Using an object detection algorithm, it extracts the recognized subject's characteristics (shape, color, material pattern, etc.), searches a product database based on these, identifies the corresponding product information, and sends it back to the device.

[0565] The terminal is a device held by the user (smartphone, tablet, PC, etc.) that receives and displays video content. It obtains the coordinate data of the location where the user taps and captures the video frame at the moment of tapping as a screenshot. It sends the tap information (X and Y coordinates), screenshot, and user authentication information to the server. It receives the product information returned from the server and displays it to the user.

[0566] Users can watch video content and tap on items they are interested in. This allows them to obtain product information through their device and purchase the product if necessary.

[0567] As a specific example, let's say a user sees a particular jacket in a movie scene and becomes interested in it. The user taps on the jacket on the device screen, and the device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server. The server inputs the received data into the generated AI model, extracts the jacket's features, and uses those features to search a product database. It then retrieves product information for similar jackets and sends it back to the device. The device displays this information to the user, and if the user likes it, they can click on the displayed link to go to an e-commerce site and purchase the jacket on the spot.

[0568] In this way, MediaTouch can enhance viewers' video content viewing experience and increase sales on e-commerce sites.

[0569] An example prompt is:

[0570] "Please implement a system that recognizes the item that the user taps on in the video frame and provides detailed information about the product.

[0571] Input the image data of the tapped coordinates into the AI ​​model

[0572] Perform product recognition and obtain relevant product information

[0573] Display detailed product information (product name, description, price, link, etc.)

[0574] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0575] Step 1:

[0576] The server encodes the video content and delivers it to the terminal in streaming format. The terminal decodes the received stream data and displays it in a format that the user can view. The input is the video content data, and the output is the terminal screen displaying the video. Specifically, the operation involves receiving the video data, decoding it, and displaying it on the screen.

[0577] Step 2:

[0578] The user watches video content and taps on an item that interests them. The device detects the user's tapping and obtains the coordinate data of the tapped location. The input is the user's tapping operation, and the output is coordinate data (X and Y coordinates). Specifically, the device detects the tapped location on the touch screen and stores the coordinates in memory.

[0579] Step 3:

[0580] The device captures the video frame at the moment of tapping as a screenshot. The input is the current frame of the video content, and the output is the screenshot image data. Specifically, the device generates a still image of the currently displayed frame, saves it, and uses it for subsequent processing.

[0581] Step 4:

[0582] The device sends tap information (X and Y coordinates), screenshot, and user authentication information to the server. The input is coordinate data, screenshot image, and user authentication information, and the output is a notification to the server that data transmission is complete. Specifically, the device generates a data packet and sends it to the server via the network.

[0583] Step 5:

[0584] The server inputs the received tap information and screenshot into the generated AI model and recognizes the subject based on the tap location. The input is the tap information and screenshot image, and the output is the subject's feature information. Specifically, the server uses an image processing algorithm to analyze the area around the tap location and identify the object.

[0585] Step 6:

[0586] The server extracts the subject's feature information and uses it to search the product database. The input is the subject's feature information, and the output is the corresponding product information. Specifically, the server executes a database query using the feature information as keywords to retrieve highly relevant product records.

[0587] Step 7:

[0588] The server returns the acquired product information to the terminal. The input is the product information, and the output is a notification of completion of data transmission to the terminal. Specifically, the product information is compiled into a data packet and sent to the terminal.

[0589] Step 8:

[0590] The terminal receives the product information returned from the server and displays it to the user. The input is the returned product information, and the output is the product details displayed on the user's screen. Specifically, it analyzes the received data and displays the information using a GUI. The user can check the presented information, and if they like it, click the displayed link to go to the e-commerce site and purchase the product on the spot.

[0591] In this way, a system is provided that enhances the viewing experience of video content through a series of processing steps, and enables users to easily obtain product information and make purchases on the spot.

[0592] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0593] This system allows viewers to tap on items of interest on their device while watching video content to obtain detailed information and easily purchase them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it also provides a function to filter or highlight product information based on the user's interests and emotional state. This system is implemented primarily through three entities: a server, a device, and a user.

[0594] System Configuration

[0595] server

[0596] The server delivers video content in streaming format and uses an AI model to recognize the subject based on user input. The server then searches a product database based on this information, retrieves the relevant product information, and returns it. Furthermore, it uses an emotion engine to analyze the user's emotions and uses the results to optimize the provision of product information.

[0597] Terminal

[0598] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal has the function of displaying video content, acquiring user tap information, and sending this information to the server. It also displays product information returned from the server and analyzes user emotions in real time through an emotion engine.

[0599] User

[0600] Users watch video content and tap on items they are interested in. They obtain product information through their device and purchase the products as needed. Furthermore, the user's emotions are analyzed through the emotion engine, and they receive customized product information based on the analysis results.

[0601] Program processing flow

[0602] Video Streaming and Display

[0603] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0604] The terminal decodes the received stream data and displays it as a video to the user.

[0605] User taps on an item

[0606] The user becomes interested in a particular item in the video content and taps on that item.

[0607] Acquiring and sending tap information

[0608] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[0609] In addition, the video frame at the moment of tapping is captured as a screenshot, and the tap information and screenshot are sent to the server.

[0610] Subject recognition and feature extraction

[0611] The server inputs the received image data and location information into the AI ​​model and recognizes the subject based on the tapped location.

[0612] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[0613] Product database search and information return

[0614] The server searches the product database based on the extracted feature information.

[0615] Information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.) is obtained and returned to the terminal.

[0616] Emotion analysis using an emotion engine

[0617] The device runs an emotion engine that analyzes the user's facial expressions, voice, operation patterns, etc. in real time.

[0618] The emotion engine analyzes the user's emotional state and sends the results to the server.

[0619] Providing customized information

[0620] Based on the results of the sentiment analysis, the server filters or emphasizes product information according to the user's interests and emotions.

[0621] The customized product information is returned to the terminal and displayed on the user's screen.

[0622] Specific examples

[0623] A user sees a scene in a movie where the main character is wearing a special jacket, and becomes interested in the jacket. The user taps the location of the jacket on the device screen. The device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server.

[0624] The server inputs the received data into an AI model to extract the jacket's characteristics (design, color, material, etc.), which are then used to search a product database to retrieve product information for similar jackets.

[0625] At the same time, the emotion engine analyzes the user's facial expressions, tone of voice, and operation patterns to determine whether the user is particularly excited. Based on the results of this emotion analysis, the server returns product information that it believes will be most appealing to the user.

[0626] The acquired information is sent back from the server to the device, which then displays it to the user. The user can check the information presented, and if they like it, they can click on the displayed link to go to the e-commerce site and purchase the jacket on the spot.

[0627] In this way, the present invention can enhance the viewer's video content viewing experience and increase sales on e-commerce sites. The introduction of an emotion engine makes it possible to provide customized product information according to the user's emotional state, further improving the user experience.

[0628] The processing flow will be explained below.

[0629] Step 1:

[0630] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0631] Step 2:

[0632] The terminal decodes the received stream data and displays it as a video to the user.

[0633] Step 3:

[0634] The user finds an item of interest within the video content and taps on it.

[0635] Step 4:

[0636] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[0637] Step 5:

[0638] The device will capture a video frame at the moment of tapping as a screenshot.

[0639] Step 6:

[0640] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[0641] Step 7:

[0642] The server inputs the received data into an AI model and recognizes the subject based on the tapped location.

[0643] Step 8:

[0644] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[0645] Step 9:

[0646] The server searches the product database based on the extracted feature information.

[0647] Step 10:

[0648] The server obtains information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.).

[0649] Step 11:

[0650] The device collects the user's facial expressions, tone of voice, and operation patterns in real time using a camera and microphone.

[0651] Step 12:

[0652] The emotion engine analyzes the collected data and determines the user's emotional state (e.g., joy, surprise, excitement, etc.).

[0653] Step 13:

[0654] The server filters or emphasizes product information based on the emotional state information obtained from the emotion engine, in accordance with the emotional state of the user.

[0655] Step 14:

[0656] The server returns the customized product information to the terminal.

[0657] Step 15:

[0658] The terminal displays the returned product information on the user's screen using a pop-up or overlay window.

[0659] Step 16:

[0660] The user checks the displayed product information and, if interested, taps the sales URL.

[0661] Step 17:

[0662] The device will open the link in a browser and redirect the user to the corresponding product page on the e-commerce site.

[0663] Step 18:

[0664] Users check out the products on the e-commerce site and, if they like them, proceed with the purchase.

[0665] This series of processing flows allows users to easily obtain detailed information about items of interest while watching a video, receive product information customized according to their emotional state, and make a purchase on the spot.

[0666] Example 2

[0667] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] There is a demand for a system that allows users viewing video content to instantly obtain detailed information about items they are interested in and easily purchase them. However, previous systems have had problems with not being able to quickly and accurately detect items and provide information, which does not improve the user experience. Furthermore, they are unable to analyze user emotions and provide customized information, resulting in a decrease in user satisfaction. The present invention aims to solve these problems and provide a system that simultaneously improves the viewing experience and sales on e-commerce sites.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0670] In this invention, the server includes means for streaming video content, means for extracting feature information of a subject, and means for analyzing the user's emotional state and filtering or emphasizing information based on the result. This allows the user to instantly obtain detailed information about items that interest the user while watching the video content, and provides information customized based on the user's emotions.

[0671] "Video content" refers to information that includes images and audio for viewing by a user.

[0672] "Streaming" refers to the technology of receiving data continuously and playing it instantly.

[0673] "Device" refers to an electronic device that can be operated by a user, such as a computer, smartphone, or tablet.

[0674] "Tap" refers to the act of touching a specific location on a device's touchscreen with a finger or stylus.

[0675] A "server" refers to a computer system that provides services to other computers and devices over a network.

[0676] A "subject" refers to a particular object or person in the video content that is of interest to the user.

[0677] "Feature information" refers to identifiable attribute data such as the shape, color, pattern, and material of the subject.

[0678] A "database" refers to a system that systematically manages various data and allows for efficient search and retrieval.

[0679] "Emotional state" refers to the internal psychological state of the user, which is analyzed from facial expressions, voice, operation patterns, etc.

[0680] A "generative AI model" refers to a program that generates, recognizes, and analyzes various data based on machine learning or deep learning.

[0681] A "prompt sentence" refers to an input sentence that gives instructions to a generative AI model.

[0682] This system allows viewers to tap on items of interest on their device while watching video content to obtain detailed information and easily purchase them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it also provides a function to filter or highlight product information based on the user's interests and emotional state. This system is implemented primarily through three entities: a server, a device, and a user.

[0683] System Configuration

[0684] server

[0685] The server encodes the video content in real time and transmits it continuously to the device in streaming format, allowing users to watch high-quality video without interruption. The server inputs the received tap information and image data into an AI model to recognize the subject and extract its features. The AI ​​model uses a machine learning framework such as TensorFlow. The server searches a product database based on the extracted feature information to retrieve relevant product information. It then uses an emotion engine to analyze the user's emotional state and filters or emphasizes the product information based on the results. Facial expression analysis tools such as OpenCV are used for the emotion engine's analysis.

[0686] Terminal

[0687] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal decodes the stream data received from the server and displays it as an image to the user. It also has the function of obtaining tap information from the user, capturing a screenshot of the corresponding field, and sending it to the server. JavaScript is used to obtain the coordinate data of the tap position. HTML5 functions can be used to capture screenshots. Furthermore, the terminal displays the product information returned from the server, and analyzes the user's emotions in real time through an emotion engine. The emotion data is sent to the server and used to provide customized information.

[0688] User

[0689] Users watch video content and tap on items of interest on the device screen. The device then sends information about the tap location and a screenshot of the relevant frame to the server. The user then checks the customized product information returned by the server and purchases the product if necessary. The user's facial expressions, tone of voice, and operation patterns are analyzed in real time by the emotion engine.

[0690] Specific examples

[0691] For example, suppose a user is interested in the jacket worn by the main character in a movie scene. The user taps on the location of the jacket on the screen of their device (which can be a smartphone or tablet). The device sends the coordinate data of the tapped location and a screenshot of the relevant frame to the server. The server inputs the received data into an AI model and extracts features such as the jacket's design, color, and material. The feature information is then used to search a product database and retrieve product information for similar jackets.

[0692] At the same time, the device's emotion engine analyzes the user's facial expressions, tone of voice, and emotional state based on their actions. Based on the results of this analysis, the server returns product information that it believes will be most appealing to the user, highlighting it. The user can review the customized product information displayed on their device, click on the provided link to go to the e-commerce site, and purchase the jacket on the spot.

[0693] Prompt Sentence Examples

[0694] "Please explain the system that retrieves detailed information about a specific item in video content when the user taps on it, and provides customized product information based on sentiment analysis."

[0695] In this way, the present invention can enhance the viewer's video content viewing experience and increase sales on e-commerce sites. The introduction of an emotion engine makes it possible to provide customized product information according to the user's emotional state, which is expected to further improve the user experience.

[0696] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0697] Step 1: Video Streaming and Display

[0698] server

[0699] The server encodes the video content and continuously transmits it to the terminal as streaming data. Specifically, it uses FFmpeg to compress and encode the video data in real time. The input is unencoded video data, and the output is encoded stream data.

[0700] Terminal

[0701] The terminal decodes the received stream data and plays it on the user's device. <video>Display video using tags. The input is the encoded stream data, and the output is the video being played.

[0702] Specific behavior:

[0703] The server encodes the movie video data and transmits it in streaming format. The terminal receives the stream data and displays the video on the user's device screen.

[0704] Step 2: User taps on an item

[0705] User

[0706] The user finds an item of interest in the video content and taps on it. The user's input is a tap on a specific location on the device screen.

[0707] Specific behavior:

[0708] The user becomes interested in the jacket the protagonist is wearing and taps the location of the jacket on the smartphone screen.

[0709] Step 3: Acquiring and sending tap information

[0710] Terminal

[0711] The device obtains the coordinate data (X and Y coordinates) of the user's tap position. It then captures the video frame at the moment of the tap as a screenshot and sends the tap information and screenshot to the server. The coordinate data is obtained using JavaScript, and the screenshot is taken using HTML5 functions. The input is the user's tap operation, and the output is the coordinate data and screenshot image.

[0712] Specific behavior:

[0713] The device captures the user's tap position as X and Y coordinates, captures a screenshot of that moment, and sends it to the server.

[0714] Step 4: Object recognition and feature extraction

[0715] server

[0716] The server inputs the received screenshot and location information into an AI model, recognizes the subject based on the tapped location, and extracts the subject's features (shape, color, pattern, etc.). Image processing is performed using TensorFlow and other tools. The input is the screenshot and coordinate data, and the output is the subject's feature information.

[0717] Specific behavior:

[0718] The image data received by the server is input into a TensorFlow model to extract features such as the jacket's design, color, and material.

[0719] Step 5: Search product database and return information

[0720] server

[0721] The server searches a product database based on the extracted feature information. It obtains information about the relevant product or similar products and returns that information to the terminal. This search is performed using an SQL database. The input is the feature information of the subject, and the output is product data (product name, description, price, stock status, sales URL, etc.).

[0722] Specific behavior:

[0723] The server searches the product database, acquires product information for similar jackets, and returns it to the terminal.

[0724] Step 6: Emotion analysis using the emotion engine

[0725] Terminal

[0726] The device uses an emotion engine to analyze the user's facial expressions, voice, and operation patterns in real time. Facial expression analysis is performed using OpenCV. The input is the user's facial expression and voice data, and the output is analyzed emotional state data. The analysis results are sent to the server.

[0727] Specific behavior:

[0728] The device captures the user's face with a camera, analyzes facial expressions in real time, and sends the results to a server.

[0729] Step 7: Provide customized information

[0730] server

[0731] The server filters or emphasizes product information based on the user's interests and emotions based on the results of the emotion analysis. The customized product information is sent back to the terminal and displayed to the user. The input is the emotional state data and product information, and the output is the customized product information.

[0732] Specific behavior:

[0733] The server highlights the most suitable product information for the user based on the analysis results and returns this information to the terminal, which then displays it to the user.

[0734] (Application example 2)

[0735] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0736] There is a need for a system that allows consumers to easily search for and purchase products that interest them while watching video content. Currently, it takes a great deal of time and effort to identify an item of interest, obtain detailed information about it, and then proceed to purchase it. In addition, there is a lack of product information that takes into account the viewer's emotional state, resulting in an insufficient personalization of the shopping experience. Therefore, there is a need for a system that can optimize product information based on the viewer's interests and emotions and facilitate the purchase process.

[0737] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for streaming video content, means for a user to tap a specific position on the device, means for transmitting the tapped position information and image data of the corresponding frame to the server, means for recognizing a subject using the received image data and position information, means for extracting feature information of the subject, means for searching a product database based on the extracted feature information, means for returning related product information to the user device, means for displaying the returned product information, means for recognizing the emotional state by analyzing the user's facial expressions and voice, and means for filtering or highlighting and providing product information based on the emotional state. This allows viewers to easily search for and purchase products of interest while watching video content, and makes it possible to provide personalized product information according to the viewer's emotional state.

[0738] "Video content" refers to visual and audio digital information, such as movies, dramas, music videos, advertisements, and news, that is streamed over the Internet or devices.

[0739] "Streaming" refers to a technology in which digital data (such as videos and music) is continuously distributed over the Internet, allowing users to download and play it in real time.

[0740] "Device" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to view and interact with video content.

[0741] "Tapping" refers to the operation of touching a specified position on a touch screen with a user's finger.

[0742] "Location information" refers to the coordinate data (X and Y coordinates) of the point where the user tapped on the touchscreen.

[0743] The "relevant frame" refers to a specific image of the video content at the moment when the user performs the tap operation.

[0744] "Image data" refers to digital data containing visual information for a frame.

[0745] A "server" refers to a computer system that provides data in response to requests from client devices over a network.

[0746] The term "subject" refers to an object or person that exists within the video content and is identified based on the position where the user taps.

[0747] "Feature information" refers to identifiable attribute data such as the shape, color, and pattern of the subject.

[0748] "Product database" refers to a digital database that stores information about multiple products (such as product names, prices, and descriptions).

[0749] "Product information" refers to detailed information about a specific product (product name, description, price, stock status, sales URL, etc.) obtained from a product database.

[0750] "Facial expression analysis" refers to a technology that analyzes a user's facial expressions and estimates their emotional state.

[0751] "Voice analysis" refers to technology that analyzes a user's voice and estimates their emotional state.

[0752] "Emotional state" refers to a state that represents the user's current emotion (e.g., excitement, joy, surprise).

[0753] "Filtering" refers to the process of selecting data (such as product information) based on specific conditions.

[0754] "Highlighting" refers to the process of visually highlighting particular data to make important information stand out to the user.

[0755] The present invention is a system that allows users to tap on a specific item while watching video content to obtain detailed information about that item and purchase it. The system also uses an emotion engine to provide a personalized shopping experience based on the user's emotional state.

[0756] System Configuration

[0757] server

[0758] The server is built using web server software such as Flask and has the following functions: First, it encodes the video content and sends it to the client device in streaming format. It also receives tap location information and image data for the corresponding frame, which it inputs into an AI model to recognize the subject. Next, it extracts the subject's feature information and uses that information to search a product database. It then returns related product information to the client device. During this process, it analyzes the user's facial expressions and voice in real time and filters or highlights product information based on their emotional state.

[0759] Terminal

[0760] The device can be a user's smartphone, tablet, or PC. It has the functions of displaying video content, acquiring user tap information, and sending this information to a server. It also has the function of displaying product information returned from the server and analyzing user emotions in real time through an emotion engine.

[0761] User

[0762] Users can view video content and tap on items they are interested in to obtain detailed product information. The emotion engine also analyzes the user's facial expressions and voice, and the user receives customized product information based on the analysis results.

[0763] Program processing flow

[0764] The server uses Flask to stream video content. The video content is encoded frame by frame using OpenCV and sent to the client device. When a user taps an item on the screen, the device captures the coordinate data of the tapped position and sends a screenshot of the corresponding frame to the server.

[0765] The server inputs the received image data and coordinate information into a TensorFlow AI model to recognize the object. It then extracts feature information such as the object's shape, color, and pattern and searches a product database. The product database contains information such as the product name, description, price, stock status, and sales URL, and retrieves the relevant product information.

[0766] Furthermore, an emotion recognition model using Keras is used to analyze the user's facial expressions and voice in real time. The emotion analysis results are sent to the server, and the product information is filtered or highlighted based on the results.

[0767] Specific examples

[0768] For example, suppose a user is watching a scene in a movie where the main character is wearing a special jacket. The user becomes interested in the jacket and taps on the location of the jacket on their smartphone screen. The device sends the coordinates of the tapped location and a screenshot of the relevant frame to the server. The server's AI model extracts the jacket's features and searches a product database to retrieve related product information.

[0769] At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is excited. Based on the results of this emotion analysis, the server highlights the most relevant product information and sends it back to the client device. The user can review the displayed product information and, if they like it, click the link to proceed with the purchase.

[0770] Example prompts to input to the generative AI model

[0771] We want an application that, when a user taps on a particular item (say, a jacket) in a movie scene, retrieves and displays information about that item. The application will analyze the user's facial expressions and filter or highlight relevant products based on the user's emotions. To achieve this, write the following Python code:

[0772] 1. Stream video content with a Flask server.

[0773] 2. Obtain the user's tap information and send it to the server.

[0774] 3. The AI ​​model recognizes the subject and obtains product information.

[0775] 4. Analyze user emotions with the emotion engine.

[0776] 5. Send related product information back to the client for display.

[0777] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0778] Step 1:

[0779] The server uses Flask to stream video content. It processes the video file that the user is watching using OpenCV, encodes each frame, and sends it continuously to the client. The input data is the video file, and the output data is the encoded streaming data.

[0780] Step 2:

[0781] The device receives the streamed video content and uses JavaScript to obtain user tap information. When a user taps a specific position on the device screen, the coordinate data (X and Y coordinates) of that position is obtained. The input data is the user's tap operation, and the output data is the coordinate data of the tap position.

[0782] Step 3:

[0783] The device sends the tapped location information and a screenshot of the corresponding frame to the server. The coordinate data obtained by JavaScript and the screenshot captured using a library such as html2canvas are sent to the server. The input data is the tapped location coordinate data and the screenshot, and the output data is the data sent to the server.

[0784] Step 4:

[0785] The server uses the received image data and location information to input it into a TensorFlow AI model to recognize the subject. The input data is image data and location information, and the output data is the subject recognition result. Specifically, the server identifies the object based on the tapped position in the image and extracts its features.

[0786] Step 5:

[0787] The server searches a product database based on the extracted feature information. The input data is the feature information, and the output data is product information obtained from the product database. Specifically, the server searches the product database based on identifiable attributes such as the shape, color, and pattern of the subject, and identifies related products.

[0788] Step 6:

[0789] The server returns product information to the terminal. The input data is the product information, and the output data is the data returned to the terminal. Specifically, detailed product information such as the product name, description, price, stock status, and sales URL is sent to the user device.

[0790] Step 7:

[0791] The device displays the product information returned from the server. At the same time, the device uses a camera and microphone to analyze the user's facial expressions and voice and executes an emotion engine. The input data is the user's facial expressions and voice, and the output data is the emotion analysis results.

[0792] Step 8:

[0793] The server filters or highlights product information based on the emotion analysis results. The input data are the emotion analysis results and product information, and the output data is the filtered or highlighted product information. Specifically, the server optimizes the display method of related product information according to the user's emotional state (e.g., excitement, joy).

[0794] Step 9:

[0795] The terminal displays customized product information to the user. The input data is filtered or highlighted product information, and the output data is information displayed to the user's visual perception. Specifically, the terminal displays product information in a format that corresponds to the user's emotions and provides a purchase link.

[0796] 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.

[0797] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0798] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0799] [Third embodiment]

[0800] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0801] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0802] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0803] 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.

[0804] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0805] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0806] 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.

[0807] 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.

[0808] 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 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.

[0809] 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.

[0810] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0811] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0812] This system allows viewers to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented mainly through three entities: a server, a device, and a user.

[0813] System Configuration

[0814] server

[0815] The server streams the video content and uses an AI model to recognize the subject based on user input. The server then searches a product database based on this information, retrieves the relevant product information, and returns it.

[0816] Terminal

[0817] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal has the function of displaying video content, acquiring user tap information, and sending this information to the server. It also displays product information returned from the server.

[0818] User

[0819] Users watch video content and tap on items they are interested in. They obtain product information through their device and purchase the products as needed.

[0820] Program processing flow

[0821] 1. Video Streaming and Display

[0822] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0823] The terminal decodes the received stream data and displays it as a video to the user.

[0824] 2. The user taps on an item

[0825] The user becomes interested in a particular item in the video content and taps on that item.

[0826] 3. Acquiring and sending tap information

[0827] The device acquires the coordinate data of the location where the user tapped, and also captures a video frame at the moment of the tap as a screenshot.

[0828] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[0829] 4. Subject Recognition

[0830] The server inputs the received information into an AI model that recognizes the subject based on the tap location, and uses an object detection algorithm to find the nearest object to a specific coordinate.

[0831] The server extracts characteristic information (shape, color, material pattern, etc.) of the recognized subject.

[0832] 5. Searching for related information

[0833] The server searches the product database based on the extracted feature information and uses an image similarity search algorithm to identify relevant or similar products.

[0834] The server obtains detailed information about the relevant product (product name, description, price, stock status, sales URL, etc.).

[0835] 6. Return and Display of Information

[0836] The server returns the acquired product information to the terminal.

[0837] The terminal displays the returned information on the user's screen and presents related product information using a pop-up or overlay window.

[0838] Specific examples

[0839] A user sees a scene in a movie where the main character is wearing a special jacket, and becomes interested in the jacket. The user taps the location of the jacket on the device screen. The device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server.

[0840] The server inputs the received data into an AI model to extract the jacket's characteristics (design, color, material, etc.), which are then used to search a product database to retrieve product information for similar jackets.

[0841] The acquired information is sent back from the server to the device, which then displays it to the user. The user can check the information presented, and if they like it, they can click on the displayed link to go to the e-commerce site and purchase the jacket on the spot.

[0842] In this way, MediaTouch can enhance viewers' video content viewing experience and increase sales on e-commerce sites.

[0843] The processing flow will be explained below.

[0844] Step 1:

[0845] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0846] Step 2:

[0847] The terminal decodes the received stream data and displays it as a video to the user.

[0848] Step 3:

[0849] The user finds an item of interest within the video content and taps on it.

[0850] Step 4:

[0851] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[0852] Step 5:

[0853] The device will capture a screenshot of the video frame at the moment of the tap.

[0854] Step 6:

[0855] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[0856] Step 7:

[0857] The server inputs the received image data and location information into the AI ​​model.

[0858] Step 8:

[0859] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[0860] Step 9:

[0861] The server searches the product database based on the extracted feature information.

[0862] Step 10:

[0863] The server obtains information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.).

[0864] Step 11:

[0865] The server returns the acquired product information to the terminal.

[0866] Step 12:

[0867] The terminal displays the returned product information on the user's screen using a pop-up or overlay window.

[0868] Step 13:

[0869] The user checks the displayed product information and, if interested, taps the sales URL.

[0870] Step 14:

[0871] The device will open the link in a browser and redirect the user to the corresponding product page on the e-commerce site.

[0872] Step 15:

[0873] Users check out the products on the e-commerce site and, if they like them, proceed with the purchase.

[0874] In this way, through a series of processing flows, the user can easily obtain detailed information about an item that interests them while watching the video, and can immediately purchase it.

[0875] Example 1

[0876] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0877] In recent years, there has been an increasing demand for systems that allow users to instantly obtain information about and purchase items of interest while watching video content. However, current systems have difficulty accurately and quickly recognizing items in a video and presenting appropriate product information. In particular, even when a user taps on a specific item in a video, accurate subject recognition and smooth acquisition of related information are often not possible. The present invention aims to solve these problems and provide a more user-friendly system.

[0878] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0879] In this invention, the server includes means for streaming video content, means for a user to tap a specific position on a terminal device, means for transmitting the tapped position information and image data of the corresponding frame to the server, means for utilizing a generative AI model that recognizes the subject using the received image data and position information, means for extracting feature information of the subject, means for searching a database based on the extracted feature information, means for searching related information using a similarity algorithm, means for returning related product information to the user terminal, and means for displaying the returned product information. This allows a user to easily tap an item of interest while watching video content to obtain detailed information and quickly purchase it.

[0880] "Video Content" refers to viewable media files such as movies, television programs, advertisements, and live streams.

[0881] "Streaming" refers to a technology that transmits and receives data in real time over the Internet, allowing users to use the data immediately.

[0882] "User" refers to an individual or group that utilizes the system to view video content and obtain information about a particular item.

[0883] "Terminal device" refers to an electronic device, such as a smartphone, tablet, or PC, that allows a user to view and operate video content.

[0884] "Tap" refers to the act of lightly pressing a specific position on the touchscreen of a terminal device using a finger or a stylus.

[0885] "Location information" refers to coordinate data (X and Y coordinates) when a user taps on a terminal device.

[0886] "Image data" refers to still image information for a particular video frame.

[0887] A "server" is a centralized computing system that processes data and provides resources on a network.

[0888] "Generative AI model" refers to a machine learning model that uses artificial intelligence techniques and is trained to perform a specific task.

[0889] "Subject" refers to a specific object or person present in a video or image.

[0890] "Feature information" refers to identifiable attributes of a subject, such as its shape, color, and material pattern.

[0891] "Database" refers to a data management system for storing data in a systematic and structured manner.

[0892] A "similarity algorithm" refers to a calculation method for calculating the similarity of given data and searching for the most similar data.

[0893] "Product information" refers to detailed information about a product, such as the product name, description, price, stock status, and sales URL.

[0894] "Return" refers to the operation of sending data back from the server to the user terminal.

[0895] "Display" refers to the visual presentation of data or information on the screen of a user terminal.

[0896] This system allows viewers to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented mainly through three entities: a server, a device, and a user.

[0897] Server Processing

[0898] The server encodes the video content and continuously sends it to the device as streaming data. FFmpeg and other encoding tools are used. The server receives the user's tap position information, screenshots, and user authentication information sent from the device. The received data is input into a generative AI model, which recognizes the subject based on the tap position. Feature information of the recognized subject is extracted and used to search a product database. Related information is searched using a similarity algorithm to obtain detailed information about the relevant product. This information (product name, description, price, stock status, sales URL, etc.) is then sent back to the device.

[0899] Terminal handling

[0900] The device receives the streaming data from the server, decodes it, and displays it as a video to the user. When the user taps on a specific item in the video content, the device acquires the coordinate data of the tapped position and a screenshot of the corresponding frame. The tap position information, screenshot, and user authentication information are sent to the server. The product information returned from the server is displayed on the device, presenting relevant product information to the user. The display format is a pop-up or overlay window.

[0901] User operations

[0902] Users can watch video content on their devices and tap on items that interest them. By tapping on an item that interests them, detailed information about that item is immediately displayed. Users can then purchase the product based on the information presented.

[0903] Specific examples

[0904] For example, if a user sees a scene in a movie where the main character is wearing a special jacket, they become interested in the jacket and tap the location of the jacket on their device screen. The device then sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server. The server then inputs the received data into a generative AI model to extract the jacket's characteristics (design, color, material, etc.). These characteristics are then used to search a product database to obtain information on similar jackets. The server then sends the obtained information back to the device, which then displays it to the user. The user can review the information presented, and if they like it, they can click on a link to go to an e-commerce site and purchase the jacket on the spot. In this way, MediaTouch can enhance the viewer's video content viewing experience and increase e-commerce site sales.

[0905] Example prompts using generative AI models

[0906] An example of a prompt to input to a generative AI model might be, "Please extract the features of the item the user taps on in the video and provide detailed information about that item."

[0907] As described above, this system allows users to instantly obtain detailed information about items that interest them while watching video content, enabling them to quickly purchase them. This is expected to improve the viewing experience and lead to commercial success.

[0908] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0909] Step 1:

[0910] Video Streaming and Display

[0911] The server encodes the video content and sends it continuously to the terminal as streaming data. The software used is an encoding tool such as FFmpeg. The input data is an unencoded video file, and the output data is streaming data that the terminal can receive. The terminal decodes the streaming data received from the server and displays it as a video to the user. Decoding is done using VLC player or HTML5 video tags. Specifically, the video is played on the terminal.

[0912] Step 2:

[0913] User taps on an item

[0914] The user taps on a specific item in the video content. The input data is the user's touch operation, and the output data is the coordinate information of the tapped location. The action here refers to the user tapping an object of interest on the device's touchscreen with a finger or stylus.

[0915] Step 3:

[0916] Acquiring and sending tap information

[0917] The device acquires the coordinate data of the location where the user tapped. Specifically, it captures the coordinate data using JavaScript or a touch event listener in the native app. It then captures the video frame at the moment of the tap as a screenshot. The input data is the coordinate information and the video frame, and the output data is the coordinate information and the screenshot image. This data is sent to the server. Specifically, the data is sent to the server.

[0918] Step 4:

[0919] Subject Recognition

[0920] The server inputs the received coordinate information and screenshot into a generative AI model and recognizes the subject based on the tapped location. The AI ​​model used is an object detection algorithm such as YOLO (You Only Look Once). The input data is the screenshot image and coordinate information, and the output data is the subject recognition result (such as a bounding box). Specifically, the AI ​​model identifies the subject in the screenshot.

[0921] Step 5:

[0922] Search for related information

[0923] The server extracts feature information (shape, color, material pattern, etc.) from the recognized object. The input data is the object recognition result, and the output data is a feature vector. Based on these features, a product database is searched and related product information is identified using a similarity algorithm. Specific operations include executing a database query and retrieving search results.

[0924] Step 6:

[0925] Returning and Displaying Information

[0926] The server returns the acquired product information (product name, description, price, stock status, sales URL, etc.) to the terminal. Specifically, data is sent from the server to the terminal. The terminal displays the returned information on the user's screen. Product information is presented to the user using a pop-up or overlay window. The input data is the information returned from the server, and the output data is the product information that is displayed to the user. Specifically, the product information is displayed on the screen.

[0927] Through the above steps, users can easily tap on items of interest while watching video content, obtain detailed information, and purchase the items.

[0928] (Application example 1)

[0929] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0930] Conventional systems that link video content viewing with e-commerce have difficulty instantly recognizing items that users are interested in and providing product information, resulting in a poor user experience and reduced purchasing motivation. Furthermore, there is a need for a highly accurate means of easily and quickly identifying specific products within video content.

[0931] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0932] In this invention, the server includes means for detecting an object using the generated AI model, means for extracting image data of the tap area based on the location information, and means for identifying product information using an image similarity search algorithm, which enables a user to easily identify a product while watching video content and instantly obtain detailed information about the product.

[0933] "Video content" refers to visual media such as videos, movies, and animations that are distributed via the Internet in streaming format.

[0934] A "device" is an information processing device such as a smartphone, tablet, or PC that a user uses to view and operate video content.

[0935] A "tap" is an operation in which a user presses a specific position on the touch screen of a device with their finger, and location information is obtained by this operation.

[0936] A "server" is a computer system that distributes video content over the cloud or network and receives and processes user operation information.

[0937] "Location information" is the specific coordinate data of a location where a user taps on the device screen.

[0938] "Image data" is still image data captured from a specific frame of video content.

[0939] The "subject" is an object that exists at the position in the video content that the user taps.

[0940] A "generated AI model" is an artificial intelligence model that has been trained using machine learning algorithms to perform a specific task, such as object recognition.

[0941] The "tap area" is a partial area of ​​image data that includes the area around the position where the user tapped.

[0942] An "image similarity search algorithm" is a computational method for identifying similar images or objects based on image feature information.

[0943] The "product database" is a database that stores product information, including product name, description, price, stock status, sales URL, and other information.

[0944] "Product information" is detailed data about a specific product obtained from a product database.

[0945] "User authentication information" is information for identifying a user, and includes a login ID, password, and the like.

[0946] "Return data" is data such as product information that is sent from the server to the user's device.

[0947] This invention relates to a system that allows users to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented by three entities: a server, a terminal, and a user.

[0948] The server encodes the video content and distributes it in streaming format. It also receives the user's tap location information and image data for the corresponding frame, and uses the generated AI model to recognize the subject based on the tap location. Using an object detection algorithm, it extracts the recognized subject's characteristics (shape, color, material pattern, etc.), searches a product database based on these, identifies the corresponding product information, and sends it back to the device.

[0949] The terminal is a device held by the user (smartphone, tablet, PC, etc.) that receives and displays video content. It obtains the coordinate data of the location where the user taps and captures the video frame at the moment of tapping as a screenshot. It sends the tap information (X and Y coordinates), screenshot, and user authentication information to the server. It receives the product information returned from the server and displays it to the user.

[0950] Users can watch video content and tap on items they are interested in. This allows them to obtain product information through their device and purchase the product if necessary.

[0951] As a specific example, let's say a user sees a particular jacket in a movie scene and becomes interested in it. The user taps on the jacket on the device screen, and the device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server. The server inputs the received data into the generated AI model, extracts the jacket's features, and uses those features to search a product database. It then retrieves product information for similar jackets and sends it back to the device. The device displays this information to the user, and if the user likes it, they can click on the displayed link to go to an e-commerce site and purchase the jacket on the spot.

[0952] In this way, MediaTouch can enhance viewers' video content viewing experience and increase sales on e-commerce sites.

[0953] An example prompt is:

[0954] "Please implement a system that recognizes the item that the user taps on in the video frame and provides detailed information about the product.

[0955] Input the image data of the tapped coordinates into the AI ​​model

[0956] Perform product recognition and obtain relevant product information

[0957] Display detailed product information (product name, description, price, link, etc.)

[0958] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0959] Step 1:

[0960] The server encodes the video content and delivers it to the terminal in streaming format. The terminal decodes the received stream data and displays it in a format that the user can view. The input is the video content data, and the output is the terminal screen displaying the video. Specifically, the operation involves receiving the video data, decoding it, and displaying it on the screen.

[0961] Step 2:

[0962] The user watches video content and taps on an item that interests them. The device detects the user's tapping and obtains the coordinate data of the tapped location. The input is the user's tapping operation, and the output is coordinate data (X and Y coordinates). Specifically, the device detects the tapped location on the touch screen and stores the coordinates in memory.

[0963] Step 3:

[0964] The device captures the video frame at the moment of tapping as a screenshot. The input is the current frame of the video content, and the output is the screenshot image data. Specifically, the device generates a still image of the currently displayed frame, saves it, and uses it for subsequent processing.

[0965] Step 4:

[0966] The device sends tap information (X and Y coordinates), screenshot, and user authentication information to the server. The input is coordinate data, screenshot image, and user authentication information, and the output is a notification to the server that data transmission is complete. Specifically, the device generates a data packet and sends it to the server via the network.

[0967] Step 5:

[0968] The server inputs the received tap information and screenshot into the generated AI model and recognizes the subject based on the tap location. The input is the tap information and screenshot image, and the output is the subject's feature information. Specifically, the server uses an image processing algorithm to analyze the area around the tap location and identify the object.

[0969] Step 6:

[0970] The server extracts the subject's feature information and uses it to search the product database. The input is the subject's feature information, and the output is the corresponding product information. Specifically, the server executes a database query using the feature information as keywords to retrieve highly relevant product records.

[0971] Step 7:

[0972] The server returns the acquired product information to the terminal. The input is the product information, and the output is a notification of completion of data transmission to the terminal. Specifically, the product information is compiled into a data packet and sent to the terminal.

[0973] Step 8:

[0974] The terminal receives the product information returned from the server and displays it to the user. The input is the returned product information, and the output is the product details displayed on the user's screen. Specifically, it analyzes the received data and displays the information using a GUI. The user can check the presented information, and if they like it, click the displayed link to go to the e-commerce site and purchase the product on the spot.

[0975] In this way, a system is provided that enhances the viewing experience of video content through a series of processing steps, and enables users to easily obtain product information and make purchases on the spot.

[0976] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0977] This system allows viewers to tap on items of interest on their device while watching video content to obtain detailed information and easily purchase them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it also provides a function to filter or highlight product information based on the user's interests and emotional state. This system is implemented primarily through three entities: a server, a device, and a user.

[0978] System Configuration

[0979] server

[0980] The server delivers video content in streaming format and uses an AI model to recognize the subject based on user input. The server then searches a product database based on this information, retrieves the relevant product information, and returns it. Furthermore, it uses an emotion engine to analyze the user's emotions and uses the results to optimize the provision of product information.

[0981] Terminal

[0982] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal has the function of displaying video content, acquiring user tap information, and sending this information to the server. It also displays product information returned from the server and analyzes user emotions in real time through an emotion engine.

[0983] User

[0984] Users watch video content and tap on items they are interested in. They obtain product information through their device and purchase the products as needed. Furthermore, the user's emotions are analyzed through the emotion engine, and they receive customized product information based on the analysis results.

[0985] Program processing flow

[0986] Video Streaming and Display

[0987] The server encodes the video content and transmits it continuously as stream data to the terminal.

[0988] The terminal decodes the received stream data and displays it as a video to the user.

[0989] User taps on an item

[0990] The user becomes interested in a particular item in the video content and taps on that item.

[0991] Acquiring and sending tap information

[0992] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[0993] In addition, the video frame at the moment of tapping is captured as a screenshot, and the tap information and screenshot are sent to the server.

[0994] Subject recognition and feature extraction

[0995] The server inputs the received image data and location information into the AI ​​model and recognizes the subject based on the tapped location.

[0996] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[0997] Product database search and information return

[0998] The server searches the product database based on the extracted feature information.

[0999] Information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.) is obtained and returned to the terminal.

[1000] Emotion analysis using an emotion engine

[1001] The device runs an emotion engine that analyzes the user's facial expressions, voice, operation patterns, etc. in real time.

[1002] The emotion engine analyzes the user's emotional state and sends the results to the server.

[1003] Providing customized information

[1004] Based on the results of the sentiment analysis, the server filters or emphasizes product information according to the user's interests and emotions.

[1005] The customized product information is returned to the terminal and displayed on the user's screen.

[1006] Specific examples

[1007] A user sees a scene in a movie where the main character is wearing a special jacket, and becomes interested in the jacket. The user taps the location of the jacket on the device screen. The device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server.

[1008] The server inputs the received data into an AI model to extract the jacket's characteristics (design, color, material, etc.), which are then used to search a product database to retrieve product information for similar jackets.

[1009] At the same time, the emotion engine analyzes the user's facial expressions, tone of voice, and operation patterns to determine whether the user is particularly excited. Based on the results of this emotion analysis, the server returns product information that it believes will be most appealing to the user.

[1010] The acquired information is sent back from the server to the device, which then displays it to the user. The user can check the information presented, and if they like it, they can click on the displayed link to go to the e-commerce site and purchase the jacket on the spot.

[1011] In this way, the present invention can enhance the viewer's video content viewing experience and increase sales on e-commerce sites. The introduction of an emotion engine makes it possible to provide customized product information according to the user's emotional state, further improving the user experience.

[1012] The processing flow will be explained below.

[1013] Step 1:

[1014] The server encodes the video content and transmits it continuously as stream data to the terminal.

[1015] Step 2:

[1016] The terminal decodes the received stream data and displays it as a video to the user.

[1017] Step 3:

[1018] The user finds an item of interest within the video content and taps on it.

[1019] Step 4:

[1020] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[1021] Step 5:

[1022] The device will capture a screenshot of the video frame at the moment of the tap.

[1023] Step 6:

[1024] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[1025] Step 7:

[1026] The server inputs the received data into an AI model and recognizes the subject based on the tapped location.

[1027] Step 8:

[1028] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[1029] Step 9:

[1030] The server searches the product database based on the extracted feature information.

[1031] Step 10:

[1032] The server obtains information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.).

[1033] Step 11:

[1034] The device collects the user's facial expressions, tone of voice, and operation patterns in real time using a camera and microphone.

[1035] Step 12:

[1036] The emotion engine analyzes the collected data and determines the user's emotional state (e.g., joy, surprise, excitement, etc.).

[1037] Step 13:

[1038] The server filters or emphasizes product information based on the emotional state information obtained from the emotion engine, in accordance with the emotional state of the user.

[1039] Step 14:

[1040] The server returns the customized product information to the terminal.

[1041] Step 15:

[1042] The terminal displays the returned product information on the user's screen using a pop-up or overlay window.

[1043] Step 16:

[1044] The user checks the displayed product information and, if interested, taps the sales URL.

[1045] Step 17:

[1046] The device will open the link in a browser and redirect the user to the corresponding product page on the e-commerce site.

[1047] Step 18:

[1048] Users check out the products on the e-commerce site and, if they like them, proceed with the purchase.

[1049] This series of processing flows allows users to easily obtain detailed information about items of interest while watching a video, receive product information customized according to their emotional state, and make a purchase on the spot.

[1050] Example 2

[1051] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1052] There is a demand for a system that allows users viewing video content to instantly obtain detailed information about items they are interested in and easily purchase them. However, previous systems have had problems with not being able to quickly and accurately detect items and provide information, which does not improve the user experience. Furthermore, they are unable to analyze user emotions and provide customized information, resulting in a decrease in user satisfaction. The present invention aims to solve these problems and provide a system that simultaneously improves the viewing experience and sales on e-commerce sites.

[1053] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1054] In this invention, the server includes means for streaming video content, means for extracting feature information of a subject, and means for analyzing the user's emotional state and filtering or emphasizing information based on the result. This allows the user to instantly obtain detailed information about items that interest the user while watching the video content, and provides information customized based on the user's emotions.

[1055] "Video content" refers to information that includes images and audio for viewing by a user.

[1056] "Streaming" refers to the technology of receiving data continuously and playing it instantly.

[1057] "Device" refers to an electronic device that can be operated by a user, such as a computer, smartphone, or tablet.

[1058] "Tap" refers to the act of touching a specific location on a device's touchscreen with a finger or stylus.

[1059] A "server" refers to a computer system that provides services to other computers and devices over a network.

[1060] A "subject" refers to a particular object or person in the video content that is of interest to the user.

[1061] "Feature information" refers to identifiable attribute data such as the shape, color, pattern, and material of the subject.

[1062] A "database" refers to a system that systematically manages various data and allows for efficient search and retrieval.

[1063] "Emotional state" refers to the internal psychological state of the user, which is analyzed from facial expressions, voice, operation patterns, etc.

[1064] A "generative AI model" refers to a program that generates, recognizes, and analyzes various data based on machine learning or deep learning.

[1065] A "prompt sentence" refers to an input sentence that gives instructions to a generative AI model.

[1066] This system allows viewers to tap on items of interest on their device while watching video content to obtain detailed information and easily purchase them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it also provides a function to filter or highlight product information based on the user's interests and emotional state. This system is implemented primarily through three entities: a server, a device, and a user.

[1067] System Configuration

[1068] server

[1069] The server encodes the video content in real time and transmits it continuously to the device in streaming format, allowing users to watch high-quality video without interruption. The server inputs the received tap information and image data into an AI model to recognize the subject and extract its features. The AI ​​model uses a machine learning framework such as TensorFlow. The server searches a product database based on the extracted feature information to retrieve relevant product information. It then uses an emotion engine to analyze the user's emotional state and filters or emphasizes the product information based on the results. Facial expression analysis tools such as OpenCV are used for the emotion engine's analysis.

[1070] Terminal

[1071] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal decodes the stream data received from the server and displays it as an image to the user. It also has the function of obtaining tap information from the user, capturing a screenshot of the corresponding field, and sending it to the server. JavaScript is used to obtain the coordinate data of the tap position. HTML5 functions can be used to capture screenshots. Furthermore, the terminal displays the product information returned from the server, and analyzes the user's emotions in real time through an emotion engine. The emotion data is sent to the server and used to provide customized information.

[1072] User

[1073] Users watch video content and tap on items of interest on the device screen. The device then sends information about the tap location and a screenshot of the relevant frame to the server. The user then checks the customized product information returned by the server and purchases the product if necessary. The user's facial expressions, tone of voice, and operation patterns are analyzed in real time by the emotion engine.

[1074] Specific examples

[1075] For example, suppose a user is interested in the jacket worn by the main character in a movie scene. The user taps on the location of the jacket on the screen of their device (which can be a smartphone or tablet). The device sends the coordinate data of the tapped location and a screenshot of the relevant frame to the server. The server inputs the received data into an AI model and extracts features such as the jacket's design, color, and material. The feature information is then used to search a product database and retrieve product information for similar jackets.

[1076] At the same time, the device's emotion engine analyzes the user's facial expressions, tone of voice, and emotional state based on their actions. Based on the results of this analysis, the server returns product information that it believes will be most appealing to the user, highlighting it. The user can review the customized product information displayed on their device, click on the provided link to go to the e-commerce site, and purchase the jacket on the spot.

[1077] Prompt Sentence Examples

[1078] "Please explain the system that retrieves detailed information about a specific item in video content when the user taps on it, and provides customized product information based on sentiment analysis."

[1079] In this way, the present invention can enhance the viewer's video content viewing experience and increase sales on e-commerce sites. The introduction of an emotion engine makes it possible to provide customized product information according to the user's emotional state, which is expected to further improve the user experience.

[1080] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1081] Step 1: Video Streaming and Display

[1082] server

[1083] The server encodes the video content and continuously transmits it to the terminal as streaming data. Specifically, it uses FFmpeg to compress and encode the video data in real time. The input is unencoded video data, and the output is encoded stream data.

[1084] Terminal

[1085] The terminal decodes the received stream data and plays it on the user's device. <video>Display video using tags. The input is the encoded stream data, and the output is the video being played.

[1086] Specific behavior:

[1087] The server encodes the movie video data and transmits it in streaming format. The terminal receives the stream data and displays the video on the user's device screen.

[1088] Step 2: User taps on an item

[1089] User

[1090] The user finds an item of interest in the video content and taps on it. The user's input is a tap on a specific location on the device screen.

[1091] Specific behavior:

[1092] The user becomes interested in the jacket the protagonist is wearing and taps the location of the jacket on the smartphone screen.

[1093] Step 3: Acquiring and sending tap information

[1094] Terminal

[1095] The device obtains the coordinate data (X and Y coordinates) of the user's tap position. It then captures the video frame at the moment of the tap as a screenshot and sends the tap information and screenshot to the server. The coordinate data is obtained using JavaScript, and the screenshot is taken using HTML5 functions. The input is the user's tap operation, and the output is the coordinate data and screenshot image.

[1096] Specific behavior:

[1097] The device captures the user's tap position as X and Y coordinates, captures a screenshot of that moment, and sends it to the server.

[1098] Step 4: Object recognition and feature extraction

[1099] server

[1100] The server inputs the received screenshot and location information into an AI model, recognizes the subject based on the tapped location, and extracts the subject's features (shape, color, pattern, etc.). Image processing is performed using TensorFlow and other tools. The input is the screenshot and coordinate data, and the output is the subject's feature information.

[1101] Specific behavior:

[1102] The image data received by the server is input into a TensorFlow model to extract features such as the jacket's design, color, and material.

[1103] Step 5: Search product database and return information

[1104] server

[1105] The server searches a product database based on the extracted feature information. It obtains information about the relevant product or similar products and returns that information to the terminal. This search is performed using an SQL database. The input is the feature information of the subject, and the output is product data (product name, description, price, stock status, sales URL, etc.).

[1106] Specific behavior:

[1107] The server searches the product database, acquires product information for similar jackets, and returns it to the terminal.

[1108] Step 6: Emotion analysis using the emotion engine

[1109] Terminal

[1110] The device uses an emotion engine to analyze the user's facial expressions, voice, and operation patterns in real time. Facial expression analysis is performed using OpenCV. The input is the user's facial expression and voice data, and the output is analyzed emotional state data. The analysis results are sent to the server.

[1111] Specific behavior:

[1112] The device captures the user's face with a camera, analyzes facial expressions in real time, and sends the results to a server.

[1113] Step 7: Provide customized information

[1114] server

[1115] The server filters or emphasizes product information based on the user's interests and emotions based on the results of the emotion analysis. The customized product information is sent back to the terminal and displayed to the user. The input is the emotional state data and product information, and the output is the customized product information.

[1116] Specific behavior:

[1117] The server highlights the most suitable product information for the user based on the analysis results and returns this information to the terminal, which then displays it to the user.

[1118] (Application example 2)

[1119] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1120] There is a need for a system that allows consumers to easily search for and purchase products that interest them while watching video content. Currently, it takes a great deal of time and effort to identify an item of interest, obtain detailed information about it, and then proceed to purchase it. In addition, there is a lack of product information that takes into account the viewer's emotional state, resulting in an insufficient personalization of the shopping experience. Therefore, there is a need for a system that can optimize product information based on the viewer's interests and emotions and facilitate the purchase process.

[1121] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for streaming video content, means for a user to tap a specific position on the device, means for transmitting the tapped position information and image data of the corresponding frame to the server, means for recognizing a subject using the received image data and position information, means for extracting feature information of the subject, means for searching a product database based on the extracted feature information, means for returning related product information to the user device, means for displaying the returned product information, means for recognizing the emotional state by analyzing the user's facial expressions and voice, and means for filtering or highlighting and providing product information based on the emotional state. This allows viewers to easily search for and purchase products of interest while watching video content, and makes it possible to provide personalized product information according to the viewer's emotional state.

[1122] "Video content" refers to visual and audio digital information, such as movies, dramas, music videos, advertisements, and news, that is streamed over the Internet or devices.

[1123] "Streaming" refers to a technology in which digital data (such as videos and music) is continuously distributed over the Internet, allowing users to download and play it in real time.

[1124] "Device" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to view and interact with video content.

[1125] "Tapping" refers to the operation of touching a specified position on a touch screen with a user's finger.

[1126] "Location information" refers to the coordinate data (X and Y coordinates) of the point where the user tapped on the touchscreen.

[1127] The "relevant frame" refers to a specific image of the video content at the moment when the user performs the tap operation.

[1128] "Image data" refers to digital data containing visual information for a frame.

[1129] A "server" refers to a computer system that provides data in response to requests from client devices over a network.

[1130] The term "subject" refers to an object or person that exists within the video content and is identified based on the position where the user taps.

[1131] "Feature information" refers to identifiable attribute data such as the shape, color, and pattern of the subject.

[1132] "Product database" refers to a digital database that stores information about multiple products (such as product names, prices, and descriptions).

[1133] "Product information" refers to detailed information about a specific product (product name, description, price, stock status, sales URL, etc.) obtained from a product database.

[1134] "Facial expression analysis" refers to a technology that analyzes a user's facial expressions and estimates their emotional state.

[1135] "Voice analysis" refers to technology that analyzes a user's voice and estimates their emotional state.

[1136] "Emotional state" refers to a state that represents the user's current emotion (e.g., excitement, joy, surprise).

[1137] "Filtering" refers to the process of selecting data (such as product information) based on specific conditions.

[1138] "Highlighting" refers to the process of visually highlighting particular data to make important information stand out to the user.

[1139] The present invention is a system that allows users to tap on a specific item while watching video content to obtain detailed information about that item and purchase it. The system also uses an emotion engine to provide a personalized shopping experience based on the user's emotional state.

[1140] System Configuration

[1141] server

[1142] The server is built using web server software such as Flask and has the following functions: First, it encodes the video content and sends it to the client device in streaming format. It also receives tap location information and image data for the corresponding frame, which it inputs into an AI model to recognize the subject. Next, it extracts the subject's feature information and uses that information to search a product database. It then returns related product information to the client device. During this process, it analyzes the user's facial expressions and voice in real time and filters or highlights product information based on their emotional state.

[1143] Terminal

[1144] The device can be a user's smartphone, tablet, or PC. It has the functions of displaying video content, acquiring user tap information, and sending this information to a server. It also has the function of displaying product information returned from the server and analyzing user emotions in real time through an emotion engine.

[1145] User

[1146] Users can view video content and tap on items they are interested in to obtain detailed product information. The emotion engine also analyzes the user's facial expressions and voice, and the user receives customized product information based on the analysis results.

[1147] Program processing flow

[1148] The server uses Flask to stream video content. The video content is encoded frame by frame using OpenCV and sent to the client device. When a user taps an item on the screen, the device captures the coordinate data of the tapped position and sends a screenshot of the corresponding frame to the server.

[1149] The server inputs the received image data and coordinate information into a TensorFlow AI model to recognize the object. It then extracts feature information such as the object's shape, color, and pattern and searches a product database. The product database contains information such as the product name, description, price, stock status, and sales URL, and retrieves the relevant product information.

[1150] Furthermore, an emotion recognition model using Keras is used to analyze the user's facial expressions and voice in real time. The emotion analysis results are sent to the server, and the product information is filtered or highlighted based on the results.

[1151] Specific examples

[1152] For example, suppose a user is watching a scene in a movie where the main character is wearing a special jacket. The user becomes interested in the jacket and taps on the location of the jacket on their smartphone screen. The device sends the coordinates of the tapped location and a screenshot of the relevant frame to the server. The server's AI model extracts the jacket's features and searches a product database to retrieve related product information.

[1153] At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is excited. Based on the results of this emotion analysis, the server highlights the most relevant product information and sends it back to the client device. The user can review the displayed product information and, if they like it, click the link to proceed with the purchase.

[1154] Example prompts to input to the generative AI model

[1155] We want an application that, when a user taps on a particular item (say, a jacket) in a movie scene, retrieves and displays information about that item. The application will analyze the user's facial expressions and filter or highlight relevant products based on the user's emotions. To achieve this, write the following Python code:

[1156] 1. Stream video content with a Flask server.

[1157] 2. Obtain the user's tap information and send it to the server.

[1158] 3. The AI ​​model recognizes the subject and obtains product information.

[1159] 4. Analyze user emotions with the emotion engine.

[1160] 5. Send related product information back to the client for display.

[1161] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1162] Step 1:

[1163] The server uses Flask to stream video content. It processes the video file that the user is watching using OpenCV, encodes each frame, and sends it continuously to the client. The input data is the video file, and the output data is the encoded streaming data.

[1164] Step 2:

[1165] The device receives the streamed video content and uses JavaScript to obtain user tap information. When a user taps a specific position on the device screen, the coordinate data (X and Y coordinates) of that position is obtained. The input data is the user's tap operation, and the output data is the coordinate data of the tap position.

[1166] Step 3:

[1167] The device sends the tapped location information and a screenshot of the corresponding frame to the server. The coordinate data obtained by JavaScript and the screenshot captured using a library such as html2canvas are sent to the server. The input data is the tapped location coordinate data and the screenshot, and the output data is the data sent to the server.

[1168] Step 4:

[1169] The server uses the received image data and location information to input it into a TensorFlow AI model to recognize the subject. The input data is image data and location information, and the output data is the subject recognition result. Specifically, the server identifies the object based on the tapped position in the image and extracts its features.

[1170] Step 5:

[1171] The server searches a product database based on the extracted feature information. The input data is the feature information, and the output data is product information obtained from the product database. Specifically, the server searches the product database based on identifiable attributes such as the shape, color, and pattern of the subject, and identifies related products.

[1172] Step 6:

[1173] The server returns product information to the terminal. The input data is the product information, and the output data is the data returned to the terminal. Specifically, detailed product information such as the product name, description, price, stock status, and sales URL is sent to the user device.

[1174] Step 7:

[1175] The device displays the product information returned from the server. At the same time, the device uses a camera and microphone to analyze the user's facial expressions and voice and executes an emotion engine. The input data is the user's facial expressions and voice, and the output data is the emotion analysis results.

[1176] Step 8:

[1177] The server filters or highlights product information based on the emotion analysis results. The input data are the emotion analysis results and product information, and the output data is the filtered or highlighted product information. Specifically, the server optimizes the display method of related product information according to the user's emotional state (e.g., excitement, joy).

[1178] Step 9:

[1179] The terminal displays customized product information to the user. The input data is filtered or highlighted product information, and the output data is information displayed to the user's visual perception. Specifically, the terminal displays product information in a format that corresponds to the user's emotions and provides a purchase link.

[1180] 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.

[1181] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1182] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1183] [Fourth embodiment]

[1184] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1185] 7, a 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.

[1186] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1187] 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.

[1188] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1189] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1190] 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.

[1191] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1192] 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.

[1193] 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 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.

[1194] 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.

[1195] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1196] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1197] This system allows viewers to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented mainly through three entities: a server, a device, and a user.

[1198] System Configuration

[1199] server

[1200] The server streams the video content and uses an AI model to recognize the subject based on user input. The server then searches a product database based on this information, retrieves the relevant product information, and returns it.

[1201] Terminal

[1202] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal has the function of displaying video content, acquiring user tap information, and sending this information to the server. It also displays product information returned from the server.

[1203] User

[1204] Users watch video content and tap on items they are interested in. They obtain product information through their device and purchase the products as needed.

[1205] Program processing flow

[1206] 1. Video Streaming and Display

[1207] The server encodes the video content and transmits it continuously as stream data to the terminal.

[1208] The terminal decodes the received stream data and displays it as a video to the user.

[1209] 2. The user taps on an item

[1210] The user becomes interested in a particular item in the video content and taps on that item.

[1211] 3. Acquiring and sending tap information

[1212] The device acquires the coordinate data of the location where the user tapped, and also captures the video frame at the moment of the tap as a screenshot.

[1213] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[1214] 4. Subject Recognition

[1215] The server inputs the received information into an AI model that recognizes the subject based on the tap location, and uses an object detection algorithm to find the object closest to the specified coordinates.

[1216] The server extracts characteristic information (shape, color, material pattern, etc.) of the recognized subject.

[1217] 5. Searching for related information

[1218] The server searches the product database based on the extracted feature information and uses an image similarity search algorithm to identify relevant or similar products.

[1219] The server obtains detailed information about the relevant product (product name, description, price, stock status, sales URL, etc.).

[1220] 6. Return and Display of Information

[1221] The server returns the acquired product information to the terminal.

[1222] The terminal displays the returned information on the user's screen and presents related product information using a pop-up or overlay window.

[1223] Specific examples

[1224] A user sees a scene in a movie where the main character is wearing a special jacket, and becomes interested in the jacket. The user taps the location of the jacket on the device screen. The device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server.

[1225] The server inputs the received data into an AI model to extract the jacket's characteristics (design, color, material, etc.), which are then used to search a product database to retrieve product information for similar jackets.

[1226] The acquired information is sent back from the server to the device, which then displays it to the user. The user can check the information presented, and if they like it, they can click on the displayed link to go to the e-commerce site and purchase the jacket on the spot.

[1227] In this way, MediaTouch can enhance viewers' video content viewing experience and increase sales on e-commerce sites.

[1228] The processing flow will be explained below.

[1229] Step 1:

[1230] The server encodes the video content and transmits it continuously as stream data to the terminal.

[1231] Step 2:

[1232] The terminal decodes the received stream data and displays it as a video to the user.

[1233] Step 3:

[1234] The user finds an item of interest within the video content and taps on it.

[1235] Step 4:

[1236] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[1237] Step 5:

[1238] The device will capture a screenshot of the video frame at the moment of the tap.

[1239] Step 6:

[1240] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[1241] Step 7:

[1242] The server inputs the received image data and location information into the AI ​​model.

[1243] Step 8:

[1244] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[1245] Step 9:

[1246] The server searches the product database based on the extracted feature information.

[1247] Step 10:

[1248] The server obtains information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.).

[1249] Step 11:

[1250] The server returns the acquired product information to the terminal.

[1251] Step 12:

[1252] The terminal displays the returned product information on the user's screen using a pop-up or overlay window.

[1253] Step 13:

[1254] The user checks the displayed product information and, if interested, taps the sales URL.

[1255] Step 14:

[1256] The device will open the link in a browser and redirect the user to the corresponding product page on the e-commerce site.

[1257] Step 15:

[1258] Users check out the products on the e-commerce site and, if they like them, proceed with the purchase.

[1259] In this way, through a series of processing flows, the user can easily obtain detailed information about an item that interests them while watching the video, and can immediately purchase it.

[1260] Example 1

[1261] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1262] In recent years, there has been an increasing demand for systems that allow users to instantly obtain information about and purchase items of interest while watching video content. However, current systems have difficulty accurately and quickly recognizing items in a video and presenting appropriate product information. In particular, even when a user taps on a specific item in a video, accurate subject recognition and smooth acquisition of related information are often not possible. The present invention aims to solve these problems and provide a more user-friendly system.

[1263] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1264] In this invention, the server includes means for streaming video content, means for a user to tap a specific position on a terminal device, means for transmitting the tapped position information and image data of the corresponding frame to the server, means for utilizing a generative AI model that recognizes the subject using the received image data and position information, means for extracting feature information of the subject, means for searching a database based on the extracted feature information, means for searching related information using a similarity algorithm, means for returning related product information to the user terminal, and means for displaying the returned product information. This allows a user to easily tap an item of interest while watching video content to obtain detailed information and quickly purchase it.

[1265] "Video Content" refers to viewable media files such as movies, television programs, advertisements, and live streams.

[1266] "Streaming" refers to a technology that transmits and receives data in real time over the Internet, allowing users to use the data immediately.

[1267] "User" refers to an individual or group that utilizes the system to view video content and obtain information about a particular item.

[1268] "Terminal device" refers to an electronic device, such as a smartphone, tablet, or PC, that allows a user to view and operate video content.

[1269] "Tap" refers to the act of lightly pressing a specific position on the touchscreen of a terminal device using a finger or a stylus.

[1270] "Location information" refers to coordinate data (X and Y coordinates) when a user taps on a terminal device.

[1271] "Image data" refers to still image information for a particular video frame.

[1272] A "server" is a centralized computing system that processes data and provides resources on a network.

[1273] "Generative AI model" refers to a machine learning model that uses artificial intelligence techniques and is trained to perform a specific task.

[1274] "Subject" refers to a specific object or person present in a video or image.

[1275] "Feature information" refers to identifiable attributes of a subject, such as its shape, color, and material pattern.

[1276] "Database" refers to a data management system for storing data in a systematic and structured manner.

[1277] A "similarity algorithm" refers to a calculation method for calculating the similarity of given data and searching for the most similar data.

[1278] "Product information" refers to detailed information about a product, such as the product name, description, price, stock status, and sales URL.

[1279] "Return" refers to the operation of sending data back from the server to the user terminal.

[1280] "Display" refers to the visual presentation of data or information on the screen of a user terminal.

[1281] This system allows viewers to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented mainly through three entities: a server, a device, and a user.

[1282] Server Processing

[1283] The server encodes the video content and continuously sends it to the device as streaming data. FFmpeg and other encoding tools are used. The server receives the user's tap position information, screenshots, and user authentication information sent from the device. The received data is input into a generative AI model, which recognizes the subject based on the tap position. Feature information of the recognized subject is extracted and used to search a product database. Related information is searched using a similarity algorithm to obtain detailed information about the relevant product. This information (product name, description, price, stock status, sales URL, etc.) is then sent back to the device.

[1284] Terminal handling

[1285] The device receives the streaming data from the server, decodes it, and displays it as a video to the user. When the user taps on a specific item in the video content, the device acquires the coordinate data of the tapped position and a screenshot of the corresponding frame. The tap position information, screenshot, and user authentication information are sent to the server. The product information returned from the server is displayed on the device, presenting relevant product information to the user. The display format is a pop-up or overlay window.

[1286] User operations

[1287] Users can watch video content on their devices and tap on items that interest them. By tapping on an item that interests them, detailed information about that item is immediately displayed. Users can then purchase the product based on the information presented.

[1288] Specific examples

[1289] For example, if a user sees a scene in a movie where the main character is wearing a special jacket, they become interested in the jacket and tap the location of the jacket on their device screen. The device then sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server. The server then inputs the received data into a generative AI model to extract the jacket's characteristics (design, color, material, etc.). These characteristics are then used to search a product database to obtain information on similar jackets. The server then sends the obtained information back to the device, which then displays it to the user. The user can review the information presented, and if they like it, they can click on a link to go to an e-commerce site and purchase the jacket on the spot. In this way, MediaTouch can enhance the viewer's video content viewing experience and increase e-commerce site sales.

[1290] Example prompts using generative AI models

[1291] An example of a prompt to input to a generative AI model might be, "Please extract the features of the item the user taps on in the video and provide detailed information about that item."

[1292] As described above, this system allows users to instantly obtain detailed information about items that interest them while watching video content, enabling them to quickly purchase them. This is expected to improve the viewing experience and lead to commercial success.

[1293] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1294] Step 1:

[1295] Video Streaming and Display

[1296] The server encodes the video content and sends it continuously to the terminal as streaming data. The software used is an encoding tool such as FFmpeg. The input data is an unencoded video file, and the output data is streaming data that the terminal can receive. The terminal decodes the streaming data received from the server and displays it as a video to the user. Decoding is done using VLC player or HTML5 video tags. Specifically, the video is played on the terminal.

[1297] Step 2:

[1298] User taps on an item

[1299] The user taps on a specific item in the video content. The input data is the user's touch operation, and the output data is the coordinate information of the tapped location. The action here refers to the user tapping an object of interest on the device's touchscreen with a finger or stylus.

[1300] Step 3:

[1301] Acquiring and sending tap information

[1302] The device acquires the coordinate data of the location where the user tapped. Specifically, it captures the coordinate data using JavaScript or a touch event listener in the native app. It then captures the video frame at the moment of the tap as a screenshot. The input data is the coordinate information and the video frame, and the output data is the coordinate information and the screenshot image. This data is sent to the server. Specifically, the data is sent to the server.

[1303] Step 4:

[1304] Subject Recognition

[1305] The server inputs the received coordinate information and screenshot into a generative AI model and recognizes the subject based on the tapped location. The AI ​​model used is an object detection algorithm such as YOLO (You Only Look Once). The input data is the screenshot image and coordinate information, and the output data is the subject recognition result (such as a bounding box). Specifically, the AI ​​model identifies the subject in the screenshot.

[1306] Step 5:

[1307] Search for related information

[1308] The server extracts feature information (shape, color, material pattern, etc.) from the recognized object. The input data is the object recognition result, and the output data is a feature vector. Based on these features, a product database is searched and related product information is identified using a similarity algorithm. Specific operations include executing a database query and retrieving search results.

[1309] Step 6:

[1310] Returning and Displaying Information

[1311] The server returns the acquired product information (product name, description, price, stock status, sales URL, etc.) to the terminal. Specifically, data is sent from the server to the terminal. The terminal displays the returned information on the user's screen. Product information is presented to the user using a pop-up or overlay window. The input data is the information returned from the server, and the output data is the product information that is displayed to the user. Specifically, the product information is displayed on the screen.

[1312] Through the above steps, users can easily tap on items of interest while watching video content, obtain detailed information, and purchase the items.

[1313] (Application example 1)

[1314] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1315] Conventional systems that link video content viewing with e-commerce have difficulty instantly recognizing items that users are interested in and providing product information, resulting in a poor user experience and reduced purchasing motivation. Furthermore, there is a need for a highly accurate means of easily and quickly identifying specific products within video content.

[1316] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1317] In this invention, the server includes means for detecting an object using the generated AI model, means for extracting image data of the tap area based on the location information, and means for identifying product information using an image similarity search algorithm, which enables a user to easily identify a product while watching video content and instantly obtain detailed information about the product.

[1318] "Video content" refers to visual media such as videos, movies, and animations that are distributed via the Internet in streaming format.

[1319] A "device" is an information processing device such as a smartphone, tablet, or PC that a user uses to view and operate video content.

[1320] A "tap" is an operation in which a user presses a specific position on the touch screen of a device with their finger, and location information is obtained by this operation.

[1321] A "server" is a computer system that distributes video content over the cloud or network and receives and processes user operation information.

[1322] "Location information" is the specific coordinate data of a location where a user taps on the device screen.

[1323] "Image data" is still image data captured from a specific frame of video content.

[1324] The "subject" is an object that exists at the position in the video content that the user taps.

[1325] A "generated AI model" is an artificial intelligence model that has been trained using machine learning algorithms to perform a specific task, such as object recognition.

[1326] The "tap area" is a partial area of ​​image data that includes the area around the position where the user tapped.

[1327] An "image similarity search algorithm" is a computational method for identifying similar images or objects based on image feature information.

[1328] The "product database" is a database that stores product information, including product name, description, price, stock status, sales URL, and other information.

[1329] "Product information" is detailed data about a specific product obtained from a product database.

[1330] "User authentication information" is information for identifying a user, and includes a login ID, password, and the like.

[1331] "Return data" is data such as product information that is sent from the server to the user's device.

[1332] This invention relates to a system that allows users to tap on an item they are interested in while watching video content on their device to obtain detailed information about it and easily purchase it. This system is implemented by three entities: a server, a terminal, and a user.

[1333] The server encodes the video content and distributes it in streaming format. It also receives the user's tap location information and image data for the corresponding frame, and uses the generated AI model to recognize the subject based on the tap location. Using an object detection algorithm, it extracts the recognized subject's characteristics (shape, color, material pattern, etc.), searches a product database based on these, identifies the corresponding product information, and sends it back to the device.

[1334] The terminal is a device held by the user (smartphone, tablet, PC, etc.) that receives and displays video content. It obtains the coordinate data of the location where the user taps and captures the video frame at the moment of tapping as a screenshot. It sends the tap information (X and Y coordinates), screenshot, and user authentication information to the server. It receives the product information returned from the server and displays it to the user.

[1335] Users can watch video content and tap on items they are interested in. This allows them to obtain product information through their device and purchase the product if necessary.

[1336] As a specific example, let's say a user sees a particular jacket in a movie scene and becomes interested in it. The user taps on the jacket on the device screen, and the device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server. The server inputs the received data into the generated AI model, extracts the jacket's features, and uses those features to search a product database. It then retrieves product information for similar jackets and sends it back to the device. The device displays this information to the user, and if the user likes it, they can click on the displayed link to go to an e-commerce site and purchase the jacket on the spot.

[1337] In this way, MediaTouch can enhance viewers' video content viewing experience and increase sales on e-commerce sites.

[1338] An example prompt is:

[1339] "Please implement a system that recognizes the item that the user taps on in the video frame and provides detailed information about the product.

[1340] Input the image data of the tapped coordinates into the AI ​​model

[1341] Perform product recognition and obtain relevant product information

[1342] Display detailed product information (product name, description, price, link, etc.)

[1343] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1344] Step 1:

[1345] The server encodes the video content and delivers it to the terminal in streaming format. The terminal decodes the received stream data and displays it in a format that the user can view. The input is the video content data, and the output is the terminal screen displaying the video. Specifically, the operation involves receiving the video data, decoding it, and displaying it on the screen.

[1346] Step 2:

[1347] The user watches video content and taps on an item that interests them. The device detects the user's tapping and obtains the coordinate data of the tapped location. The input is the user's tapping operation, and the output is coordinate data (X and Y coordinates). Specifically, the device detects the tapped location on the touch screen and stores the coordinates in memory.

[1348] Step 3:

[1349] The device captures the video frame at the moment of tapping as a screenshot. The input is the current frame of the video content, and the output is the screenshot image data. Specifically, the device generates a still image of the currently displayed frame, saves it, and uses it for subsequent processing.

[1350] Step 4:

[1351] The device sends tap information (X and Y coordinates), screenshot, and user authentication information to the server. The input is coordinate data, screenshot image, and user authentication information, and the output is a notification to the server that data transmission is complete. Specifically, the device generates a data packet and sends it to the server via the network.

[1352] Step 5:

[1353] The server inputs the received tap information and screenshot into the generated AI model and recognizes the subject based on the tap location. The input is the tap information and screenshot image, and the output is the subject's feature information. Specifically, the server uses an image processing algorithm to analyze the area around the tap location and identify the object.

[1354] Step 6:

[1355] The server extracts the subject's feature information and uses it to search the product database. The input is the subject's feature information, and the output is the corresponding product information. Specifically, the server executes a database query using the feature information as keywords to retrieve highly relevant product records.

[1356] Step 7:

[1357] The server returns the acquired product information to the terminal. The input is the product information, and the output is a notification of completion of data transmission to the terminal. Specifically, the product information is compiled into a data packet and sent to the terminal.

[1358] Step 8:

[1359] The terminal receives the product information returned from the server and displays it to the user. The input is the returned product information, and the output is the product details displayed on the user's screen. Specifically, it analyzes the received data and displays the information using a GUI. The user can check the presented information, and if they like it, click the displayed link to go to the e-commerce site and purchase the product on the spot.

[1360] In this way, a system is provided that enhances the viewing experience of video content through a series of processing steps, and enables users to easily obtain product information and make purchases on the spot.

[1361] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1362] This system allows viewers to tap on items of interest on their device while watching video content to obtain detailed information and easily purchase them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it also provides a function to filter or highlight product information based on the user's interests and emotional state. This system is implemented primarily through three entities: a server, a device, and a user.

[1363] System Configuration

[1364] server

[1365] The server delivers video content in streaming format and uses an AI model to recognize the subject based on user input. The server then searches a product database based on this information, retrieves the relevant product information, and returns it. Furthermore, it uses an emotion engine to analyze the user's emotions and uses the results to optimize the provision of product information.

[1366] Terminal

[1367] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal has the function of displaying video content, acquiring user tap information, and sending this information to the server. It also displays product information returned from the server and analyzes user emotions in real time through an emotion engine.

[1368] User

[1369] Users watch video content and tap on items they are interested in. They obtain product information through their device and purchase the products as needed. Furthermore, the user's emotions are analyzed through the emotion engine, and they receive customized product information based on the analysis results.

[1370] Program processing flow

[1371] Video Streaming and Display

[1372] The server encodes the video content and transmits it continuously as stream data to the terminal.

[1373] The terminal decodes the received stream data and displays it as a video to the user.

[1374] User taps on an item

[1375] The user becomes interested in a particular item in the video content and taps on that item.

[1376] Acquiring and sending tap information

[1377] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[1378] In addition, the video frame at the moment of tapping is captured as a screenshot, and the tap information and screenshot are sent to the server.

[1379] Subject recognition and feature extraction

[1380] The server inputs the received image data and location information into the AI ​​model and recognizes the subject based on the tapped location.

[1381] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[1382] Product database search and information return

[1383] The server searches the product database based on the extracted feature information.

[1384] Information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.) is obtained and returned to the terminal.

[1385] Emotion analysis using an emotion engine

[1386] The device runs an emotion engine that analyzes the user's facial expressions, voice, operation patterns, etc. in real time.

[1387] The emotion engine analyzes the user's emotional state and sends the results to the server.

[1388] Providing customized information

[1389] Based on the results of the sentiment analysis, the server filters or emphasizes product information according to the user's interests and emotions.

[1390] The customized product information is returned to the terminal and displayed on the user's screen.

[1391] Specific examples

[1392] A user sees a scene in a movie where the main character is wearing a special jacket, and becomes interested in the jacket. The user taps the location of the jacket on the device screen. The device sends the coordinates of the tapped location and a screenshot of the corresponding frame to the server.

[1393] The server inputs the received data into an AI model to extract the jacket's characteristics (design, color, material, etc.), which are then used to search a product database to retrieve product information for similar jackets.

[1394] At the same time, the emotion engine analyzes the user's facial expressions, tone of voice, and operation patterns to determine whether the user is particularly excited. Based on the results of this emotion analysis, the server returns product information that it believes will be most appealing to the user.

[1395] The acquired information is sent back from the server to the device, which then displays it to the user. The user can check the information presented, and if they like it, they can click on the displayed link to go to the e-commerce site and purchase the jacket on the spot.

[1396] In this way, the present invention can enhance the viewer's video content viewing experience and increase sales on e-commerce sites. The introduction of an emotion engine makes it possible to provide customized product information according to the user's emotional state, further improving the user experience.

[1397] The processing flow will be explained below.

[1398] Step 1:

[1399] The server encodes the video content and transmits it continuously as stream data to the terminal.

[1400] Step 2:

[1401] The terminal decodes the received stream data and displays it as a video to the user.

[1402] Step 3:

[1403] The user finds an item of interest within the video content and taps on it.

[1404] Step 4:

[1405] The device acquires the coordinate data (X and Y coordinates) of the position where the user tapped.

[1406] Step 5:

[1407] The device will capture a screenshot of the video frame at the moment of the tap.

[1408] Step 6:

[1409] The device sends the tap location information (X and Y coordinates), a screenshot, and user authentication information to the server.

[1410] Step 7:

[1411] The server inputs the received data into an AI model and recognizes the subject based on the tapped location.

[1412] Step 8:

[1413] The AI ​​model recognizes the subject based on where you tap in the screenshot and extracts its features (shape, color, pattern, etc.).

[1414] Step 9:

[1415] The server searches the product database based on the extracted feature information.

[1416] Step 10:

[1417] The server obtains information about the relevant product or similar products (product name, description, price, stock status, sales URL, etc.).

[1418] Step 11:

[1419] The device collects the user's facial expressions, tone of voice, and operation patterns in real time using a camera and microphone.

[1420] Step 12:

[1421] The emotion engine analyzes the collected data and determines the user's emotional state (e.g., joy, surprise, excitement, etc.).

[1422] Step 13:

[1423] The server filters or emphasizes product information based on the emotional state information obtained from the emotion engine, in accordance with the emotional state of the user.

[1424] Step 14:

[1425] The server returns the customized product information to the terminal.

[1426] Step 15:

[1427] The terminal displays the returned product information on the user's screen using a pop-up or overlay window.

[1428] Step 16:

[1429] The user checks the displayed product information and, if interested, taps the sales URL.

[1430] Step 17:

[1431] The device will open the link in a browser and redirect the user to the corresponding product page on the e-commerce site.

[1432] Step 18:

[1433] Users check out the products on the e-commerce site and, if they like them, proceed with the purchase.

[1434] This series of processing flows allows users to easily obtain detailed information about items of interest while watching a video, receive product information customized according to their emotional state, and make a purchase on the spot.

[1435] Example 2

[1436] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1437] There is a demand for a system that allows users viewing video content to instantly obtain detailed information about items they are interested in and easily purchase them. However, previous systems have had problems with not being able to quickly and accurately detect items and provide information, which does not improve the user experience. Furthermore, they are unable to analyze user emotions and provide customized information, resulting in a decrease in user satisfaction. The present invention aims to solve these problems and provide a system that simultaneously improves the viewing experience and sales on e-commerce sites.

[1438] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1439] In this invention, the server includes means for streaming video content, means for extracting feature information of a subject, and means for analyzing the user's emotional state and filtering or emphasizing information based on the result. This allows the user to instantly obtain detailed information about items that interest the user while watching the video content, and provides information customized based on the user's emotions.

[1440] "Video content" refers to information that includes images and audio for viewing by a user.

[1441] "Streaming" refers to the technology of receiving data continuously and playing it instantly.

[1442] "Device" refers to an electronic device that can be operated by a user, such as a computer, smartphone, or tablet.

[1443] "Tap" refers to the act of touching a specific location on a device's touchscreen with a finger or stylus.

[1444] A "server" refers to a computer system that provides services to other computers and devices over a network.

[1445] A "subject" refers to a particular object or person in the video content that is of interest to the user.

[1446] "Feature information" refers to identifiable attribute data such as the shape, color, pattern, and material of the subject.

[1447] A "database" refers to a system that systematically manages various data and allows for efficient search and retrieval.

[1448] "Emotional state" refers to the internal psychological state of the user, which is analyzed from facial expressions, voice, operation patterns, etc.

[1449] A "generative AI model" refers to a program that generates, recognizes, and analyzes various data based on machine learning or deep learning.

[1450] A "prompt sentence" refers to an input sentence that gives instructions to a generative AI model.

[1451] This system allows viewers to tap on items of interest on their device while watching video content to obtain detailed information and easily purchase them. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it also provides a function to filter or highlight product information based on the user's interests and emotional state. This system is implemented primarily through three entities: a server, a device, and a user.

[1452] System Configuration

[1453] server

[1454] The server encodes the video content in real time and transmits it continuously to the device in streaming format, allowing users to watch high-quality video without interruption. The server inputs the received tap information and image data into an AI model to recognize the subject and extract its features. The AI ​​model uses a machine learning framework such as TensorFlow. The server searches a product database based on the extracted feature information to retrieve relevant product information. It then uses an emotion engine to analyze the user's emotional state and filters or emphasizes the product information based on the results. Facial expression analysis tools such as OpenCV are used for the emotion engine's analysis.

[1455] Terminal

[1456] The terminal is a device held by the user (smartphone, tablet, PC, etc.). The terminal decodes the stream data received from the server and displays it as an image to the user. It also has the function of obtaining tap information from the user, capturing a screenshot of the corresponding field, and sending it to the server. JavaScript is used to obtain the coordinate data of the tap position. HTML5 functions can be used to capture screenshots. Furthermore, the terminal displays the product information returned from the server, and analyzes the user's emotions in real time through an emotion engine. The emotion data is sent to the server and used to provide customized information.

[1457] User

[1458] Users watch video content and tap on items of interest on the device screen. The device then sends information about the tap location and a screenshot of the relevant frame to the server. The user then checks the customized product information returned by the server and purchases the product if necessary. The user's facial expressions, tone of voice, and operation patterns are analyzed in real time by the emotion engine.

[1459] Specific examples

[1460] For example, suppose a user is interested in the jacket worn by the main character in a movie scene. The user taps on the location of the jacket on the screen of their device (which can be a smartphone or tablet). The device sends the coordinate data of the tapped location and a screenshot of the relevant frame to the server. The server inputs the received data into an AI model and extracts features such as the jacket's design, color, and material. The feature information is then used to search a product database and retrieve product information for similar jackets.

[1461] At the same time, the device's emotion engine analyzes the user's facial expressions, tone of voice, and emotional state based on their actions. Based on the results of this analysis, the server returns product information that it believes will be most appealing to the user, highlighting it. The user can review the customized product information displayed on their device, click on the provided link to go to the e-commerce site, and purchase the jacket on the spot.

[1462] Prompt Sentence Examples

[1463] "Please explain the system that retrieves detailed information about a specific item in video content when the user taps on it, and provides customized product information based on sentiment analysis."

[1464] In this way, the present invention can enhance the viewer's video content viewing experience and increase sales on e-commerce sites. The introduction of an emotion engine makes it possible to provide customized product information according to the user's emotional state, which is expected to further improve the user experience.

[1465] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1466] Step 1: Video Streaming and Display

[1467] server

[1468] The server encodes the video content and continuously transmits it to the terminal as streaming data. Specifically, it uses FFmpeg to compress and encode the video data in real time. The input is unencoded video data, and the output is encoded stream data.

[1469] Terminal

[1470] The terminal decodes the received stream data and plays it on the user's device. <video>Display video using tags. The input is the encoded stream data, and the output is the video being played.

[1471] Specific behavior:

[1472] The server encodes the movie video data and transmits it in streaming format. The terminal receives the stream data and displays the video on the user's device screen.

[1473] Step 2: User taps on an item

[1474] User

[1475] The user finds an item of interest in the video content and taps on it. The user's input is a tap on a specific location on the device screen.

[1476] Specific behavior:

[1477] The user becomes interested in the jacket the protagonist is wearing and taps the location of the jacket on the smartphone screen.

[1478] Step 3: Acquiring and sending tap information

[1479] Terminal

[1480] The device obtains the coordinate data (X and Y coordinates) of the user's tap position. It then captures the video frame at the moment of the tap as a screenshot and sends the tap information and screenshot to the server. The coordinate data is obtained using JavaScript, and the screenshot is taken using HTML5 functions. The input is the user's tap operation, and the output is the coordinate data and screenshot image.

[1481] Specific behavior:

[1482] The device captures the user's tap position as X and Y coordinates, captures a screenshot of that moment, and sends it to the server.

[1483] Step 4: Object recognition and feature extraction

[1484] server

[1485] The server inputs the received screenshot and location information into an AI model, recognizes the subject based on the tapped location, and extracts the subject's features (shape, color, pattern, etc.). Image processing is performed using TensorFlow and other tools. The input is the screenshot and coordinate data, and the output is the subject's feature information.

[1486] Specific behavior:

[1487] The image data received by the server is input into a TensorFlow model to extract features such as the jacket's design, color, and material.

[1488] Step 5: Search product database and return information

[1489] server

[1490] The server searches a product database based on the extracted feature information. It obtains information about the relevant product or similar products and returns that information to the terminal. This search is performed using an SQL database. The input is the feature information of the subject, and the output is product data (product name, description, price, stock status, sales URL, etc.).

[1491] Specific behavior:

[1492] The server searches the product database, acquires product information for similar jackets, and returns it to the terminal.

[1493] Step 6: Emotion analysis using the emotion engine

[1494] Terminal

[1495] The device uses an emotion engine to analyze the user's facial expressions, voice, and operation patterns in real time. Facial expression analysis is performed using OpenCV. The input is the user's facial expression and voice data, and the output is analyzed emotional state data. The analysis results are sent to the server.

[1496] Specific behavior:

[1497] The device captures the user's face with a camera, analyzes facial expressions in real time, and sends the results to a server.

[1498] Step 7: Provide customized information

[1499] server

[1500] The server filters or emphasizes product information based on the user's interests and emotions based on the results of the emotion analysis. The customized product information is sent back to the terminal and displayed to the user. The input is the emotional state data and product information, and the output is the customized product information.

[1501] Specific behavior:

[1502] The server highlights the most suitable product information for the user based on the analysis results and returns this information to the terminal, which then displays it to the user.

[1503] (Application example 2)

[1504] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1505] There is a need for a system that allows consumers to easily search for and purchase products that interest them while watching video content. Currently, it takes a great deal of time and effort to identify an item of interest, obtain detailed information about it, and then proceed to purchase it. In addition, there is a lack of product information that takes into account the viewer's emotional state, resulting in an insufficient personalization of the shopping experience. Therefore, there is a need for a system that can optimize product information based on the viewer's interests and emotions and facilitate the purchase process.

[1506] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for streaming video content, means for a user to tap a specific position on the device, means for transmitting the tapped position information and image data of the corresponding frame to the server, means for recognizing a subject using the received image data and position information, means for extracting feature information of the subject, means for searching a product database based on the extracted feature information, means for returning related product information to the user device, means for displaying the returned product information, means for recognizing the emotional state by analyzing the user's facial expressions and voice, and means for filtering or highlighting and providing product information based on the emotional state. This allows viewers to easily search for and purchase products of interest while watching video content, and makes it possible to provide personalized product information according to the viewer's emotional state.

[1507] "Video content" refers to visual and audio digital information, such as movies, dramas, music videos, advertisements, and news, that is streamed over the Internet or devices.

[1508] "Streaming" refers to a technology in which digital data (such as videos and music) is continuously distributed over the Internet, allowing users to download and play it in real time.

[1509] "Device" refers to an electronic device, such as a smartphone, tablet, or computer, that a user uses to view and interact with video content.

[1510] "Tapping" refers to the operation of touching a specified position on a touch screen with a user's finger.

[1511] "Location information" refers to the coordinate data (X and Y coordinates) of the point where the user tapped on the touchscreen.

[1512] The "relevant frame" refers to a specific image of the video content at the moment when the user performs the tap operation.

[1513] "Image data" refers to digital data containing visual information for a frame.

[1514] A "server" refers to a computer system that provides data in response to requests from client devices over a network.

[1515] The term "subject" refers to an object or person that exists within the video content and is identified based on the position where the user taps.

[1516] "Feature information" refers to identifiable attribute data such as the shape, color, and pattern of the subject.

[1517] "Product database" refers to a digital database that stores information about multiple products (such as product names, prices, and descriptions).

[1518] "Product information" refers to detailed information about a specific product (product name, description, price, stock status, sales URL, etc.) obtained from a product database.

[1519] "Facial expression analysis" refers to a technology that analyzes a user's facial expressions and estimates their emotional state.

[1520] "Voice analysis" refers to technology that analyzes a user's voice and estimates their emotional state.

[1521] "Emotional state" refers to a state that represents the user's current emotion (e.g., excitement, joy, surprise).

[1522] "Filtering" refers to the process of selecting data (such as product information) based on specific conditions.

[1523] "Highlighting" refers to the process of visually highlighting particular data to make important information stand out to the user.

[1524] The present invention is a system that allows users to tap on a specific item while watching video content to obtain detailed information about that item and purchase it. The system also uses an emotion engine to provide a personalized shopping experience based on the user's emotional state.

[1525] System Configuration

[1526] server

[1527] The server is built using web server software such as Flask and has the following functions: First, it encodes the video content and sends it to the client device in streaming format. It also receives tap location information and image data for the corresponding frame, which it inputs into an AI model to recognize the subject. Next, it extracts the subject's feature information and uses that information to search a product database. It then returns related product information to the client device. During this process, it analyzes the user's facial expressions and voice in real time and filters or highlights product information based on their emotional state.

[1528] Terminal

[1529] The device can be a user's smartphone, tablet, or PC. It has the functions of displaying video content, acquiring user tap information, and sending this information to a server. It also has the function of displaying product information returned from the server and analyzing user emotions in real time through an emotion engine.

[1530] User

[1531] Users can view video content and tap on items they are interested in to obtain detailed product information. The emotion engine also analyzes the user's facial expressions and voice, and the user receives customized product information based on the analysis results.

[1532] Program processing flow

[1533] The server uses Flask to stream video content. The video content is encoded frame by frame using OpenCV and sent to the client device. When a user taps an item on the screen, the device captures the coordinate data of the tapped position and sends a screenshot of the corresponding frame to the server.

[1534] The server inputs the received image data and coordinate information into a TensorFlow AI model to recognize the object. It then extracts feature information such as the object's shape, color, and pattern and searches a product database. The product database contains information such as the product name, description, price, stock status, and sales URL, and retrieves the relevant product information.

[1535] Furthermore, an emotion recognition model using Keras is used to analyze the user's facial expressions and voice in real time. The emotion analysis results are sent to the server, and the product information is filtered or highlighted based on the results.

[1536] Specific examples

[1537] For example, suppose a user is watching a scene in a movie where the main character is wearing a special jacket. The user becomes interested in the jacket and taps on the location of the jacket on their smartphone screen. The device sends the coordinates of the tapped location and a screenshot of the relevant frame to the server. The server's AI model extracts the jacket's features and searches a product database to retrieve related product information.

[1538] At the same time, the emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is excited. Based on the results of this emotion analysis, the server highlights the most relevant product information and sends it back to the client device. The user can review the displayed product information and, if they like it, click the link to proceed with the purchase.

[1539] Example prompts to input to the generative AI model

[1540] We want an application that, when a user taps on a particular item (say, a jacket) in a movie scene, retrieves and displays information about that item. The application will analyze the user's facial expressions and filter or highlight relevant products based on the user's emotions. To achieve this, write the following Python code:

[1541] 1. Stream video content with a Flask server.

[1542] 2. Obtain the user's tap information and send it to the server.

[1543] 3. The AI ​​model recognizes the subject and obtains product information.

[1544] 4. Analyze user emotions with the emotion engine.

[1545] 5. Send related product information back to the client for display.

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] The server uses Flask to stream video content. It processes the video file that the user is watching using OpenCV, encodes each frame, and continuously sends it to the client. The input data is the video file, and the output data is the encoded streaming data.

[1549] Step 2:

[1550] The device receives the streamed video content and uses JavaScript to obtain user tap information. When a user taps a specific position on the device screen, the coordinate data (X and Y coordinates) of that position is obtained. The input data is the user's tap operation, and the output data is the coordinate data of the tap position.

[1551] Step 3:

[1552] The device sends the tapped location information and a screenshot of the corresponding frame to the server. The coordinate data obtained by JavaScript and the screenshot captured using a library such as html2canvas are sent to the server. The input data is the tapped location coordinate data and the screenshot, and the output data is the data sent to the server.

[1553] Step 4:

[1554] The server uses the received image data and location information to input it into a TensorFlow AI model to recognize the subject. The input data is image data and location information, and the output data is the subject recognition result. Specifically, the server identifies the object based on the tapped position in the image and extracts its features.

[1555] Step 5:

[1556] The server searches a product database based on the extracted feature information. The input data is the feature information, and the output data is product information obtained from the product database. Specifically, the server searches the product database based on identifiable attributes such as the shape, color, and pattern of the subject, and identifies related products.

[1557] Step 6:

[1558] The server returns product information to the terminal. The input data is the product information, and the output data is the data returned to the terminal. Specifically, detailed product information such as the product name, description, price, stock status, and sales URL is sent to the user device.

[1559] Step 7:

[1560] The device displays the product information returned from the server. At the same time, the device uses a camera and microphone to analyze the user's facial expressions and voice and executes an emotion engine. The input data is the user's facial expressions and voice, and the output data is the emotion analysis results.

[1561] Step 8:

[1562] The server filters or highlights product information based on the emotion analysis results. The input data are the emotion analysis results and product information, and the output data is the filtered or highlighted product information. Specifically, the server optimizes the display method of related product information according to the user's emotional state (e.g., excitement, joy).

[1563] Step 9:

[1564] The terminal displays customized product information to the user. The input data is filtered or highlighted product information, and the output data is information displayed to the user's visual perception. Specifically, the terminal displays product information in a format that corresponds to the user's emotions and provides a purchase link.

[1565] 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.

[1566] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1567] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1568] 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.

[1569] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1570] 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.

[1571] 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).

[1572] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1573] 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."

[1574] 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.

[1575] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1576] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1581] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] The following is further disclosed regarding the above embodiment.

[1587] (Claim 1)

[1588] means for streaming video content;

[1589] a means for a user to tap a specific location on the device;

[1590] means for transmitting tapped position information and image data of the corresponding frame to a server;

[1591] a means for recognizing a subject using the received image data and position information;

[1592] A means for extracting feature information of a subject;

[1593] A means for searching a product database based on the extracted feature information;

[1594] means for transmitting relevant product information back to the user device;

[1595] The system includes a means for displaying returned product information.

[1596] (Claim 2)

[1597] 10. The system of claim 1, further comprising means for transmitting and receiving user authentication information.

[1598] (Claim 3)

[1599] The system of claim 1, wherein the returned product information data includes the product name, description, price, stock status, and sales URL.

[1600] "Example 1"

[1601] (Claim 1)

[1602] means for streaming video content;

[1603] A means for a user to tap a specific position on a terminal device;

[1604] means for transmitting tapped position information and image data of the corresponding frame to a server;

[1605] A means for utilizing a generative AI model that recognizes a subject using the received image data and location information; and

[1606] A means for extracting feature information of a subject;

[1607] a means for searching a database based on the extracted feature information;

[1608] means for retrieving related information using a similarity algorithm;

[1609] means for returning related product information to a user terminal;

[1610] The system includes a means for displaying returned product information.

[1611] (Claim 2)

[1612] 10. The system of claim 1, further comprising means for transmitting and receiving user authentication information.

[1613] (Claim 3)

[1614] 2. The system of claim 1, wherein the returned product information includes product name, description, price, availability, and a purchase link.

[1615] "Application Example 1"

[1616] (Claim 1)

[1617] means for streaming video content;

[1618] a means for a user to tap a specific location on the device;

[1619] means for transmitting tapped position information and image data of the corresponding frame to a server;

[1620] a means for recognizing a subject using the received image data and position information;

[1621] A means for extracting feature information of a subject;

[1622] A means for searching a product database based on the extracted feature information;

[1623] means for transmitting relevant product information back to the user device;

[1624] A means for displaying returned product information;

[1625] a means for detecting an object using the generated AI model;

[1626] means for extracting image data of a tap area based on the position information;

[1627] A system including means for identifying product information using an image similarity search algorithm.

[1628] (Claim 2)

[1629] 10. The system of claim 1, further comprising means for transmitting and receiving user authentication information.

[1630] (Claim 3)

[1631] The system of claim 1, wherein the returned product information data includes the product name, description, price, stock status, and sales URL.

[1632] "Example 2: Combining Emotion Engines"

[1633] (Claim 1)

[1634] means for streaming video content;

[1635] a means for a user to tap a specific location on the device;

[1636] means for transmitting tapped position information and image data of the corresponding frame to a server;

[1637] a means for recognizing a subject using the received image data and position information;

[1638] A means for extracting feature information of a subject;

[1639] a means for searching a database based on the extracted feature information;

[1640] means for transmitting the relevant information back to the user device;

[1641] means for displaying the returned information;

[1642] A means of analyzing the user's emotional state and filtering or highlighting information based on the results.

[1643] Including system.

[1644] (Claim 2)

[1645] 10. The system of claim 1, further comprising means for transmitting and receiving user authentication information.

[1646] (Claim 3)

[1647] 2. The system of claim 1, wherein the information return data includes product name, description, price, availability, and sales URL.

[1648] (Claim 4)

[1649] The system of claim 1, which recognizes a subject using a generative AI model.

[1650] (Claim 5)

[1651] 10. The system of claim 1, further comprising means for generating a prompt sentence for analyzing the user's emotional state.

[1652] "Application example 2 when combining emotion engines"

[1653] (Claim 1)

[1654] means for streaming video content;

[1655] a means for a user to tap a specific location on the device;

[1656] means for transmitting tapped position information and image data of the corresponding frame to a server;

[1657] a means for recognizing a subject using the received image data and position information;

[1658] A means for extracting feature information of a subject;

[1659] A means for searching a product database based on the extracted feature information;

[1660] means for transmitting relevant product information back to the user device;

[1661] A means for displaying returned product information;

[1662] A means for recognizing an emotional state of a user by analyzing the user's facial expressions and voice;

[1663] a means for filtering or highlighting product information based on an emotional state;

[1664] A system including:

[1665] (Claim 2)

[1666] 10. The system of claim 1, further comprising means for transmitting and receiving user authentication information.

[1667] (Claim 3)

[1668] The system of claim 1, wherein the returned product information data includes the product name, description, price, stock status, and sales URL. [Explanation of symbols]

[1669] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / video> < / url:> < / video> < / url:> < / video> < / url:> < / video>

Claims

1. means for streaming video content; a means for a user to tap a specific location on the device; means for transmitting tapped position information and image data of the corresponding frame to a server; a means for recognizing a subject using the received image data and position information; A means for extracting feature information of a subject; A means for searching a product database based on the extracted feature information; means for transmitting relevant product information back to the user device; The system includes a means for displaying returned product information.

2. 10. The system of claim 1, further comprising means for transmitting and receiving user authentication information.

3. The system according to claim 1, wherein the returned product information data includes the product name, description, price, stock status, and sales URL.

Images