System

Abstract

A system is provided.SOLUTION: Means for receiving a moving image captured by a user, means for extracting a frame from the received moving image at regular intervals, means for analyzing the extracted frame and sorting the frame into categories such as a store appearance, a store interior, a menu, a dish, and a person, means for performing a blurring process on the frame sorted into the person category, means for storing the frame sorted into the category for each use, means for generating a promotion material for a store by using the stored frame, means for providing the generated promotion material to a store manager, and means for giving a point to posting of the moving image. Means for using the points for local payment by a user.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] When tourists visit Japan, the challenge is to increase the use of video posting platforms while promoting cashless payments at local stores. Furthermore, it is also necessary to automatically generate promotional materials for stores using videos posted by tourists, thereby efficiently increasing the value of stores. Currently, creating promotional materials is time-consuming and costly for many stores, so automating this process is important. Furthermore, from the perspective of protecting personal information, it is essential to properly handle people appearing in videos. [Means for solving the problem]

[0005] The present invention provides a system including: means for receiving videos shot by a user; means for extracting frames from the received videos at regular intervals; means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people; means for blurring frames sorted into the people category; means for saving the categorized frames by purpose; means for generating promotional materials for the store using the saved frames; means for providing the generated promotional materials to a store manager; means for awarding points for posting videos; and means for users to use the points for on-site payments. The means for analyzing the frames uses an object recognition algorithm, and the means for blurring uses a face detection algorithm and an image processing library, thereby efficiently and automatically generating promotional materials and improving user convenience while protecting personal information.

[0006] A "user" is an individual or organization that uses the system of the present invention to shoot and upload videos.

[0007] "Terminal" means the device (e.g., smartphone, tablet, PC) used by a User to capture Video and access the System to upload the Video.

[0008] A "server" is a computing device that stores and analyzes videos received from users and generates promotional materials.

[0009] "Video" is a video file about a store or tourist spot that the user has visited.

[0010] A "frame" is a still image taken from a video at regular intervals, and is a unit used in video analysis.

[0011] A "category" is a group such as store exterior, store interior, menu, food, person, etc., classified according to the content of the analyzed frame.

[0012] An "object recognition algorithm" is a program process for detecting and identifying specific objects (e.g., facilities, food, people, etc.) contained within a frame.

[0013] A "face detection algorithm" is a program process for identifying the faces of people contained within a frame.

[0014] The "image processing library" is a software component for performing image processing such as blurring on frames.

[0015] "Blurring" is a process of blurring an image to make it difficult to identify personal information within the frame.

[0016] "Promotional materials" are digital content such as images and slideshows that are generated based on the analyzed frames and are used for advertising and promotion of the store.

[0017] "Points" are rewards that users can earn by posting videos and can be used to make payments locally. [Brief explanation of the drawings]

[0018] [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 illustrating 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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The system of the present invention automatically generates promotional materials for stores based on videos taken by tourists visiting Japan and promotes the use of cashless payments. A specific embodiment for implementing this system will be described below.

[0040] System configuration

[0041] 1. User Device

[0042] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device.

[0043] 2. Server

[0044] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. The server is equipped with a high-performance processor and large-capacity storage.

[0045] Program processing overview

[0046] Receiving and storing videos

[0047] The user device uploads the captured video to the server via the VOOM app.

[0048] The server stores the video received from the user terminal and records the associated metadata (user ID, video ID, upload time, visited store ID) in a database.

[0049] Video Analysis

[0050] The server extracts frames from the received video at regular intervals, for example, one frame per second.

[0051] The server applies object recognition algorithms to identify objects in the frame and sort them into categories (store exterior, store interior, menu, food, people).

[0052] Blurring

[0053] The server selects frames that are classified into the person category based on the analysis results.

[0054] The server uses a face detection algorithm to identify people's faces and then uses an image processing library to blur the facial areas, thereby protecting personal information.

[0055] Generate and provide promotional materials

[0056] The server stores categorized frames by theme.

[0057] The server uses the saved frames to automatically generate promotional materials for the store (e.g., slideshows, banner images, etc.).

[0058] The server provides the generated promotional materials to the management screen of the store manager so that the promotional materials can be used directly.

[0059] Points allocation and use

[0060] After the video is uploaded, the server awards points to the user. The criteria for awarding points is, for example, 500 points per video upload.

[0061] Users can use the smartphone app to earn points when shopping or eating at cashless payment affiliated stores in Japan, and the points are applied as discounts according to the purchase amount.

[0062] Specific examples

[0063] Users visit tourist spots in Japan and take videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the videos they have taken to a server via the VOOM app. The server receives the videos, analyzes each frame, and categorizes them into categories such as the store's exterior, interior, menu, food, and people. Frames that show people are blurred. Promotional materials are then automatically generated and provided to the store manager. Users are also awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[0064] In this way, the present invention realizes a system that is convenient for tourists and effectively supports store promotions.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] Users use their smartphones to shoot videos at stores and tourist spots in Japan, and the videos are saved in a dedicated application folder on the device.

[0068] Step 2:

[0069] Users upload the saved video to the server through the VOOM application. On the upload screen, they enter the video title and information about the store they visited, select the video file, and press the send button.

[0070] Step 3:

[0071] The server stores the video file received from the user. Upon receiving the file, it extracts the video metadata (user ID, video ID, upload time, visited store ID, etc.) and records it in a database.

[0072] Step 4:

[0073] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[0074] Step 5:

[0075] The server applies object recognition algorithms to detect objects in the extracted frames and classify each frame into a specific category (store exterior, store interior, menu, food, people, etc.).

[0076] Step 6:

[0077] The server identifies frames that fall into the people category and uses a face detection algorithm to detect human faces within the frames.

[0078] Step 7:

[0079] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[0080] Step 8:

[0081] The server stores the categorized frames according to their purpose, and the frames are stored in specific folders according to their classification.

[0082] Step 9:

[0083] The server automatically generates promotional materials based on the saved frames, for example, creating slideshows and banner images using images of the store's exterior and food.

[0084] Step 10:

[0085] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0086] Step 11:

[0087] After the video has been posted, the server awards points to the user. The amount of points awarded is determined based on preset rules.

[0088] Step 12:

[0089] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0090] The above are the specific processing steps in this system.

[0091] Example 1

[0092] 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."

[0093] In recent years, the number of video content shot by tourists and users visiting Japan has increased, but it is difficult to automatically generate promotional materials for stores that effectively utilize these videos. There is also a need for technology that can efficiently create promotional materials while also protecting personal information. Furthermore, incentives for video contributors are also necessary, and it is desirable that these incentives contribute to the spread of cashless payments.

[0094] 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.

[0095] In this invention, the server includes means for receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for saving the categorized frames by purpose, means for generating promotional materials for the store using the saved frames, means for providing the generated promotional materials to the store manager, means for awarding points for posting videos, and means for users to use the points for local payments. This makes it possible to efficiently analyze videos shot by tourists visiting Japan and users and convert them into promotional materials while protecting personal information, and further promote the spread of cashless payments by awarding points to posters.

[0096] A "user terminal" is a device such as a smartphone, tablet, or computer that a user uses to shoot videos and upload them to the system.

[0097] "Server" refers to a computer system equipped with a high-performance processor and large-capacity storage that communicates with user terminals via the Internet, stores and analyzes received videos, and generates promotional materials.

[0098] "Receiving video" is a process in which a user sends a video taken by the user from the user terminal to a server, and the server receives the video.

[0099] "Frame extraction" is the process of extracting still images (frames) from the received video at regular intervals.

[0100] "Frame analysis" is the process of using object recognition algorithms to identify and categorize objects within extracted frames.

[0101] An "object recognition algorithm" is an artificial intelligence technique used to identify and classify objects within a frame.

[0102] "Blurring" is an image processing technique that blurs the facial area of ​​frames classified as people to protect personal information.

[0103] A "face detection algorithm" is an artificial intelligence technique used to identify the faces of people within a frame.

[0104] An "image processing library" is a collection of software tools for editing and processing images.

[0105] "Promotional materials" are content such as slideshows and banner images used for store promotion and advertising.

[0106] "Point awarding" is the process of providing points as an incentive to users who post videos.

[0107] "Cashless payment" is a system in which payments are made electronically without using cash.

[0108] MODE FOR CARRYING OUT THE INVENTION

[0109] The system of the present invention automatically generates promotional materials based on videos taken by tourists visiting Japan or users on-site, and promotes the use of cashless payments. Specific embodiments for implementing this system are described below.

[0110] System configuration

[0111] 1. User Device

[0112] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device. This application is responsible for sending videos, adding metadata, and uploading them.

[0113] 2. Server

[0114] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. The server is equipped with a high-performance processor and large-capacity storage. The main software used includes object recognition algorithms (e.g., YOLO, Mask R-CNN), face detection algorithms (e.g., OpenCV's Haar Cascade), and image processing libraries (e.g., OpenCV, PIL).

[0115] Receiving and storing videos

[0116] The user device uploads the video they have taken to the server using a dedicated video posting app. The server receives the uploaded video and stores it in a temporary storage area. It also records metadata related to the video (user ID, video ID, upload time, visited store ID) in a database.

[0117] Video Analysis

[0118] The server reads the saved video file and extracts frames at regular intervals. Specifically, it is set to extract one frame every second. The extracted frames are analyzed using an object recognition algorithm and sorted into categories such as store exteriors, store interiors, menus, dishes, and people.

[0119] Blurring

[0120] The server applies a face detection algorithm to frames classified as people to identify faces, then uses an image processing library to blur the face area to protect personal information, making it difficult to identify individuals.

[0121] Generate and provide promotional materials

[0122] The server stores the categorized frames by theme and automatically generates promotional materials such as slideshows and banner images. The generated promotional materials are provided to the store administrator's management screen and can be easily used.

[0123] Points allocation and use

[0124] The server awards points to users after they have posted a video. The standard for awarding points is, for example, 500 points per video post. Users can use the smartphone app to redeem points at cashless payment affiliated stores in Japan. Points are applied as discounts according to the purchase amount.

[0125] Specific examples

[0126] Users visit tourist spots in Japan and take videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the videos they have taken to a server using a dedicated app. The server receives the videos, analyzes each frame, and categorizes them into categories such as the store's exterior, interior, menu, food, and people. Frames that show people are blurred. Promotional materials are then automatically generated and provided to the store manager. Users are rewarded for posting videos with points, which can be used at cashless payment affiliated stores in Japan.

[0127] Prompt Sentence Examples

[0128] "A user visits a tourist spot in Japan and takes a video of a store's exterior, interior, menu, and food. The user uploads this video to a server via the app, and the server analyzes the video and automatically generates promotional materials. Please give us an overview of this system."

[0129] The AI ​​model generated based on this prompt can provide detailed explanations of the system's detailed operation and the technical elements of each step.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] Recording and uploading videos

[0133] Input: Videos taken by users using devices such as smartphones

[0134] How it works: A user launches a video posting application, selects a video they have taken, and presses the submit button to upload the video.

[0135] Output: Sending video data from the user device to the server

[0136] Step 2:

[0137] Receiving and storing videos

[0138] Input: Video data sent from the user device

[0139] How it works: The server receives the video via the Internet and stores it in a temporary storage area. At the same time, it analyzes the metadata associated with the video (user ID, video ID, upload time, visited store ID) and records it in a database.

[0140] Output: Video files stored in temporary storage and metadata stored in a database

[0141] Step 3:

[0142] Extracting Frames

[0143] Input: Saved video file

[0144] Operation: The server loads the video file and extracts frames at regular intervals. Specifically, it is set to extract one frame every second.

[0145] Output: Individual frames stored in temporary storage

[0146] Step 4:

[0147] Frame analysis and categorization

[0148] Input: Extracted individual frames

[0149] How it works: The server applies object recognition algorithms (e.g., YOLO, Mask R-CNN) to identify objects in the frame, then categorizes them into categories such as store exteriors, store interiors, menus, dishes, people, etc.

[0150] Output: Frames sorted by category

[0151] Step 5:

[0152] Blurring

[0153] Input: Frames sorted into the people category

[0154] How it works: The server uses a face detection algorithm (e.g., OpenCV's Haar Cascade) to identify faces in the frame, and then uses an image processing library (e.g., OpenCV, PIL) to blur the identified faces.

[0155] Output: Frame with face blurred

[0156] Step 6:

[0157] Save Frame

[0158] Input: Categorized and blurred frames

[0159] How it works: The server stores these frames in folders by theme, for example, store exterior frames in an "Exterior" folder, food frames in a "Food" folder, etc.

[0160] Output: Thematically organized frame data

[0161] Step 7:

[0162] Generate promotional materials

[0163] Input: Frame data organized by theme

[0164] How it works: The server selects the saved frames and automatically generates promotional materials such as slideshows and banner images, stitching the selected frames together and adding text.

[0165] Output: Auto-generated promotional materials

[0166] Step 8:

[0167] Providing promotional materials

[0168] Input: Auto-generated promotional material

[0169] Operation: The server uploads the generated promotional materials to the store manager's management screen, where the manager can easily use the materials.

[0170] Output: Promotional materials uploaded to the dashboard

[0171] Step 9:

[0172] Points awarded

[0173] Input: Video submission completion data

[0174] Operation: The server confirms the completion of video posting and awards points to the user. Points are awarded at a rate of 500 points per video posted. The point information is updated in the user database.

[0175] Output: Update of awarded points to user database

[0176] Step 10:

[0177] Using points

[0178] Input: Points awarded

[0179] How it works: Users use a smartphone app to redeem points at cashless payment affiliated stores in Japan. The app provides a function to select the application of points at the time of payment, specifically by scanning a QR code or presenting a barcode.

[0180] Output: Discount applied by using points at cashless payment affiliated stores

[0181] (Application example 1)

[0182] 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."

[0183] In today's tourism industry, it is extremely important to effectively promote stores visited by tourists visiting Japan. However, in many cases, videos taken by tourists are not effectively utilized. Furthermore, there are issues with privacy protection and promoting cashless payments. Specifically, there is a lack of methods to automatically generate promotional materials for stores and encourage visitors to use cashless payments. As a result, not only are store promotional activities ineffective, but convenience for tourists is also diminished.

[0184] 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.

[0185] In this invention, the server includes a terminal device for a user to shoot videos at a store and upload the videos to the server, a means for analyzing the shot and uploaded videos on the server and classifying them into store exteriors, interiors, menus, dishes, people, etc. using an object recognition algorithm, a means for blurring facial portions of the analyzed frames using an image processing library, a means for automatically generating store promotional materials from the analyzed frames and providing them to a store manager, and a means for awarding points that can be used for cashless payments to users. This makes it possible to effectively utilize videos shot by tourists visiting Japan to automatically generate store promotional materials and promote cashless payments while simultaneously protecting their privacy.

[0186] A "user terminal" is a device that a user uses to shoot video and upload the video to a server.

[0187] "Server" refers to a device equipped with a high-performance processor and large-capacity storage that communicates with user terminals via the Internet, stores and analyzes received videos, and generates promotional materials.

[0188] An "object recognition algorithm" is an algorithm that identifies objects within video frames and classifies them into store exteriors, interiors, menus, dishes, people, etc.

[0189] An "image processing library" is a library for processing and editing images, and is used for blurring and other processes.

[0190] "Promotional Materials" refers to generated visual content, such as slideshows and banner images, used in store promotional activities.

[0191] The "point awarding means" is a means for awarding points to users for posting videos, and provides points that users can use for cashless payments.

[0192] The "analysis means" is a processing device or software that has the function of allowing the server to analyze uploaded videos and classify them into specific categories.

[0193] The "blurring means" is a means for protecting privacy by blurring the facial portion of a person in an image.

[0194] An embodiment of the present invention will be described. This is a system that automatically generates promotional materials for stores based on videos taken by tourists visiting Japan and promotes the use of cashless payment. This system includes the following components.

[0195] System configuration

[0196] User terminal

[0197] A user device is a device that a user uses to shoot videos and upload them to a server. This includes smartphones, tablets, and PCs. A dedicated video posting application (hereafter referred to as the SmartPromo app) is installed on these devices.

[0198] server

[0199] The server communicates with user devices via the Internet, stores and analyzes the received video, and generates promotional materials. The server is equipped with a high-performance processor (e.g., Intel Xeon or AMD Ryzen series) and large-capacity storage (HDD or SSD). The server runs an object recognition algorithm (e.g., YOLO or SSD) and an image processing library (e.g., OpenCV), which analyzes frames and performs blurring.

[0200] Receiving and storing videos

[0201] A user shoots a video using the SmartPromo app and uploads it to the server. The server receives the video data along with metadata (user ID, video ID, upload time, store ID). This data is recorded in a database (e.g., MySQL or SQLite), and the video itself is stored in storage.

[0202] Video analysis and processing

[0203] The server extracts frames from the stored video at regular intervals. For example, it can be set to extract one frame every second. The extracted frames are then categorized using an object recognition algorithm into categories such as store exteriors, interiors, menus, food, and people. For frames classified as people, a face detection algorithm (for example, OpenCV's face recognition function) is used to blur the faces.

[0204] Generate promotional materials

[0205] The server saves the analyzed frames by theme and automatically generates promotional materials such as slideshows and banner images. The generated materials are provided to store managers through a dedicated management screen, allowing store managers to use these materials to carry out effective store promotions.

[0206] Points allocation and use

[0207] When a user posts a video, the server will award the user points. The points can be viewed within the SmartPromo app and can be used at cashless payment affiliated stores in Japan. The standard for awarding points could be 500 points per video post.

[0208] Specific Examples

[0209] For example, a user shoots a video at a restaurant in a tourist spot in Japan and uploads it to the server using the SmartPromo app. The server receives the video, extracts and analyzes frames every second, and classifies them into categories such as the restaurant's exterior, interior, menu, food, and people. Frames containing people are subjected to facial recognition and blurring. Promotional materials are then generated and provided to the restaurant manager, and points are awarded to the user. These points can be used for the next cashless payment, increasing user convenience.

[0210] Prompt Sentence Examples

[0211] Users can upload videos of their restaurants. The videos are analyzed into categories of restaurant exterior, interior, menu, food, and people. Faces of people are blurred and promotional materials for the restaurant are generated. The generated promotional materials are provided to the restaurant manager, and the user is awarded 500 points. The points can be used for the next cashless payment.

[0212] In this way, the present invention provides a system that contributes to the promotion of the tourism industry and cashless payments by effectively utilizing videos taken by tourists visiting Japan and automatically generating promotional materials for stores.

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] A user takes a video in a store. The user uses the SmartPromo app to take a video and temporarily saves it on the device. The input of this step is the "taken video data" and the output is the "video file saved on the device."

[0216] Step 2:

[0217] The user uploads the video they have taken to the server. On the user's device, they use the SmartPromo app to send the video to the server. At this time, metadata such as the user ID, video ID, upload time, and store ID are also sent. The input for this step is the "video file and metadata on the device," and the output is the "video file and metadata stored on the server."

[0218] Step 3:

[0219] The server stores the received video data. The server stores the video data in storage (e.g., HDD or SSD) and records the metadata in a database (e.g., MySQL or SQLite). The input of this step is the "video data and metadata uploaded to the server," and the output is the "video file stored in storage and the metadata recorded in the database."

[0220] Step 4:

[0221] The server extracts frames from the video at regular intervals. For example, it can be set to extract one frame every second. The input of this step is the "saved video file" and the output is the "extracted frames."

[0222] Step 5:

[0223] The server analyzes the extracted frames using an object recognition algorithm. The server uses object recognition algorithms such as YOLO and SSD to categorize them into store exteriors, interiors, menus, dishes, people, etc. The input to this step is the "extracted frame group," and the output is the "categorized frame information."

[0224] Step 6:

[0225] The server blurs frames that contain people among the categorized frames. The server identifies faces using OpenCV's face recognition function and performs blurring. The input to this step is "categorized frame information that includes people," and the output is "frames that have been blurred."

[0226] Step 7:

[0227] The server saves the analyzed and processed frames. The categorized frames are saved to generate promotional materials by theme. The input of this step is "blurred frames" and "other categorized frames", and the output is "saved frame data".

[0228] Step 8:

[0229] The server uses the saved frames to generate promotional materials for the store. For example, it automatically generates slideshows and banner images. The input of this step is the "saved frame data" and the output is the "generated promotional materials."

[0230] Step 9:

[0231] The server provides the generated promotional materials to the store manager, who can then view and use them through the management screen. The input to this step is the "generated promotional materials" and the output is the "promotional materials provided to the store manager."

[0232] Step 10:

[0233] The server assigns points to the user. After the video is posted, the server adds the points to the user ID. The input of this step is the user ID and posting information in the metadata, and the output is the points assigned to the user account.

[0234] Step 11:

[0235] The user uses the points awarded for cashless payment. Using the SmartPromo app, the user can use the points to pay at cashless payment affiliated stores. The input for this step is the "point information stored in the app," and the output is the "payment result using the points."

[0236] 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.

[0237] The system of this invention automatically generates promotional materials for stores based on videos taken by tourists visiting Japan, promoting the use of cashless payments. Furthermore, by combining it with an emotion engine that recognizes the emotions of users in the videos and improves the quality of the promotional materials, more effective marketing can be achieved.

[0238] System configuration

[0239] 1. User Device

[0240] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device.

[0241] 2. Server

[0242] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. It also uses an emotion engine to recognize user emotions and reflect them in the promotional materials.

[0243] Program processing overview

[0244] Receiving and storing videos

[0245] The user device uploads the captured video to the server via the VOOM app.

[0246] The server stores the video received from the user terminal, extracts related metadata (user ID, video ID, upload time, visited store ID, etc.), and records it in a database.

[0247] Video Analysis

[0248] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[0249] The server applies object recognition algorithms to detect objects in the frame and classify them into categories (store exterior, store interior, menu, food, people).

[0250] Blurring

[0251] The server identifies frames that fall into the people category based on the analysis results, and uses a face detection algorithm to detect human faces within the frames.

[0252] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[0253] emotion recognition

[0254] The server applies an emotion engine to recognize the user's emotion from the extracted frames. The emotion engine analyzes the user's facial expression data to identify emotions (e.g., joy, anger, sadness, surprise, etc.).

[0255] Generate and provide promotional materials

[0256] The server stores the categorized frames by purpose, and the frames are stored in specific folders according to their category.

[0257] The server automatically generates promotional materials based on the stored frames and emotion data, for example creating slideshows and banner images that include many frames of the user's happy facial expressions.

[0258] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0259] Points allocation and use

[0260] After the video has been uploaded, the server awards points to the user based on preset rules.

[0261] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0262] Specific examples

[0263] Users visit tourist spots in Japan and film videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the video to a server via the VOOM app. The server receives the video, analyzes each frame, and categorizes it into categories such as store exterior, interior, menu, food, and people. Frames that show people are blurred. The emotion engine then analyzes the user's facial expressions in the frames, selecting frames with many happy expressions, automatically generating promotional materials, and providing them to store managers. In return, users are awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[0264] In this way, the present invention realizes a system that optimizes store marketing activities by providing effective promotional materials that are convenient for tourists and reflect the emotions of users.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] Users use their smartphones to shoot videos at stores and tourist spots in Japan, and the videos are saved in a dedicated application folder on the device.

[0268] Step 2:

[0269] Users upload the saved video to the server through the VOOM application. On the upload screen, they enter the video title and information about the store they visited, select the video file, and press the send button.

[0270] Step 3:

[0271] The server stores the video file received from the user. Upon receiving the file, it extracts the video metadata (user ID, video ID, upload time, visited store ID, etc.) and records it in a database.

[0272] Step 4:

[0273] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[0274] Step 5:

[0275] The server applies object recognition algorithms to detect objects in the extracted frames and classify each frame into a specific category (store exterior, store interior, menu, food, people, etc.).

[0276] Step 6:

[0277] The server identifies frames that fall into the people category and uses a face detection algorithm to detect human faces within the frames.

[0278] Step 7:

[0279] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[0280] Step 8:

[0281] The server applies an emotion engine to recognize the user's emotion from the extracted frames. The emotion engine analyzes the user's facial expression data in the frames and identifies emotions such as joy, surprise, sadness, and anger.

[0282] Step 9:

[0283] The server automatically generates promotional materials based on the categorized frames and emotion data. For example, it selects frames showing the most happy expressions of users and creates images for slideshows and banners.

[0284] Step 10:

[0285] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0286] Step 11:

[0287] After a video is posted, the server awards points to the user. The amount of points awarded is determined based on pre-defined rules. For example, 500 points are awarded for each video posted.

[0288] Step 12:

[0289] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0290] As a result, the system of the present invention can provide convenience and benefits to tourists, while also automatically generating effective promotional materials for stores, thereby improving marketing effectiveness.

[0291] Example 2

[0292] 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."

[0293] There is a need for a system that can effectively utilize videos taken by tourists visiting Japan to automatically generate promotional materials for stores and promote the use of cashless payments. However, current methods require a great deal of time and effort to analyze the videos and generate promotional materials, so an efficient and highly accurate method is needed. Other challenges include protecting personal information contained in the videos and making it difficult to implement marketing that reflects user emotions.

[0294] 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.

[0295] In this invention, the server includes means for receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for analyzing facial expression data of users in the frames to recognize emotions, means for generating promotional materials using the emotion data, means for providing the generated promotional materials to store managers, means for awarding points for posting videos, and means for users to use the points for payments on site. This makes it possible to efficiently analyze videos shot by tourists and automatically generate high-quality promotional materials, and further promote the use of cashless payments.

[0296] "Means for receiving videos taken by users" refers to a combination of infrastructure and software for receiving and storing video data uploaded from users' devices via a network.

[0297] The "means for extracting frames at regular intervals" refers to a process for extracting still images from video data at specified time intervals and dividing them into analyzable units.

[0298] "Means for analyzing frames and categorizing them into categories such as store exteriors, store interiors, menus, food, people, etc." refers to a technology that applies object recognition algorithms to identify objects in each frame and classify them into specific categories.

[0299] The "means of blurring frames classified into the person category" is a method of using a face detection algorithm and an image processing library to blur the face of a detected person in order to protect personal information.

[0300] "Means for recognizing emotions by analyzing the user's facial expression data in the frames" refers to a technology that analyzes the user's facial expressions from the extracted frames and uses an emotion engine to recognize specific emotions (joy, anger, sadness, surprise, etc.).

[0301] The "means for generating promotional materials using emotional data" is a method for automatically generating promotional materials such as slideshows and banner images, based on the results of emotional analysis, by preferentially using frames that express positive emotions.

[0302] The "means for providing the generated promotional materials to the store manager" is a system that uploads the generated promotional materials from a server to a management screen that can be accessed by the store manager, making them available for use.

[0303] The "means for awarding points for video submissions" is a reward system for awarding points to users who upload videos based on predefined rules.

[0304] "Means for users to use points for local payments" refers to a set of infrastructure and applications that provide an environment in which users can use their awarded points as an alternative to cash when making purchases or paying for services at physical stores.

[0305] This system automatically generates promotional materials for stores based on videos taken by tourists visiting Japan, promoting the use of cashless payments. The system's main hardware components include a user terminal and a server. Specifically, the user terminal is a device such as a smartphone, tablet, or PC, while the server provides high-performance cloud-based storage and computing resources.

[0306] Recording and uploading videos

[0307] The user device uses a dedicated video posting application (e.g., the VOOM app) to shoot videos of the store's exterior, interior, menu, food, etc. The videos are then uploaded to the server via the app.

[0308] Receiving and storing videos

[0309] The server receives the video data sent from the user's device. Along with the received video data, metadata such as the user ID, video ID, upload time, and visited store ID are sent. This data is stored in a database on the server. The video data itself is stored in highly secure storage.

[0310] Video Analysis

[0311] The server extracts frames from the received video at regular intervals, for example, using a technique to extract one frame per second. Then, an object recognition algorithm is applied to each extracted frame, and it is classified into store exteriors, store interiors, menus, dishes, people, etc.

[0312] Blurring and Emotion Recognition

[0313] The server identifies frames classified as "people" based on the object recognition results. Faces detected using a face detection algorithm (e.g., OpenCV) are blurred to ensure privacy. The emotion engine then analyzes the user's facial expression data in the extracted frames to recognize the user's emotions (e.g., joy, anger, sadness, surprise, etc.).

[0314] Producing and providing promotional materials

[0315] The server automatically generates promotional materials based on the classified frames and emotion data. The generated promotional materials include, for example, slideshows containing many frames of happy expressions from users and banner images, and these are saved in specific folders. The generated promotional materials are then automatically uploaded to the store manager's management screen, where they can be easily accessed and used by the store manager.

[0316] Points allocation and use

[0317] The server awards points to users who have completed uploading a video based on certain rules. These points can be used at cashless payment affiliated stores in Japan, and users can use the points via a smartphone app to make payments locally.

[0318] Examples of concrete examples and prompts

[0319] For example, a user might visit a famous ramen restaurant in a Tokyo tourist spot and film a video of the restaurant's exterior, interior, menu, and food. The user then uploads the video to a server via the VOOM app. The server receives the video and stores it in a database along with the metadata. The server then analyzes the video frame by frame, categorizing each frame into categories such as the restaurant's exterior, interior, menu, food, and people. A facial recognition algorithm is applied to frames containing people to blur them, and an emotion engine then analyzes the user's facial expressions and selects frames with many happy expressions to automatically generate promotional materials. These materials are then uploaded to the restaurant administrator's management screen. Users are then awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[0320] Example prompt sentence:

[0321] "Please upload videos of ramen shops in tourist spots in Japan that show their exteriors, interiors, menus, and dishes, and automatically generate promotional materials. Please prioritize happy expressions in promotional materials that reflect the emotions of users. After that, provide these materials to store managers, who can award points to users who posted videos, which can be used for cashless payments."

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] Step 1:

[0324] The user device uses the VOOM app to shoot videos of the store's exterior, interior, menu, food, etc. The input is the video data shot by the user, and the output is the video data and metadata (user ID, shooting date and time, store ID, etc.).

[0325] Step 2:

[0326] The user device uploads the captured video to the server via the VOOM app. Specifically, the app sends UDP / TCP packets to the server via an Internet connection to transfer the video data and metadata. The input is the video data and metadata in the user device, and the output is the data sent to the server.

[0327] Step 3:

[0328] The server receives the uploaded video data and metadata. The received data is stored in a database. The input is the video data and metadata sent from the user terminal, and the output is the data stored in the database. Specifically, the server splits the received data and stores it in the metadata table and video storage, respectively.

[0329] Step 4:

[0330] The server extracts frames from the video data at regular intervals, for example, using a technique to extract one frame per second. The input is the received video data, and the output is the extracted still frame images. Specifically, a video decoding algorithm is applied to extract frames at each time interval.

[0331] Step 5:

[0332] The server applies an object recognition algorithm to each extracted frame, detects objects in the frame, and classifies them into categories (store exterior, store interior, menu, food, people). The input is a still frame image, and the output is frame data tagged by category. Specifically, the server runs an image recognition algorithm using a machine learning model and saves the recognition results.

[0333] Step 6:

[0334] The server applies a face detection algorithm to frames classified as "people" to identify faces. It then uses an image processing library (e.g., OpenCV) to blur the detected face area. The input is a frame image in the "people" category, and the output is a frame image with the face blurred. Specifically, the server uses a face detection algorithm to identify the face area, and then applies a filter to blur that area.

[0335] Step 7:

[0336] The server analyzes the user's facial expression data in the blurred frames using an emotion engine. The input is the processed frame image, and the output is the analyzed emotion data. Specifically, it uses face recognition and expression analysis algorithms to estimate the emotional state for each frame.

[0337] Step 8:

[0338] The server automatically generates promotional materials based on the analysis results. It uses the emotional data to create slideshows and banner images that prioritize frames with a particular emotion (e.g., joy). The input is frames categorized by category and emotional data, and the output is the completed promotional materials. Specifically, it edits the selected frames and generates materials in a specific format.

[0339] Step 9:

[0340] The server saves the generated promotional materials in a specific folder and automatically uploads the data to the store manager's management screen. The input is the generated promotional materials, and the output is the data uploaded to the management screen. Specifically, the server uses a file transfer protocol to save the materials in a specified folder on the server and display them on the management screen.

[0341] Step 10:

[0342] The server awards points to users who have completed posting a video. The criteria for awarding points are determined based on pre-defined rules. The input is the metadata of the video post, and the output is the points added to the user account. Specifically, the server uses a point management system to add points to the user ID.

[0343] Step 11:

[0344] The user makes a payment using points at a cashless payment affiliated store. The payment is made using a smartphone app, with points applied. The input is the smartphone's point balance, and the output is a payment completion notification with points applied. Specifically, the points are applied via the payment API, and the result is notified to the user.

[0345] (Application example 2)

[0346] 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."

[0347] With conventional store promotion methods, creating promotional materials requires a great deal of effort and time, making it difficult to achieve effective marketing. It is also difficult to quickly generate materials that reflect the experiences of tourists visiting Japan, which means that the system lacks real-time capabilities. Furthermore, previous systems were unable to create materials that reflect users' emotions, preventing improvements in promotional effectiveness.

[0348] The identification process 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 receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for saving the categorized frames by purpose, means for using an emotion recognition engine to analyze user emotions, means for generating promotional materials using the saved frames and emotion data, means for providing the generated promotional materials to an administrator, means for awarding points for video submissions, and means for users to use the points for on-site payments. This enables automatic generation of promotional materials for stores and rapid, effective promotions that reflect user emotions.

[0349] "Videos taken by users" are video data taken by users using devices such as smartphones or smart glasses.

[0350] The "receiving means" refers to a communication method and function for transmitting a video taken by a user to a server via the Internet and for the server to receive the video.

[0351] "Means for extracting frames at regular intervals" refers to a processing method and function for extracting individual images (frames) from a received video at set intervals.

[0352] The "means for analyzing frames" refers to techniques and methods for analyzing extracted frames and classifying them into specific categories such as store exteriors, store interiors, menus, dishes, people, etc.

[0353] The "categorization means" refers to a method and function for dividing the analyzed frames into specific categories such as store exteriors, store interiors, menus, dishes, people, etc.

[0354] The "means for performing blurring processing" refers to a method and function for applying a blurring effect to the face part of a frame in the analyzed person category in order to protect personal information.

[0355] An "emotion recognition engine for analyzing emotions" is an algorithm and technology that detects the facial expressions of a user captured in a frame and recognizes the user's emotions (e.g., joy, surprise, sadness, etc.) from those expressions.

[0356] The "means for generating promotional materials" refers to a technology and method for automatically creating promotional materials (e.g., slideshows and banners) for a store using the stored frame and emotion data for each category.

[0357] The "means for providing to the store manager" refers to a method and function for providing the generated promotional material so that it can be used by the store manager.

[0358] The "means for awarding points" refers to a method and function for awarding points as a reward to a user who has posted a video.

[0359] "Means for users to use points for local payments" refers to methods and functions that allow users to use the accumulated points as a certain value when making cashless payments.

[0360] The system of the present invention automatically generates promotional materials from videos taken by users and promotes cashless payments. In particular, it realizes effective marketing by recognizing users' emotions and improving the quality of the materials based on those emotions.

[0361] System configuration

[0362] 1. User Device

[0363] Users use their smartphones or smart glasses to take videos of the inside and outside of the store. A dedicated video uploading application is installed on the device, allowing users to easily upload videos to the server.

[0364] 2. Server

[0365] The server plays a central role in receiving, storing, analyzing, and recognizing emotions in videos, as well as generating and providing promotional materials. Specific processes include:

[0366] Receiving and storing videos

[0367] The user terminal uploads the captured video to a server via a dedicated application.

[0368] The server stores the received video and records the associated metadata (e.g., user ID, video ID, upload time, visited store ID) in a database.

[0369] Video Analysis

[0370] The server extracts frames from the received video at regular intervals, for example, one frame per second, and treats the entire video as a series of still images.

[0371] The server uses object recognition algorithms to detect objects in the frame and classify them into categories (e.g., store exterior, store interior, menu, food, people).

[0372] Blurring

[0373] The server applies a face detection algorithm to frames classified as people and blurs the facial areas, ensuring the protection of personal information.

[0374] emotion recognition

[0375] The server uses an emotion recognition engine to analyze the user's facial expressions in the extracted frames and identify emotions (e.g., joy, surprise, sadness, etc.).

[0376] Generate and provide promotional materials

[0377] The server automatically generates promotional materials based on the stored frames and emotion data, for example, creating slideshows and banner images that include many frames of the user's happy facial expressions.

[0378] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0379] Points allocation and use

[0380] After a video is uploaded, the server awards points to the user based on preset rules.

[0381] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0382] Technology used

[0383] Hardware: Smart glasses, smartphones

[0384] Software: Python, OpenCV, FER library

[0385] Specific examples

[0386] A user visits a tourist spot and takes videos of the exterior, interior, menu, and food of a store (e.g., a restaurant). The user takes videos using smart glasses or a smartphone and uploads them to a server using a dedicated application. The server analyzes the videos, categorizes each frame, and blurs people's faces. It also uses an emotion recognition engine to analyze the user's emotions and generates promotional materials that include many happy expressions. These promotional materials are provided to the store manager's screen, and the user is awarded points.

[0387] Prompt Sentence Examples

[0388] "Generate promotional materials using videos of users having fun in the store. Analyze the emotions in the videos and create a slideshow that includes many scenes where users are particularly happy."

[0389] This system makes it possible to quickly generate effective promotional materials that reflect the actual experiences of tourists visiting Japan.

[0390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0391] Step 1:

[0392] Users use their smartphones or smart glasses to take videos of the inside and outside of a store. The videos are then uploaded to a server via a dedicated video posting application. The input is the video taken by the user, and the output is the video data uploaded to the server.

[0393] Step 2:

[0394] The server receives the uploaded video, extracts the associated metadata (e.g., user ID, video ID, upload time, visited store ID) and records it in a database. The input is the uploaded video data, and the output is the video data and metadata stored in the database.

[0395] Step 3:

[0396] The server extracts frames from the received video at regular intervals. For example, it extracts one frame per second and treats the entire video as a series of still images. The input is the received video data, and the output is a list of extracted frame data.

[0397] Step 4:

[0398] The server applies an object recognition algorithm to detect objects in each frame and classify them into categories (e.g., store exterior, store interior, menu, food, person). The input is the extracted frames, and the output is the categorized frame data.

[0399] Step 5:

[0400] The server applies a face detection algorithm to frames classified as people and blurs the face, thereby protecting privacy. The input is a frame classified as people, and the output is a blurred frame.

[0401] Step 6:

[0402] The server uses an emotion recognition engine to analyze the user's facial expressions in each frame and identify emotions (e.g., happiness, surprise, sadness, etc.). The input is the blurred frames, and the output is the emotion data.

[0403] Step 7:

[0404] The server automatically generates promotional materials based on the stored frames and emotion data. For example, it creates slideshows or banner images that include many frames of the user's happy facial expressions. The input is emotion data and the stored frames, and the output is the generated promotional materials.

[0405] Step 8:

[0406] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager. The input is the generated promotional materials, and the output is the promotional materials that can be viewed by the store manager.

[0407] Step 9:

[0408] The server awards points to users after they have completed uploading a video. The criteria for awarding points are determined based on pre-defined rules. The input is the video upload data, and the output is the awarded points.

[0409] Step 10:

[0410] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan. When paying, they can use their points to get a discount. The input is the points held by the user, and the output is the discount amount at the time of payment.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Second embodiment]

[0415] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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."

[0427] The system of the present invention automatically generates promotional materials for stores based on videos taken by tourists visiting Japan and promotes the use of cashless payments. A specific embodiment for implementing this system will be described below.

[0428] System configuration

[0429] 1. User Device

[0430] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device.

[0431] 2. Server

[0432] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. The server is equipped with a high-performance processor and large-capacity storage.

[0433] Program processing overview

[0434] Receiving and storing videos

[0435] The user device uploads the captured video to the server via the VOOM app.

[0436] The server stores the video received from the user terminal and records the associated metadata (user ID, video ID, upload time, visited store ID) in a database.

[0437] Video Analysis

[0438] The server extracts frames from the received video at regular intervals, for example, one frame per second.

[0439] The server applies object recognition algorithms to identify objects in the frame and sort them into categories (store exterior, store interior, menu, food, people).

[0440] Blurring

[0441] The server selects frames that are classified into the person category based on the analysis results.

[0442] The server uses a face detection algorithm to identify people's faces and then uses an image processing library to blur the facial areas, thereby protecting personal information.

[0443] Generate and provide promotional materials

[0444] The server stores categorized frames by theme.

[0445] The server uses the saved frames to automatically generate promotional materials for the store (e.g., slideshows, banner images, etc.).

[0446] The server provides the generated promotional materials to the management screen of the store manager so that the promotional materials can be used directly.

[0447] Points allocation and use

[0448] After the video is uploaded, the server awards points to the user. The criteria for awarding points is, for example, 500 points per video upload.

[0449] Users can use the smartphone app to earn points when shopping or eating at cashless payment affiliated stores in Japan, and the points are applied as discounts according to the purchase amount.

[0450] Specific examples

[0451] Users visit tourist spots in Japan and take videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the videos they have taken to a server via the VOOM app. The server receives the videos, analyzes each frame, and categorizes them into categories such as the store's exterior, interior, menu, food, and people. Frames that show people are blurred. Promotional materials are then automatically generated and provided to the store manager. Users are also awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[0452] In this way, the present invention realizes a system that is convenient for tourists and effectively supports store promotions.

[0453] The processing flow will be explained below.

[0454] Step 1:

[0455] Users use their smartphones to shoot videos at stores and tourist spots in Japan, and the videos are saved in a dedicated application folder on the device.

[0456] Step 2:

[0457] Users upload the saved video to the server through the VOOM application. On the upload screen, they enter the video title and information about the store they visited, select the video file, and press the send button.

[0458] Step 3:

[0459] The server stores the video file received from the user. Upon receiving the file, it extracts the video metadata (user ID, video ID, upload time, visited store ID, etc.) and records it in a database.

[0460] Step 4:

[0461] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[0462] Step 5:

[0463] The server applies object recognition algorithms to detect objects in the extracted frames and classify each frame into a specific category (store exterior, store interior, menu, food, people, etc.).

[0464] Step 6:

[0465] The server identifies frames that fall into the people category and uses a face detection algorithm to detect human faces within the frames.

[0466] Step 7:

[0467] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[0468] Step 8:

[0469] The server stores the categorized frames according to their purpose, and the frames are stored in specific folders according to their classification.

[0470] Step 9:

[0471] The server automatically generates promotional materials based on the saved frames, for example, creating slideshows and banner images using images of the store's exterior and food.

[0472] Step 10:

[0473] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0474] Step 11:

[0475] After the video has been posted, the server awards points to the user. The amount of points awarded is determined based on preset rules.

[0476] Step 12:

[0477] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0478] The above are the specific processing steps in this system.

[0479] Example 1

[0480] 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."

[0481] In recent years, the number of video content shot by tourists and users visiting Japan has increased, but it is difficult to automatically generate promotional materials for stores that effectively utilize these videos. There is also a need for technology that can efficiently create promotional materials while also protecting personal information. Furthermore, incentives for video contributors are also necessary, and it is desirable that these incentives contribute to the spread of cashless payments.

[0482] 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.

[0483] In this invention, the server includes means for receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for saving the categorized frames by purpose, means for generating promotional materials for the store using the saved frames, means for providing the generated promotional materials to the store manager, means for awarding points for posting videos, and means for users to use the points for local payments. This makes it possible to efficiently analyze videos shot by tourists visiting Japan and users and convert them into promotional materials while protecting personal information, and further promote the spread of cashless payments by awarding points to posters.

[0484] A "user terminal" is a device such as a smartphone, tablet, or computer that a user uses to shoot videos and upload them to the system.

[0485] "Server" refers to a computer system equipped with a high-performance processor and large-capacity storage that communicates with user terminals via the Internet, stores and analyzes received videos, and generates promotional materials.

[0486] "Receiving video" is a process in which a user sends a video taken by the user from the user terminal to a server, and the server receives the video.

[0487] "Frame extraction" is the process of extracting still images (frames) from the received video at regular intervals.

[0488] "Frame analysis" is the process of using object recognition algorithms to identify and categorize objects within extracted frames.

[0489] An "object recognition algorithm" is an artificial intelligence technique used to identify and classify objects within a frame.

[0490] "Blurring" is an image processing technique that blurs the facial area of ​​frames classified as people to protect personal information.

[0491] A "face detection algorithm" is an artificial intelligence technique used to identify the faces of people within a frame.

[0492] An "image processing library" is a collection of software tools for editing and processing images.

[0493] "Promotional materials" are content such as slideshows and banner images used for store promotion and advertising.

[0494] "Point awarding" is the process of providing points as an incentive to users who post videos.

[0495] "Cashless payment" is a system in which payments are made electronically without using cash.

[0496] MODE FOR CARRYING OUT THE INVENTION

[0497] The system of the present invention automatically generates promotional materials based on videos taken by tourists visiting Japan or users on-site, and promotes the use of cashless payments. Specific embodiments for implementing this system are described below.

[0498] System configuration

[0499] 1. User Device

[0500] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device. This application is responsible for sending videos, adding metadata, and uploading them.

[0501] 2. Server

[0502] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. The server is equipped with a high-performance processor and large-capacity storage. The main software used includes object recognition algorithms (e.g., YOLO, Mask R-CNN), face detection algorithms (e.g., OpenCV's Haar Cascade), and image processing libraries (e.g., OpenCV, PIL).

[0503] Receiving and storing videos

[0504] The user device uploads the video they have taken to the server using a dedicated video posting app. The server receives the uploaded video and stores it in a temporary storage area. It also records metadata related to the video (user ID, video ID, upload time, visited store ID) in a database.

[0505] Video Analysis

[0506] The server reads the saved video file and extracts frames at regular intervals. Specifically, it is set to extract one frame every second. The extracted frames are analyzed using an object recognition algorithm and sorted into categories such as store exteriors, store interiors, menus, dishes, and people.

[0507] Blurring

[0508] The server applies a face detection algorithm to frames classified as people to identify faces, then uses an image processing library to blur the face area to protect personal information, making it difficult to identify individuals.

[0509] Generate and provide promotional materials

[0510] The server stores the categorized frames by theme and automatically generates promotional materials such as slideshows and banner images. The generated promotional materials are provided to the store administrator's management screen and can be easily used.

[0511] Points allocation and use

[0512] The server awards points to users after they have posted a video. The standard for awarding points is, for example, 500 points per video post. Users can use the smartphone app to redeem points at cashless payment affiliated stores in Japan. Points are applied as discounts according to the purchase amount.

[0513] Specific examples

[0514] Users visit tourist spots in Japan and take videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the videos they have taken to a server using a dedicated app. The server receives the videos, analyzes each frame, and categorizes them into categories such as the store's exterior, interior, menu, food, and people. Frames that show people are blurred. Promotional materials are then automatically generated and provided to the store manager. Users are rewarded for posting videos with points, which can be used at cashless payment affiliated stores in Japan.

[0515] Prompt Sentence Examples

[0516] "A user visits a tourist spot in Japan and takes a video of a store's exterior, interior, menu, and food. The user uploads this video to a server via the app, and the server analyzes the video and automatically generates promotional materials. Please give us an overview of this system."

[0517] The AI ​​model generated based on this prompt can provide detailed explanations of the system's detailed operation and the technical elements of each step.

[0518] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0519] Step 1:

[0520] Recording and uploading videos

[0521] Input: Videos taken by users using devices such as smartphones

[0522] How it works: A user launches a video posting application, selects a video they have taken, and presses the submit button to upload the video.

[0523] Output: Sending video data from the user device to the server

[0524] Step 2:

[0525] Receiving and storing videos

[0526] Input: Video data sent from the user device

[0527] How it works: The server receives the video via the Internet and stores it in a temporary storage area. At the same time, it analyzes the metadata associated with the video (user ID, video ID, upload time, visited store ID) and records it in a database.

[0528] Output: Video files stored in temporary storage and metadata stored in a database

[0529] Step 3:

[0530] Extracting Frames

[0531] Input: Saved video file

[0532] Operation: The server loads the video file and extracts frames at regular intervals. Specifically, it is set to extract one frame every second.

[0533] Output: Individual frames stored in temporary storage

[0534] Step 4:

[0535] Frame analysis and categorization

[0536] Input: Extracted individual frames

[0537] How it works: The server applies object recognition algorithms (e.g., YOLO, Mask R-CNN) to identify objects in the frame, then categorizes them into categories such as store exteriors, store interiors, menus, dishes, people, etc.

[0538] Output: Frames sorted by category

[0539] Step 5:

[0540] Blurring

[0541] Input: Frames sorted into the people category

[0542] How it works: The server uses a face detection algorithm (e.g., OpenCV's Haar Cascade) to identify faces in the frame, and then uses an image processing library (e.g., OpenCV, PIL) to blur the identified faces.

[0543] Output: Frame with face blurred

[0544] Step 6:

[0545] Save Frame

[0546] Input: Categorized and blurred frames

[0547] How it works: The server stores these frames in folders by theme, for example, store exterior frames in an "Exterior" folder, food frames in a "Food" folder, etc.

[0548] Output: Thematically organized frame data

[0549] Step 7:

[0550] Generate promotional materials

[0551] Input: Frame data organized by theme

[0552] How it works: The server selects the saved frames and automatically generates promotional materials such as slideshows and banner images, stitching the selected frames together and adding text.

[0553] Output: Auto-generated promotional materials

[0554] Step 8:

[0555] Providing promotional materials

[0556] Input: Auto-generated promotional material

[0557] Operation: The server uploads the generated promotional materials to the store manager's management screen, where the manager can easily use the materials.

[0558] Output: Promotional materials uploaded to the dashboard

[0559] Step 9:

[0560] Points awarded

[0561] Input: Video submission completion data

[0562] Operation: The server confirms the completion of video posting and awards points to the user. Points are awarded at a rate of 500 points per video posted. The point information is updated in the user database.

[0563] Output: Update of awarded points to user database

[0564] Step 10:

[0565] Using points

[0566] Input: Points awarded

[0567] How it works: Users use a smartphone app to redeem points at cashless payment affiliated stores in Japan. The app provides a function to select the application of points at the time of payment, specifically by scanning a QR code or presenting a barcode.

[0568] Output: Discount applied by using points at cashless payment affiliated stores

[0569] (Application example 1)

[0570] 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."

[0571] In today's tourism industry, it is extremely important to effectively promote stores visited by tourists visiting Japan. However, in many cases, videos taken by tourists are not effectively utilized. Furthermore, there are issues with privacy protection and promoting cashless payments. Specifically, there is a lack of methods to automatically generate promotional materials for stores and encourage visitors to use cashless payments. As a result, not only are store promotional activities ineffective, but convenience for tourists is also diminished.

[0572] 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.

[0573] In this invention, the server includes a terminal device for a user to shoot videos at a store and upload the videos to the server, a means for analyzing the shot and uploaded videos on the server and classifying them into store exteriors, interiors, menus, dishes, people, etc. using an object recognition algorithm, a means for blurring facial portions of the analyzed frames using an image processing library, a means for automatically generating store promotional materials from the analyzed frames and providing them to a store manager, and a means for awarding points that can be used for cashless payments to users. This makes it possible to effectively utilize videos shot by tourists visiting Japan to automatically generate store promotional materials and promote cashless payments while simultaneously protecting their privacy.

[0574] A "user terminal" is a device that a user uses to shoot video and upload the video to a server.

[0575] "Server" refers to a device equipped with a high-performance processor and large-capacity storage that communicates with user terminals via the Internet, stores and analyzes received videos, and generates promotional materials.

[0576] An "object recognition algorithm" is an algorithm that identifies objects within video frames and classifies them into store exteriors, interiors, menus, dishes, people, etc.

[0577] An "image processing library" is a library for processing and editing images, and is used for blurring and other processes.

[0578] "Promotional Materials" refers to generated visual content, such as slideshows and banner images, used in store promotional activities.

[0579] The "point awarding means" is a means for awarding points to users for posting videos, and provides points that users can use for cashless payments.

[0580] The "analysis means" is a processing device or software that has the function of allowing the server to analyze uploaded videos and classify them into specific categories.

[0581] The "blurring means" is a means for protecting privacy by blurring the facial portion of a person in an image.

[0582] An embodiment of the present invention will be described. This is a system that automatically generates promotional materials for stores based on videos taken by tourists visiting Japan and promotes the use of cashless payment. This system includes the following components.

[0583] System configuration

[0584] User terminal

[0585] A user device is a device that a user uses to shoot videos and upload them to a server. This includes smartphones, tablets, and PCs. A dedicated video posting application (hereafter referred to as the SmartPromo app) is installed on these devices.

[0586] server

[0587] The server communicates with user devices via the Internet, stores and analyzes the received video, and generates promotional materials. The server is equipped with a high-performance processor (e.g., Intel Xeon or AMD Ryzen series) and large-capacity storage (HDD or SSD). The server runs an object recognition algorithm (e.g., YOLO or SSD) and an image processing library (e.g., OpenCV), which analyzes frames and performs blurring.

[0588] Receiving and storing videos

[0589] A user shoots a video using the SmartPromo app and uploads it to the server. The server receives the video data along with metadata (user ID, video ID, upload time, store ID). This data is recorded in a database (e.g., MySQL or SQLite), and the video itself is stored in storage.

[0590] Video analysis and processing

[0591] The server extracts frames from the stored video at regular intervals. For example, it can be set to extract one frame every second. The extracted frames are then categorized using an object recognition algorithm into categories such as store exteriors, interiors, menus, food, and people. For frames classified as people, a face detection algorithm (for example, OpenCV's face recognition function) is used to blur the faces.

[0592] Generate promotional materials

[0593] The server saves the analyzed frames by theme and automatically generates promotional materials such as slideshows and banner images. The generated materials are provided to store managers through a dedicated management screen, allowing store managers to use these materials to carry out effective store promotions.

[0594] Points allocation and use

[0595] When a user posts a video, the server will award the user points. The points can be viewed within the SmartPromo app and can be used at cashless payment affiliated stores in Japan. The standard for awarding points could be 500 points per video post.

[0596] Specific Examples

[0597] For example, a user shoots a video at a restaurant in a tourist spot in Japan and uploads it to the server using the SmartPromo app. The server receives the video, extracts and analyzes frames every second, and classifies them into categories such as the restaurant's exterior, interior, menu, food, and people. Frames containing people are subjected to facial recognition and blurring. Promotional materials are then generated and provided to the restaurant manager, and points are awarded to the user. These points can be used for the next cashless payment, increasing user convenience.

[0598] Prompt Sentence Examples

[0599] Users can upload videos of their restaurants. The videos are analyzed into categories of restaurant exterior, interior, menu, food, and people. Faces of people are blurred and promotional materials for the restaurant are generated. The generated promotional materials are provided to the restaurant manager, and the user is awarded 500 points. The points can be used for the next cashless payment.

[0600] In this way, the present invention provides a system that contributes to the promotion of the tourism industry and cashless payments by effectively utilizing videos taken by tourists visiting Japan and automatically generating promotional materials for stores.

[0601] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0602] Step 1:

[0603] A user takes a video in a store. The user uses the SmartPromo app to take a video and temporarily saves it on the device. The input of this step is the "taken video data" and the output is the "video file saved on the device."

[0604] Step 2:

[0605] The user uploads the video they have taken to the server. On the user's device, they use the SmartPromo app to send the video to the server. At this time, metadata such as the user ID, video ID, upload time, and store ID are also sent. The input for this step is the "video file and metadata on the device," and the output is the "video file and metadata stored on the server."

[0606] Step 3:

[0607] The server stores the received video data. The server stores the video data in storage (e.g., HDD or SSD) and records the metadata in a database (e.g., MySQL or SQLite). The input of this step is the "video data and metadata uploaded to the server," and the output is the "video file stored in storage and the metadata recorded in the database."

[0608] Step 4:

[0609] The server extracts frames from the video at regular intervals. For example, it can be set to extract one frame every second. The input of this step is the "saved video file" and the output is the "extracted frames."

[0610] Step 5:

[0611] The server analyzes the extracted frames using an object recognition algorithm. The server uses object recognition algorithms such as YOLO and SSD to categorize them into store exteriors, interiors, menus, dishes, people, etc. The input to this step is the "extracted frame group," and the output is the "categorized frame information."

[0612] Step 6:

[0613] The server blurs frames that contain people among the categorized frames. The server identifies faces using OpenCV's face recognition function and performs blurring. The input to this step is "categorized frame information that includes people," and the output is "frames that have been blurred."

[0614] Step 7:

[0615] The server saves the analyzed and processed frames. The categorized frames are saved to generate promotional materials by theme. The input of this step is "blurred frames" and "other categorized frames", and the output is "saved frame data".

[0616] Step 8:

[0617] The server uses the saved frames to generate promotional materials for the store. For example, it automatically generates slideshows and banner images. The input of this step is the "saved frame data" and the output is the "generated promotional materials."

[0618] Step 9:

[0619] The server provides the generated promotional materials to the store manager, who can then view and use them through the management screen. The input to this step is the "generated promotional materials" and the output is the "promotional materials provided to the store manager."

[0620] Step 10:

[0621] The server assigns points to the user. After the video is posted, the server adds the points to the user ID. The input of this step is the user ID and posting information in the metadata, and the output is the points assigned to the user account.

[0622] Step 11:

[0623] The user uses the points awarded for cashless payment. Using the SmartPromo app, the user can use the points to pay at cashless payment affiliated stores. The input for this step is the "point information stored in the app," and the output is the "payment result using the points."

[0624] 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.

[0625] The system of this invention automatically generates promotional materials for stores based on videos taken by tourists visiting Japan, promoting the use of cashless payments. Furthermore, by combining it with an emotion engine that recognizes the emotions of users in the videos and improves the quality of the promotional materials, more effective marketing can be achieved.

[0626] System configuration

[0627] 1. User Device

[0628] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device.

[0629] 2. Server

[0630] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. It also uses an emotion engine to recognize user emotions and reflect them in the promotional materials.

[0631] Program processing overview

[0632] Receiving and storing videos

[0633] The user device uploads the captured video to the server via the VOOM app.

[0634] The server stores the video received from the user terminal, extracts related metadata (user ID, video ID, upload time, visited store ID, etc.), and records it in a database.

[0635] Video Analysis

[0636] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[0637] The server applies object recognition algorithms to detect objects in the frame and classify them into categories (store exterior, store interior, menu, food, people).

[0638] Blurring

[0639] The server identifies frames that fall into the people category based on the analysis results, and uses a face detection algorithm to detect human faces within the frames.

[0640] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[0641] emotion recognition

[0642] The server applies an emotion engine to recognize the user's emotion from the extracted frames. The emotion engine analyzes the user's facial expression data to identify emotions (e.g., joy, anger, sadness, surprise, etc.).

[0643] Generate and provide promotional materials

[0644] The server stores the categorized frames by purpose, and the frames are stored in specific folders according to their category.

[0645] The server automatically generates promotional materials based on the stored frames and emotion data, for example creating slideshows and banner images that include many frames of the user's happy facial expressions.

[0646] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0647] Points allocation and use

[0648] After the video has been uploaded, the server awards points to the user based on preset rules.

[0649] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0650] Specific examples

[0651] Users visit tourist spots in Japan and film videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the video to a server via the VOOM app. The server receives the video, analyzes each frame, and categorizes it into categories such as store exterior, interior, menu, food, and people. Frames that show people are blurred. The emotion engine then analyzes the user's facial expressions in the frames, selecting frames with many happy expressions, automatically generating promotional materials, and providing them to store managers. In return, users are awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[0652] In this way, the present invention realizes a system that optimizes store marketing activities by providing effective promotional materials that are convenient for tourists and reflect the emotions of users.

[0653] The processing flow will be explained below.

[0654] Step 1:

[0655] Users use their smartphones to shoot videos at stores and tourist spots in Japan, and the videos are saved in a dedicated application folder on the device.

[0656] Step 2:

[0657] Users upload the saved video to the server through the VOOM application. On the upload screen, they enter the video title and information about the store they visited, select the video file, and press the send button.

[0658] Step 3:

[0659] The server stores the video file received from the user. Upon receiving the file, it extracts the video metadata (user ID, video ID, upload time, visited store ID, etc.) and records it in a database.

[0660] Step 4:

[0661] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[0662] Step 5:

[0663] The server applies object recognition algorithms to detect objects in the extracted frames and classify each frame into a specific category (store exterior, store interior, menu, food, people, etc.).

[0664] Step 6:

[0665] The server identifies frames that fall into the people category and uses a face detection algorithm to detect human faces within the frames.

[0666] Step 7:

[0667] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[0668] Step 8:

[0669] The server applies an emotion engine to recognize the user's emotion from the extracted frames. The emotion engine analyzes the user's facial expression data in the frames and identifies emotions such as joy, surprise, sadness, and anger.

[0670] Step 9:

[0671] The server automatically generates promotional materials based on the categorized frames and emotion data. For example, it selects frames showing the most happy expressions of users and creates images for slideshows and banners.

[0672] Step 10:

[0673] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0674] Step 11:

[0675] After a video is posted, the server awards points to the user. The amount of points awarded is determined based on pre-defined rules. For example, 500 points are awarded for each video posted.

[0676] Step 12:

[0677] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0678] As a result, the system of the present invention can provide convenience and benefits to tourists, while also automatically generating effective promotional materials for stores, thereby improving marketing effectiveness.

[0679] Example 2

[0680] 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."

[0681] There is a need for a system that can effectively utilize videos taken by tourists visiting Japan to automatically generate promotional materials for stores and promote the use of cashless payments. However, current methods require a great deal of time and effort to analyze the videos and generate promotional materials, so an efficient and highly accurate method is needed. Other challenges include protecting personal information contained in the videos and making it difficult to implement marketing that reflects user emotions.

[0682] 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.

[0683] In this invention, the server includes means for receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for analyzing facial expression data of users in the frames to recognize emotions, means for generating promotional materials using the emotion data, means for providing the generated promotional materials to store managers, means for awarding points for posting videos, and means for users to use the points for payments on site. This makes it possible to efficiently analyze videos shot by tourists and automatically generate high-quality promotional materials, and further promote the use of cashless payments.

[0684] "Means for receiving videos taken by users" refers to a combination of infrastructure and software for receiving and storing video data uploaded from users' devices via a network.

[0685] The "means for extracting frames at regular intervals" refers to a process for extracting still images from video data at specified time intervals and dividing them into analyzable units.

[0686] "Means for analyzing frames and categorizing them into categories such as store exteriors, store interiors, menus, food, people, etc." refers to a technology that applies object recognition algorithms to identify objects in each frame and classify them into specific categories.

[0687] The "means of blurring frames classified into the person category" is a method of using a face detection algorithm and an image processing library to blur the face of a detected person in order to protect personal information.

[0688] "Means for recognizing emotions by analyzing the user's facial expression data in the frames" refers to a technology that analyzes the user's facial expressions from the extracted frames and uses an emotion engine to recognize specific emotions (joy, anger, sadness, surprise, etc.).

[0689] The "means for generating promotional materials using emotional data" is a method for automatically generating promotional materials such as slideshows and banner images, based on the results of emotional analysis, by preferentially using frames that express positive emotions.

[0690] The "means for providing the generated promotional materials to the store manager" is a system that uploads the generated promotional materials from a server to a management screen that can be accessed by the store manager, making them available for use.

[0691] The "means for awarding points for video submissions" is a reward system for awarding points to users who upload videos based on predefined rules.

[0692] "Means for users to use points for local payments" refers to a set of infrastructure and applications that provide an environment in which users can use their awarded points as an alternative to cash when making purchases or paying for services at physical stores.

[0693] This system automatically generates promotional materials for stores based on videos taken by tourists visiting Japan, promoting the use of cashless payments. The system's main hardware components include a user terminal and a server. Specifically, the user terminal is a device such as a smartphone, tablet, or PC, while the server provides high-performance cloud-based storage and computing resources.

[0694] Recording and uploading videos

[0695] The user device uses a dedicated video posting application (e.g., the VOOM app) to shoot videos of the store's exterior, interior, menu, food, etc. The videos are then uploaded to the server via the app.

[0696] Receiving and storing videos

[0697] The server receives the video data sent from the user's device. Along with the received video data, metadata such as the user ID, video ID, upload time, and visited store ID are sent. This data is stored in a database on the server. The video data itself is stored in highly secure storage.

[0698] Video Analysis

[0699] The server extracts frames from the received video at regular intervals, for example, using a technique to extract one frame per second. Then, an object recognition algorithm is applied to each extracted frame, and it is classified into store exteriors, store interiors, menus, dishes, people, etc.

[0700] Blurring and Emotion Recognition

[0701] The server identifies frames classified as "people" based on the object recognition results. Faces detected using a face detection algorithm (e.g., OpenCV) are blurred to ensure privacy. The emotion engine then analyzes the user's facial expression data in the extracted frames to recognize the user's emotions (e.g., joy, anger, sadness, surprise, etc.).

[0702] Producing and providing promotional materials

[0703] The server automatically generates promotional materials based on the classified frames and emotion data. The generated promotional materials include, for example, slideshows containing many frames of happy expressions from users and banner images, and these are saved in specific folders. The generated promotional materials are then automatically uploaded to the store manager's management screen, where they can be easily accessed and used by the store manager.

[0704] Points allocation and use

[0705] The server awards points to users who have completed uploading a video based on certain rules. These points can be used at cashless payment affiliated stores in Japan, and users can use the points via a smartphone app to make payments locally.

[0706] Examples of concrete examples and prompts

[0707] For example, a user might visit a famous ramen restaurant in a Tokyo tourist spot and film a video of the restaurant's exterior, interior, menu, and food. The user then uploads the video to a server via the VOOM app. The server receives the video and stores it in a database along with the metadata. The server then analyzes the video frame by frame, categorizing each frame into categories such as the restaurant's exterior, interior, menu, food, and people. A facial recognition algorithm is applied to frames containing people to blur them, and an emotion engine then analyzes the user's facial expressions and selects frames with many happy expressions to automatically generate promotional materials. These materials are then uploaded to the restaurant administrator's management screen. Users are then awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[0708] Example prompt sentence:

[0709] "Please upload videos of ramen shops in tourist spots in Japan that show their exteriors, interiors, menus, and dishes, and automatically generate promotional materials. Please prioritize happy expressions in promotional materials that reflect the emotions of users. After that, provide these materials to store managers, who can award points to users who posted videos, which can be used for cashless payments."

[0710] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0711] Step 1:

[0712] The user device uses the VOOM app to shoot videos of the store's exterior, interior, menu, food, etc. The input is the video data shot by the user, and the output is the video data and metadata (user ID, shooting date and time, store ID, etc.).

[0713] Step 2:

[0714] The user device uploads the captured video to the server via the VOOM app. Specifically, the app sends UDP / TCP packets to the server via an Internet connection to transfer the video data and metadata. The input is the video data and metadata in the user device, and the output is the data sent to the server.

[0715] Step 3:

[0716] The server receives the uploaded video data and metadata. The received data is stored in a database. The input is the video data and metadata sent from the user terminal, and the output is the data stored in the database. Specifically, the server splits the received data and stores it in the metadata table and video storage, respectively.

[0717] Step 4:

[0718] The server extracts frames from the video data at regular intervals, for example, using a technique to extract one frame per second. The input is the received video data, and the output is the extracted still frame images. Specifically, a video decoding algorithm is applied to extract frames at each time interval.

[0719] Step 5:

[0720] The server applies an object recognition algorithm to each extracted frame, detects objects in the frame, and classifies them into categories (store exterior, store interior, menu, food, people). The input is a still frame image, and the output is frame data tagged by category. Specifically, the server runs an image recognition algorithm using a machine learning model and saves the recognition results.

[0721] Step 6:

[0722] The server applies a face detection algorithm to frames classified as "people" to identify faces. It then uses an image processing library (e.g., OpenCV) to blur the detected face area. The input is a frame image in the "people" category, and the output is a frame image with the face blurred. Specifically, the server uses a face detection algorithm to identify the face area, and then applies a filter to blur that area.

[0723] Step 7:

[0724] The server analyzes the user's facial expression data in the blurred frames using an emotion engine. The input is the processed frame image, and the output is the analyzed emotion data. Specifically, it uses face recognition and expression analysis algorithms to estimate the emotional state for each frame.

[0725] Step 8:

[0726] The server automatically generates promotional materials based on the analysis results. It uses the emotional data to create slideshows and banner images that prioritize frames with a particular emotion (e.g., joy). The input is frames categorized by category and emotional data, and the output is the completed promotional materials. Specifically, it edits the selected frames and generates materials in a specific format.

[0727] Step 9:

[0728] The server saves the generated promotional materials in a specific folder and automatically uploads the data to the store manager's management screen. The input is the generated promotional materials, and the output is the data uploaded to the management screen. Specifically, the server uses a file transfer protocol to save the materials in a specified folder on the server and display them on the management screen.

[0729] Step 10:

[0730] The server awards points to users who have completed posting a video. The criteria for awarding points are determined based on pre-defined rules. The input is the metadata of the video post, and the output is the points added to the user account. Specifically, the server uses a point management system to add points to the user ID.

[0731] Step 11:

[0732] The user makes a payment using points at a cashless payment affiliated store. The payment is made using a smartphone app, with points applied. The input is the smartphone's point balance, and the output is a payment completion notification with points applied. Specifically, the points are applied via the payment API, and the result is notified to the user.

[0733] (Application example 2)

[0734] 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."

[0735] With conventional store promotion methods, creating promotional materials requires a great deal of effort and time, making it difficult to achieve effective marketing. It is also difficult to quickly generate materials that reflect the experiences of tourists visiting Japan, which means that the system lacks real-time capabilities. Furthermore, previous systems were unable to create materials that reflect users' emotions, preventing improvements in promotional effectiveness.

[0736] The identification process 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 receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for saving the categorized frames by purpose, means for using an emotion recognition engine to analyze user emotions, means for generating promotional materials using the saved frames and emotion data, means for providing the generated promotional materials to an administrator, means for awarding points for video submissions, and means for users to use the points for on-site payments. This enables automatic generation of promotional materials for stores and rapid, effective promotions that reflect user emotions.

[0737] "Videos taken by users" are video data taken by users using devices such as smartphones or smart glasses.

[0738] The "receiving means" refers to a communication method and function for transmitting a video taken by a user to a server via the Internet and for the server to receive the video.

[0739] "Means for extracting frames at regular intervals" refers to a processing method and function for extracting individual images (frames) from a received video at set intervals.

[0740] The "means for analyzing frames" refers to techniques and methods for analyzing extracted frames and classifying them into specific categories such as store exteriors, store interiors, menus, dishes, people, etc.

[0741] The "categorization means" refers to a method and function for dividing the analyzed frames into specific categories such as store exteriors, store interiors, menus, dishes, people, etc.

[0742] The "means for performing blurring processing" refers to a method and function for applying a blurring effect to the face part of a frame in the analyzed person category in order to protect personal information.

[0743] An "emotion recognition engine for analyzing emotions" is an algorithm and technology that detects the facial expressions of a user captured in a frame and recognizes the user's emotions (e.g., joy, surprise, sadness, etc.) from those expressions.

[0744] The "means for generating promotional materials" refers to a technology and method for automatically creating promotional materials (e.g., slideshows and banners) for a store using the stored frame and emotion data for each category.

[0745] The "means for providing to the store manager" refers to a method and function for providing the generated promotional material so that it can be used by the store manager.

[0746] The "means for awarding points" refers to a method and function for awarding points as a reward to a user who has posted a video.

[0747] "Means for users to use points for local payments" refers to methods and functions that allow users to use the accumulated points as a certain value when making cashless payments.

[0748] The system of the present invention automatically generates promotional materials from videos taken by users and promotes cashless payments. In particular, it realizes effective marketing by recognizing users' emotions and improving the quality of the materials based on those emotions.

[0749] System configuration

[0750] 1. User Device

[0751] Users use their smartphones or smart glasses to take videos of the inside and outside of the store. A dedicated video uploading application is installed on the device, allowing users to easily upload videos to the server.

[0752] 2. Server

[0753] The server plays a central role in receiving, storing, analyzing, and recognizing emotions in videos, as well as generating and providing promotional materials. Specific processes include:

[0754] Receiving and storing videos

[0755] The user terminal uploads the captured video to a server via a dedicated application.

[0756] The server stores the received video and records the associated metadata (e.g., user ID, video ID, upload time, visited store ID) in a database.

[0757] Video Analysis

[0758] The server extracts frames from the received video at regular intervals, for example, one frame per second, and treats the entire video as a series of still images.

[0759] The server uses object recognition algorithms to detect objects in the frame and classify them into categories (e.g., store exterior, store interior, menu, food, people).

[0760] Blurring

[0761] The server applies a face detection algorithm to frames classified as people and blurs the facial areas, ensuring the protection of personal information.

[0762] emotion recognition

[0763] The server uses an emotion recognition engine to analyze the user's facial expressions in the extracted frames and identify emotions (e.g., joy, surprise, sadness, etc.).

[0764] Generate and provide promotional materials

[0765] The server automatically generates promotional materials based on the stored frames and emotion data, for example, creating slideshows and banner images that include many frames of the user's happy facial expressions.

[0766] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0767] Points allocation and use

[0768] After a video is uploaded, the server awards points to the user based on preset rules.

[0769] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0770] Technology used

[0771] Hardware: Smart glasses, smartphones

[0772] Software: Python, OpenCV, FER library

[0773] Specific examples

[0774] A user visits a tourist spot and takes videos of the exterior, interior, menu, and food of a store (e.g., a restaurant). The user takes videos using smart glasses or a smartphone and uploads them to a server using a dedicated application. The server analyzes the videos, categorizes each frame, and blurs people's faces. It also uses an emotion recognition engine to analyze the user's emotions and generates promotional materials that include many happy expressions. These promotional materials are provided to the store manager's screen, and the user is awarded points.

[0775] Prompt Sentence Examples

[0776] "Generate promotional materials using videos of users having fun in the store. Analyze the emotions in the videos and create a slideshow that includes many scenes where users are particularly happy."

[0777] This system makes it possible to quickly generate effective promotional materials that reflect the actual experiences of tourists visiting Japan.

[0778] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0779] Step 1:

[0780] Users use their smartphones or smart glasses to take videos of the inside and outside of a store. The videos are then uploaded to a server via a dedicated video posting application. The input is the video taken by the user, and the output is the video data uploaded to the server.

[0781] Step 2:

[0782] The server receives the uploaded video, extracts the associated metadata (e.g., user ID, video ID, upload time, visited store ID) and records it in a database. The input is the uploaded video data, and the output is the video data and metadata stored in the database.

[0783] Step 3:

[0784] The server extracts frames from the received video at regular intervals. For example, it extracts one frame per second and treats the entire video as a series of still images. The input is the received video data, and the output is a list of extracted frame data.

[0785] Step 4:

[0786] The server applies an object recognition algorithm to detect objects in each frame and classify them into categories (e.g., store exterior, store interior, menu, food, person). The input is the extracted frames, and the output is the categorized frame data.

[0787] Step 5:

[0788] The server applies a face detection algorithm to frames classified as people and blurs the face, thereby protecting privacy. The input is a frame classified as people, and the output is a blurred frame.

[0789] Step 6:

[0790] The server uses an emotion recognition engine to analyze the user's facial expressions in each frame and identify emotions (e.g., happiness, surprise, sadness, etc.). The input is the blurred frames, and the output is the emotion data.

[0791] Step 7:

[0792] The server automatically generates promotional materials based on the stored frames and emotion data. For example, it creates slideshows or banner images that include many frames of the user's happy facial expressions. The input is emotion data and the stored frames, and the output is the generated promotional materials.

[0793] Step 8:

[0794] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager. The input is the generated promotional materials, and the output is the promotional materials that can be viewed by the store manager.

[0795] Step 9:

[0796] The server awards points to users after they have completed uploading a video. The criteria for awarding points are determined based on pre-defined rules. The input is the video upload data, and the output is the awarded points.

[0797] Step 10:

[0798] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan. When paying, they can use their points to get a discount. The input is the points held by the user, and the output is the discount amount at the time of payment.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] [Third embodiment]

[0803] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0804] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0805] 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).

[0806] 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.

[0807] 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.

[0808] 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).

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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."

[0815] The system of the present invention automatically generates promotional materials for stores based on videos taken by tourists visiting Japan and promotes the use of cashless payments. A specific embodiment for implementing this system will be described below.

[0816] System configuration

[0817] 1. User Device

[0818] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device.

[0819] 2. Server

[0820] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. The server is equipped with a high-performance processor and large-capacity storage.

[0821] Program processing overview

[0822] Receiving and storing videos

[0823] The user device uploads the captured video to the server via the VOOM app.

[0824] The server stores the video received from the user terminal and records the associated metadata (user ID, video ID, upload time, visited store ID) in a database.

[0825] Video Analysis

[0826] The server extracts frames from the received video at regular intervals, for example, one frame per second.

[0827] The server applies object recognition algorithms to identify objects in the frame and sort them into categories (store exterior, store interior, menu, food, people).

[0828] Blurring

[0829] The server selects frames that are classified into the person category based on the analysis results.

[0830] The server uses a face detection algorithm to identify people's faces and then uses an image processing library to blur the facial areas, thereby protecting personal information.

[0831] Generate and provide promotional materials

[0832] The server stores categorized frames by theme.

[0833] The server uses the saved frames to automatically generate promotional materials for the store (e.g., slideshows, banner images, etc.).

[0834] The server provides the generated promotional materials to the management screen of the store manager so that the promotional materials can be used directly.

[0835] Points allocation and use

[0836] After the video is uploaded, the server awards points to the user. The criteria for awarding points is, for example, 500 points per video upload.

[0837] Users can use the smartphone app to earn points when shopping or eating at cashless payment affiliated stores in Japan, and the points are applied as discounts according to the purchase amount.

[0838] Specific examples

[0839] Users visit tourist spots in Japan and take videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the videos they have taken to a server via the VOOM app. The server receives the videos, analyzes each frame, and categorizes them into categories such as the store's exterior, interior, menu, food, and people. Frames that show people are blurred. Promotional materials are then automatically generated and provided to the store manager. Users are also awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[0840] In this way, the present invention realizes a system that is convenient for tourists and effectively supports store promotions.

[0841] The processing flow will be explained below.

[0842] Step 1:

[0843] Users use their smartphones to shoot videos at stores and tourist spots in Japan, and the videos are saved in a dedicated application folder on the device.

[0844] Step 2:

[0845] Users upload the saved video to the server through the VOOM application. On the upload screen, they enter the video title and information about the store they visited, select the video file, and press the send button.

[0846] Step 3:

[0847] The server stores the video file received from the user. Upon receiving the file, it extracts the video metadata (user ID, video ID, upload time, visited store ID, etc.) and records it in a database.

[0848] Step 4:

[0849] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[0850] Step 5:

[0851] The server applies object recognition algorithms to detect objects in the extracted frames and classify each frame into a specific category (store exterior, store interior, menu, food, people, etc.).

[0852] Step 6:

[0853] The server identifies frames that fall into the people category and uses a face detection algorithm to detect human faces within the frames.

[0854] Step 7:

[0855] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[0856] Step 8:

[0857] The server stores the categorized frames according to their purpose, and the frames are stored in specific folders according to their classification.

[0858] Step 9:

[0859] The server automatically generates promotional materials based on the saved frames, for example, creating slideshows and banner images using images of the store's exterior and food.

[0860] Step 10:

[0861] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[0862] Step 11:

[0863] After the video has been posted, the server awards points to the user. The amount of points awarded is determined based on preset rules.

[0864] Step 12:

[0865] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[0866] The above are the specific processing steps in this system.

[0867] Example 1

[0868] 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."

[0869] In recent years, the number of video content shot by tourists and users visiting Japan has increased, but it is difficult to automatically generate promotional materials for stores that effectively utilize these videos. There is also a need for technology that can efficiently create promotional materials while also protecting personal information. Furthermore, incentives for video contributors are also necessary, and it is desirable that these incentives contribute to the spread of cashless payments.

[0870] 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.

[0871] In this invention, the server includes means for receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for saving the categorized frames by purpose, means for generating promotional materials for the store using the saved frames, means for providing the generated promotional materials to the store manager, means for awarding points for posting videos, and means for users to use the points for local payments. This makes it possible to efficiently analyze videos shot by tourists visiting Japan and users and convert them into promotional materials while protecting personal information, and further promote the spread of cashless payments by awarding points to posters.

[0872] A "user terminal" is a device such as a smartphone, tablet, or computer that a user uses to shoot videos and upload them to the system.

[0873] "Server" refers to a computer system equipped with a high-performance processor and large-capacity storage that communicates with user terminals via the Internet, stores and analyzes received videos, and generates promotional materials.

[0874] "Receiving video" is a process in which a user sends a video taken by the user from the user terminal to a server, and the server receives the video.

[0875] "Frame extraction" is the process of extracting still images (frames) from the received video at regular intervals.

[0876] "Frame analysis" is the process of using object recognition algorithms to identify and categorize objects within extracted frames.

[0877] An "object recognition algorithm" is an artificial intelligence technique used to identify and classify objects within a frame.

[0878] "Blurring" is an image processing technique that blurs the facial area of ​​frames classified as people to protect personal information.

[0879] A "face detection algorithm" is an artificial intelligence technique used to identify the faces of people within a frame.

[0880] An "image processing library" is a collection of software tools for editing and processing images.

[0881] "Promotional materials" are content such as slideshows and banner images used for store promotion and advertising.

[0882] "Point awarding" is the process of providing points as an incentive to users who post videos.

[0883] "Cashless payment" is a system in which payments are made electronically without using cash.

[0884] MODE FOR CARRYING OUT THE INVENTION

[0885] The system of the present invention automatically generates promotional materials based on videos taken by tourists visiting Japan or users on-site, and promotes the use of cashless payments. Specific embodiments for implementing this system are described below.

[0886] System configuration

[0887] 1. User Device

[0888] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device. This application is responsible for sending videos, adding metadata, and uploading them.

[0889] 2. Server

[0890] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. The server is equipped with a high-performance processor and large-capacity storage. The main software used includes object recognition algorithms (e.g., YOLO, Mask R-CNN), face detection algorithms (e.g., OpenCV's Haar Cascade), and image processing libraries (e.g., OpenCV, PIL).

[0891] Receiving and storing videos

[0892] The user device uploads the video they have taken to the server using a dedicated video posting app. The server receives the uploaded video and stores it in a temporary storage area. It also records metadata related to the video (user ID, video ID, upload time, visited store ID) in a database.

[0893] Video Analysis

[0894] The server reads the saved video file and extracts frames at regular intervals. Specifically, it is set to extract one frame every second. The extracted frames are analyzed using an object recognition algorithm and sorted into categories such as store exteriors, store interiors, menus, dishes, and people.

[0895] Blurring

[0896] The server applies a face detection algorithm to frames classified as people to identify faces, then uses an image processing library to blur the face area to protect personal information, making it difficult to identify individuals.

[0897] Generate and provide promotional materials

[0898] The server stores the categorized frames by theme and automatically generates promotional materials such as slideshows and banner images. The generated promotional materials are provided to the store administrator's management screen and can be easily used.

[0899] Points allocation and use

[0900] The server awards points to users after they have posted a video. The standard for awarding points is, for example, 500 points per video post. Users can use the smartphone app to redeem points at cashless payment affiliated stores in Japan. Points are applied as discounts according to the purchase amount.

[0901] Specific examples

[0902] Users visit tourist spots in Japan and take videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the videos they have taken to a server using a dedicated app. The server receives the videos, analyzes each frame, and categorizes them into categories such as the store's exterior, interior, menu, food, and people. Frames that show people are blurred. Promotional materials are then automatically generated and provided to the store manager. Users are rewarded for posting videos with points, which can be used at cashless payment affiliated stores in Japan.

[0903] Prompt Sentence Examples

[0904] "A user visits a tourist spot in Japan and takes a video of a store's exterior, interior, menu, and food. The user uploads this video to a server via the app, and the server analyzes the video and automatically generates promotional materials. Please give us an overview of this system."

[0905] The AI ​​model generated based on this prompt can provide detailed explanations of the system's detailed operation and the technical elements of each step.

[0906] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0907] Step 1:

[0908] Recording and uploading videos

[0909] Input: Videos taken by users using devices such as smartphones

[0910] How it works: A user launches a video posting application, selects a video they have taken, and presses the submit button to upload the video.

[0911] Output: Sending video data from the user device to the server

[0912] Step 2:

[0913] Receiving and storing videos

[0914] Input: Video data sent from the user device

[0915] How it works: The server receives the video via the Internet and stores it in a temporary storage area. At the same time, it analyzes the metadata associated with the video (user ID, video ID, upload time, visited store ID) and records it in a database.

[0916] Output: Video files stored in temporary storage and metadata stored in a database

[0917] Step 3:

[0918] Extracting Frames

[0919] Input: Saved video file

[0920] Operation: The server loads the video file and extracts frames at regular intervals. Specifically, it is set to extract one frame every second.

[0921] Output: Individual frames stored in temporary storage

[0922] Step 4:

[0923] Frame analysis and categorization

[0924] Input: Extracted individual frames

[0925] How it works: The server applies object recognition algorithms (e.g., YOLO, Mask R-CNN) to identify objects in the frame, then categorizes them into categories such as store exteriors, store interiors, menus, dishes, people, etc.

[0926] Output: Frames sorted by category

[0927] Step 5:

[0928] Blurring

[0929] Input: Frames sorted into the people category

[0930] How it works: The server uses a face detection algorithm (e.g., OpenCV's Haar Cascade) to identify faces in the frame, and then uses an image processing library (e.g., OpenCV, PIL) to blur the identified faces.

[0931] Output: Frame with face blurred

[0932] Step 6:

[0933] Save Frame

[0934] Input: Categorized and blurred frames

[0935] How it works: The server stores these frames in folders by theme, for example, store exterior frames in an "Exterior" folder, food frames in a "Food" folder, etc.

[0936] Output: Thematically organized frame data

[0937] Step 7:

[0938] Generate promotional materials

[0939] Input: Frame data organized by theme

[0940] How it works: The server selects the saved frames and automatically generates promotional materials such as slideshows and banner images, stitching the selected frames together and adding text.

[0941] Output: Auto-generated promotional materials

[0942] Step 8:

[0943] Providing promotional materials

[0944] Input: Auto-generated promotional material

[0945] Operation: The server uploads the generated promotional materials to the store manager's management screen, where the manager can easily use the materials.

[0946] Output: Promotional materials uploaded to the dashboard

[0947] Step 9:

[0948] Points awarded

[0949] Input: Video submission completion data

[0950] Operation: The server confirms the completion of video posting and awards points to the user. Points are awarded at a rate of 500 points per video posted. The point information is updated in the user database.

[0951] Output: Update of awarded points to user database

[0952] Step 10:

[0953] Using points

[0954] Input: Points awarded

[0955] How it works: Users use a smartphone app to redeem points at cashless payment affiliated stores in Japan. The app provides a function to select the application of points at the time of payment, specifically by scanning a QR code or presenting a barcode.

[0956] Output: Discount applied by using points at cashless payment affiliated stores

[0957] (Application example 1)

[0958] 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."

[0959] In today's tourism industry, it is extremely important to effectively promote stores visited by tourists visiting Japan. However, in many cases, videos taken by tourists are not effectively utilized. Furthermore, there are issues with privacy protection and promoting cashless payments. Specifically, there is a lack of methods to automatically generate promotional materials for stores and encourage visitors to use cashless payments. As a result, not only are store promotional activities ineffective, but convenience for tourists is also diminished.

[0960] 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.

[0961] In this invention, the server includes a terminal device for a user to shoot videos at a store and upload the videos to the server, a means for analyzing the shot and uploaded videos on the server and classifying them into store exteriors, interiors, menus, dishes, people, etc. using an object recognition algorithm, a means for blurring facial portions of the analyzed frames using an image processing library, a means for automatically generating store promotional materials from the analyzed frames and providing them to a store manager, and a means for awarding points that can be used for cashless payments to users. This makes it possible to effectively utilize videos shot by tourists visiting Japan to automatically generate store promotional materials and promote cashless payments while simultaneously protecting their privacy.

[0962] A "user terminal" is a device that a user uses to shoot video and upload the video to a server.

[0963] "Server" refers to a device equipped with a high-performance processor and large-capacity storage that communicates with user terminals via the Internet, stores and analyzes received videos, and generates promotional materials.

[0964] An "object recognition algorithm" is an algorithm that identifies objects within video frames and classifies them into store exteriors, interiors, menus, dishes, people, etc.

[0965] An "image processing library" is a library for processing and editing images, and is used for blurring and other processes.

[0966] "Promotional Materials" refers to generated visual content, such as slideshows and banner images, used in store promotional activities.

[0967] The "point awarding means" is a means for awarding points to users for posting videos, and provides points that users can use for cashless payments.

[0968] The "analysis means" is a processing device or software that has the function of allowing the server to analyze uploaded videos and classify them into specific categories.

[0969] The "blurring means" is a means for protecting privacy by blurring the facial portion of a person in an image.

[0970] An embodiment of the present invention will be described. This is a system that automatically generates promotional materials for stores based on videos taken by tourists visiting Japan and promotes the use of cashless payment. This system includes the following components.

[0971] System configuration

[0972] User terminal

[0973] A user device is a device that a user uses to shoot videos and upload them to a server. This includes smartphones, tablets, and PCs. A dedicated video posting application (hereafter referred to as the SmartPromo app) is installed on these devices.

[0974] server

[0975] The server communicates with user devices via the Internet, stores and analyzes the received video, and generates promotional materials. The server is equipped with a high-performance processor (e.g., Intel Xeon or AMD Ryzen series) and large-capacity storage (HDD or SSD). The server runs an object recognition algorithm (e.g., YOLO or SSD) and an image processing library (e.g., OpenCV), which analyzes frames and performs blurring.

[0976] Receiving and storing videos

[0977] A user shoots a video using the SmartPromo app and uploads it to the server. The server receives the video data along with metadata (user ID, video ID, upload time, store ID). This data is recorded in a database (e.g., MySQL or SQLite), and the video itself is stored in storage.

[0978] Video analysis and processing

[0979] The server extracts frames from the stored video at regular intervals. For example, it can be set to extract one frame every second. The extracted frames are then categorized using an object recognition algorithm into categories such as store exteriors, interiors, menus, food, and people. For frames classified as people, a face detection algorithm (for example, OpenCV's face recognition function) is used to blur the faces.

[0980] Generate promotional materials

[0981] The server saves the analyzed frames by theme and automatically generates promotional materials such as slideshows and banner images. The generated materials are provided to store managers through a dedicated management screen, allowing store managers to use these materials to carry out effective store promotions.

[0982] Points allocation and use

[0983] When a user posts a video, the server will award the user points. The points can be viewed within the SmartPromo app and can be used at cashless payment affiliated stores in Japan. The standard for awarding points could be 500 points per video post.

[0984] Specific Examples

[0985] For example, a user shoots a video at a restaurant in a tourist spot in Japan and uploads it to the server using the SmartPromo app. The server receives the video, extracts and analyzes frames every second, and classifies them into categories such as the restaurant's exterior, interior, menu, food, and people. Frames containing people are subjected to facial recognition and blurring. Promotional materials are then generated and provided to the restaurant manager, and points are awarded to the user. These points can be used for the next cashless payment, increasing user convenience.

[0986] Prompt Sentence Examples

[0987] Users can upload videos of their restaurants. The videos are analyzed into categories of restaurant exterior, interior, menu, food, and people. Faces of people are blurred and promotional materials for the restaurant are generated. The generated promotional materials are provided to the restaurant manager, and the user is awarded 500 points. The points can be used for the next cashless payment.

[0988] In this way, the present invention provides a system that contributes to the promotion of the tourism industry and cashless payments by effectively utilizing videos taken by tourists visiting Japan and automatically generating promotional materials for stores.

[0989] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0990] Step 1:

[0991] A user takes a video in a store. The user uses the SmartPromo app to take a video and temporarily saves it on the device. The input of this step is the "taken video data" and the output is the "video file saved on the device."

[0992] Step 2:

[0993] The user uploads the video they have taken to the server. On the user's device, they use the SmartPromo app to send the video to the server. At this time, metadata such as the user ID, video ID, upload time, and store ID are also sent. The input for this step is the "video file and metadata on the device," and the output is the "video file and metadata stored on the server."

[0994] Step 3:

[0995] The server stores the received video data. The server stores the video data in storage (e.g., HDD or SSD) and records the metadata in a database (e.g., MySQL or SQLite). The input of this step is the "video data and metadata uploaded to the server," and the output is the "video file stored in storage and the metadata recorded in the database."

[0996] Step 4:

[0997] The server extracts frames from the video at regular intervals. For example, it can be set to extract one frame every second. The input of this step is the "saved video file" and the output is the "extracted frames."

[0998] Step 5:

[0999] The server analyzes the extracted frames using an object recognition algorithm. The server uses object recognition algorithms such as YOLO and SSD to categorize them into store exteriors, interiors, menus, dishes, people, etc. The input to this step is the "extracted frame group," and the output is the "categorized frame information."

[1000] Step 6:

[1001] The server blurs frames that contain people among the categorized frames. The server identifies faces using OpenCV's face recognition function and performs blurring. The input to this step is "categorized frame information that includes people," and the output is "frames that have been blurred."

[1002] Step 7:

[1003] The server saves the analyzed and processed frames. The categorized frames are saved to generate promotional materials by theme. The input of this step is "blurred frames" and "other categorized frames", and the output is "saved frame data".

[1004] Step 8:

[1005] The server uses the saved frames to generate promotional materials for the store. For example, it automatically generates slideshows and banner images. The input of this step is the "saved frame data" and the output is the "generated promotional materials."

[1006] Step 9:

[1007] The server provides the generated promotional materials to the store manager, who can then view and use them through the management screen. The input to this step is the "generated promotional materials" and the output is the "promotional materials provided to the store manager."

[1008] Step 10:

[1009] The server assigns points to the user. After the video is posted, the server adds the points to the user ID. The input of this step is the user ID and posting information in the metadata, and the output is the points assigned to the user account.

[1010] Step 11:

[1011] The user uses the points awarded for cashless payment. Using the SmartPromo app, the user can use the points to pay at cashless payment affiliated stores. The input for this step is the "point information stored in the app," and the output is the "payment result using the points."

[1012] 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.

[1013] The system of this invention automatically generates promotional materials for stores based on videos taken by tourists visiting Japan, promoting the use of cashless payments. Furthermore, by combining it with an emotion engine that recognizes the emotions of users in the videos and improves the quality of the promotional materials, more effective marketing can be achieved.

[1014] System configuration

[1015] 1. User Device

[1016] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device.

[1017] 2. Server

[1018] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. It also uses an emotion engine to recognize user emotions and reflect them in the promotional materials.

[1019] Program processing overview

[1020] Receiving and storing videos

[1021] The user device uploads the captured video to the server via the VOOM app.

[1022] The server stores the video received from the user terminal, extracts related metadata (user ID, video ID, upload time, visited store ID, etc.), and records it in a database.

[1023] Video Analysis

[1024] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[1025] The server applies object recognition algorithms to detect objects in the frame and classify them into categories (store exterior, store interior, menu, food, people).

[1026] Blurring

[1027] The server identifies frames that fall into the people category based on the analysis results, and uses a face detection algorithm to detect human faces within the frames.

[1028] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[1029] emotion recognition

[1030] The server applies an emotion engine to recognize the user's emotion from the extracted frames. The emotion engine analyzes the user's facial expression data to identify emotions (e.g., joy, anger, sadness, surprise, etc.).

[1031] Generate and provide promotional materials

[1032] The server stores the categorized frames by purpose, and the frames are stored in specific folders according to their category.

[1033] The server automatically generates promotional materials based on the stored frames and emotion data, for example creating slideshows and banner images that include many frames of the user's happy facial expressions.

[1034] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[1035] Points allocation and use

[1036] After the video has been uploaded, the server awards points to the user based on preset rules.

[1037] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[1038] Specific examples

[1039] Users visit tourist spots in Japan and film videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the video to a server via the VOOM app. The server receives the video, analyzes each frame, and categorizes it into categories such as store exterior, interior, menu, food, and people. Frames that show people are blurred. The emotion engine then analyzes the user's facial expressions in the frames, selecting frames with many happy expressions, automatically generating promotional materials, and providing them to store managers. In return, users are awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[1040] In this way, the present invention realizes a system that optimizes store marketing activities by providing effective promotional materials that are convenient for tourists and reflect the emotions of users.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] Users use their smartphones to shoot videos at stores and tourist spots in Japan, and the videos are saved in a dedicated application folder on the device.

[1044] Step 2:

[1045] Users upload the saved video to the server through the VOOM application. On the upload screen, they enter the video title and information about the store they visited, select the video file, and press the send button.

[1046] Step 3:

[1047] The server stores the video file received from the user. Upon receiving the file, it extracts the video metadata (user ID, video ID, upload time, visited store ID, etc.) and records it in a database.

[1048] Step 4:

[1049] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[1050] Step 5:

[1051] The server applies object recognition algorithms to detect objects in the extracted frames and classify each frame into a specific category (store exterior, store interior, menu, food, people, etc.).

[1052] Step 6:

[1053] The server identifies frames that fall into the people category and uses a face detection algorithm to detect human faces within the frames.

[1054] Step 7:

[1055] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[1056] Step 8:

[1057] The server applies an emotion engine to recognize the user's emotion from the extracted frames. The emotion engine analyzes the user's facial expression data in the frames and identifies emotions such as joy, surprise, sadness, and anger.

[1058] Step 9:

[1059] The server automatically generates promotional materials based on the categorized frames and emotion data. For example, it selects frames showing the most happy expressions of users and creates images for slideshows and banners.

[1060] Step 10:

[1061] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[1062] Step 11:

[1063] After a video is posted, the server awards points to the user. The amount of points awarded is determined based on pre-defined rules. For example, 500 points are awarded for each video posted.

[1064] Step 12:

[1065] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[1066] As a result, the system of the present invention can provide convenience and benefits to tourists, while also automatically generating effective promotional materials for stores, thereby improving marketing effectiveness.

[1067] Example 2

[1068] 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."

[1069] There is a need for a system that can effectively utilize videos taken by tourists visiting Japan to automatically generate promotional materials for stores and promote the use of cashless payments. However, current methods require a great deal of time and effort to analyze the videos and generate promotional materials, so an efficient and highly accurate method is needed. Other challenges include protecting personal information contained in the videos and making it difficult to implement marketing that reflects user emotions.

[1070] 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.

[1071] In this invention, the server includes means for receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for analyzing facial expression data of users in the frames to recognize emotions, means for generating promotional materials using the emotion data, means for providing the generated promotional materials to store managers, means for awarding points for posting videos, and means for users to use the points for payments on site. This makes it possible to efficiently analyze videos shot by tourists and automatically generate high-quality promotional materials, and further promote the use of cashless payments.

[1072] "Means for receiving videos taken by users" refers to a combination of infrastructure and software for receiving and storing video data uploaded from users' devices via a network.

[1073] The "means for extracting frames at regular intervals" refers to a process for extracting still images from video data at specified time intervals and dividing them into analyzable units.

[1074] "Means for analyzing frames and categorizing them into categories such as store exteriors, store interiors, menus, food, people, etc." refers to a technology that applies object recognition algorithms to identify objects in each frame and classify them into specific categories.

[1075] The "means of blurring frames classified into the person category" is a method of using a face detection algorithm and an image processing library to blur the face of a detected person in order to protect personal information.

[1076] "Means for recognizing emotions by analyzing the user's facial expression data in the frames" refers to a technology that analyzes the user's facial expressions from the extracted frames and uses an emotion engine to recognize specific emotions (joy, anger, sadness, surprise, etc.).

[1077] The "means for generating promotional materials using emotional data" is a method for automatically generating promotional materials such as slideshows and banner images, based on the results of emotional analysis, by preferentially using frames that express positive emotions.

[1078] The "means for providing the generated promotional materials to the store manager" is a system that uploads the generated promotional materials from a server to a management screen that can be accessed by the store manager, making them available for use.

[1079] The "means for awarding points for video submissions" is a reward system for awarding points to users who upload videos based on predefined rules.

[1080] "Means for users to use points for local payments" refers to a set of infrastructure and applications that provide an environment in which users can use their awarded points as an alternative to cash when making purchases or paying for services at physical stores.

[1081] This system automatically generates promotional materials for stores based on videos taken by tourists visiting Japan, promoting the use of cashless payments. The system's main hardware components include a user terminal and a server. Specifically, the user terminal is a device such as a smartphone, tablet, or PC, while the server provides high-performance cloud-based storage and computing resources.

[1082] Recording and uploading videos

[1083] The user device uses a dedicated video posting application (e.g., the VOOM app) to shoot videos of the store's exterior, interior, menu, food, etc. The videos are then uploaded to the server via the app.

[1084] Receiving and storing videos

[1085] The server receives the video data sent from the user's device. Along with the received video data, metadata such as the user ID, video ID, upload time, and visited store ID are sent. This data is stored in a database on the server. The video data itself is stored in highly secure storage.

[1086] Video Analysis

[1087] The server extracts frames from the received video at regular intervals, for example, using a technique to extract one frame per second. Then, an object recognition algorithm is applied to each extracted frame, and it is classified into store exteriors, store interiors, menus, dishes, people, etc.

[1088] Blurring and Emotion Recognition

[1089] The server identifies frames classified as "people" based on the object recognition results. Faces detected using a face detection algorithm (e.g., OpenCV) are blurred to ensure privacy. The emotion engine then analyzes the user's facial expression data in the extracted frames to recognize the user's emotions (e.g., joy, anger, sadness, surprise, etc.).

[1090] Producing and providing promotional materials

[1091] The server automatically generates promotional materials based on the classified frames and emotion data. The generated promotional materials include, for example, slideshows containing many frames of happy expressions from users and banner images, and these are saved in specific folders. The generated promotional materials are then automatically uploaded to the store manager's management screen, where they can be easily accessed and used by the store manager.

[1092] Points allocation and use

[1093] The server awards points to users who have completed uploading a video based on certain rules. These points can be used at cashless payment affiliated stores in Japan, and users can use the points via a smartphone app to make payments locally.

[1094] Examples of concrete examples and prompts

[1095] For example, a user might visit a famous ramen restaurant in a Tokyo tourist spot and film a video of the restaurant's exterior, interior, menu, and food. The user then uploads the video to a server via the VOOM app. The server receives the video and stores it in a database along with the metadata. The server then analyzes the video frame by frame, categorizing each frame into categories such as the restaurant's exterior, interior, menu, food, and people. A facial recognition algorithm is applied to frames containing people to blur them, and an emotion engine then analyzes the user's facial expressions and selects frames with many happy expressions to automatically generate promotional materials. These materials are then uploaded to the restaurant administrator's management screen. Users are then awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[1096] Example prompt sentence:

[1097] "Please upload videos of ramen shops in tourist spots in Japan that show their exteriors, interiors, menus, and dishes, and automatically generate promotional materials. Please prioritize happy expressions in promotional materials that reflect the emotions of users. After that, provide these materials to store managers, who can award points to users who posted videos, which can be used for cashless payments."

[1098] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1099] Step 1:

[1100] The user device uses the VOOM app to shoot videos of the store's exterior, interior, menu, food, etc. The input is the video data shot by the user, and the output is the video data and metadata (user ID, shooting date and time, store ID, etc.).

[1101] Step 2:

[1102] The user device uploads the captured video to the server via the VOOM app. Specifically, the app sends UDP / TCP packets to the server via an Internet connection to transfer the video data and metadata. The input is the video data and metadata in the user device, and the output is the data sent to the server.

[1103] Step 3:

[1104] The server receives the uploaded video data and metadata. The received data is stored in a database. The input is the video data and metadata sent from the user terminal, and the output is the data stored in the database. Specifically, the server splits the received data and stores it in the metadata table and video storage, respectively.

[1105] Step 4:

[1106] The server extracts frames from the video data at regular intervals, for example, using a technique to extract one frame per second. The input is the received video data, and the output is the extracted still frame images. Specifically, a video decoding algorithm is applied to extract frames at each time interval.

[1107] Step 5:

[1108] The server applies an object recognition algorithm to each extracted frame, detects objects in the frame, and classifies them into categories (store exterior, store interior, menu, food, people). The input is a still frame image, and the output is frame data tagged by category. Specifically, the server runs an image recognition algorithm using a machine learning model and saves the recognition results.

[1109] Step 6:

[1110] The server applies a face detection algorithm to frames classified as "people" to identify faces. It then uses an image processing library (e.g., OpenCV) to blur the detected face area. The input is a frame image in the "people" category, and the output is a frame image with the face blurred. Specifically, the server uses a face detection algorithm to identify the face area, and then applies a filter to blur that area.

[1111] Step 7:

[1112] The server analyzes the user's facial expression data in the blurred frames using an emotion engine. The input is the processed frame image, and the output is the analyzed emotion data. Specifically, it uses face recognition and expression analysis algorithms to estimate the emotional state for each frame.

[1113] Step 8:

[1114] The server automatically generates promotional materials based on the analysis results. It uses the emotional data to create slideshows and banner images that prioritize frames with a particular emotion (e.g., joy). The input is frames categorized by category and emotional data, and the output is the completed promotional materials. Specifically, it edits the selected frames and generates materials in a specific format.

[1115] Step 9:

[1116] The server saves the generated promotional materials in a specific folder and automatically uploads the data to the store manager's management screen. The input is the generated promotional materials, and the output is the data uploaded to the management screen. Specifically, the server uses a file transfer protocol to save the materials in a specified folder on the server and display them on the management screen.

[1117] Step 10:

[1118] The server awards points to users who have completed posting a video. The criteria for awarding points are determined based on pre-defined rules. The input is the metadata of the video post, and the output is the points added to the user account. Specifically, the server uses a point management system to add points to the user ID.

[1119] Step 11:

[1120] The user makes a payment using points at a cashless payment affiliated store. The payment is made using a smartphone app, with points applied. The input is the smartphone's point balance, and the output is a payment completion notification with points applied. Specifically, the points are applied via the payment API, and the result is notified to the user.

[1121] (Application example 2)

[1122] 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."

[1123] With conventional store promotion methods, creating promotional materials requires a great deal of effort and time, making it difficult to achieve effective marketing. It is also difficult to quickly generate materials that reflect the experiences of tourists visiting Japan, which means that the system lacks real-time capabilities. Furthermore, previous systems were unable to create materials that reflect users' emotions, preventing improvements in promotional effectiveness.

[1124] The identification process 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 receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for saving the categorized frames by purpose, means for using an emotion recognition engine to analyze user emotions, means for generating promotional materials using the saved frames and emotion data, means for providing the generated promotional materials to an administrator, means for awarding points for video submissions, and means for users to use the points for on-site payments. This enables automatic generation of promotional materials for stores and rapid, effective promotions that reflect user emotions.

[1125] "Videos taken by users" are video data taken by users using devices such as smartphones or smart glasses.

[1126] The "receiving means" refers to a communication method and function for transmitting a video taken by a user to a server via the Internet and for the server to receive the video.

[1127] "Means for extracting frames at regular intervals" refers to a processing method and function for extracting individual images (frames) from a received video at set intervals.

[1128] The "means for analyzing frames" refers to techniques and methods for analyzing extracted frames and classifying them into specific categories such as store exteriors, store interiors, menus, dishes, people, etc.

[1129] The "categorization means" refers to a method and function for dividing the analyzed frames into specific categories such as store exteriors, store interiors, menus, dishes, people, etc.

[1130] The "means for performing blurring processing" refers to a method and function for applying a blurring effect to the face part of a frame in the analyzed person category in order to protect personal information.

[1131] An "emotion recognition engine for analyzing emotions" is an algorithm and technology that detects the facial expressions of a user captured in a frame and recognizes the user's emotions (e.g., joy, surprise, sadness, etc.) from those expressions.

[1132] The "means for generating promotional materials" refers to a technology and method for automatically creating promotional materials (e.g., slideshows and banners) for a store using the stored frame and emotion data for each category.

[1133] The "means for providing to the store manager" refers to a method and function for providing the generated promotional material so that it can be used by the store manager.

[1134] The "means for awarding points" refers to a method and function for awarding points as a reward to a user who has posted a video.

[1135] "Means for users to use points for local payments" refers to methods and functions that allow users to use the accumulated points as a certain value when making cashless payments.

[1136] The system of the present invention automatically generates promotional materials from videos taken by users and promotes cashless payments. In particular, it realizes effective marketing by recognizing users' emotions and improving the quality of the materials based on those emotions.

[1137] System configuration

[1138] 1. User Device

[1139] Users use their smartphones or smart glasses to take videos of the inside and outside of the store. A dedicated video uploading application is installed on the device, allowing users to easily upload videos to the server.

[1140] 2. Server

[1141] The server plays a central role in receiving, storing, analyzing, and recognizing emotions in videos, as well as generating and providing promotional materials. Specific processes include:

[1142] Receiving and storing videos

[1143] The user terminal uploads the captured video to a server via a dedicated application.

[1144] The server stores the received video and records the associated metadata (e.g., user ID, video ID, upload time, visited store ID) in a database.

[1145] Video Analysis

[1146] The server extracts frames from the received video at regular intervals, for example, one frame per second, and treats the entire video as a series of still images.

[1147] The server uses object recognition algorithms to detect objects in the frame and classify them into categories (e.g., store exterior, store interior, menu, food, people).

[1148] Blurring

[1149] The server applies a face detection algorithm to frames classified as people and blurs the facial areas, ensuring the protection of personal information.

[1150] emotion recognition

[1151] The server uses an emotion recognition engine to analyze the user's facial expressions in the extracted frames and identify emotions (e.g., joy, surprise, sadness, etc.).

[1152] Generate and provide promotional materials

[1153] The server automatically generates promotional materials based on the stored frames and emotion data, for example, creating slideshows and banner images that include many frames of the user's happy facial expressions.

[1154] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[1155] Points allocation and use

[1156] After a video is uploaded, the server awards points to the user based on preset rules.

[1157] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[1158] Technology used

[1159] Hardware: Smart glasses, smartphones

[1160] Software: Python, OpenCV, FER library

[1161] Specific examples

[1162] A user visits a tourist spot and takes videos of the exterior, interior, menu, and food of a store (e.g., a restaurant). The user takes videos using smart glasses or a smartphone and uploads them to a server using a dedicated application. The server analyzes the videos, categorizes each frame, and blurs people's faces. It also uses an emotion recognition engine to analyze the user's emotions and generates promotional materials that include many happy expressions. These promotional materials are provided to the store manager's screen, and the user is awarded points.

[1163] Prompt Sentence Examples

[1164] "Generate promotional materials using videos of users having fun in the store. Analyze the emotions in the videos and create a slideshow that includes many scenes where users are particularly happy."

[1165] This system makes it possible to quickly generate effective promotional materials that reflect the actual experiences of tourists visiting Japan.

[1166] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1167] Step 1:

[1168] Users use their smartphones or smart glasses to take videos of the inside and outside of a store. The videos are then uploaded to a server via a dedicated video posting application. The input is the video taken by the user, and the output is the video data uploaded to the server.

[1169] Step 2:

[1170] The server receives the uploaded video, extracts the associated metadata (e.g., user ID, video ID, upload time, visited store ID) and records it in a database. The input is the uploaded video data, and the output is the video data and metadata stored in the database.

[1171] Step 3:

[1172] The server extracts frames from the received video at regular intervals. For example, it extracts one frame per second and treats the entire video as a series of still images. The input is the received video data, and the output is a list of extracted frame data.

[1173] Step 4:

[1174] The server applies an object recognition algorithm to detect objects in each frame and classify them into categories (e.g., store exterior, store interior, menu, food, person). The input is the extracted frames, and the output is the categorized frame data.

[1175] Step 5:

[1176] The server applies a face detection algorithm to frames classified as people and blurs the face, thereby protecting privacy. The input is a frame classified as people, and the output is a blurred frame.

[1177] Step 6:

[1178] The server uses an emotion recognition engine to analyze the user's facial expressions in each frame and identify emotions (e.g., happiness, surprise, sadness, etc.). The input is the blurred frames, and the output is the emotion data.

[1179] Step 7:

[1180] The server automatically generates promotional materials based on the stored frames and emotion data. For example, it creates slideshows or banner images that include many frames of the user's happy facial expressions. The input is emotion data and the stored frames, and the output is the generated promotional materials.

[1181] Step 8:

[1182] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager. The input is the generated promotional materials, and the output is the promotional materials that can be viewed by the store manager.

[1183] Step 9:

[1184] The server awards points to users after they have completed uploading a video. The criteria for awarding points are determined based on pre-defined rules. The input is the video upload data, and the output is the awarded points.

[1185] Step 10:

[1186] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan. When paying, they can use their points to get a discount. The input is the points held by the user, and the output is the discount amount at the time of payment.

[1187] 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.

[1188] 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.

[1189] 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.

[1190] [Fourth embodiment]

[1191] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1192] 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.

[1193] 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).

[1194] 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.

[1195] 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.

[1196] 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).

[1197] 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.

[1198] 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.

[1199] 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.

[1200] 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.

[1201] 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.

[1202] 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.

[1203] 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."

[1204] The system of the present invention automatically generates promotional materials for stores based on videos taken by tourists visiting Japan and promotes the use of cashless payments. A specific embodiment for implementing this system will be described below.

[1205] System configuration

[1206] 1. User Device

[1207] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device.

[1208] 2. Server

[1209] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. The server is equipped with a high-performance processor and large-capacity storage.

[1210] Program processing overview

[1211] Receiving and storing videos

[1212] The user device uploads the captured video to the server via the VOOM app.

[1213] The server stores the video received from the user terminal and records the associated metadata (user ID, video ID, upload time, visited store ID) in a database.

[1214] Video Analysis

[1215] The server extracts frames from the received video at regular intervals, for example, one frame per second.

[1216] The server applies object recognition algorithms to identify objects in the frame and sort them into categories (store exterior, store interior, menu, food, people).

[1217] Blurring

[1218] The server selects frames that are classified into the person category based on the analysis results.

[1219] The server uses a face detection algorithm to identify people's faces and then uses an image processing library to blur the facial areas, thereby protecting personal information.

[1220] Generate and provide promotional materials

[1221] The server stores categorized frames by theme.

[1222] The server uses the saved frames to automatically generate promotional materials for the store (e.g., slideshows, banner images, etc.).

[1223] The server provides the generated promotional materials to the management screen of the store manager so that the promotional materials can be used directly.

[1224] Points allocation and use

[1225] After the video is uploaded, the server awards points to the user. The criteria for awarding points is, for example, 500 points per video upload.

[1226] Users can use the smartphone app to earn points when shopping or eating at cashless payment affiliated stores in Japan, and the points are applied as discounts according to the purchase amount.

[1227] Specific examples

[1228] Users visit tourist spots in Japan and take videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the videos they have taken to a server via the VOOM app. The server receives the videos, analyzes each frame, and categorizes them into categories such as the store's exterior, interior, menu, food, and people. Frames that show people are blurred. Promotional materials are then automatically generated and provided to the store manager. Users are also awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[1229] In this way, the present invention realizes a system that is convenient for tourists and effectively supports store promotions.

[1230] The processing flow will be explained below.

[1231] Step 1:

[1232] Users use their smartphones to shoot videos at stores and tourist spots in Japan, and the videos are saved in a dedicated application folder on the device.

[1233] Step 2:

[1234] Users upload the saved video to the server through the VOOM application. On the upload screen, they enter the video title and information about the store they visited, select the video file, and press the send button.

[1235] Step 3:

[1236] The server stores the video file received from the user. Upon receiving the file, it extracts the video metadata (user ID, video ID, upload time, visited store ID, etc.) and records it in a database.

[1237] Step 4:

[1238] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[1239] Step 5:

[1240] The server applies object recognition algorithms to detect objects in the extracted frames and classify each frame into a specific category (store exterior, store interior, menu, food, people, etc.).

[1241] Step 6:

[1242] The server identifies frames that fall into the people category and uses a face detection algorithm to detect human faces within the frames.

[1243] Step 7:

[1244] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[1245] Step 8:

[1246] The server stores the categorized frames according to their purpose, and the frames are stored in specific folders according to their classification.

[1247] Step 9:

[1248] The server automatically generates promotional materials based on the saved frames, for example, creating slideshows and banner images using images of the store's exterior and food.

[1249] Step 10:

[1250] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[1251] Step 11:

[1252] After the video has been posted, the server awards points to the user. The amount of points awarded is determined based on preset rules.

[1253] Step 12:

[1254] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[1255] The above are the specific processing steps in this system.

[1256] Example 1

[1257] 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."

[1258] In recent years, the number of video content shot by tourists and users visiting Japan has increased, but it is difficult to automatically generate promotional materials for stores that effectively utilize these videos. There is also a need for technology that can efficiently create promotional materials while also protecting personal information. Furthermore, incentives for video contributors are also necessary, and it is desirable that these incentives contribute to the spread of cashless payments.

[1259] 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.

[1260] In this invention, the server includes means for receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for saving the categorized frames by purpose, means for generating promotional materials for the store using the saved frames, means for providing the generated promotional materials to the store manager, means for awarding points for posting videos, and means for users to use the points for local payments. This makes it possible to efficiently analyze videos shot by tourists visiting Japan and users and convert them into promotional materials while protecting personal information, and further promote the spread of cashless payments by awarding points to posters.

[1261] A "user terminal" is a device such as a smartphone, tablet, or computer that a user uses to shoot videos and upload them to the system.

[1262] "Server" refers to a computer system equipped with a high-performance processor and large-capacity storage that communicates with user terminals via the Internet, stores and analyzes received videos, and generates promotional materials.

[1263] "Receiving video" is a process in which a user sends a video taken by the user from the user terminal to a server, and the server receives the video.

[1264] "Frame extraction" is the process of extracting still images (frames) from the received video at regular intervals.

[1265] "Frame analysis" is the process of using object recognition algorithms to identify and categorize objects within extracted frames.

[1266] An "object recognition algorithm" is an artificial intelligence technique used to identify and classify objects within a frame.

[1267] "Blurring" is an image processing technique that blurs the facial area of ​​frames classified as people to protect personal information.

[1268] A "face detection algorithm" is an artificial intelligence technique used to identify the faces of people within a frame.

[1269] An "image processing library" is a collection of software tools for editing and processing images.

[1270] "Promotional materials" are content such as slideshows and banner images used for store promotion and advertising.

[1271] "Point awarding" is the process of providing points as an incentive to users who post videos.

[1272] "Cashless payment" is a system in which payments are made electronically without using cash.

[1273] MODE FOR CARRYING OUT THE INVENTION

[1274] The system of the present invention automatically generates promotional materials based on videos taken by tourists visiting Japan or users on-site, and promotes the use of cashless payments. Specific embodiments for implementing this system are described below.

[1275] System configuration

[1276] 1. User Device

[1277] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device. This application is responsible for sending videos, adding metadata, and uploading them.

[1278] 2. Server

[1279] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. The server is equipped with a high-performance processor and large-capacity storage. The main software used includes object recognition algorithms (e.g., YOLO, Mask R-CNN), face detection algorithms (e.g., OpenCV's Haar Cascade), and image processing libraries (e.g., OpenCV, PIL).

[1280] Receiving and storing videos

[1281] The user device uploads the video they have taken to the server using a dedicated video posting app. The server receives the uploaded video and stores it in a temporary storage area. It also records metadata related to the video (user ID, video ID, upload time, visited store ID) in a database.

[1282] Video Analysis

[1283] The server reads the saved video file and extracts frames at regular intervals. Specifically, it is set to extract one frame every second. The extracted frames are analyzed using an object recognition algorithm and sorted into categories such as store exteriors, store interiors, menus, dishes, and people.

[1284] Blurring

[1285] The server applies a face detection algorithm to frames classified as people to identify faces, then uses an image processing library to blur the face area to protect personal information, making it difficult to identify individuals.

[1286] Generate and provide promotional materials

[1287] The server stores the categorized frames by theme and automatically generates promotional materials such as slideshows and banner images. The generated promotional materials are provided to the store administrator's management screen and can be easily used.

[1288] Points allocation and use

[1289] The server awards points to users after they have posted a video. The standard for awarding points is, for example, 500 points per video post. Users can use the smartphone app to redeem points at cashless payment affiliated stores in Japan. Points are applied as discounts according to the purchase amount.

[1290] Specific examples

[1291] Users visit tourist spots in Japan and take videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the videos they have taken to a server using a dedicated app. The server receives the videos, analyzes each frame, and categorizes them into categories such as the store's exterior, interior, menu, food, and people. Frames that show people are blurred. Promotional materials are then automatically generated and provided to the store manager. Users are rewarded for posting videos with points, which can be used at cashless payment affiliated stores in Japan.

[1292] Prompt Sentence Examples

[1293] "A user visits a tourist spot in Japan and takes a video of a store's exterior, interior, menu, and food. The user uploads this video to a server via the app, and the server analyzes the video and automatically generates promotional materials. Please give us an overview of this system."

[1294] The AI ​​model generated based on this prompt can provide detailed explanations of the system's detailed operation and the technical elements of each step.

[1295] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1296] Step 1:

[1297] Recording and uploading videos

[1298] Input: Videos taken by users using devices such as smartphones

[1299] How it works: A user launches a video posting application, selects a video they have taken, and presses the submit button to upload the video.

[1300] Output: Sending video data from the user device to the server

[1301] Step 2:

[1302] Receiving and storing videos

[1303] Input: Video data sent from the user device

[1304] How it works: The server receives the video via the Internet and stores it in a temporary storage area. At the same time, it analyzes the metadata associated with the video (user ID, video ID, upload time, visited store ID) and records it in a database.

[1305] Output: Video files stored in temporary storage and metadata stored in a database

[1306] Step 3:

[1307] Extracting Frames

[1308] Input: Saved video file

[1309] Operation: The server loads the video file and extracts frames at regular intervals. Specifically, it is set to extract one frame every second.

[1310] Output: Individual frames stored in temporary storage

[1311] Step 4:

[1312] Frame analysis and categorization

[1313] Input: Extracted individual frames

[1314] How it works: The server applies object recognition algorithms (e.g., YOLO, Mask R-CNN) to identify objects in the frame, then categorizes them into categories such as store exteriors, store interiors, menus, dishes, people, etc.

[1315] Output: Frames sorted by category

[1316] Step 5:

[1317] Blurring

[1318] Input: Frames sorted into the people category

[1319] How it works: The server uses a face detection algorithm (e.g., OpenCV's Haar Cascade) to identify faces in the frame, and then uses an image processing library (e.g., OpenCV, PIL) to blur the identified faces.

[1320] Output: Frame with face blurred

[1321] Step 6:

[1322] Save Frame

[1323] Input: Categorized and blurred frames

[1324] How it works: The server stores these frames in folders by theme, for example, store exterior frames in an "Exterior" folder, food frames in a "Food" folder, etc.

[1325] Output: Thematically organized frame data

[1326] Step 7:

[1327] Generate promotional materials

[1328] Input: Frame data organized by theme

[1329] How it works: The server selects the saved frames and automatically generates promotional materials such as slideshows and banner images, stitching the selected frames together and adding text.

[1330] Output: Auto-generated promotional materials

[1331] Step 8:

[1332] Providing promotional materials

[1333] Input: Auto-generated promotional material

[1334] Operation: The server uploads the generated promotional materials to the store manager's management screen, where the manager can easily use the materials.

[1335] Output: Promotional materials uploaded to the dashboard

[1336] Step 9:

[1337] Points awarded

[1338] Input: Video submission completion data

[1339] Operation: The server confirms the completion of video posting and awards points to the user. Points are awarded at a rate of 500 points per video posted. The point information is updated in the user database.

[1340] Output: Update of awarded points to user database

[1341] Step 10:

[1342] Using points

[1343] Input: Points awarded

[1344] How it works: Users use a smartphone app to redeem points at cashless payment affiliated stores in Japan. The app provides a function to select the application of points at the time of payment, specifically by scanning a QR code or presenting a barcode.

[1345] Output: Discount applied by using points at cashless payment affiliated stores

[1346] (Application example 1)

[1347] 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."

[1348] In today's tourism industry, it is extremely important to effectively promote stores visited by tourists visiting Japan. However, in many cases, videos taken by tourists are not effectively utilized. Furthermore, there are issues with privacy protection and promoting cashless payments. Specifically, there is a lack of methods to automatically generate promotional materials for stores and encourage visitors to use cashless payments. As a result, not only are store promotional activities ineffective, but convenience for tourists is also diminished.

[1349] 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.

[1350] In this invention, the server includes a terminal device for a user to shoot videos at a store and upload the videos to the server, a means for analyzing the shot and uploaded videos on the server and classifying them into store exteriors, interiors, menus, dishes, people, etc. using an object recognition algorithm, a means for blurring facial portions of the analyzed frames using an image processing library, a means for automatically generating store promotional materials from the analyzed frames and providing them to a store manager, and a means for awarding points that can be used for cashless payments to users. This makes it possible to effectively utilize videos shot by tourists visiting Japan to automatically generate store promotional materials and promote cashless payments while simultaneously protecting their privacy.

[1351] A "user terminal" is a device that a user uses to shoot video and upload the video to a server.

[1352] "Server" refers to a device equipped with a high-performance processor and large-capacity storage that communicates with user terminals via the Internet, stores and analyzes received videos, and generates promotional materials.

[1353] An "object recognition algorithm" is an algorithm that identifies objects within video frames and classifies them into store exteriors, interiors, menus, dishes, people, etc.

[1354] An "image processing library" is a library for processing and editing images, and is used for blurring and other processes.

[1355] "Promotional Materials" refers to generated visual content, such as slideshows and banner images, used in store promotional activities.

[1356] The "point awarding means" is a means for awarding points to users for posting videos, and provides points that users can use for cashless payments.

[1357] The "analysis means" is a processing device or software that has the function of allowing the server to analyze uploaded videos and classify them into specific categories.

[1358] The "blurring means" is a means for protecting privacy by blurring the facial portion of a person in an image.

[1359] An embodiment of the present invention will be described. This is a system that automatically generates promotional materials for stores based on videos taken by tourists visiting Japan and promotes the use of cashless payment. This system includes the following components.

[1360] System configuration

[1361] User terminal

[1362] A user device is a device that a user uses to shoot videos and upload them to a server. This includes smartphones, tablets, and PCs. A dedicated video posting application (hereafter referred to as the SmartPromo app) is installed on these devices.

[1363] server

[1364] The server communicates with user devices via the Internet, stores and analyzes the received video, and generates promotional materials. The server is equipped with a high-performance processor (e.g., Intel Xeon or AMD Ryzen series) and large-capacity storage (HDD or SSD). The server runs an object recognition algorithm (e.g., YOLO or SSD) and an image processing library (e.g., OpenCV), which analyzes frames and performs blurring.

[1365] Receiving and storing videos

[1366] A user shoots a video using the SmartPromo app and uploads it to the server. The server receives the video data along with metadata (user ID, video ID, upload time, store ID). This data is recorded in a database (e.g., MySQL or SQLite), and the video itself is stored in storage.

[1367] Video analysis and processing

[1368] The server extracts frames from the stored video at regular intervals. For example, it can be set to extract one frame every second. The extracted frames are then categorized using an object recognition algorithm into categories such as store exteriors, interiors, menus, food, and people. For frames classified as people, a face detection algorithm (for example, OpenCV's face recognition function) is used to blur the faces.

[1369] Generate promotional materials

[1370] The server saves the analyzed frames by theme and automatically generates promotional materials such as slideshows and banner images. The generated materials are provided to store managers through a dedicated management screen, allowing store managers to use these materials to carry out effective store promotions.

[1371] Points allocation and use

[1372] When a user posts a video, the server will award the user points. The points can be viewed within the SmartPromo app and can be used at cashless payment affiliated stores in Japan. The standard for awarding points could be 500 points per video post.

[1373] Specific Examples

[1374] For example, a user shoots a video at a restaurant in a tourist spot in Japan and uploads it to the server using the SmartPromo app. The server receives the video, extracts and analyzes frames every second, and classifies them into categories such as the restaurant's exterior, interior, menu, food, and people. Frames containing people are subjected to facial recognition and blurring. Promotional materials are then generated and provided to the restaurant manager, and points are awarded to the user. These points can be used for the next cashless payment, increasing user convenience.

[1375] Prompt Sentence Examples

[1376] Users can upload videos of their restaurants. The videos are analyzed into categories of restaurant exterior, interior, menu, food, and people. Faces of people are blurred and promotional materials for the restaurant are generated. The generated promotional materials are provided to the restaurant manager, and the user is awarded 500 points. The points can be used for the next cashless payment.

[1377] In this way, the present invention provides a system that contributes to the promotion of the tourism industry and cashless payments by effectively utilizing videos taken by tourists visiting Japan and automatically generating promotional materials for stores.

[1378] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1379] Step 1:

[1380] A user takes a video in a store. The user uses the SmartPromo app to take a video and temporarily saves it on the device. The input of this step is the "taken video data" and the output is the "video file saved on the device."

[1381] Step 2:

[1382] The user uploads the video they have taken to the server. On the user's device, they use the SmartPromo app to send the video to the server. At this time, metadata such as the user ID, video ID, upload time, and store ID are also sent. The input for this step is the "video file and metadata on the device," and the output is the "video file and metadata stored on the server."

[1383] Step 3:

[1384] The server stores the received video data. The server stores the video data in storage (e.g., HDD or SSD) and records the metadata in a database (e.g., MySQL or SQLite). The input of this step is the "video data and metadata uploaded to the server," and the output is the "video file stored in storage and the metadata recorded in the database."

[1385] Step 4:

[1386] The server extracts frames from the video at regular intervals. For example, it can be set to extract one frame every second. The input of this step is the "saved video file" and the output is the "extracted frames."

[1387] Step 5:

[1388] The server analyzes the extracted frames using an object recognition algorithm. The server uses object recognition algorithms such as YOLO and SSD to categorize them into store exteriors, interiors, menus, dishes, people, etc. The input to this step is the "extracted frame group," and the output is the "categorized frame information."

[1389] Step 6:

[1390] The server blurs frames that contain people among the categorized frames. The server identifies faces using OpenCV's face recognition function and performs blurring. The input to this step is "categorized frame information that includes people," and the output is "frames that have been blurred."

[1391] Step 7:

[1392] The server saves the analyzed and processed frames. The categorized frames are saved to generate promotional materials by theme. The input of this step is "blurred frames" and "other categorized frames", and the output is "saved frame data".

[1393] Step 8:

[1394] The server uses the saved frames to generate promotional materials for the store. For example, it automatically generates slideshows and banner images. The input of this step is the "saved frame data" and the output is the "generated promotional materials."

[1395] Step 9:

[1396] The server provides the generated promotional materials to the store manager, who can then view and use them through the management screen. The input to this step is the "generated promotional materials" and the output is the "promotional materials provided to the store manager."

[1397] Step 10:

[1398] The server assigns points to the user. After the video is posted, the server adds the points to the user ID. The input of this step is the user ID and posting information in the metadata, and the output is the points assigned to the user account.

[1399] Step 11:

[1400] The user uses the points awarded for cashless payment. Using the SmartPromo app, the user can use the points to pay at cashless payment affiliated stores. The input for this step is the "point information stored in the app," and the output is the "payment result using the points."

[1401] 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.

[1402] The system of this invention automatically generates promotional materials for stores based on videos taken by tourists visiting Japan, promoting the use of cashless payments. Furthermore, by combining it with an emotion engine that recognizes the emotions of users in the videos and improves the quality of the promotional materials, more effective marketing can be achieved.

[1403] System configuration

[1404] 1. User Device

[1405] This is a device such as a smartphone, tablet, or PC that allows users to shoot videos and upload them to the system. A dedicated video posting application is installed on the device.

[1406] 2. Server

[1407] The server communicates with user devices via the Internet, stores and analyzes the received videos, and generates promotional materials. It also uses an emotion engine to recognize user emotions and reflect them in the promotional materials.

[1408] Program processing overview

[1409] Receiving and storing videos

[1410] The user device uploads the captured video to the server via the VOOM app.

[1411] The server stores the video received from the user terminal, extracts related metadata (user ID, video ID, upload time, visited store ID, etc.), and records it in a database.

[1412] Video Analysis

[1413] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[1414] The server applies object recognition algorithms to detect objects in the frame and classify them into categories (store exterior, store interior, menu, food, people).

[1415] Blurring

[1416] The server identifies frames that fall into the people category based on the analysis results, and uses a face detection algorithm to detect human faces within the frames.

[1417] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[1418] emotion recognition

[1419] The server applies an emotion engine to recognize the user's emotion from the extracted frames. The emotion engine analyzes the user's facial expression data to identify emotions (e.g., joy, anger, sadness, surprise, etc.).

[1420] Generate and provide promotional materials

[1421] The server stores the categorized frames by purpose, and the frames are stored in specific folders according to their category.

[1422] The server automatically generates promotional materials based on the stored frames and emotion data, for example creating slideshows and banner images that include many frames of the user's happy facial expressions.

[1423] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[1424] Points allocation and use

[1425] After the video has been uploaded, the server awards points to the user based on preset rules.

[1426] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[1427] Specific examples

[1428] Users visit tourist spots in Japan and film videos of the exterior, interior, menu, and food of a store (e.g., a ramen shop). The user then uploads the video to a server via the VOOM app. The server receives the video, analyzes each frame, and categorizes it into categories such as store exterior, interior, menu, food, and people. Frames that show people are blurred. The emotion engine then analyzes the user's facial expressions in the frames, selecting frames with many happy expressions, automatically generating promotional materials, and providing them to store managers. In return, users are awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[1429] In this way, the present invention realizes a system that optimizes store marketing activities by providing effective promotional materials that are convenient for tourists and reflect the emotions of users.

[1430] The processing flow will be explained below.

[1431] Step 1:

[1432] Users use their smartphones to shoot videos at stores and tourist spots in Japan, and the videos are saved in a dedicated application folder on the device.

[1433] Step 2:

[1434] Users upload the saved video to the server through the VOOM application. On the upload screen, they enter the video title and information about the store they visited, select the video file, and press the send button.

[1435] Step 3:

[1436] The server stores the video file received from the user. Upon receiving the file, it extracts the video metadata (user ID, video ID, upload time, visited store ID, etc.) and records it in a database.

[1437] Step 4:

[1438] The server extracts frames from the received video at regular intervals, for example, one frame per second, treating the entire video as a series of still images.

[1439] Step 5:

[1440] The server applies object recognition algorithms to detect objects in the extracted frames and classify each frame into a specific category (store exterior, store interior, menu, food, people, etc.).

[1441] Step 6:

[1442] The server identifies frames that fall into the people category and uses a face detection algorithm to detect human faces within the frames.

[1443] Step 7:

[1444] Based on the results of the face detection algorithm, the server uses an image processing library to blur the face, ensuring the protection of personal information.

[1445] Step 8:

[1446] The server applies an emotion engine to recognize the user's emotion from the extracted frames. The emotion engine analyzes the user's facial expression data in the frames and identifies emotions such as joy, surprise, sadness, and anger.

[1447] Step 9:

[1448] The server automatically generates promotional materials based on the categorized frames and emotion data. For example, it selects frames showing the most happy expressions of users and creates images for slideshows and banners.

[1449] Step 10:

[1450] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[1451] Step 11:

[1452] After a video is posted, the server awards points to the user. The amount of points awarded is determined based on pre-defined rules. For example, 500 points are awarded for each video posted.

[1453] Step 12:

[1454] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[1455] As a result, the system of the present invention can provide convenience and benefits to tourists, while also automatically generating effective promotional materials for stores, thereby improving marketing effectiveness.

[1456] Example 2

[1457] 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."

[1458] There is a need for a system that can effectively utilize videos taken by tourists visiting Japan to automatically generate promotional materials for stores and promote the use of cashless payments. However, current methods require a great deal of time and effort to analyze the videos and generate promotional materials, so an efficient and highly accurate method is needed. Other challenges include protecting personal information contained in the videos and making it difficult to implement marketing that reflects user emotions.

[1459] 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.

[1460] In this invention, the server includes means for receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for analyzing facial expression data of users in the frames to recognize emotions, means for generating promotional materials using the emotion data, means for providing the generated promotional materials to store managers, means for awarding points for posting videos, and means for users to use the points for payments on site. This makes it possible to efficiently analyze videos shot by tourists and automatically generate high-quality promotional materials, and further promote the use of cashless payments.

[1461] "Means for receiving videos taken by users" refers to a combination of infrastructure and software for receiving and storing video data uploaded from users' devices via a network.

[1462] The "means for extracting frames at regular intervals" refers to a process for extracting still images from video data at specified time intervals and dividing them into analyzable units.

[1463] "Means for analyzing frames and categorizing them into categories such as store exteriors, store interiors, menus, food, people, etc." refers to a technology that applies object recognition algorithms to identify objects in each frame and classify them into specific categories.

[1464] The "means of blurring frames classified into the person category" is a method of using a face detection algorithm and an image processing library to blur the face of a detected person in order to protect personal information.

[1465] "Means for recognizing emotions by analyzing the user's facial expression data in the frames" refers to a technology that analyzes the user's facial expressions from the extracted frames and uses an emotion engine to recognize specific emotions (joy, anger, sadness, surprise, etc.).

[1466] The "means for generating promotional materials using emotional data" is a method for automatically generating promotional materials such as slideshows and banner images, based on the results of emotional analysis, by preferentially using frames that express positive emotions.

[1467] The "means for providing the generated promotional materials to the store manager" is a system that uploads the generated promotional materials from a server to a management screen that can be accessed by the store manager, making them available for use.

[1468] The "means for awarding points for video submissions" is a reward system for awarding points to users who upload videos based on predefined rules.

[1469] "Means for users to use points for local payments" refers to a set of infrastructure and applications that provide an environment in which users can use their awarded points as an alternative to cash when making purchases or paying for services at physical stores.

[1470] This system automatically generates promotional materials for stores based on videos taken by tourists visiting Japan, promoting the use of cashless payments. The system's main hardware components include a user terminal and a server. Specifically, the user terminal is a device such as a smartphone, tablet, or PC, while the server provides high-performance cloud-based storage and computing resources.

[1471] Recording and uploading videos

[1472] The user device uses a dedicated video posting application (e.g., the VOOM app) to shoot videos of the store's exterior, interior, menu, food, etc. The videos are then uploaded to the server via the app.

[1473] Receiving and storing videos

[1474] The server receives the video data sent from the user's device. Along with the received video data, metadata such as the user ID, video ID, upload time, and visited store ID are sent. This data is stored in a database on the server. The video data itself is stored in highly secure storage.

[1475] Video Analysis

[1476] The server extracts frames from the received video at regular intervals, for example, using a technique to extract one frame per second. Then, an object recognition algorithm is applied to each extracted frame, and it is classified into store exteriors, store interiors, menus, dishes, people, etc.

[1477] Blurring and Emotion Recognition

[1478] The server identifies frames classified as "people" based on the object recognition results. Faces detected using a face detection algorithm (e.g., OpenCV) are blurred to ensure privacy. The emotion engine then analyzes the user's facial expression data in the extracted frames to recognize the user's emotions (e.g., joy, anger, sadness, surprise, etc.).

[1479] Producing and providing promotional materials

[1480] The server automatically generates promotional materials based on the classified frames and emotion data. The generated promotional materials include, for example, slideshows containing many frames of happy expressions from users and banner images, and these are saved in specific folders. The generated promotional materials are then automatically uploaded to the store manager's management screen, where they can be easily accessed and used by the store manager.

[1481] Points allocation and use

[1482] The server awards points to users who have completed uploading a video based on certain rules. These points can be used at cashless payment affiliated stores in Japan, and users can use the points via a smartphone app to make payments locally.

[1483] Examples of concrete examples and prompts

[1484] For example, a user might visit a famous ramen restaurant in a Tokyo tourist spot and film a video of the restaurant's exterior, interior, menu, and food. The user then uploads the video to a server via the VOOM app. The server receives the video and stores it in a database along with the metadata. The server then analyzes the video frame by frame, categorizing each frame into categories such as the restaurant's exterior, interior, menu, food, and people. A facial recognition algorithm is applied to frames containing people to blur them, and an emotion engine then analyzes the user's facial expressions and selects frames with many happy expressions to automatically generate promotional materials. These materials are then uploaded to the restaurant administrator's management screen. Users are then awarded points as a reward for posting videos, which can be used at cashless payment affiliated stores in Japan.

[1485] Example prompt sentence:

[1486] "Please upload videos of ramen shops in tourist spots in Japan that show their exteriors, interiors, menus, and dishes, and automatically generate promotional materials. Please prioritize happy expressions in promotional materials that reflect the emotions of users. After that, provide these materials to store managers, who can award points to users who posted videos, which can be used for cashless payments."

[1487] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1488] Step 1:

[1489] The user device uses the VOOM app to shoot videos of the store's exterior, interior, menu, food, etc. The input is the video data shot by the user, and the output is the video data and metadata (user ID, shooting date and time, store ID, etc.).

[1490] Step 2:

[1491] The user device uploads the captured video to the server via the VOOM app. Specifically, the app sends UDP / TCP packets to the server via an Internet connection to transfer the video data and metadata. The input is the video data and metadata in the user device, and the output is the data sent to the server.

[1492] Step 3:

[1493] The server receives the uploaded video data and metadata. The received data is stored in a database. The input is the video data and metadata sent from the user terminal, and the output is the data stored in the database. Specifically, the server splits the received data and stores it in the metadata table and video storage, respectively.

[1494] Step 4:

[1495] The server extracts frames from the video data at regular intervals, for example, using a technique to extract one frame per second. The input is the received video data, and the output is the extracted still frame images. Specifically, a video decoding algorithm is applied to extract frames at each time interval.

[1496] Step 5:

[1497] The server applies an object recognition algorithm to each extracted frame, detects objects in the frame, and classifies them into categories (store exterior, store interior, menu, food, people). The input is a still frame image, and the output is frame data tagged by category. Specifically, the server runs an image recognition algorithm using a machine learning model and saves the recognition results.

[1498] Step 6:

[1499] The server applies a face detection algorithm to frames classified as "people" to identify faces. It then uses an image processing library (e.g., OpenCV) to blur the detected face area. The input is a frame image in the "people" category, and the output is a frame image with the face blurred. Specifically, the server uses a face detection algorithm to identify the face area, and then applies a filter to blur that area.

[1500] Step 7:

[1501] The server analyzes the user's facial expression data in the blurred frames using an emotion engine. The input is the processed frame image, and the output is the analyzed emotion data. Specifically, it uses face recognition and expression analysis algorithms to estimate the emotional state for each frame.

[1502] Step 8:

[1503] The server automatically generates promotional materials based on the analysis results. It uses the emotional data to create slideshows and banner images that prioritize frames with a particular emotion (e.g., joy). The input is frames categorized by category and emotional data, and the output is the completed promotional materials. Specifically, it edits the selected frames and generates materials in a specific format.

[1504] Step 9:

[1505] The server saves the generated promotional materials in a specific folder and automatically uploads the data to the store manager's management screen. The input is the generated promotional materials, and the output is the data uploaded to the management screen. Specifically, the server uses a file transfer protocol to save the materials in a specified folder on the server and display them on the management screen.

[1506] Step 10:

[1507] The server awards points to users who have completed posting a video. The criteria for awarding points are determined based on pre-defined rules. The input is the metadata of the video post, and the output is the points added to the user account. Specifically, the server uses a point management system to add points to the user ID.

[1508] Step 11:

[1509] The user makes a payment using points at a cashless payment affiliated store. The payment is made using a smartphone app, with points applied. The input is the smartphone's point balance, and the output is a payment completion notification with points applied. Specifically, the points are applied via the payment API, and the result is notified to the user.

[1510] (Application example 2)

[1511] 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."

[1512] With conventional store promotion methods, creating promotional materials requires a great deal of effort and time, making it difficult to achieve effective marketing. It is also difficult to quickly generate materials that reflect the experiences of tourists visiting Japan, which means that the system lacks real-time capabilities. Furthermore, previous systems were unable to create materials that reflect users' emotions, preventing improvements in promotional effectiveness.

[1513] The identification process 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 receiving videos shot by users, means for extracting frames from the received videos at regular intervals, means for analyzing the extracted frames and sorting them into categories such as store exteriors, store interiors, menus, dishes, and people, means for blurring frames sorted into the people category, means for saving the categorized frames by purpose, means for using an emotion recognition engine to analyze user emotions, means for generating promotional materials using the saved frames and emotion data, means for providing the generated promotional materials to an administrator, means for awarding points for video submissions, and means for users to use the points for on-site payments. This enables automatic generation of promotional materials for stores and rapid, effective promotions that reflect user emotions.

[1514] "Videos taken by users" are video data taken by users using devices such as smartphones or smart glasses.

[1515] The "receiving means" refers to a communication method and function for transmitting a video taken by a user to a server via the Internet and for the server to receive the video.

[1516] "Means for extracting frames at regular intervals" refers to a processing method and function for extracting individual images (frames) from a received video at set intervals.

[1517] The "means for analyzing frames" refers to techniques and methods for analyzing extracted frames and classifying them into specific categories such as store exteriors, store interiors, menus, dishes, people, etc.

[1518] The "categorization means" refers to a method and function for dividing the analyzed frames into specific categories such as store exteriors, store interiors, menus, dishes, people, etc.

[1519] The "means for performing blurring processing" refers to a method and function for applying a blurring effect to the face part of a frame in the analyzed person category in order to protect personal information.

[1520] An "emotion recognition engine for analyzing emotions" is an algorithm and technology that detects the facial expressions of a user captured in a frame and recognizes the user's emotions (e.g., joy, surprise, sadness, etc.) from those expressions.

[1521] The "means for generating promotional materials" refers to a technology and method for automatically creating promotional materials (e.g., slideshows and banners) for a store using the stored frame and emotion data for each category.

[1522] The "means for providing to the store manager" refers to a method and function for providing the generated promotional material so that it can be used by the store manager.

[1523] The "means for awarding points" refers to a method and function for awarding points as a reward to a user who has posted a video.

[1524] "Means for users to use points for local payments" refers to methods and functions that allow users to use the accumulated points as a certain value when making cashless payments.

[1525] The system of the present invention automatically generates promotional materials from videos taken by users and promotes cashless payments. In particular, it realizes effective marketing by recognizing users' emotions and improving the quality of the materials based on those emotions.

[1526] System configuration

[1527] 1. User Device

[1528] Users use their smartphones or smart glasses to take videos of the inside and outside of the store. A dedicated video uploading application is installed on the device, allowing users to easily upload videos to the server.

[1529] 2. Server

[1530] The server plays a central role in receiving, storing, analyzing, and recognizing emotions in videos, as well as generating and providing promotional materials. Specific processes include:

[1531] Receiving and storing videos

[1532] The user terminal uploads the captured video to a server via a dedicated application.

[1533] The server stores the received video and records the associated metadata (e.g., user ID, video ID, upload time, visited store ID) in a database.

[1534] Video Analysis

[1535] The server extracts frames from the received video at regular intervals, for example, one frame per second, and treats the entire video as a series of still images.

[1536] The server uses object recognition algorithms to detect objects in the frame and classify them into categories (e.g., store exterior, store interior, menu, food, people).

[1537] Blurring

[1538] The server applies a face detection algorithm to frames classified as people and blurs the facial areas, ensuring the protection of personal information.

[1539] emotion recognition

[1540] The server uses an emotion recognition engine to analyze the user's facial expressions in the extracted frames and identify emotions (e.g., joy, surprise, sadness, etc.).

[1541] Generate and provide promotional materials

[1542] The server automatically generates promotional materials based on the stored frames and emotion data, for example, creating slideshows and banner images that include many frames of the user's happy facial expressions.

[1543] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager.

[1544] Points allocation and use

[1545] After a video is uploaded, the server awards points to the user based on preset rules.

[1546] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan, and can use their points to get discounts on payments.

[1547] Technology used

[1548] Hardware: Smart glasses, smartphones

[1549] Software: Python, OpenCV, FER library

[1550] Specific examples

[1551] A user visits a tourist spot and takes videos of the exterior, interior, menu, and food of a store (e.g., a restaurant). The user takes videos using smart glasses or a smartphone and uploads them to a server using a dedicated application. The server analyzes the videos, categorizes each frame, and blurs people's faces. It also uses an emotion recognition engine to analyze the user's emotions and generates promotional materials that include many happy expressions. These promotional materials are provided to the store manager's screen, and the user is awarded points.

[1552] Prompt Sentence Examples

[1553] "Generate promotional materials using videos of users having fun in the store. Analyze the emotions in the videos and create a slideshow that includes many scenes where users are particularly happy."

[1554] This system makes it possible to quickly generate effective promotional materials that reflect the actual experiences of tourists visiting Japan.

[1555] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1556] Step 1:

[1557] Users use their smartphones or smart glasses to take videos of the inside and outside of a store. The videos are then uploaded to a server via a dedicated video posting application. The input is the video taken by the user, and the output is the video data uploaded to the server.

[1558] Step 2:

[1559] The server receives the uploaded video, extracts the associated metadata (e.g., user ID, video ID, upload time, visited store ID) and records it in a database. The input is the uploaded video data, and the output is the video data and metadata stored in the database.

[1560] Step 3:

[1561] The server extracts frames from the received video at regular intervals. For example, it extracts one frame per second and treats the entire video as a series of still images. The input is the received video data, and the output is a list of extracted frame data.

[1562] Step 4:

[1563] The server applies an object recognition algorithm to detect objects in each frame and classify them into categories (e.g., store exterior, store interior, menu, food, person). The input is the extracted frames, and the output is the categorized frame data.

[1564] Step 5:

[1565] The server applies a face detection algorithm to frames classified as people and blurs the face, thereby protecting privacy. The input is a frame classified as people, and the output is a blurred frame.

[1566] Step 6:

[1567] The server uses an emotion recognition engine to analyze the user's facial expressions in each frame and identify emotions (e.g., happiness, surprise, sadness, etc.). The input is the blurred frames, and the output is the emotion data.

[1568] Step 7:

[1569] The server automatically generates promotional materials based on the stored frames and emotion data. For example, it creates slideshows or banner images that include many frames of the user's happy facial expressions. The input is emotion data and the stored frames, and the output is the generated promotional materials.

[1570] Step 8:

[1571] The server automatically uploads the generated promotional materials to the store manager's management screen, making them easily accessible and usable by the store manager. The input is the generated promotional materials, and the output is the promotional materials that can be viewed by the store manager.

[1572] Step 9:

[1573] The server awards points to users after they have completed uploading a video. The criteria for awarding points are determined based on pre-defined rules. The input is the video upload data, and the output is the awarded points.

[1574] Step 10:

[1575] Users can use points on their smartphones when shopping or eating at cashless payment affiliated stores in Japan. When paying, they can use their points to get a discount. The input is the points held by the user, and the output is the discount amount at the time of payment.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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).

[1583] 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.

[1584] 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."

[1585] 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.

[1586] 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).

[1587] 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.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] 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.

[1592] 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.

[1593] 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.

[1594] 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.

[1595] 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.

[1596] 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.

[1597] The following is further disclosed regarding the above embodiment.

[1598] (Claim 1)

[1599] means for receiving a video captured by a user;

[1600] means for extracting frames from the received video at regular intervals;

[1601] A means for analyzing the extracted frames and sorting them into categories such as store exterior, store interior, menu, food, people, etc.;

[1602] means for performing blurring processing on frames sorted into the person category;

[1603] a means for storing the frames sorted into the categories by purpose;

[1604] means for generating promotional materials for a store using said stored frames;

[1605] means for providing the generated promotional material to a store manager;

[1606] A means of awarding points for posting videos,

[1607] A means for a user to use the points for local payments;

[1608] A system including:

[1609] (Claim 2)

[1610] 10. The system of claim 1, wherein the means for analyzing the frames uses an object recognition algorithm.

[1611] (Claim 3)

[1612] 2. The system of claim 1, wherein the means for blurring the frame uses a face detection algorithm and an image processing library.

[1613] "Example 1"

[1614] (Claim 1)

[1615] means for receiving a video captured by a user;

[1616] means for extracting frames from the received video at regular intervals;

[1617] A means for analyzing the extracted frames and sorting them into categories such as store exterior, store interior, menu, food, people, etc.;

[1618] means for performing blurring processing on frames sorted into the person category;

[1619] a means for storing the frames sorted into the categories by purpose;

[1620] means for generating promotional materials for a store using said stored frames;

[1621] means for providing the generated promotional material to a store manager;

[1622] A means of awarding points for posting videos,

[1623] A means for a user to use the points for local payments;

[1624] A system including:

[1625] (Claim 2)

[1626] 10. The system of claim 1, wherein the means for analyzing the frames uses an object recognition algorithm.

[1627] (Claim 3)

[1628] 2. The system of claim 1, wherein the means for blurring the frame uses a face detection algorithm and an image processing library.

[1629] "Application Example 1"

[1630] (Claim 1)

[1631] means for receiving a video captured by a user;

[1632] means for extracting frames from the received video at regular intervals;

[1633] A means for analyzing the extracted frames and sorting them into categories such as store exterior, store interior, menu, food, people, etc.;

[1634] means for performing blurring processing on frames sorted into the person category;

[1635] a means for storing the frames sorted into the categories by purpose;

[1636] means for generating promotional materials for a store using said stored frames;

[1637] means for providing the generated promotional material to a store manager;

[1638] A means of awarding points for posting videos,

[1639] A means for a user to use the points for local payments;

[1640] a terminal device for users to take videos in the store and upload the videos to a server;

[1641] A means that has the function of analyzing the filmed and uploaded videos on a server and classifying them into store exteriors, interiors, menus, dishes, people, etc. using an object recognition algorithm;

[1642] A means for blurring the face portion of the analyzed frame using an image processing library;

[1643] a means for automatically generating store promotion materials from the analyzed frames and providing the materials to a store manager;

[1644] A means for granting points that can be used for cashless payments to users;

[1645] A system including:

[1646] (Claim 2)

[1647] 10. The system of claim 1, wherein the means for analyzing the frames uses an object recognition algorithm and a high performance processor.

[1648] (Claim 3)

[1649] 2. The system according to claim 1, wherein the means for blurring the frames uses a face detection algorithm and an image processing library to process the frames extracted at regular intervals.

[1650] "Example 2: Combining Emotion Engines"

[1651] (Claim 1)

[1652] means for receiving a video captured by a user;

[1653] means for extracting frames from the received video at regular intervals;

[1654] A means for analyzing the extracted frames and sorting them into categories such as store exterior, store interior, menu, food, people, etc.;

[1655] means for performing blurring processing on frames sorted into the person category;

[1656] means for analyzing the facial expression data of the user in the frame and recognizing emotions;

[1657] means for generating promotional materials using said emotion data;

[1658] means for providing the generated promotional material to a store manager;

[1659] A means of awarding points for posting videos,

[1660] A means for a user to use the points for local payments;

[1661] A system including:

[1662] (Claim 2)

[1663] 10. The system of claim 1, wherein the means for analyzing the frames uses an object recognition algorithm.

[1664] (Claim 3)

[1665] 2. The system of claim 1, wherein the means for blurring the frame uses a face detection algorithm and an image processing library.

[1666] "Application example 2 when combining emotion engines"

[1667] (Claim 1)

[1668] means for receiving a video captured by a user;

[1669] means for extracting frames from the received video at regular intervals;

[1670] A means for analyzing the extracted frames and sorting them into categories such as store exterior, store interior, menu, food, people, etc.;

[1671] means for performing blurring processing on frames sorted into the person category;

[1672] a means for storing the frames sorted into the categories by purpose;

[1673] a means for utilizing an emotion recognition engine to analyze the user's emotions;

[1674] means for generating promotional material using said stored frame and emotion data;

[1675] means for providing the generated promotional material to an administrator;

[1676] A means of awarding points for posting videos,

[1677] A means for a user to use the points for local payments;

[1678] A system including:

[1679] (Claim 2)

[1680] 10. The system of claim 1, wherein the means for analyzing the frames uses an object recognition algorithm.

[1681] (Claim 3)

[1682] 2. The system of claim 1, wherein the means for blurring the frame uses a face detection algorithm and an image processing library. [Explanation of symbols]

[1683] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a video captured by a user; means for extracting frames from the received video at regular intervals; A means for analyzing the extracted frames and sorting them into categories such as store exterior, store interior, menu, food, people, etc.; means for performing blurring processing on frames sorted into the person category; a means for storing the frames sorted into the categories by purpose; means for generating promotional materials for a store using said stored frames; means for providing the generated promotional material to a store manager; A means of awarding points for posting videos, A means for a user to use the points for local payments; A system including:

2. 2. The system of claim 1, wherein said means for analyzing said frames uses an object recognition algorithm.

3. 2. The system of claim 1, wherein the means for blurring the frame uses a face detection algorithm and an image processing library.

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