System

Abstract

A system is provided.SOLUTION: A system comprising: means for capturing video footage of a user in real-time; means for transmitting the captured video footage to a server; means for analyzing the video footage received by the server using multi-modal AI and facial recognition techniques to detect changes in the user's emotions; means for generating various types of feedback based on the analysis results; and means for transmitting and displaying the generated feedback to the user's device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In traditional online education, therapy, entertainment, and product development environments, it is difficult to accurately analyze user emotions in real time, making it difficult to obtain specific feedback to improve the user experience. If this challenge is not addressed, it can lead to lower user satisfaction and engagement, and delay improvements to services and products. [Means for solving the problem]

[0005] The present invention provides a means for capturing a user's video footage in real time and transmitting it to a server. The server then employs a means for analyzing the received video footage using multimodal AI and facial recognition technologies to detect changes in the user's emotions. This allows various feedback to be generated based on the analysis results and transmitted to the user's device for display. The present invention also includes a means for storing the user's emotional data in a cloud-based database, thereby enabling effective data utilization. The present invention also includes a means for using the user's emotional data to provide personalized experiences for users in the fields of education, healthcare, entertainment, and product development. The present invention also provides a data encryption means for protecting user privacy and data security, thereby achieving a highly reliable emotion analysis system.

[0006] "User" means an individual or organization that uses this system.

[0007] "Video Footage" is real-time video data captured by a user's camera.

[0008] "Capture" is the process of capturing video footage using a camera or other image acquisition device.

[0009] "Terminal" refers to a device (e.g., PC, tablet, smartphone) that a user uses to access and operate the System.

[0010] "Send" is the act of transferring data from a terminal to a server.

[0011] A "server" is a computer system for receiving video footage, analyzing it, and managing the results.

[0012] "Multimodal AI technology" is an artificial intelligence technology that combines and analyzes multiple data modes (e.g., images, audio, text).

[0013] "Facial recognition technology" is a technology that detects a user's face from video footage and identifies their facial expressions and features.

[0014] "Analysis" is the act of analyzing video footage to extract useful information (e.g., the user's emotional state).

[0015] "Emotional changes" refers to changes in a user's emotional state over time.

[0016] "Feedback" refers to response information or actions to be taken by the user based on the analysis results.

[0017] A "cloud-based database" is a remote data storage system that is accessible via the internet.

[0018] "Data encryption" is the process of converting data into an unreadable form to protect it from unauthorized access.

[0019] "Privacy" refers to a state in which a user's personal information and emotional data are protected and not leaked to third parties. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The system of the present invention improves the user experience by capturing a user's video footage in real time and analyzing changes in the user's emotions from the video footage. This system is composed of the following means and operations.

[0042] System Configuration

[0043] 1. User Interface

[0044] The terminal provides a user interface that allows users to access and operate the system, including functions such as starting video capture, selecting a session, and checking feedback.

[0045] 2. Video capture and transmission

[0046] A user logs into the system and begins a session.

[0047] The device captures the user's video in real time and sends it to the server. The video capture is performed using the device's camera device.

[0048] 3. Video reception and analysis

[0049] The server receives the video image transmitted from the terminal.

[0050] The server analyzes video footage using multimodal AI and facial recognition technology, identifying the user's facial expressions and gestures and detecting changes in the user's emotions in real time.

[0051] 4. Feedback generation and display

[0052] The server generates feedback based on the analysis, which is tailored to the user's situation and may include additional educational material or help links if the user is confused, for example.

[0053] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[0054] 5. Data storage and utilization

[0055] The server stores the user's emotional data in a cloud-based database, which is encrypted to protect user privacy and data security.

[0056] Service providers analyze the stored data and plan improvements to their services and products. The data can be used in education, healthcare, entertainment, and product development to provide users with personalized experiences.

[0057] Specific examples

[0058] Specific examples of online education

[0059] In an online education platform, the system functions as follows:

[0060] 1. User Registration and Login

[0061] A user registers and logs in to the education platform.

[0062] The terminal sends authentication information to the server, and the server performs authentication.

[0063] 2. Starting the lesson and capturing video

[0064] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[0065] 3. Video Analysis

[0066] The server receives the video footage and performs analysis: it detects the user's facial expressions and identifies emotions such as interest and confusion.

[0067] 4. Feedback generation and display

[0068] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[0069] 5. Data storage and utilization

[0070] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[0071] Examples of online therapy

[0072] On the online therapy platform, the system works as follows:

[0073] 1. User Registration and Login

[0074] A user registers and logs in to the therapy platform.

[0075] The terminal sends authentication information to the server, and the server performs authentication.

[0076] 2. Therapy session begins

[0077] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[0078] 3. Video Analysis

[0079] The server receives the video footage and performs analysis to detect changes in emotions from the user's facial expressions and gestures.

[0080] 4. Feedback generation and display

[0081] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[0082] 5. Data storage and utilization

[0083] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[0084] As a result, the system of the present invention can analyze the user's emotions in real time and provide various feedback, thereby improving the user experience in various fields.

[0085] The processing flow will be explained below.

[0086] Step 1:

[0087] The user accesses the Kimochi Scope website or app and fills in the required information on the new registration form.

[0088] Step 2:

[0089] The device sends the entered information (name, email address, password, etc.) to the server.

[0090] Step 3:

[0091] The server stores the received information in its database and completes the new user's registration.

[0092] Step 4:

[0093] The user enters their registered email address and password on the login screen.

[0094] Step 5:

[0095] The terminal transmits the entered authentication information to the server.

[0096] Step 6:

[0097] The server checks the database and, if authentication is successful, generates a session ID and sends it to the terminal.

[0098] Step 7:

[0099] Users select from a range of online education, therapy, entertainment, product testing and other sessions.

[0100] Step 8:

[0101] The terminal transmits the selected session information to the server.

[0102] Step 9:

[0103] The device activates the camera and captures live video footage of the user.

[0104] Step 10:

[0105] The video captured by the device is streamed to the server in real time.

[0106] Step 11:

[0107] The server receives the video image transmitted from the terminal.

[0108] Step 12:

[0109] The server temporarily stores the received video and prepares it for processing.

[0110] Step 13:

[0111] The server analyzes the stored video data using multimodal AI technology and facial recognition technology.

[0112] Step 14:

[0113] The server identifies the user's facial expressions and gestures and evaluates the changes in real time.

[0114] Step 15:

[0115] The server quantifies the emotional state and classifies it into categories (e.g., happy, sad, confused, etc.).

[0116] Step 16:

[0117] The server generates feedback data (e.g., "The user is confused") based on the analysis results.

[0118] Step 17:

[0119] The server transmits the generated feedback data to the terminal.

[0120] Step 18:

[0121] Based on the feedback data received by the terminal, appropriate messages and materials are displayed on the user interface.

[0122] Step 19:

[0123] The user reviews the displayed feedback and materials and decides on their next course of action.

[0124] Step 20:

[0125] The server stores the user's emotional data during the session in a cloud-based database.

[0126] Step 21:

[0127] The server encrypts data at rest for data security and privacy.

[0128] Step 22:

[0129] Service providers analyze the stored data and develop improvements to their services and products.

[0130] Step 23:

[0131] Service providers can use data to tailor online education curricula, therapy plans, entertainment content, and more to improve user experience.

[0132] Example 1

[0133] 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."

[0134] The present invention relates to a system that captures a user's video footage in real time, analyzes the footage, and detects changes in the user's emotions. Conventional systems have difficulty accurately capturing a user's emotions in real time, and have not sufficiently improved the user experience. Furthermore, there are many challenges in storing and utilizing emotional data and protecting privacy. Therefore, there is a need for a system that can significantly improve the user experience by accurately analyzing a user's emotions in real time and effectively providing feedback based on the results.

[0135] 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.

[0136] In this invention, the server includes means for capturing a user's video image in real time, means for transmitting the captured video image to an information processing device, means for analyzing the video image received by the information processing device using multimodal artificial intelligence technology and face recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's output device and displaying it, and means for storing the user's emotion data in a network-based storage device, thereby enabling the user's emotions to be accurately analyzed in real time and appropriate feedback to be provided promptly.

[0137] "User" refers to any individual or entity that uses the System.

[0138] "Video Footage" refers to visual data captured through a camera device.

[0139] "Real-time" refers to the temporal characteristic of being processed immediately without delay.

[0140] "Information processing device" refers to a computer system for analyzing and processing received data.

[0141] "Multimodal AI technology" refers to AI technology that combines and evaluates multiple different data formats (e.g., video, audio, text).

[0142] "Facial recognition technology" refers to technology that detects a person's face from video captured using a camera device and analyzes its features.

[0143] "Changes in emotion" refers to changes in the emotional state detected based on changes in the user's facial expressions and gestures.

[0144] "Feedback" refers to information, instructions, or advice provided to the user based on the analysis results.

[0145] "Output device" refers to a device (e.g., a display or speaker) for providing visual or audible feedback to a user.

[0146] "Network-based storage" refers to a remotely located data storage device that is accessible over a network (e.g., cloud storage).

[0147] "Data encryption" refers to a technology that protects the confidentiality of data by converting it into a format that cannot be understood by third parties during transmission or storage.

[0148] "Education" refers to activities aimed at imparting knowledge and skills and promoting learning.

[0149] "Medical care" refers to diagnostic, therapeutic and preventive activities aimed at maintaining and restoring human health.

[0150] "Leisure" refers to any activity that people undertake for enjoyment or relaxation.

[0151] "Product development" refers to the process of planning, designing, and producing new products and services.

[0152] The present invention is a system that captures a user's video footage in real time and analyzes changes in the user's emotions from the footage. This system is characterized by the configuration and operation described below.

[0153] System Configuration

[0154] 1. User Interface

[0155] The terminal provides a user interface through which the user can access and operate the system, including functions such as starting a video capture, selecting a session, and viewing feedback.

[0156] 2. Video capture and transmission

[0157] A user logs into the system and begins a session.

[0158] The terminal uses a camera device to capture video images of the user in real time and transmits them to an information processing device (server).

[0159] 3. Video reception and analysis

[0160] The server receives the video image transmitted from the terminal.

[0161] The server analyzes video footage using multimodal artificial intelligence and facial recognition technology, identifying the user's facial expressions and gestures and detecting changes in the user's emotions in real time.

[0162] 4. Feedback generation and display

[0163] The server generates feedback based on the analysis, which is tailored to the user's situation and may include additional educational materials or help links if the user is confused.

[0164] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[0165] 5. Data storage and utilization

[0166] The server stores the user's emotional data in a network-based storage device, and the data is encrypted to protect the user's privacy and data security.

[0167] Service providers analyze the stored data to plan improvements to their services and products, and the data can be used in education, healthcare, entertainment, and product development to provide users with a personalized experience.

[0168] Technical details

[0169] Camera device: The device's built-in front camera or an externally connected camera device is used.

[0170] Multimodal artificial intelligence technology: Machine learning models using Tensorflow (registered trademark) and PyTorch are used. Libraries such as OpenCV and Dlib are used for face recognition technology.

[0171] Network-based storage: Amazon RDS, Google® Cloud Firestore, etc. are used to store and analyze data.

[0172] Specific examples

[0173] Specific examples of online education

[0174] 1. User Registration and Login

[0175] A user registers and logs in to the education platform.

[0176] The terminal sends authentication information to the server, and the server performs authentication.

[0177] 2. Starting the lesson and capturing video

[0178] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[0179] 3. Video Analysis

[0180] The server receives the video footage and performs analysis: it detects the user's facial expressions and identifies emotions such as interest and confusion.

[0181] 4. Feedback generation and display

[0182] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[0183] 5. Data storage and utilization

[0184] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[0185] Examples of online therapy

[0186] 1. User Registration and Login

[0187] A user registers and logs in to the therapy platform.

[0188] The terminal sends authentication information to the server, and the server performs authentication.

[0189] 2. Therapy session begins

[0190] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[0191] 3. Video Analysis

[0192] The server receives the video footage and performs analysis to detect changes in emotions from the user's facial expressions and gestures.

[0193] 4. Feedback generation and display

[0194] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[0195] 5. Data storage and utilization

[0196] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[0197] Prompt Sentence Examples

[0198] Example prompt:

[0199] "I want to design a system that captures a user's facial expressions in real time through a camera and analyzes their emotional changes. What should I use?"

[0200] Example output:

[0201] It is recommended to use a webcam or external camera as the camera device. Machine learning models using TensorFlow or PyTorch are useful for facial expression analysis, and libraries such as OpenCV or Dlib are useful for face recognition. For data storage and analysis, use a cloud-based database such as Amazon RDS or Google Cloud Firestore.

[0202] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0203] Step 1:

[0204] User Registration and Login

[0205] Input: Information entered by the user in the sign-up form (name, email address, password)

[0206] Output: Authentication information generated by the server

[0207] Specific operation: The user enters information into the new registration form on the web browser and clicks the "Register" button. The device sends the information to the server as a POST request. The server stores the received information in a database and generates and returns authentication information.

[0208] Step 2:

[0209] Start video capture

[0210] Input: Instruction to start video capture selected by the user after logging in

[0211] Output: Captured video data

[0212] Specific operation: When the user clicks the start video capture button, the device activates the camera device using JavaScript (registered trademark) and starts capturing video in real time. The captured video is passed directly to the next step.

[0213] Step 3:

[0214] Video transmission and reception

[0215] Input: Captured video footage data

[0216] Output: Video data received by the server

[0217] Specific operation: The device sends captured video frame by frame to the server using WebSocket or HTTP streaming. The server receives this data in real time and puts it into an analysis queue.

[0218] Step 4:

[0219] Video Analysis

[0220] Input: Received video data

[0221] Output: Analyzed emotion change data

[0222] How it works: The server uses OpenCV and TensorFlow libraries to analyze the user's facial expressions and gestures for each frame. In particular, it detects expressions such as smiles and confusion, and identifies emotional changes based on them. The identified emotional change data is generated and passed to the next step.

[0223] Step 5:

[0224] Feedback Generation

[0225] Input: Analyzed emotion change data

[0226] Output: Generated feedback data

[0227] How it works: Based on the analysis results, the server creates appropriate feedback using Python scripts or generative AI models (e.g., GPT). The feedback data is converted to JSON format and sent to the device.

[0228] Step 6:

[0229] Feedback Display

[0230] Input: Generated feedback data

[0231] Output: A feedback message that is displayed on the user's terminal.

[0232] Specific behavior: When the device receives feedback data from the server, it uses JavaScript and HTML to display appropriate messages and content on the interface. The user checks the displayed feedback and decides on the next action.

[0233] Step 7:

[0234] Data storage and utilization

[0235] Input: Analyzed emotion change data and generated feedback data

[0236] Output: Data stored on a network-based storage device

[0237] How it works: The server stores the analysis results and feedback in a database. This storage is encrypted to protect the confidentiality of the data. The stored data is later analyzed by the service provider using analysis tools and used to plan and implement improvements to services and products.

[0238] (Application example 1)

[0239] 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."

[0240] While conventional user experience improvement systems generate appropriate feedback based on user sentiment analysis, they have not been adequately applied to customer service and guidance in brick-and-mortar stores. As a result, customers often become confused or dissatisfied in-store, making improving customer satisfaction a challenge. Furthermore, it is difficult to introduce technology that provides guidance and services tailored to individual customers in real time, making it difficult to improve the efficiency of store operations.

[0241] 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.

[0242] In this invention, the server includes means for capturing video footage of users in real time, means for transmitting the captured video footage to the server, means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's terminal and displaying it, means for saving user emotion data in a cloud-based database, and means for capturing video footage of customers and providing in-store guidance and services based on the analysis results. This makes it possible to analyze customer emotions in real stores in real time and provide optimal guidance and services based on the analysis results.

[0243] "User video footage" refers to video data captured by a user in real time.

[0244] "Means for capturing in real time" refers to a system that uses a photographing device to obtain video data instantly.

[0245] "Means for transmitting to a server" refers to a method for transferring captured video data to an external computing device via a network.

[0246] "Multimodal AI technology" refers to artificial intelligence technology that combines and analyzes multiple data inputs (e.g., video, audio, text).

[0247] "Facial recognition technology" is a technology for detecting and identifying facial features from video data.

[0248] "Changes in user's emotions" refers to the transitions in the user's emotional state indicated by the user's facial expressions and behavior detected from the video.

[0249] "Means for generating various feedback" refers to methods for generating responses and information according to the analysis results and providing them to users.

[0250] "User terminal" refers to an electronic device that a user uses to directly operate or display information.

[0251] A "cloud-based database" is online storage that is accessed and managed over the internet.

[0252] A "store visitor" is a customer who visits a physical store.

[0253] "Means for providing guidance and services in-store" refers to methods for providing appropriate information and support to customers in-store in real time.

[0254] MODE FOR CARRYING OUT THE INVENTION

[0255] The system of the present invention aims to improve the customer experience in physical stores based on a program configured as follows. The configuration and processing procedure will be described in detail below.

[0256] System Configuration

[0257] Hardware:

[0258] 1. A smartphone or in-store camera is used as the terminal, which can capture video footage of customers in real time.

[0259] 2. Customer service robots will be used to provide guidance and services within the store, such as providing voice guidance and displaying information on screens.

[0260] software:

[0261] 1. OpenCV is used for video capture and pre-processing, which allows for real-time video capture.

[0262] 2. EmotionRecognizer (emotion analysis library) is used to analyze changes in users' emotions. This library uses multimodal AI technology and facial recognition technology to identify the emotions of customers.

[0263] 3. REST APIs are used to send and receive video footage and analysis results, as well as generate and receive feedback.

[0264] Program processing

[0265] 1. The device captures video footage of customers in real time. This role is played by smartphones or in-store cameras.

[0266] 2. The captured video data is sent to the server, which receives it and analyzes it.

[0267] 3. The server uses OpenCV and EmotionRecognizer to analyze the video data and identify changes in the customers' emotions.

[0268] 4. Based on the analysis results, the server generates appropriate feedback. For example, if a customer is confused, the server may provide guidance through a customer service robot, asking, "Is there anything I can help you with?"

[0269] 5. Feedback is sent to the device in real time and displayed on the customer service robot or smartphone, allowing the store to provide appropriate guidance and services to customers.

[0270] Specific examples

[0271] Example 1:

[0272] For example, if a customer is confused and unable to find a particular product in a store, a smartphone camera captures their facial expression. EmotionRecognizer analyzes the confusion, and the server sends a message to a customer service robot saying, "Are you having trouble?" The customer service robot then plays the message aloud and guides the customer to the product.

[0273] Example 2:

[0274] When a customer shows a satisfied expression, the capture device also captures that expression and the server analyzes it. Based on the analysis results, the customer service robot will say to the customer, "Thank you. We look forward to seeing you again."

[0275] In this way, appropriate feedback and services can be provided based on real-time analysis of customers' emotions.

[0276] Prompt Sentence Examples

[0277] An example prompt phrase might take the following form:

[0278] When a customer enters the store, a camera captures the video. The captured video is analyzed in real time to identify the customer's emotional state. Depending on the identified emotion, for example, if the customer is confused, the customer service robot will ask, "Is there anything I can help you with?" If the customer is satisfied, it will display, "Thank you. We look forward to seeing you again."

[0279] The above is an embodiment of the present invention, and this system will improve the customer experience in physical stores.

[0280] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0281] Step 1:

[0282] When a user enters a store, the device (smartphone or in-store camera) captures video footage in real time. Video data of the customer is obtained as input, and real-time video footage is generated as output. Specifically, the device's camera device is turned on and begins capturing video data.

[0283] Step 2:

[0284] The captured video is sent from the device to the server. The input is real-time video, and the output is uploading video data to the server. Specifically, the device sends video data to the server via the network.

[0285] Step 3:

[0286] The server analyzes the received video using multimodal AI and facial recognition technologies. The input is the video sent to the server, and the output is data on the user's emotional state. Specifically, the server preprocesses the video using OpenCV and then analyzes changes in emotions using EmotionRecognizer.

[0287] Step 4:

[0288] Based on the analysis results, the server generates optimal feedback. The input is data on the user's emotional state, and the output is the generated feedback. Specifically, the server generates prompts based on the analysis data and constructs appropriate messages and guidance.

[0289] Step 5:

[0290] The generated feedback is sent from the server to the terminal, which then displays it or provides audio guidance. The input is the generated feedback, and the output is the information presented to the user. Specifically, the terminal displays the feedback by voice or on a display, and provides guidance and services to customers.

[0291] Step 6:

[0292] The server stores the user's emotional data in a cloud-based database. The input is the analyzed emotional data, and the output is saved in the database. Specifically, the server properly encrypts the emotional data and uploads it to cloud storage.

[0293] Step 7:

[0294] Customer service robots and in-store displays provide appropriate service to customers based on the displayed feedback. The input is the generated feedback, and the output is the actual service provided. Specifically, the robot will provide voice guidance or the display will display guidance information to alleviate customer confusion.

[0295] 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.

[0296] The system of the present invention captures a user's video footage in real time and analyzes changes in the user's emotions from the video footage. In this process, an emotion engine is used to perform detailed classification and recognition of emotions, thereby improving the quality of the user experience. This system is composed of the following means and operations.

[0297] System Configuration

[0298] 1. User Interface

[0299] The terminal provides a user interface that allows the user to access and operate the system, including functions such as starting video capture, selecting a session, and checking feedback.

[0300] 2. Video capture and transmission

[0301] A user logs into the system and begins a session.

[0302] The device captures the user's video in real time and sends it to the server. The video capture is performed using the device's camera device.

[0303] 3. Video reception and analysis

[0304] The server receives the video image transmitted from the terminal.

[0305] The server temporarily stores the received video and prepares it for processing.

[0306] The server analyzes the stored video data using multimodal AI technology and facial recognition technology to identify the user's facial expressions and gestures.

[0307] 4. Emotion Recognition by Emotion Engine

[0308] The server uses an emotion engine to classify the user's emotions in detail based on the analysis results, specifically into emotion categories such as happiness, sadness, surprise, and confusion.

[0309] The emotion engine continuously learns from the user's emotion data to improve the accuracy of emotion recognition.

[0310] 5. Feedback Generation and Display

[0311] The server generates feedback based on the emotion engine's analysis, which is tailored to the user's state and may include additional educational materials or help links if the user is confused, for example.

[0312] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[0313] 6. Data storage and utilization

[0314] The server stores the user's emotional data in a cloud-based database, which is encrypted to protect user privacy and data security.

[0315] Service providers analyze the stored data and plan improvements to their services and products. The data can be used in education, healthcare, entertainment, and product development to provide users with personalized experiences.

[0316] Specific examples

[0317] Specific examples of online education

[0318] In an online education platform, the system functions as follows:

[0319] 1. User Registration and Login

[0320] A user registers and logs in to the education platform.

[0321] The terminal sends authentication information to the server, and the server performs authentication.

[0322] 2. Starting the lesson and capturing video

[0323] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[0324] 3. Video Analysis and Emotion Recognition

[0325] The server receives the video footage and analyzes the user's facial expressions using multimodal AI and facial recognition technologies. The server then uses an emotion engine to classify the user's emotions in detail.

[0326] 4. Feedback generation and display

[0327] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[0328] 5. Data storage and utilization

[0329] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[0330] Examples of online therapy

[0331] On the online therapy platform, the system works as follows:

[0332] 1. User Registration and Login

[0333] A user registers and logs in to the therapy platform.

[0334] The terminal sends authentication information to the server, and the server performs authentication.

[0335] 2. Therapy session begins

[0336] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[0337] 3. Video Analysis and Emotion Recognition

[0338] The server receives the video footage and analyzes the user's facial expressions and gestures using multimodal AI and facial recognition technology, then uses an emotion engine to classify the user's emotions in detail.

[0339] 4. Feedback generation and display

[0340] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[0341] 5. Data storage and utilization

[0342] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[0343] This allows the system of the present invention to integrate an emotion engine and perform detailed analysis of user emotions, thereby providing personalized, high-quality experiences in various fields.

[0344] The processing flow will be explained below.

[0345] Step 1:

[0346] The user accesses the Kimochi Scope website or app and fills in the required information on the new registration form.

[0347] Step 2:

[0348] The device sends the entered information (name, email address, password, etc.) to the server.

[0349] Step 3:

[0350] The server stores the received information in its database and completes the new user's registration.

[0351] Step 4:

[0352] The user enters their registered email address and password on the login screen.

[0353] Step 5:

[0354] The terminal transmits the entered authentication information to the server.

[0355] Step 6:

[0356] The server checks the database and, if authentication is successful, generates a session ID and sends it to the terminal.

[0357] Step 7:

[0358] The user selects one of the following sessions: online education, therapy, entertainment, etc.

[0359] Step 8:

[0360] The terminal transmits the selected session information to the server.

[0361] Step 9:

[0362] The device activates the camera and captures live video footage of the user.

[0363] Step 10:

[0364] The video captured by the device is streamed to the server in real time.

[0365] Step 11:

[0366] The server receives the video image transmitted from the terminal.

[0367] Step 12:

[0368] The server temporarily stores the received video data and prepares it for processing.

[0369] Step 13:

[0370] The server analyzes the stored video data using multimodal AI technology and facial recognition technology.

[0371] Step 14:

[0372] The server identifies the user's facial expressions and gestures and evaluates the changes in real time.

[0373] Step 15:

[0374] The server uses an emotion engine to classify the emotional state in detail and divide it into categories (e.g., happy, sad, confused, etc.).

[0375] Step 16:

[0376] The emotion engine learns the user's emotion data and improves the accuracy of emotion recognition.

[0377] Step 17:

[0378] The server generates feedback data (e.g., "The user is confused") based on the analysis results.

[0379] Step 18:

[0380] The server transmits the generated feedback data to the terminal.

[0381] Step 19:

[0382] Based on the feedback data received by the terminal, appropriate messages and materials are displayed on the user interface.

[0383] Step 20:

[0384] The user reviews the displayed feedback and materials and decides on their next course of action.

[0385] Step 21:

[0386] The server stores the user's emotional data during the session in a cloud-based database.

[0387] Step 22:

[0388] The server encrypts data at rest for data security and privacy.

[0389] Step 23:

[0390] Service providers analyze the stored data and develop improvements to their services and products.

[0391] Step 24:

[0392] Service providers can use data to tailor online education curricula, therapy plans, entertainment content, and more to improve user experience.

[0393] Example 2

[0394] 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."

[0395] Current online platforms lack systems that can identify users' emotions in real time and provide feedback based on those emotions. This results in a uniform user experience, making it difficult to respond to individual needs, and improving user satisfaction is a challenge. Protecting the privacy and security of collected emotion data is also a major challenge.

[0396] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for temporarily storing video footage, a means for identifying the user's facial expressions and gestures using multimodal AI technology and face recognition technology, and a means for classifying the user's emotions in detail using an emotion engine based on the analysis results. This makes it possible to analyze the user's emotions in real time and provide feedback tailored to individual needs. Furthermore, emotion data is encrypted and securely stored in a cloud-based database, ensuring privacy and data security.

[0397] "User" refers to any individual or entity that uses the System.

[0398] "Video footage" refers to real-time moving images of a user.

[0399] "Means of capturing in real time" refers to devices or programs that instantly acquire current video and digitally process it on the spot.

[0400] A "server" refers to a computer system that processes, stores, and manages data over a network.

[0401] "Multimodal AI technology" refers to artificial intelligence technology that simultaneously analyzes multiple different data formats (e.g., video, audio, text) and integrates the results.

[0402] "Facial recognition technology" refers to technology that detects facial features from video footage and identifies individuals and facial expressions.

[0403] An "emotion engine" refers to an algorithm or program that analyzes data such as a user's facial expressions and gestures, and classifies and recognizes their emotional state.

[0404] "Feedback" refers to information and suggested actions provided to the user based on the system's analysis results.

[0405] A "cloud-based database" is a data storage system accessible via the internet that stores and manages data on a cloud service.

[0406] "Data encryption" refers to a technology that converts data into a format that cannot be deciphered by third parties, thereby increasing data security.

[0407] "Privacy" refers to the state in which a user's personal information and activities are prevented from being known to third parties without permission.

[0408] "Data security" refers to a set of measures and techniques for protecting the integrity, confidentiality, and availability of data.

[0409] "Education" refers to the activity of imparting knowledge and skills to people and promoting growth through learning.

[0410] "Healthcare" refers to a range of medical and health-related services aimed at maintaining health and preventing and treating disease.

[0411] "Entertainment" refers to activities and content that provide people with fun and joy.

[0412] "Product development" refers to the entire process of planning, designing, and realizing new products and services.

[0413] MODE FOR CARRYING OUT THE INVENTION

[0414] The system of this invention captures a user's video footage in real time and analyzes changes in the user's emotions from the video footage. The system is composed of the following main components:

[0415] 1. User Interface

[0416] The terminal provides a user interface that allows users to access and operate the system. The user interface includes functions such as starting video capture, selecting a session, and checking feedback. This interface is implemented using HTML, CSS, JavaScript, etc.

[0417] 2. User login and session initiation

[0418] The user logs into the system and enters their account information. Once the login information is entered, the device sends this information to the server, which performs authentication. Once authentication is complete, the user can select and start a video capture session. The authentication process uses standard authentication technologies such as OAuth.

[0419] 3. Video capture and transmission

[0420] When a user starts a session, the device's camera captures the user's video in real time, and the captured video is encoded by the device and then sent to the server using the H.264 encoding format and the RTSP protocol.

[0421] 4. Receiving and temporarily storing video

[0422] The server receives the video footage sent from the device. The received footage is temporarily stored in the server's storage. The stored data is used for analysis in subsequent processing steps. The storage uses a large-capacity HDD or SSD.

[0423] 5. Video Analysis and Facial Expression Recognition

[0424] The server then applies multimodal AI and facial recognition technology to the stored video data. Specifically, it applies face detection algorithms using libraries such as OpenCV and Dlib to analyze the movement and relative positions of facial features. This process uses Haar cascade classifiers and DNN-based face detection models.

[0425] 6. Emotion Recognition by Emotion Engine

[0426] The server further analyzes the results of the facial expression recognition using an emotion engine. The emotion engine uses a deep learning model (e.g., CNN or RNN) to classify emotions. Frameworks such as TensorFlow or PyTorch are used. The trained model analyzes the features of facial features and outputs an emotion label (e.g., happiness, sadness, surprise, etc.).

[0427] 7. Generating and Displaying Feedback

[0428] The server generates appropriate feedback for the user based on the emotion recognition results of the emotion engine. For example, if the user is confused, feedback including supplementary learning materials and help links will be generated. This feedback is sent from the server to the device, where it is displayed on the user interface. NLP technology and conditional branching algorithms are used to generate the feedback.

[0429] 8. Data storage and utilization

[0430] The server stores the emotional data collected during the session in a cloud-based database. The data is encrypted and uses AES encryption technology to protect user privacy and data security. This data can be used in a variety of fields, including education, healthcare, entertainment, and product development.

[0431] Specific examples

[0432] Specific examples of online education

[0433] In an online education platform, the system works as follows:

[0434] 1. The user registers and logs in to the educational platform.

[0435] 2. The device sends authentication information to the server, and the server performs authentication.

[0436] 3. The user selects a lesson and starts video capture. The device captures the video and sends it to the server.

[0437] 4. The server receives the video footage and analyzes the user's facial expressions using multimodal AI technology and facial recognition technology. The server then uses an emotion engine to classify the user's emotions in detail.

[0438] 5. The server generates a link to the supplementary learning material based on the analysis results and sends it to the device. The device displays the feedback and the user can view the supplementary learning material.

[0439] 6. The server stores the user's emotional data during class in a database. The educational institution analyzes this data and improves the content of the class.

[0440] Examples of online therapy

[0441] On the online therapy platform, this system works as follows:

[0442] 1. The user registers and logs in to the therapy platform.

[0443] 2. The device sends authentication information to the server, and the server performs authentication.

[0444] 3. The user starts a therapy session. The device starts capturing video and sending it to the server.

[0445] 4. The server receives the video footage and analyzes the user's facial expressions and gestures using multimodal AI and facial recognition technologies. The server then uses an emotion engine to classify the user's emotions in detail.

[0446] 5. The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[0447] 6. The server stores the emotional data from the session in a database. The therapist uses this data to plan the next session and provide more effective treatment.

[0448] Example prompts for generative AI models

[0449] "Please tell me the specific processing steps for video analysis in this system."

[0450] "Please explain in detail how the emotion engine classifies the user's emotions."

[0451] This allows users to understand the specific operations and processes and learn how the system achieves emotion recognition.

[0452] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0453] Step 1: User Login

[0454] Input: The user enters their authentication information (username, password).

[0455] What happens: The authentication information provided by the user is sent from the device to the server.

[0456] Data processing: The server checks the received authentication information against the registration information in its database.

[0457] Output: If authentication is successful, the server generates a session ID and sends it to the device. If authentication fails, an error message is returned.

[0458] Step 2: Start video capture

[0459] Input: The user presses the Start Video Capture button to begin the session.

[0460] Specific behavior: The device's camera device will start up and begin capturing video footage.

[0461] Data processing: The captured video is encoded in H.264 format.

[0462] Output: The encoded video data is sent to the server in real time.

[0463] Step 3: Receiving and temporarily saving the video

[0464] Input: Captured video data is sent to the server.

[0465] Specific operation: The server temporarily stores the received video data in storage.

[0466] Data processing: Video data is kept in storage and prepared for analysis.

[0467] Output: Stored video data for subsequent analysis process.

[0468] Step 4: Video analysis and facial expression recognition

[0469] Input: The stored video data is passed to the server's analysis module.

[0470] Specific operation: The server uses OpenCV and Dlib to detect the user's face from the video frame.

[0471] Data processing: The detected facial landmarks (eyes, mouth, nose, etc.) are identified and facial expressions are analyzed based on them.

[0472] Output: The analysis results in the generation of facial expression information of the user.

[0473] Step 5: Emotion Recognition with the Emotion Engine

[0474] Input: Facial expression information is input to the emotion engine.

[0475] How it works: The server runs the emotion engine using TensorFlow and PyTorch. The deep learning model analyzes facial expression information and issues emotion labels.

[0476] Data processing: Facial expression information is input into the model as features, and emotion labels (e.g., happiness, sadness, surprise, etc.) are output.

[0477] Output: Emotion labels are generated and sent to the next feedback generation process.

[0478] Step 6: Generate and display feedback

[0479] Input: Emotion labels are input to the feedback generation module.

[0480] Specific behavior: The server generates appropriate feedback for the user based on the analysis results. For example, if the user is confused, feedback containing additional learning materials or help links is generated.

[0481] Data processing: Feedback content is generated and configured in HTML and JSON formats.

[0482] Output: The generated feedback is sent from the server to the device and displayed on the user interface.

[0483] Step 7: Store and use your data

[0484] Input: Emotion labels and feedback are input into the database storage module.

[0485] What it does: The server stores the emotional data and feedback collected during the session in a cloud-based database.

[0486] Data processing: Data is encrypted using encryption techniques such as AES.

[0487] Output: The encrypted data is securely stored in a database for later analysis and service improvement.

[0488] Through the above processing steps, the system can perform real-time emotion analysis of the user and provide feedback.

[0489] (Application example 2)

[0490] 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."

[0491] Conventional content distribution services lack the ability to analyze users' emotions in real time and provide appropriate feedback to improve their viewing experience. As a result, viewers are unable to enjoy appropriate content and a personalized experience is not provided. Furthermore, they lack the ability to ensure the security of users' viewing data and protect their privacy.

[0492] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0493] In this invention, the server includes means for capturing a user's video footage in real time, means for transmitting the captured video footage to the server, means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's terminal and displaying it, means for storing the user's emotional data in a cloud-based database, and, particularly in the case of a content distribution service, means for analyzing the user's emotions while watching and providing appropriate feedback and recommended content in real time.

[0494] This allows for personalized feedback and content recommendations to enhance the user's viewing experience, while data encryption protects user privacy and data security.

[0495] The "means for capturing a user's video footage in real time" refers to a technology that allows a user to obtain video footage in real time through the device's camera and immediately transmit it to the system.

[0496] The "means for transmitting captured video images to a server" is a technique for transmitting captured video data to a remote computer via a communication network such as the Internet.

[0497] "Means of analyzing video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions" refers to technology that analyzes video data using machine learning algorithms and facial detection technology to identify changes in the user's emotional state.

[0498] "Means for generating various feedback based on analysis results" refers to technology that provides users with appropriate information and recommendations in real time based on the results of emotion analysis.

[0499] The "means for transmitting the generated feedback to the user's terminal and displaying it" refers to a technique for transmitting the generated feedback information to the user's device and visually displaying it.

[0500] "Means for storing user emotion data in a cloud-based database" refers to technology for securely storing the results of a user's emotion analysis in remote data storage.

[0501] "A means of analyzing the user's emotions while watching, particularly in content distribution services, and providing appropriate feedback and recommended content in real time" refers to technology that analyzes emotions while the user is watching media such as movies or videos, and instantly provides optimal recommended information.

[0502] MODE FOR CARRYING OUT THE INVENTION

[0503] The present invention is a system that captures a user's video footage in real time and analyzes changes in the user's emotions from that footage. The system of the present invention has the function of analyzing the user's emotions while watching, and providing appropriate feedback and recommended content in real time, particularly in content distribution services.

[0504] System Configuration

[0505] 1. User Interface and Login

[0506] Users log in to the system using their device (e.g., smartphone, PC, smart glasses). After logging in, users can select the content they want to watch.

[0507] 2. Video capture and transmission

[0508] When a user watches content, the device's camera captures video footage, which is then sent to the server in real time.

[0509] 3. Video Analysis and Emotion Recognition

[0510] The server temporarily stores the received video footage and analyzes it using multimodal AI technology (e.g., OpenAI's GPT-4, Google Cloud Vision, Amazon Rekognition) and facial recognition technology (e.g., Microsoft's Azure Face API, DeepFace). As a result of the analysis, the user's emotions (e.g., happiness, sadness, surprise, confusion, etc.) are identified.

[0511] 4. Feedback generation and display

[0512] The server generates various feedback based on the results of the sentiment analysis. This feedback includes comments about the content being viewed and information about recommended content to watch next. The generated feedback is sent to the user's device in real time and displayed.

[0513] 5. Data storage and analysis

[0514] User sentiment data is stored in a cloud-based database (e.g., Google Cloud Firestore, Amazon RDS). This data is encrypted to protect user privacy. The stored data is used for analysis and to improve content delivery services.

[0515] Hardware and Software Used

[0516] Camera devices: Cameras installed in smartphones, smart glasses, and head-mounted displays are used.

[0517] Multimodal AI technology: Uses OpenAI's GPT-4, Google Cloud Vision, and Amazon Rekognition.

[0518] Facial recognition technology: Uses Microsoft Azure's Face API and DeepFace.

[0519] Cloud-based database: Google Cloud Firestore and Amazon RDS are used.

[0520] Specific examples

[0521] For example, imagine a user watching a movie on a video streaming service. At this time, the user's facial expressions are captured in real time by a camera and sent to the server. The server analyzes the video and recognizes emotional changes such as surprise or laughter at a particular scene. Based on this, feedback such as "It looks like you're enjoying this scene! Here are some movies that other users also enjoyed" is displayed.

[0522] Prompt Sentence Examples

[0523] The prompts for the generative AI model to capture specific emotional changes and generate appropriate feedback are as follows:

[0524] Analyze user video footage in real time and classify the user's emotional changes as happy, sad, surprised, confused, etc. Then determine the appropriate feedback and content recommendations to provide next. Use the following video footage data: [Video Data Link]

[0525] In this way, the system of the present invention significantly improves the user's viewing experience, providing personalized feedback and content recommendations in real time. This system, which combines ease of use with high security, adds new value to content distribution services.

[0526] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0527] Specific explanation of processing steps

[0528] Step 1: User Login

[0529] Input: User authentication information (user ID, password)

[0530] How it works: The user enters their authentication information on the device and presses the login button. The device sends the authentication information to the server.

[0531] Data processing: The server compares the received authentication information with the database and performs login authentication.

[0532] Output: Authentication result (success or failure)

[0533] Step 2: Content Selection

[0534] Input: User interface after login (list of content provided)

[0535] What it does: The user selects the content to watch.

[0536] Data processing: The selected content ID is sent to the server.

[0537] Output: Selected content ID

[0538] Step 3: Start video capture

[0539] Input: Content playback start instruction

[0540] How it works: The device's camera begins capturing video footage of the user in real time.

[0541] Data processing: Process the captured video footage as a data stream.

[0542] Output: Real-time video data

[0543] Step 4: Sending video to the server

[0544] Input: Real-time video data

[0545] Operation: The device sends captured video to the server.

[0546] Data processing: Compress the video data and send it using network protocols.

[0547] Output: Video data sent to the server

[0548] Step 5: Temporarily save the video data

[0549] Input: Video data sent to the server

[0550] Operation: The server stores the received video data in a storage device for short-term storage.

[0551] Data processing: Temporarily save the video data in an appropriate format.

[0552] Output: Temporarily saved video data

[0553] Step 6: Sentiment Analysis

[0554] Input: Temporarily saved video data

[0555] How it works: The server analyzes the video data using multimodal AI technology and facial recognition technology.

[0556] Data processing: Facial recognition is performed for each video frame, emotion labels are assigned, and emotional changes are identified using an AI model.

[0557] Output: Analysis results (user emotion data)

[0558] Step 7: Feedback Generation

[0559] Input: Analysis results (user emotion data)

[0560] Operation: The server generates feedback and recommended content for the content being viewed based on the emotional data.

[0561] Data processing: Feedback sentences are created using a generative AI model (e.g., GPT-4) based on emotion data.

[0562] Output: Generated feedback

[0563] Step 8: Submit and view feedback

[0564] Input: Generated feedback

[0565] Operation: The server sends the generated feedback to the user's device.

[0566] Data processing: The feedback data is converted into an appropriate format and sent using the network protocol.

[0567] Output: Feedback displayed on the terminal

[0568] Step 9: Storing Emotion Data

[0569] Input: Analysis results (user emotion data)

[0570] How it works: The server stores user emotion data in a cloud-based database.

[0571] Data processing: Emotional data is encrypted and stored securely in a database.

[0572] Output: Saved emotion data

[0573] Prompt Sentence Examples

[0574] "Prompt sentence for GPT-4"

[0575] Analyze user video footage in real time and classify the user's emotional changes as happy, sad, surprised, confused, etc. Then determine the appropriate feedback and content recommendations to provide next. Use the following video footage data: [Video Data Link]

[0576] 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.

[0577] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0578] 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.

[0579] [Second embodiment]

[0580] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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).

[0586] 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. 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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."

[0592] The system of the present invention improves the user experience by capturing a user's video footage in real time and analyzing changes in the user's emotions from the video footage. This system is composed of the following means and operations.

[0593] System Configuration

[0594] 1. User Interface

[0595] The terminal provides a user interface that allows users to access and operate the system, including functions such as starting video capture, selecting a session, and checking feedback.

[0596] 2. Video capture and transmission

[0597] A user logs into the system and begins a session.

[0598] The device captures the user's video in real time and sends it to the server. The video capture is performed using the device's camera device.

[0599] 3. Video reception and analysis

[0600] The server receives the video image transmitted from the terminal.

[0601] The server analyzes video footage using multimodal AI and facial recognition technology, identifying the user's facial expressions and gestures and detecting changes in the user's emotions in real time.

[0602] 4. Feedback generation and display

[0603] The server generates feedback based on the analysis, which is tailored to the user's situation and may include additional educational material or help links if the user is confused, for example.

[0604] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[0605] 5. Data storage and utilization

[0606] The server stores the user's emotional data in a cloud-based database, which is encrypted to protect user privacy and data security.

[0607] Service providers analyze the stored data and plan improvements to their services and products. The data can be used in education, healthcare, entertainment, and product development to provide users with personalized experiences.

[0608] Specific examples

[0609] Specific examples of online education

[0610] In an online education platform, the system functions as follows:

[0611] 1. User Registration and Login

[0612] A user registers and logs in to the education platform.

[0613] The terminal sends authentication information to the server, and the server performs authentication.

[0614] 2. Starting the lesson and capturing video

[0615] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[0616] 3. Video Analysis

[0617] The server receives the video footage and performs analysis: it detects the user's facial expressions and identifies emotions such as interest and confusion.

[0618] 4. Feedback generation and display

[0619] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[0620] 5. Data storage and utilization

[0621] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[0622] Examples of online therapy

[0623] On the online therapy platform, the system works as follows:

[0624] 1. User Registration and Login

[0625] A user registers and logs in to the therapy platform.

[0626] The terminal sends authentication information to the server, and the server performs authentication.

[0627] 2. Therapy session begins

[0628] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[0629] 3. Video Analysis

[0630] The server receives the video footage and performs analysis to detect changes in emotions from the user's facial expressions and gestures.

[0631] 4. Feedback generation and display

[0632] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[0633] 5. Data storage and utilization

[0634] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[0635] As a result, the system of the present invention can analyze the user's emotions in real time and provide various feedback, thereby improving the user experience in various fields.

[0636] The processing flow will be explained below.

[0637] Step 1:

[0638] The user accesses the Kimochi Scope website or app and fills in the required information on the new registration form.

[0639] Step 2:

[0640] The device sends the entered information (name, email address, password, etc.) to the server.

[0641] Step 3:

[0642] The server stores the received information in its database and completes the new user's registration.

[0643] Step 4:

[0644] The user enters their registered email address and password on the login screen.

[0645] Step 5:

[0646] The terminal transmits the entered authentication information to the server.

[0647] Step 6:

[0648] The server checks the database and, if authentication is successful, generates a session ID and sends it to the terminal.

[0649] Step 7:

[0650] Users select from a range of online education, therapy, entertainment, product testing and other sessions.

[0651] Step 8:

[0652] The terminal transmits the selected session information to the server.

[0653] Step 9:

[0654] The device activates the camera and captures live video footage of the user.

[0655] Step 10:

[0656] The video captured by the device is streamed to the server in real time.

[0657] Step 11:

[0658] The server receives the video image transmitted from the terminal.

[0659] Step 12:

[0660] The server temporarily stores the received video and prepares it for processing.

[0661] Step 13:

[0662] The server analyzes the stored video data using multimodal AI technology and facial recognition technology.

[0663] Step 14:

[0664] The server identifies the user's facial expressions and gestures and evaluates the changes in real time.

[0665] Step 15:

[0666] The server quantifies the emotional state and classifies it into categories (e.g., happy, sad, confused, etc.).

[0667] Step 16:

[0668] The server generates feedback data (e.g., "The user is confused") based on the analysis results.

[0669] Step 17:

[0670] The server transmits the generated feedback data to the terminal.

[0671] Step 18:

[0672] Based on the feedback data received by the terminal, appropriate messages and materials are displayed on the user interface.

[0673] Step 19:

[0674] The user reviews the displayed feedback and materials and decides on their next course of action.

[0675] Step 20:

[0676] The server stores the user's emotional data during the session in a cloud-based database.

[0677] Step 21:

[0678] The server encrypts data at rest for data security and privacy.

[0679] Step 22:

[0680] Service providers analyze the stored data and develop improvements to their services and products.

[0681] Step 23:

[0682] Service providers can use data to tailor online education curricula, therapy plans, entertainment content, and more to improve user experience.

[0683] Example 1

[0684] 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."

[0685] The present invention relates to a system that captures a user's video footage in real time, analyzes the footage, and detects changes in the user's emotions. Conventional systems have difficulty accurately capturing a user's emotions in real time, and have not sufficiently improved the user experience. Furthermore, there are many challenges in storing and utilizing emotional data and protecting privacy. Therefore, there is a need for a system that can significantly improve the user experience by accurately analyzing a user's emotions in real time and effectively providing feedback based on the results.

[0686] 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.

[0687] In this invention, the server includes means for capturing a user's video image in real time, means for transmitting the captured video image to an information processing device, means for analyzing the video image received by the information processing device using multimodal artificial intelligence technology and face recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's output device and displaying it, and means for storing the user's emotion data in a network-based storage device, thereby enabling the user's emotions to be accurately analyzed in real time and appropriate feedback to be provided promptly.

[0688] "User" refers to any individual or entity that uses the System.

[0689] "Video Footage" refers to visual data captured through a camera device.

[0690] "Real-time" refers to the temporal characteristic of being processed immediately without delay.

[0691] "Information processing device" refers to a computer system for analyzing and processing received data.

[0692] "Multimodal AI technology" refers to AI technology that combines and evaluates multiple different data formats (e.g., video, audio, text).

[0693] "Facial recognition technology" refers to technology that detects a person's face from video captured using a camera device and analyzes its features.

[0694] "Changes in emotion" refers to changes in the emotional state detected based on changes in the user's facial expressions and gestures.

[0695] "Feedback" refers to information, instructions, or advice provided to the user based on the analysis results.

[0696] "Output device" refers to a device (e.g., a display or speaker) for providing visual or audible feedback to a user.

[0697] "Network-based storage" refers to a remotely located data storage device that is accessible over a network (e.g., cloud storage).

[0698] "Data encryption" refers to a technology that protects the confidentiality of data by converting it into a format that cannot be understood by third parties during transmission or storage.

[0699] "Education" refers to activities aimed at imparting knowledge and skills and promoting learning.

[0700] "Medical care" refers to diagnostic, therapeutic and preventive activities aimed at maintaining and restoring human health.

[0701] "Leisure" refers to any activity that people undertake for enjoyment or relaxation.

[0702] "Product development" refers to the process of planning, designing, and producing new products and services.

[0703] The present invention is a system that captures a user's video footage in real time and analyzes changes in the user's emotions from the footage. This system is characterized by the configuration and operation described below.

[0704] System Configuration

[0705] 1. User Interface

[0706] The terminal provides a user interface through which the user can access and operate the system, including functions such as starting a video capture, selecting a session, and viewing feedback.

[0707] 2. Video capture and transmission

[0708] A user logs into the system and begins a session.

[0709] The terminal uses a camera device to capture video images of the user in real time and transmits them to an information processing device (server).

[0710] 3. Video reception and analysis

[0711] The server receives the video image transmitted from the terminal.

[0712] The server analyzes video footage using multimodal artificial intelligence and facial recognition technology, identifying the user's facial expressions and gestures and detecting changes in the user's emotions in real time.

[0713] 4. Feedback generation and display

[0714] The server generates feedback based on the analysis, which is tailored to the user's situation and may include additional educational materials or help links if the user is confused.

[0715] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[0716] 5. Data storage and utilization

[0717] The server stores the user's emotional data in a network-based storage device, and the data is encrypted to protect the user's privacy and data security.

[0718] Service providers analyze the stored data to plan improvements to their services and products, and the data can be used in education, healthcare, entertainment, and product development to provide users with a personalized experience.

[0719] Technical details

[0720] Camera device: The device's built-in front camera or an externally connected camera device is used.

[0721] Multimodal artificial intelligence technology: Machine learning models using TensorFlow and PyTorch are used, and libraries such as OpenCV and Dlib are used for face recognition technology.

[0722] Network-based storage: Use Amazon RDS, Google Cloud Firestore, etc. to store and analyze data.

[0723] Specific examples

[0724] Specific examples of online education

[0725] 1. User Registration and Login

[0726] A user registers and logs in to the education platform.

[0727] The terminal sends authentication information to the server, and the server performs authentication.

[0728] 2. Starting the lesson and capturing video

[0729] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[0730] 3. Video Analysis

[0731] The server receives the video footage and performs analysis: it detects the user's facial expressions and identifies emotions such as interest and confusion.

[0732] 4. Feedback generation and display

[0733] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[0734] 5. Data storage and utilization

[0735] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[0736] Examples of online therapy

[0737] 1. User Registration and Login

[0738] A user registers and logs in to the therapy platform.

[0739] The terminal sends authentication information to the server, and the server performs authentication.

[0740] 2. Therapy session begins

[0741] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[0742] 3. Video Analysis

[0743] The server receives the video footage and performs analysis to detect changes in emotions from the user's facial expressions and gestures.

[0744] 4. Feedback generation and display

[0745] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[0746] 5. Data storage and utilization

[0747] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[0748] Prompt Sentence Examples

[0749] Example prompt:

[0750] "I want to design a system that captures a user's facial expressions in real time through a camera and analyzes their emotional changes. What should I use?"

[0751] Example output:

[0752] It is recommended to use a webcam or external camera as the camera device. Machine learning models using TensorFlow or PyTorch are useful for facial expression analysis, and libraries such as OpenCV or Dlib are useful for face recognition. For data storage and analysis, use a cloud-based database such as Amazon RDS or Google Cloud Firestore.

[0753] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0754] Step 1:

[0755] User Registration and Login

[0756] Input: Information entered by the user in the sign-up form (name, email address, password)

[0757] Output: Authentication information generated by the server

[0758] Specific operation: The user enters information into the new registration form on the web browser and clicks the "Register" button. The device sends the information to the server as a POST request. The server stores the received information in a database and generates and returns authentication information.

[0759] Step 2:

[0760] Start video capture

[0761] Input: Instruction to start video capture selected by the user after logging in

[0762] Output: Captured video data

[0763] Specific operation: When the user clicks the start video capture button, the device activates the camera device using JavaScript and starts capturing video in real time. The captured video is passed directly to the next step.

[0764] Step 3:

[0765] Video transmission and reception

[0766] Input: Captured video footage data

[0767] Output: Video data received by the server

[0768] Specific operation: The device sends captured video frame by frame to the server using WebSocket or HTTP streaming. The server receives this data in real time and puts it into an analysis queue.

[0769] Step 4:

[0770] Video Analysis

[0771] Input: Received video data

[0772] Output: Analyzed emotion change data

[0773] How it works: The server uses OpenCV and TensorFlow libraries to analyze the user's facial expressions and gestures for each frame. In particular, it detects expressions such as smiles and confusion, and identifies emotional changes based on them. The identified emotional change data is generated and passed to the next step.

[0774] Step 5:

[0775] Feedback Generation

[0776] Input: Analyzed emotion change data

[0777] Output: Generated feedback data

[0778] How it works: Based on the analysis results, the server creates appropriate feedback using Python scripts or generative AI models (e.g., GPT). The feedback data is converted to JSON format and sent to the device.

[0779] Step 6:

[0780] Feedback Display

[0781] Input: Generated feedback data

[0782] Output: A feedback message that is displayed on the user's terminal.

[0783] Specific behavior: When the device receives feedback data from the server, it uses JavaScript and HTML to display appropriate messages and content on the interface. The user checks the displayed feedback and decides on the next action.

[0784] Step 7:

[0785] Data storage and utilization

[0786] Input: Analyzed emotion change data and generated feedback data

[0787] Output: Data stored on a network-based storage device

[0788] How it works: The server stores the analysis results and feedback in a database. This storage is encrypted to protect the confidentiality of the data. The stored data is later analyzed by the service provider using analysis tools and used to plan and implement improvements to services and products.

[0789] (Application example 1)

[0790] 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."

[0791] While conventional user experience improvement systems generate appropriate feedback based on user sentiment analysis, they have not been adequately applied to customer service and guidance in brick-and-mortar stores. As a result, customers often become confused or dissatisfied in-store, making improving customer satisfaction a challenge. Furthermore, it is difficult to introduce technology that provides guidance and services tailored to individual customers in real time, making it difficult to improve the efficiency of store operations.

[0792] 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.

[0793] In this invention, the server includes means for capturing video footage of users in real time, means for transmitting the captured video footage to the server, means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's terminal and displaying it, means for saving user emotion data in a cloud-based database, and means for capturing video footage of customers and providing in-store guidance and services based on the analysis results. This makes it possible to analyze customer emotions in real stores in real time and provide optimal guidance and services based on the analysis results.

[0794] "User video footage" refers to video data captured by a user in real time.

[0795] "Means for capturing in real time" refers to a system that uses a photographing device to obtain video data instantly.

[0796] "Means for transmitting to a server" refers to a method for transferring captured video data to an external computing device via a network.

[0797] "Multimodal AI technology" refers to artificial intelligence technology that combines and analyzes multiple data inputs (e.g., video, audio, text).

[0798] "Facial recognition technology" is a technology for detecting and identifying facial features from video data.

[0799] "Changes in user's emotions" refers to the transitions in the user's emotional state indicated by the user's facial expressions and behavior detected from the video.

[0800] "Means for generating various feedback" refers to methods for generating responses and information according to the analysis results and providing them to users.

[0801] "User terminal" refers to an electronic device that a user uses to directly operate or display information.

[0802] A "cloud-based database" is online storage that is accessed and managed over the internet.

[0803] A "store visitor" is a customer who visits a physical store.

[0804] "Means for providing guidance and services in-store" refers to methods for providing appropriate information and support to customers in-store in real time.

[0805] MODE FOR CARRYING OUT THE INVENTION

[0806] The system of the present invention aims to improve the customer experience in physical stores based on a program configured as follows. The configuration and processing procedure will be described in detail below.

[0807] System Configuration

[0808] Hardware:

[0809] 1. A smartphone or in-store camera is used as the terminal, which can capture video footage of customers in real time.

[0810] 2. Customer service robots will be used to provide guidance and services within the store, such as providing voice guidance and displaying information on screens.

[0811] software:

[0812] 1. OpenCV is used for video capture and pre-processing, which allows for real-time video capture.

[0813] 2. EmotionRecognizer (emotion analysis library) is used to analyze changes in users' emotions. This library uses multimodal AI technology and facial recognition technology to identify the emotions of customers.

[0814] 3. REST APIs are used to send and receive video footage and analysis results, as well as generate and receive feedback.

[0815] Program processing

[0816] 1. The device captures video footage of customers in real time. This role is played by smartphones or in-store cameras.

[0817] 2. The captured video data is sent to the server, which receives it and analyzes it.

[0818] 3. The server uses OpenCV and EmotionRecognizer to analyze the video data and identify changes in the customers' emotions.

[0819] 4. Based on the analysis results, the server generates appropriate feedback. For example, if a customer is confused, the server may provide guidance through a customer service robot, asking, "Is there anything I can help you with?"

[0820] 5. Feedback is sent to the device in real time and displayed on the customer service robot or smartphone, allowing the store to provide appropriate guidance and services to customers.

[0821] Specific examples

[0822] Example 1:

[0823] For example, if a customer is confused and unable to find a particular product in a store, a smartphone camera captures their facial expression. EmotionRecognizer analyzes the confusion, and the server sends a message to a customer service robot saying, "Are you having trouble?" The customer service robot then plays the message aloud and guides the customer to the product.

[0824] Example 2:

[0825] When a customer shows a satisfied expression, the capture device also captures that expression and the server analyzes it. Based on the analysis results, the customer service robot will say to the customer, "Thank you. We look forward to seeing you again."

[0826] In this way, appropriate feedback and services can be provided based on real-time analysis of customers' emotions.

[0827] Prompt Sentence Examples

[0828] An example prompt phrase might take the following form:

[0829] When a customer enters the store, a camera captures the video. The captured video is analyzed in real time to identify the customer's emotional state. Depending on the identified emotion, for example, if the customer is confused, the customer service robot will ask, "Is there anything I can help you with?" If the customer is satisfied, it will display, "Thank you. We look forward to seeing you again."

[0830] The above is an embodiment of the present invention, and this system will improve the customer experience in physical stores.

[0831] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0832] Step 1:

[0833] When a user enters a store, the device (smartphone or in-store camera) captures video footage in real time. Video data of the customer is obtained as input, and real-time video footage is generated as output. Specifically, the device's camera device is turned on and begins capturing video data.

[0834] Step 2:

[0835] The captured video is sent from the device to the server. The input is real-time video, and the output is uploading video data to the server. Specifically, the device sends video data to the server via the network.

[0836] Step 3:

[0837] The server analyzes the received video using multimodal AI and facial recognition technologies. The input is the video sent to the server, and the output is data on the user's emotional state. Specifically, the server preprocesses the video using OpenCV and then analyzes changes in emotions using EmotionRecognizer.

[0838] Step 4:

[0839] Based on the analysis results, the server generates optimal feedback. The input is data on the user's emotional state, and the output is the generated feedback. Specifically, the server generates prompts based on the analysis data and constructs appropriate messages and guidance.

[0840] Step 5:

[0841] The generated feedback is sent from the server to the terminal, which then displays it or provides audio guidance. The input is the generated feedback, and the output is the information presented to the user. Specifically, the terminal displays the feedback by voice or on a display, and provides guidance and services to customers.

[0842] Step 6:

[0843] The server stores the user's emotional data in a cloud-based database. The input is the analyzed emotional data, and the output is saved in the database. Specifically, the server properly encrypts the emotional data and uploads it to cloud storage.

[0844] Step 7:

[0845] Customer service robots and in-store displays provide appropriate service to customers based on the displayed feedback. The input is the generated feedback, and the output is the actual service provided. Specifically, the robot will provide voice guidance or the display will display guidance information to alleviate customer confusion.

[0846] 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.

[0847] The system of the present invention captures a user's video footage in real time and analyzes changes in the user's emotions from the video footage. In this process, an emotion engine is used to perform detailed classification and recognition of emotions, thereby improving the quality of the user experience. This system is composed of the following means and operations.

[0848] System Configuration

[0849] 1. User Interface

[0850] The terminal provides a user interface that allows the user to access and operate the system, including functions such as starting video capture, selecting a session, and checking feedback.

[0851] 2. Video capture and transmission

[0852] A user logs into the system and begins a session.

[0853] The device captures the user's video in real time and sends it to the server. The video capture is performed using the device's camera device.

[0854] 3. Video reception and analysis

[0855] The server receives the video image transmitted from the terminal.

[0856] The server temporarily stores the received video and prepares it for processing.

[0857] The server analyzes the stored video data using multimodal AI technology and facial recognition technology to identify the user's facial expressions and gestures.

[0858] 4. Emotion Recognition by Emotion Engine

[0859] The server uses an emotion engine to classify the user's emotions in detail based on the analysis results, specifically into emotion categories such as happiness, sadness, surprise, and confusion.

[0860] The emotion engine continuously learns from the user's emotion data to improve the accuracy of emotion recognition.

[0861] 5. Feedback Generation and Display

[0862] The server generates feedback based on the emotion engine's analysis, which is tailored to the user's state and may include additional educational materials or help links if the user is confused, for example.

[0863] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[0864] 6. Data storage and utilization

[0865] The server stores the user's emotional data in a cloud-based database, which is encrypted to protect user privacy and data security.

[0866] Service providers analyze the stored data and plan improvements to their services and products. The data can be used in education, healthcare, entertainment, and product development to provide users with personalized experiences.

[0867] Specific examples

[0868] Specific examples of online education

[0869] In an online education platform, the system functions as follows:

[0870] 1. User Registration and Login

[0871] A user registers and logs in to the education platform.

[0872] The terminal sends authentication information to the server, and the server performs authentication.

[0873] 2. Starting the lesson and capturing video

[0874] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[0875] 3. Video Analysis and Emotion Recognition

[0876] The server receives the video footage and analyzes the user's facial expressions using multimodal AI and facial recognition technologies. The server then uses an emotion engine to classify the user's emotions in detail.

[0877] 4. Feedback generation and display

[0878] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[0879] 5. Data storage and utilization

[0880] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[0881] Examples of online therapy

[0882] On the online therapy platform, the system works as follows:

[0883] 1. User Registration and Login

[0884] A user registers and logs in to the therapy platform.

[0885] The terminal sends authentication information to the server, and the server performs authentication.

[0886] 2. Therapy session begins

[0887] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[0888] 3. Video Analysis and Emotion Recognition

[0889] The server receives the video footage and analyzes the user's facial expressions and gestures using multimodal AI and facial recognition technology, then uses an emotion engine to classify the user's emotions in detail.

[0890] 4. Feedback generation and display

[0891] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[0892] 5. Data storage and utilization

[0893] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[0894] This allows the system of the present invention to integrate an emotion engine and perform detailed analysis of user emotions, thereby providing personalized, high-quality experiences in various fields.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] The user accesses the Kimochi Scope website or app and fills in the required information on the new registration form.

[0898] Step 2:

[0899] The device sends the entered information (name, email address, password, etc.) to the server.

[0900] Step 3:

[0901] The server stores the received information in its database and completes the new user's registration.

[0902] Step 4:

[0903] The user enters their registered email address and password on the login screen.

[0904] Step 5:

[0905] The terminal transmits the entered authentication information to the server.

[0906] Step 6:

[0907] The server checks the database and, if authentication is successful, generates a session ID and sends it to the terminal.

[0908] Step 7:

[0909] The user selects one of the following sessions: online education, therapy, entertainment, etc.

[0910] Step 8:

[0911] The terminal transmits the selected session information to the server.

[0912] Step 9:

[0913] The device activates the camera and captures live video footage of the user.

[0914] Step 10:

[0915] The video captured by the device is streamed to the server in real time.

[0916] Step 11:

[0917] The server receives the video image transmitted from the terminal.

[0918] Step 12:

[0919] The server temporarily stores the received video data and prepares it for processing.

[0920] Step 13:

[0921] The server analyzes the stored video data using multimodal AI technology and facial recognition technology.

[0922] Step 14:

[0923] The server identifies the user's facial expressions and gestures and evaluates the changes in real time.

[0924] Step 15:

[0925] The server uses an emotion engine to classify the emotional state in detail and divide it into categories (e.g., happy, sad, confused, etc.).

[0926] Step 16:

[0927] The emotion engine learns the user's emotion data and improves the accuracy of emotion recognition.

[0928] Step 17:

[0929] The server generates feedback data (e.g., "The user is confused") based on the analysis results.

[0930] Step 18:

[0931] The server transmits the generated feedback data to the terminal.

[0932] Step 19:

[0933] Based on the feedback data received by the terminal, appropriate messages and materials are displayed on the user interface.

[0934] Step 20:

[0935] The user reviews the displayed feedback and materials and decides on their next course of action.

[0936] Step 21:

[0937] The server stores the user's emotional data during the session in a cloud-based database.

[0938] Step 22:

[0939] The server encrypts data at rest for data security and privacy.

[0940] Step 23:

[0941] Service providers analyze the stored data and develop improvements to their services and products.

[0942] Step 24:

[0943] Service providers can use data to tailor online education curricula, therapy plans, entertainment content, and more to improve user experience.

[0944] Example 2

[0945] 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."

[0946] Current online platforms lack systems that can identify users' emotions in real time and provide feedback based on those emotions. This results in a uniform user experience, making it difficult to respond to individual needs, and improving user satisfaction is a challenge. Protecting the privacy and security of collected emotion data is also a major challenge.

[0947] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for temporarily storing video footage, a means for identifying the user's facial expressions and gestures using multimodal AI technology and face recognition technology, and a means for classifying the user's emotions in detail using an emotion engine based on the analysis results. This makes it possible to analyze the user's emotions in real time and provide feedback tailored to individual needs. Furthermore, emotion data is encrypted and securely stored in a cloud-based database, ensuring privacy and data security.

[0948] "User" refers to any individual or entity that uses the System.

[0949] "Video footage" refers to real-time moving images of a user.

[0950] "Means of capturing in real time" refers to devices or programs that instantly acquire current video and digitally process it on the spot.

[0951] A "server" refers to a computer system that processes, stores, and manages data over a network.

[0952] "Multimodal AI technology" refers to artificial intelligence technology that simultaneously analyzes multiple different data formats (e.g., video, audio, text) and integrates the results.

[0953] "Facial recognition technology" refers to technology that detects facial features from video footage and identifies individuals and facial expressions.

[0954] An "emotion engine" refers to an algorithm or program that analyzes data such as a user's facial expressions and gestures, and classifies and recognizes their emotional state.

[0955] "Feedback" refers to information and suggested actions provided to the user based on the system's analysis results.

[0956] A "cloud-based database" is a data storage system accessible via the internet that stores and manages data on a cloud service.

[0957] "Data encryption" refers to a technology that converts data into a format that cannot be deciphered by third parties, thereby increasing data security.

[0958] "Privacy" refers to the state in which a user's personal information and activities are prevented from being known to third parties without permission.

[0959] "Data security" refers to a set of measures and techniques for protecting the integrity, confidentiality, and availability of data.

[0960] "Education" refers to the activity of imparting knowledge and skills to people and promoting growth through learning.

[0961] "Healthcare" refers to a range of medical and health-related services aimed at maintaining health and preventing and treating disease.

[0962] "Entertainment" refers to activities and content that provide people with fun and joy.

[0963] "Product development" refers to the entire process of planning, designing, and realizing new products and services.

[0964] MODE FOR CARRYING OUT THE INVENTION

[0965] The system of this invention captures a user's video footage in real time and analyzes changes in the user's emotions from the video footage. The system is composed of the following main components:

[0966] 1. User Interface

[0967] The terminal provides a user interface that allows users to access and operate the system. The user interface includes functions such as starting video capture, selecting a session, and checking feedback. This interface is implemented using HTML, CSS, JavaScript, etc.

[0968] 2. User login and session initiation

[0969] The user logs into the system and enters their account information. Once the login information is entered, the device sends this information to the server, which performs authentication. Once authentication is complete, the user can select and start a video capture session. The authentication process uses standard authentication technologies such as OAuth.

[0970] 3. Video capture and transmission

[0971] When a user starts a session, the device's camera captures the user's video in real time, and the captured video is encoded by the device and then sent to the server using the H.264 encoding format and the RTSP protocol.

[0972] 4. Receiving and temporarily storing video

[0973] The server receives the video footage sent from the device. The received footage is temporarily stored in the server's storage. The stored data is used for analysis in subsequent processing steps. The storage uses a large-capacity HDD or SSD.

[0974] 5. Video Analysis and Facial Expression Recognition

[0975] The server then applies multimodal AI and facial recognition technology to the stored video data. Specifically, it applies face detection algorithms using libraries such as OpenCV and Dlib to analyze the movement and relative positions of facial features. This process uses Haar cascade classifiers and DNN-based face detection models.

[0976] 6. Emotion Recognition by Emotion Engine

[0977] The server further analyzes the results of the facial expression recognition using an emotion engine. The emotion engine uses a deep learning model (e.g., CNN or RNN) to classify emotions. Frameworks such as TensorFlow or PyTorch are used. The trained model analyzes the features of facial features and outputs an emotion label (e.g., happiness, sadness, surprise, etc.).

[0978] 7. Generating and Displaying Feedback

[0979] The server generates appropriate feedback for the user based on the emotion recognition results of the emotion engine. For example, if the user is confused, feedback including supplementary learning materials and help links will be generated. This feedback is sent from the server to the device, where it is displayed on the user interface. NLP technology and conditional branching algorithms are used to generate the feedback.

[0980] 8. Data storage and utilization

[0981] The server stores the emotional data collected during the session in a cloud-based database. The data is encrypted and uses AES encryption technology to protect user privacy and data security. This data can be used in a variety of fields, including education, healthcare, entertainment, and product development.

[0982] Specific examples

[0983] Specific examples of online education

[0984] In an online education platform, the system works as follows:

[0985] 1. The user registers and logs in to the educational platform.

[0986] 2. The device sends authentication information to the server, and the server performs authentication.

[0987] 3. The user selects a lesson and starts video capture. The device captures the video and sends it to the server.

[0988] 4. The server receives the video footage and analyzes the user's facial expressions using multimodal AI technology and facial recognition technology. The server then uses an emotion engine to classify the user's emotions in detail.

[0989] 5. The server generates a link to the supplementary learning material based on the analysis results and sends it to the device. The device displays the feedback and the user can view the supplementary learning material.

[0990] 6. The server stores the user's emotional data during class in a database. The educational institution analyzes this data and improves the content of the class.

[0991] Examples of online therapy

[0992] On the online therapy platform, this system works as follows:

[0993] 1. The user registers and logs in to the therapy platform.

[0994] 2. The device sends authentication information to the server, and the server performs authentication.

[0995] 3. The user starts a therapy session. The device starts capturing video and sending it to the server.

[0996] 4. The server receives the video footage and analyzes the user's facial expressions and gestures using multimodal AI and facial recognition technologies. The server then uses an emotion engine to classify the user's emotions in detail.

[0997] 5. The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[0998] 6. The server stores the emotional data from the session in a database. The therapist uses this data to plan the next session and provide more effective treatment.

[0999] Example prompts for generative AI models

[1000] "Please tell me the specific processing steps for video analysis in this system."

[1001] "Please explain in detail how the emotion engine classifies the user's emotions."

[1002] This allows users to understand the specific operations and processes and learn how the system achieves emotion recognition.

[1003] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1004] Step 1: User Login

[1005] Input: The user enters their authentication information (username, password).

[1006] What happens: The authentication information provided by the user is sent from the device to the server.

[1007] Data processing: The server checks the received authentication information against the registration information in its database.

[1008] Output: If authentication is successful, the server generates a session ID and sends it to the device. If authentication fails, an error message is returned.

[1009] Step 2: Start video capture

[1010] Input: The user presses the Start Video Capture button to begin the session.

[1011] Specific behavior: The device's camera device will start up and begin capturing video footage.

[1012] Data processing: The captured video is encoded in H.264 format.

[1013] Output: The encoded video data is sent to the server in real time.

[1014] Step 3: Receiving and temporarily saving the video

[1015] Input: Captured video data is sent to the server.

[1016] Specific operation: The server temporarily stores the received video data in storage.

[1017] Data processing: Video data is kept in storage and prepared for analysis.

[1018] Output: Stored video data for subsequent analysis process.

[1019] Step 4: Video analysis and facial expression recognition

[1020] Input: The stored video data is passed to the server's analysis module.

[1021] Specific operation: The server uses OpenCV and Dlib to detect the user's face from the video frame.

[1022] Data processing: The detected facial landmarks (eyes, mouth, nose, etc.) are identified and facial expressions are analyzed based on them.

[1023] Output: The analysis results in the generation of facial expression information of the user.

[1024] Step 5: Emotion Recognition with the Emotion Engine

[1025] Input: Facial expression information is input to the emotion engine.

[1026] How it works: The server runs the emotion engine using TensorFlow and PyTorch. The deep learning model analyzes facial expression information and issues emotion labels.

[1027] Data processing: Facial expression information is input into the model as features, and emotion labels (e.g., happiness, sadness, surprise, etc.) are output.

[1028] Output: Emotion labels are generated and sent to the next feedback generation process.

[1029] Step 6: Generate and display feedback

[1030] Input: Emotion labels are input to the feedback generation module.

[1031] Specific behavior: The server generates appropriate feedback for the user based on the analysis results. For example, if the user is confused, feedback containing additional learning materials or help links is generated.

[1032] Data processing: Feedback content is generated and configured in HTML and JSON formats.

[1033] Output: The generated feedback is sent from the server to the device and displayed on the user interface.

[1034] Step 7: Store and use your data

[1035] Input: Emotion labels and feedback are input into the database storage module.

[1036] What it does: The server stores the emotional data and feedback collected during the session in a cloud-based database.

[1037] Data processing: Data is encrypted using encryption techniques such as AES.

[1038] Output: The encrypted data is securely stored in a database for later analysis and service improvement.

[1039] Through the above processing steps, the system can perform real-time emotion analysis of the user and provide feedback.

[1040] (Application example 2)

[1041] 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."

[1042] Conventional content distribution services lack the ability to analyze users' emotions in real time and provide appropriate feedback to improve their viewing experience. As a result, viewers are unable to enjoy appropriate content and a personalized experience is not provided. Furthermore, they lack the ability to ensure the security of users' viewing data and protect their privacy.

[1043] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1044] In this invention, the server includes means for capturing a user's video footage in real time, means for transmitting the captured video footage to the server, means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's terminal and displaying it, means for storing the user's emotional data in a cloud-based database, and, particularly in the case of a content distribution service, means for analyzing the user's emotions while watching and providing appropriate feedback and recommended content in real time.

[1045] This allows for personalized feedback and content recommendations to enhance the user's viewing experience, while data encryption protects user privacy and data security.

[1046] The "means for capturing a user's video footage in real time" refers to a technology that allows a user to obtain video footage in real time through the device's camera and immediately transmit it to the system.

[1047] The "means for transmitting captured video images to a server" is a technique for transmitting captured video data to a remote computer via a communication network such as the Internet.

[1048] "Means of analyzing video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions" refers to technology that analyzes video data using machine learning algorithms and facial detection technology to identify changes in the user's emotional state.

[1049] "Means for generating various feedback based on analysis results" refers to technology that provides users with appropriate information and recommendations in real time based on the results of emotion analysis.

[1050] The "means for transmitting the generated feedback to the user's terminal and displaying it" refers to a technique for transmitting the generated feedback information to the user's device and visually displaying it.

[1051] "Means for storing user emotion data in a cloud-based database" refers to technology for securely storing the results of a user's emotion analysis in remote data storage.

[1052] "A means of analyzing the user's emotions while watching, particularly in content distribution services, and providing appropriate feedback and recommended content in real time" refers to technology that analyzes emotions while the user is watching media such as movies or videos, and instantly provides optimal recommended information.

[1053] MODE FOR CARRYING OUT THE INVENTION

[1054] The present invention is a system that captures a user's video footage in real time and analyzes changes in the user's emotions from that footage. The system of the present invention has the function of analyzing the user's emotions while watching, and providing appropriate feedback and recommended content in real time, particularly in content distribution services.

[1055] System Configuration

[1056] 1. User Interface and Login

[1057] Users log in to the system using their device (e.g., smartphone, PC, smart glasses). After logging in, users can select the content they want to watch.

[1058] 2. Video capture and transmission

[1059] When a user watches content, the device's camera captures video footage, which is then sent to the server in real time.

[1060] 3. Video Analysis and Emotion Recognition

[1061] The server temporarily stores the received video footage and analyzes it using multimodal AI technology (e.g., OpenAI's GPT-4, Google Cloud Vision, Amazon Rekognition) and facial recognition technology (e.g., Microsoft Azure's Face API, DeepFace). As a result of the analysis, the user's emotions (e.g., happiness, sadness, surprise, confusion, etc.) are identified.

[1062] 4. Feedback generation and display

[1063] The server generates various feedback based on the results of the sentiment analysis. This feedback includes comments about the content being viewed and information about recommended content to watch next. The generated feedback is sent to the user's device in real time and displayed.

[1064] 5. Data storage and analysis

[1065] User sentiment data is stored in a cloud-based database (e.g., Google Cloud Firestore, Amazon RDS). This data is encrypted to protect user privacy. The stored data is used for analysis and to improve content delivery services.

[1066] Hardware and Software Used

[1067] Camera devices: Cameras installed in smartphones, smart glasses, and head-mounted displays are used.

[1068] Multimodal AI technology: Uses OpenAI's GPT-4, Google Cloud Vision, and Amazon Rekognition.

[1069] Facial recognition technology: Uses Microsoft Azure's Face API and DeepFace.

[1070] Cloud-based database: Google Cloud Firestore and Amazon RDS are used.

[1071] Specific examples

[1072] For example, imagine a user watching a movie on a video streaming service. At this time, the user's facial expressions are captured in real time by a camera and sent to the server. The server analyzes the video and recognizes emotional changes such as surprise or laughter at a particular scene. Based on this, feedback such as "It looks like you're enjoying this scene! Here are some movies that other users also enjoyed" is displayed.

[1073] Prompt Sentence Examples

[1074] The prompts for the generative AI model to capture specific emotional changes and generate appropriate feedback are as follows:

[1075] Analyze user video footage in real time and classify the user's emotional changes as happy, sad, surprised, confused, etc. Then determine the appropriate feedback and content recommendations to provide next. Use the following video footage data: [Video Data Link]

[1076] In this way, the system of the present invention significantly improves the user's viewing experience, providing personalized feedback and content recommendations in real time. This system, which combines ease of use with high security, adds new value to content distribution services.

[1077] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1078] Specific explanation of processing steps

[1079] Step 1: User Login

[1080] Input: User authentication information (user ID, password)

[1081] How it works: The user enters their authentication information on the device and presses the login button. The device sends the authentication information to the server.

[1082] Data processing: The server compares the received authentication information with the database and performs login authentication.

[1083] Output: Authentication result (success or failure)

[1084] Step 2: Content Selection

[1085] Input: User interface after login (list of content provided)

[1086] What it does: The user selects the content to watch.

[1087] Data processing: The selected content ID is sent to the server.

[1088] Output: Selected content ID

[1089] Step 3: Start video capture

[1090] Input: Content playback start instruction

[1091] How it works: The device's camera begins capturing video footage of the user in real time.

[1092] Data processing: Process the captured video footage as a data stream.

[1093] Output: Real-time video data

[1094] Step 4: Sending video to the server

[1095] Input: Real-time video data

[1096] Operation: The device sends captured video to the server.

[1097] Data processing: Compress the video data and send it using network protocols.

[1098] Output: Video data sent to the server

[1099] Step 5: Temporarily save the video data

[1100] Input: Video data sent to the server

[1101] Operation: The server stores the received video data in a storage device for short-term storage.

[1102] Data processing: Temporarily save the video data in an appropriate format.

[1103] Output: Temporarily saved video data

[1104] Step 6: Sentiment Analysis

[1105] Input: Temporarily saved video data

[1106] How it works: The server analyzes the video data using multimodal AI technology and facial recognition technology.

[1107] Data processing: Facial recognition is performed for each video frame, emotion labels are assigned, and emotional changes are identified using an AI model.

[1108] Output: Analysis results (user emotion data)

[1109] Step 7: Feedback Generation

[1110] Input: Analysis results (user emotion data)

[1111] Operation: The server generates feedback and recommended content for the content being viewed based on the emotional data.

[1112] Data processing: Feedback sentences are created using a generative AI model (e.g., GPT-4) based on emotion data.

[1113] Output: Generated feedback

[1114] Step 8: Submit and view feedback

[1115] Input: Generated feedback

[1116] Operation: The server sends the generated feedback to the user's device.

[1117] Data processing: The feedback data is converted into an appropriate format and sent using the network protocol.

[1118] Output: Feedback displayed on the terminal

[1119] Step 9: Storing Emotion Data

[1120] Input: Analysis results (user emotion data)

[1121] How it works: The server stores user emotion data in a cloud-based database.

[1122] Data processing: Emotional data is encrypted and stored securely in a database.

[1123] Output: Saved emotion data

[1124] Prompt Sentence Examples

[1125] "Prompt sentence for GPT-4"

[1126] Analyze user video footage in real time and classify the user's emotional changes as happy, sad, surprised, confused, etc. Then determine the appropriate feedback and content recommendations to provide next. Use the following video footage data: [Video Data Link]

[1127] 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.

[1128] 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.

[1129] 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.

[1130] [Third embodiment]

[1131] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1132] 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.

[1133] 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).

[1134] 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.

[1135] 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.

[1136] 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).

[1137] 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. 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.

[1138] 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.

[1139] 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.

[1140] 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.

[1141] 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.

[1142] 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."

[1143] The system of the present invention improves the user experience by capturing a user's video footage in real time and analyzing changes in the user's emotions from the video footage. This system is composed of the following means and operations.

[1144] System Configuration

[1145] 1. User Interface

[1146] The terminal provides a user interface that allows users to access and operate the system, including functions such as starting video capture, selecting a session, and checking feedback.

[1147] 2. Video capture and transmission

[1148] A user logs into the system and begins a session.

[1149] The device captures the user's video in real time and sends it to the server. The video capture is performed using the device's camera device.

[1150] 3. Video reception and analysis

[1151] The server receives the video image transmitted from the terminal.

[1152] The server analyzes video footage using multimodal AI and facial recognition technology, identifying the user's facial expressions and gestures and detecting changes in the user's emotions in real time.

[1153] 4. Feedback generation and display

[1154] The server generates feedback based on the analysis, which is tailored to the user's situation and may include additional educational material or help links if the user is confused, for example.

[1155] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[1156] 5. Data storage and utilization

[1157] The server stores the user's emotional data in a cloud-based database, which is encrypted to protect user privacy and data security.

[1158] Service providers analyze the stored data and plan improvements to their services and products. The data can be used in education, healthcare, entertainment, and product development to provide users with personalized experiences.

[1159] Specific examples

[1160] Specific examples of online education

[1161] In an online education platform, the system functions as follows:

[1162] 1. User Registration and Login

[1163] A user registers and logs in to the education platform.

[1164] The terminal sends authentication information to the server, and the server performs authentication.

[1165] 2. Starting the lesson and capturing video

[1166] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[1167] 3. Video Analysis

[1168] The server receives the video footage and performs analysis: it detects the user's facial expressions and identifies emotions such as interest and confusion.

[1169] 4. Feedback generation and display

[1170] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[1171] 5. Data storage and utilization

[1172] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[1173] Examples of online therapy

[1174] On the online therapy platform, the system works as follows:

[1175] 1. User Registration and Login

[1176] A user registers and logs in to the therapy platform.

[1177] The terminal sends authentication information to the server, and the server performs authentication.

[1178] 2. Therapy session begins

[1179] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[1180] 3. Video Analysis

[1181] The server receives the video footage and performs analysis to detect changes in emotions from the user's facial expressions and gestures.

[1182] 4. Feedback generation and display

[1183] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[1184] 5. Data storage and utilization

[1185] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[1186] As a result, the system of the present invention can analyze the user's emotions in real time and provide various feedback, thereby improving the user experience in various fields.

[1187] The processing flow will be explained below.

[1188] Step 1:

[1189] The user accesses the Kimochi Scope website or app and fills in the required information on the new registration form.

[1190] Step 2:

[1191] The device sends the entered information (name, email address, password, etc.) to the server.

[1192] Step 3:

[1193] The server stores the received information in its database and completes the new user's registration.

[1194] Step 4:

[1195] The user enters their registered email address and password on the login screen.

[1196] Step 5:

[1197] The terminal transmits the entered authentication information to the server.

[1198] Step 6:

[1199] The server checks the database and, if authentication is successful, generates a session ID and sends it to the terminal.

[1200] Step 7:

[1201] Users select from a range of online education, therapy, entertainment, product testing and other sessions.

[1202] Step 8:

[1203] The terminal transmits the selected session information to the server.

[1204] Step 9:

[1205] The device activates the camera and captures live video footage of the user.

[1206] Step 10:

[1207] The video captured by the device is streamed to the server in real time.

[1208] Step 11:

[1209] The server receives the video image transmitted from the terminal.

[1210] Step 12:

[1211] The server temporarily stores the received video and prepares it for processing.

[1212] Step 13:

[1213] The server analyzes the stored video data using multimodal AI technology and facial recognition technology.

[1214] Step 14:

[1215] The server identifies the user's facial expressions and gestures and evaluates the changes in real time.

[1216] Step 15:

[1217] The server quantifies the emotional state and classifies it into categories (e.g., happy, sad, confused, etc.).

[1218] Step 16:

[1219] The server generates feedback data (e.g., "The user is confused") based on the analysis results.

[1220] Step 17:

[1221] The server transmits the generated feedback data to the terminal.

[1222] Step 18:

[1223] Based on the feedback data received by the terminal, appropriate messages and materials are displayed on the user interface.

[1224] Step 19:

[1225] The user reviews the displayed feedback and materials and decides on their next course of action.

[1226] Step 20:

[1227] The server stores the user's emotional data during the session in a cloud-based database.

[1228] Step 21:

[1229] The server encrypts data at rest for data security and privacy.

[1230] Step 22:

[1231] Service providers analyze the stored data and develop improvements to their services and products.

[1232] Step 23:

[1233] Service providers can use data to tailor online education curricula, therapy plans, entertainment content, and more to improve user experience.

[1234] Example 1

[1235] 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."

[1236] The present invention relates to a system that captures a user's video footage in real time, analyzes the footage, and detects changes in the user's emotions. Conventional systems have difficulty accurately capturing a user's emotions in real time, and have not sufficiently improved the user experience. Furthermore, there are many challenges in storing and utilizing emotional data and protecting privacy. Therefore, there is a need for a system that can significantly improve the user experience by accurately analyzing a user's emotions in real time and effectively providing feedback based on the results.

[1237] 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.

[1238] In this invention, the server includes means for capturing a user's video image in real time, means for transmitting the captured video image to an information processing device, means for analyzing the video image received by the information processing device using multimodal artificial intelligence technology and face recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's output device and displaying it, and means for storing the user's emotion data in a network-based storage device, thereby enabling the user's emotions to be accurately analyzed in real time and appropriate feedback to be provided promptly.

[1239] "User" refers to any individual or entity that uses the System.

[1240] "Video Footage" refers to visual data captured through a camera device.

[1241] "Real-time" refers to the temporal characteristic of being processed immediately without delay.

[1242] "Information processing device" refers to a computer system for analyzing and processing received data.

[1243] "Multimodal AI technology" refers to AI technology that combines and evaluates multiple different data formats (e.g., video, audio, text).

[1244] "Facial recognition technology" refers to technology that detects a person's face from video captured using a camera device and analyzes its features.

[1245] "Changes in emotion" refers to changes in the emotional state detected based on changes in the user's facial expressions and gestures.

[1246] "Feedback" refers to information, instructions, or advice provided to the user based on the analysis results.

[1247] "Output device" refers to a device (e.g., a display or speaker) for providing visual or audible feedback to a user.

[1248] "Network-based storage" refers to a remotely located data storage device that is accessible over a network (e.g., cloud storage).

[1249] "Data encryption" refers to a technology that protects the confidentiality of data by converting it into a format that cannot be understood by third parties during transmission or storage.

[1250] "Education" refers to activities aimed at imparting knowledge and skills and promoting learning.

[1251] "Medical care" refers to diagnostic, therapeutic and preventive activities aimed at maintaining and restoring human health.

[1252] "Leisure" refers to any activity that people undertake for enjoyment or relaxation.

[1253] "Product development" refers to the process of planning, designing, and producing new products and services.

[1254] The present invention is a system that captures a user's video footage in real time and analyzes changes in the user's emotions from the footage. This system is characterized by the configuration and operation described below.

[1255] System Configuration

[1256] 1. User Interface

[1257] The terminal provides a user interface through which the user can access and operate the system, including functions such as starting a video capture, selecting a session, and viewing feedback.

[1258] 2. Video capture and transmission

[1259] A user logs into the system and begins a session.

[1260] The terminal uses a camera device to capture video images of the user in real time and transmits them to an information processing device (server).

[1261] 3. Video reception and analysis

[1262] The server receives the video image transmitted from the terminal.

[1263] The server analyzes video footage using multimodal artificial intelligence and facial recognition technology, identifying the user's facial expressions and gestures and detecting changes in the user's emotions in real time.

[1264] 4. Feedback generation and display

[1265] The server generates feedback based on the analysis, which is tailored to the user's situation and may include additional educational materials or help links if the user is confused.

[1266] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[1267] 5. Data storage and utilization

[1268] The server stores the user's emotional data in a network-based storage device, and the data is encrypted to protect the user's privacy and data security.

[1269] Service providers analyze the stored data to plan improvements to their services and products, and the data can be used in education, healthcare, entertainment, and product development to provide users with a personalized experience.

[1270] Technical details

[1271] Camera device: The device's built-in front camera or an externally connected camera device is used.

[1272] Multimodal artificial intelligence technology: Machine learning models using TensorFlow and PyTorch are used, and libraries such as OpenCV and Dlib are used for face recognition technology.

[1273] Network-based storage: Use Amazon RDS, Google Cloud Firestore, etc. to store and analyze data.

[1274] Specific examples

[1275] Specific examples of online education

[1276] 1. User Registration and Login

[1277] A user registers and logs in to the education platform.

[1278] The terminal sends authentication information to the server, and the server performs authentication.

[1279] 2. Starting the lesson and capturing video

[1280] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[1281] 3. Video Analysis

[1282] The server receives the video footage and performs analysis: it detects the user's facial expressions and identifies emotions such as interest and confusion.

[1283] 4. Feedback generation and display

[1284] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[1285] 5. Data storage and utilization

[1286] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[1287] Examples of online therapy

[1288] 1. User Registration and Login

[1289] A user registers and logs in to the therapy platform.

[1290] The terminal sends authentication information to the server, and the server performs authentication.

[1291] 2. Therapy session begins

[1292] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[1293] 3. Video Analysis

[1294] The server receives the video footage and performs analysis to detect changes in emotions from the user's facial expressions and gestures.

[1295] 4. Feedback generation and display

[1296] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[1297] 5. Data storage and utilization

[1298] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[1299] Prompt Sentence Examples

[1300] Example prompt:

[1301] "I want to design a system that captures a user's facial expressions in real time through a camera and analyzes their emotional changes. What should I use?"

[1302] Example output:

[1303] It is recommended to use a webcam or external camera as the camera device. Machine learning models using TensorFlow or PyTorch are useful for facial expression analysis, and libraries such as OpenCV or Dlib are useful for face recognition. For data storage and analysis, use a cloud-based database such as Amazon RDS or Google Cloud Firestore.

[1304] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1305] Step 1:

[1306] User Registration and Login

[1307] Input: Information entered by the user in the sign-up form (name, email address, password)

[1308] Output: Authentication information generated by the server

[1309] Specific operation: The user enters information into the new registration form on the web browser and clicks the "Register" button. The device sends the information to the server as a POST request. The server stores the received information in a database and generates and returns authentication information.

[1310] Step 2:

[1311] Start video capture

[1312] Input: Instruction to start video capture selected by the user after logging in

[1313] Output: Captured video data

[1314] Specific operation: When the user clicks the start video capture button, the device activates the camera device using JavaScript and starts capturing video in real time. The captured video is passed directly to the next step.

[1315] Step 3:

[1316] Video transmission and reception

[1317] Input: Captured video footage data

[1318] Output: Video data received by the server

[1319] Specific operation: The device sends captured video frame by frame to the server using WebSocket or HTTP streaming. The server receives this data in real time and puts it into an analysis queue.

[1320] Step 4:

[1321] Video Analysis

[1322] Input: Received video data

[1323] Output: Analyzed emotion change data

[1324] How it works: The server uses OpenCV and TensorFlow libraries to analyze the user's facial expressions and gestures for each frame. In particular, it detects expressions such as smiles and confusion, and identifies emotional changes based on them. The identified emotional change data is generated and passed to the next step.

[1325] Step 5:

[1326] Feedback Generation

[1327] Input: Analyzed emotion change data

[1328] Output: Generated feedback data

[1329] How it works: Based on the analysis results, the server creates appropriate feedback using Python scripts or generative AI models (e.g., GPT). The feedback data is converted to JSON format and sent to the device.

[1330] Step 6:

[1331] Feedback Display

[1332] Input: Generated feedback data

[1333] Output: A feedback message that is displayed on the user's terminal.

[1334] Specific behavior: When the device receives feedback data from the server, it uses JavaScript and HTML to display appropriate messages and content on the interface. The user checks the displayed feedback and decides on the next action.

[1335] Step 7:

[1336] Data storage and utilization

[1337] Input: Analyzed emotion change data and generated feedback data

[1338] Output: Data stored on a network-based storage device

[1339] How it works: The server stores the analysis results and feedback in a database. This storage is encrypted to protect the confidentiality of the data. The stored data is later analyzed by the service provider using analysis tools and used to plan and implement improvements to services and products.

[1340] (Application example 1)

[1341] 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."

[1342] While conventional user experience improvement systems generate appropriate feedback based on user sentiment analysis, they have not been adequately applied to customer service and guidance in brick-and-mortar stores. As a result, customers often become confused or dissatisfied in-store, making improving customer satisfaction a challenge. Furthermore, it is difficult to introduce technology that provides guidance and services tailored to individual customers in real time, making it difficult to improve the efficiency of store operations.

[1343] 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.

[1344] In this invention, the server includes means for capturing video footage of users in real time, means for transmitting the captured video footage to the server, means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's terminal and displaying it, means for saving user emotion data in a cloud-based database, and means for capturing video footage of customers and providing in-store guidance and services based on the analysis results. This makes it possible to analyze customer emotions in real stores in real time and provide optimal guidance and services based on the analysis results.

[1345] "User video footage" refers to video data captured by a user in real time.

[1346] "Means for capturing in real time" refers to a system that uses a photographing device to obtain video data instantly.

[1347] "Means for transmitting to a server" refers to a method for transferring captured video data to an external computing device via a network.

[1348] "Multimodal AI technology" refers to artificial intelligence technology that combines and analyzes multiple data inputs (e.g., video, audio, text).

[1349] "Facial recognition technology" is a technology for detecting and identifying facial features from video data.

[1350] "Changes in user's emotions" refers to the transitions in the user's emotional state indicated by the user's facial expressions and behavior detected from the video.

[1351] "Means for generating various feedback" refers to methods for generating responses and information according to the analysis results and providing them to users.

[1352] "User terminal" refers to an electronic device that a user uses to directly operate or display information.

[1353] A "cloud-based database" is online storage that is accessed and managed over the internet.

[1354] A "store visitor" is a customer who visits a physical store.

[1355] "Means for providing guidance and services in-store" refers to methods for providing appropriate information and support to customers in-store in real time.

[1356] MODE FOR CARRYING OUT THE INVENTION

[1357] The system of the present invention aims to improve the customer experience in physical stores based on a program configured as follows. The configuration and processing procedure will be described in detail below.

[1358] System Configuration

[1359] Hardware:

[1360] 1. A smartphone or in-store camera is used as the terminal, which can capture video footage of customers in real time.

[1361] 2. Customer service robots will be used to provide guidance and services within the store, such as providing voice guidance and displaying information on screens.

[1362] software:

[1363] 1. OpenCV is used for video capture and pre-processing, which allows for real-time video capture.

[1364] 2. EmotionRecognizer (emotion analysis library) is used to analyze changes in users' emotions. This library uses multimodal AI technology and facial recognition technology to identify the emotions of customers.

[1365] 3. REST APIs are used to send and receive video footage and analysis results, as well as generate and receive feedback.

[1366] Program processing

[1367] 1. The device captures video footage of customers in real time. This role is played by smartphones or in-store cameras.

[1368] 2. The captured video data is sent to the server, which receives it and analyzes it.

[1369] 3. The server uses OpenCV and EmotionRecognizer to analyze the video data and identify changes in the customers' emotions.

[1370] 4. Based on the analysis results, the server generates appropriate feedback. For example, if a customer is confused, the server may provide guidance through a customer service robot, asking, "Is there anything I can help you with?"

[1371] 5. Feedback is sent to the device in real time and displayed on the customer service robot or smartphone, allowing the store to provide appropriate guidance and services to customers.

[1372] Specific examples

[1373] Example 1:

[1374] For example, if a customer is confused and unable to find a particular product in a store, a smartphone camera captures their facial expression. EmotionRecognizer analyzes the confusion, and the server sends a message to a customer service robot saying, "Are you having trouble?" The customer service robot then plays the message aloud and guides the customer to the product.

[1375] Example 2:

[1376] When a customer shows a satisfied expression, the capture device also captures that expression and the server analyzes it. Based on the analysis results, the customer service robot will say to the customer, "Thank you. We look forward to seeing you again."

[1377] In this way, appropriate feedback and services can be provided based on real-time analysis of customers' emotions.

[1378] Prompt Sentence Examples

[1379] An example prompt phrase might take the following form:

[1380] When a customer enters the store, a camera captures the video. The captured video is analyzed in real time to identify the customer's emotional state. Depending on the identified emotion, for example, if the customer is confused, the customer service robot will ask, "Is there anything I can help you with?" If the customer is satisfied, it will display, "Thank you. We look forward to seeing you again."

[1381] The above is an embodiment of the present invention, and this system will improve the customer experience in physical stores.

[1382] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1383] Step 1:

[1384] When a user enters a store, the device (smartphone or in-store camera) captures video footage in real time. Video data of the customer is obtained as input, and real-time video footage is generated as output. Specifically, the device's camera device is turned on and begins capturing video data.

[1385] Step 2:

[1386] The captured video is sent from the device to the server. The input is real-time video, and the output is uploading video data to the server. Specifically, the device sends video data to the server via the network.

[1387] Step 3:

[1388] The server analyzes the received video using multimodal AI and facial recognition technologies. The input is the video sent to the server, and the output is data on the user's emotional state. Specifically, the server preprocesses the video using OpenCV and then analyzes changes in emotions using EmotionRecognizer.

[1389] Step 4:

[1390] Based on the analysis results, the server generates optimal feedback. The input is data on the user's emotional state, and the output is the generated feedback. Specifically, the server generates prompts based on the analysis data and constructs appropriate messages and guidance.

[1391] Step 5:

[1392] The generated feedback is sent from the server to the terminal, which then displays it or provides audio guidance. The input is the generated feedback, and the output is the information presented to the user. Specifically, the terminal displays the feedback by voice or on a display, and provides guidance and services to customers.

[1393] Step 6:

[1394] The server stores the user's emotional data in a cloud-based database. The input is the analyzed emotional data, and the output is saved in the database. Specifically, the server properly encrypts the emotional data and uploads it to cloud storage.

[1395] Step 7:

[1396] Customer service robots and in-store displays provide appropriate service to customers based on the displayed feedback. The input is the generated feedback, and the output is the actual service provided. Specifically, the robot will provide voice guidance or the display will display guidance information to alleviate customer confusion.

[1397] 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.

[1398] The system of the present invention captures a user's video footage in real time and analyzes changes in the user's emotions from the video footage. In this process, an emotion engine is used to perform detailed classification and recognition of emotions, thereby improving the quality of the user experience. This system is composed of the following means and operations.

[1399] System Configuration

[1400] 1. User Interface

[1401] The terminal provides a user interface that allows the user to access and operate the system, including functions such as starting video capture, selecting a session, and checking feedback.

[1402] 2. Video capture and transmission

[1403] A user logs into the system and begins a session.

[1404] The device captures the user's video in real time and sends it to the server. The video capture is performed using the device's camera device.

[1405] 3. Video reception and analysis

[1406] The server receives the video image transmitted from the terminal.

[1407] The server temporarily stores the received video and prepares it for processing.

[1408] The server analyzes the stored video data using multimodal AI technology and facial recognition technology to identify the user's facial expressions and gestures.

[1409] 4. Emotion Recognition by Emotion Engine

[1410] The server uses an emotion engine to classify the user's emotions in detail based on the analysis results, specifically into emotion categories such as happiness, sadness, surprise, and confusion.

[1411] The emotion engine continuously learns from the user's emotion data to improve the accuracy of emotion recognition.

[1412] 5. Feedback Generation and Display

[1413] The server generates feedback based on the emotion engine's analysis, which is tailored to the user's state and may include additional educational materials or help links if the user is confused, for example.

[1414] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[1415] 6. Data storage and utilization

[1416] The server stores the user's emotional data in a cloud-based database, which is encrypted to protect user privacy and data security.

[1417] Service providers analyze the stored data and plan improvements to their services and products. The data can be used in education, healthcare, entertainment, and product development to provide users with personalized experiences.

[1418] Specific examples

[1419] Specific examples of online education

[1420] In an online education platform, the system functions as follows:

[1421] 1. User Registration and Login

[1422] A user registers and logs in to the education platform.

[1423] The terminal sends authentication information to the server, and the server performs authentication.

[1424] 2. Starting the lesson and capturing video

[1425] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[1426] 3. Video Analysis and Emotion Recognition

[1427] The server receives the video footage and analyzes the user's facial expressions using multimodal AI and facial recognition technologies. The server then uses an emotion engine to classify the user's emotions in detail.

[1428] 4. Feedback generation and display

[1429] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[1430] 5. Data storage and utilization

[1431] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[1432] Examples of online therapy

[1433] On the online therapy platform, the system works as follows:

[1434] 1. User Registration and Login

[1435] A user registers and logs in to the therapy platform.

[1436] The terminal sends authentication information to the server, and the server performs authentication.

[1437] 2. Therapy session begins

[1438] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[1439] 3. Video Analysis and Emotion Recognition

[1440] The server receives the video footage and analyzes the user's facial expressions and gestures using multimodal AI and facial recognition technology, then uses an emotion engine to classify the user's emotions in detail.

[1441] 4. Feedback generation and display

[1442] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[1443] 5. Data storage and utilization

[1444] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[1445] This allows the system of the present invention to integrate an emotion engine and perform detailed analysis of user emotions, thereby providing personalized, high-quality experiences in various fields.

[1446] The processing flow will be explained below.

[1447] Step 1:

[1448] The user accesses the Kimochi Scope website or app and fills in the required information on the new registration form.

[1449] Step 2:

[1450] The device sends the entered information (name, email address, password, etc.) to the server.

[1451] Step 3:

[1452] The server stores the received information in its database and completes the new user's registration.

[1453] Step 4:

[1454] The user enters their registered email address and password on the login screen.

[1455] Step 5:

[1456] The terminal transmits the entered authentication information to the server.

[1457] Step 6:

[1458] The server checks the database and, if authentication is successful, generates a session ID and sends it to the terminal.

[1459] Step 7:

[1460] The user selects one of the following sessions: online education, therapy, entertainment, etc.

[1461] Step 8:

[1462] The terminal transmits the selected session information to the server.

[1463] Step 9:

[1464] The device activates the camera and captures live video footage of the user.

[1465] Step 10:

[1466] The video captured by the device is streamed to the server in real time.

[1467] Step 11:

[1468] The server receives the video image transmitted from the terminal.

[1469] Step 12:

[1470] The server temporarily stores the received video data and prepares it for processing.

[1471] Step 13:

[1472] The server analyzes the stored video data using multimodal AI technology and facial recognition technology.

[1473] Step 14:

[1474] The server identifies the user's facial expressions and gestures and evaluates the changes in real time.

[1475] Step 15:

[1476] The server uses an emotion engine to classify the emotional state in detail and divide it into categories (e.g., happy, sad, confused, etc.).

[1477] Step 16:

[1478] The emotion engine learns the user's emotion data and improves the accuracy of emotion recognition.

[1479] Step 17:

[1480] The server generates feedback data (e.g., "The user is confused") based on the analysis results.

[1481] Step 18:

[1482] The server transmits the generated feedback data to the terminal.

[1483] Step 19:

[1484] Based on the feedback data received by the terminal, appropriate messages and materials are displayed on the user interface.

[1485] Step 20:

[1486] The user reviews the displayed feedback and materials and decides on their next course of action.

[1487] Step 21:

[1488] The server stores the user's emotional data during the session in a cloud-based database.

[1489] Step 22:

[1490] The server encrypts data at rest for data security and privacy.

[1491] Step 23:

[1492] Service providers analyze the stored data and develop improvements to their services and products.

[1493] Step 24:

[1494] Service providers can use data to tailor online education curricula, therapy plans, entertainment content, and more to improve user experience.

[1495] Example 2

[1496] 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."

[1497] Current online platforms lack systems that can identify users' emotions in real time and provide feedback based on those emotions. This results in a uniform user experience, making it difficult to respond to individual needs, and improving user satisfaction is a challenge. Protecting the privacy and security of collected emotion data is also a major challenge.

[1498] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for temporarily storing video footage, a means for identifying the user's facial expressions and gestures using multimodal AI technology and face recognition technology, and a means for classifying the user's emotions in detail using an emotion engine based on the analysis results. This makes it possible to analyze the user's emotions in real time and provide feedback tailored to individual needs. Furthermore, emotion data is encrypted and securely stored in a cloud-based database, ensuring privacy and data security.

[1499] "User" refers to any individual or entity that uses the System.

[1500] "Video footage" refers to real-time moving images of a user.

[1501] "Means of capturing in real time" refers to devices or programs that instantly acquire current video and digitally process it on the spot.

[1502] A "server" refers to a computer system that processes, stores, and manages data over a network.

[1503] "Multimodal AI technology" refers to artificial intelligence technology that simultaneously analyzes multiple different data formats (e.g., video, audio, text) and integrates the results.

[1504] "Facial recognition technology" refers to technology that detects facial features from video footage and identifies individuals and facial expressions.

[1505] An "emotion engine" refers to an algorithm or program that analyzes data such as a user's facial expressions and gestures, and classifies and recognizes their emotional state.

[1506] "Feedback" refers to information and suggested actions provided to the user based on the system's analysis results.

[1507] A "cloud-based database" is a data storage system accessible via the internet that stores and manages data on a cloud service.

[1508] "Data encryption" refers to a technology that converts data into a format that cannot be deciphered by third parties, thereby increasing data security.

[1509] "Privacy" refers to the state in which a user's personal information and activities are prevented from being known to third parties without permission.

[1510] "Data security" refers to a set of measures and techniques for protecting the integrity, confidentiality, and availability of data.

[1511] "Education" refers to the activity of imparting knowledge and skills to people and promoting growth through learning.

[1512] "Healthcare" refers to a range of medical and health-related services aimed at maintaining health and preventing and treating disease.

[1513] "Entertainment" refers to activities and content that provide people with fun and joy.

[1514] "Product development" refers to the entire process of planning, designing, and realizing new products and services.

[1515] MODE FOR CARRYING OUT THE INVENTION

[1516] The system of this invention captures a user's video footage in real time and analyzes changes in the user's emotions from the video footage. The system is composed of the following main components:

[1517] 1. User Interface

[1518] The terminal provides a user interface that allows users to access and operate the system. The user interface includes functions such as starting video capture, selecting a session, and checking feedback. This interface is implemented using HTML, CSS, JavaScript, etc.

[1519] 2. User login and session initiation

[1520] The user logs into the system and enters their account information. Once the login information is entered, the device sends this information to the server, which performs authentication. Once authentication is complete, the user can select and start a video capture session. The authentication process uses standard authentication technologies such as OAuth.

[1521] 3. Video capture and transmission

[1522] When a user starts a session, the device's camera captures the user's video in real time, and the captured video is encoded by the device and then sent to the server using the H.264 encoding format and the RTSP protocol.

[1523] 4. Receiving and temporarily storing video

[1524] The server receives the video footage sent from the device. The received footage is temporarily stored in the server's storage. The stored data is used for analysis in subsequent processing steps. The storage uses a large-capacity HDD or SSD.

[1525] 5. Video Analysis and Facial Expression Recognition

[1526] The server then applies multimodal AI and facial recognition technology to the stored video data. Specifically, it applies face detection algorithms using libraries such as OpenCV and Dlib to analyze the movement and relative positions of facial features. This process uses Haar cascade classifiers and DNN-based face detection models.

[1527] 6. Emotion Recognition by Emotion Engine

[1528] The server further analyzes the results of the facial expression recognition using an emotion engine. The emotion engine uses a deep learning model (e.g., CNN or RNN) to classify emotions. Frameworks such as TensorFlow or PyTorch are used. The trained model analyzes the features of facial features and outputs an emotion label (e.g., happiness, sadness, surprise, etc.).

[1529] 7. Generating and Displaying Feedback

[1530] The server generates appropriate feedback for the user based on the emotion recognition results of the emotion engine. For example, if the user is confused, feedback including supplementary learning materials and help links will be generated. This feedback is sent from the server to the device, where it is displayed on the user interface. NLP technology and conditional branching algorithms are used to generate the feedback.

[1531] 8. Data storage and utilization

[1532] The server stores the emotional data collected during the session in a cloud-based database. The data is encrypted and uses AES encryption technology to protect user privacy and data security. This data can be used in a variety of fields, including education, healthcare, entertainment, and product development.

[1533] Specific examples

[1534] Specific examples of online education

[1535] In an online education platform, the system works as follows:

[1536] 1. The user registers and logs in to the educational platform.

[1537] 2. The device sends authentication information to the server, and the server performs authentication.

[1538] 3. The user selects a lesson and starts video capture. The device captures the video and sends it to the server.

[1539] 4. The server receives the video footage and analyzes the user's facial expressions using multimodal AI technology and facial recognition technology. The server then uses an emotion engine to classify the user's emotions in detail.

[1540] 5. The server generates a link to the supplementary learning material based on the analysis results and sends it to the device. The device displays the feedback and the user can view the supplementary learning material.

[1541] 6. The server stores the user's emotional data during class in a database. The educational institution analyzes this data and improves the content of the class.

[1542] Examples of online therapy

[1543] On the online therapy platform, this system works as follows:

[1544] 1. The user registers and logs in to the therapy platform.

[1545] 2. The device sends authentication information to the server, and the server performs authentication.

[1546] 3. The user starts a therapy session. The device starts capturing video and sending it to the server.

[1547] 4. The server receives the video footage and analyzes the user's facial expressions and gestures using multimodal AI and facial recognition technologies. The server then uses an emotion engine to classify the user's emotions in detail.

[1548] 5. The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[1549] 6. The server stores the emotional data from the session in a database. The therapist uses this data to plan the next session and provide more effective treatment.

[1550] Example prompts for generative AI models

[1551] "Please tell me the specific processing steps for video analysis in this system."

[1552] "Please explain in detail how the emotion engine classifies the user's emotions."

[1553] This allows users to understand the specific operations and processes and learn how the system achieves emotion recognition.

[1554] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1555] Step 1: User Login

[1556] Input: The user enters their authentication information (username, password).

[1557] What happens: The authentication information provided by the user is sent from the device to the server.

[1558] Data processing: The server checks the received authentication information against the registration information in its database.

[1559] Output: If authentication is successful, the server generates a session ID and sends it to the device. If authentication fails, an error message is returned.

[1560] Step 2: Start video capture

[1561] Input: The user presses the Start Video Capture button to begin the session.

[1562] Specific behavior: The device's camera device will start up and begin capturing video footage.

[1563] Data processing: The captured video is encoded in H.264 format.

[1564] Output: The encoded video data is sent to the server in real time.

[1565] Step 3: Receiving and temporarily saving the video

[1566] Input: Captured video data is sent to the server.

[1567] Specific operation: The server temporarily stores the received video data in storage.

[1568] Data processing: Video data is kept in storage and prepared for analysis.

[1569] Output: Stored video data for subsequent analysis process.

[1570] Step 4: Video analysis and facial expression recognition

[1571] Input: The stored video data is passed to the server's analysis module.

[1572] Specific operation: The server uses OpenCV and Dlib to detect the user's face from the video frame.

[1573] Data processing: The detected facial landmarks (eyes, mouth, nose, etc.) are identified and facial expressions are analyzed based on them.

[1574] Output: The analysis results in the generation of facial expression information of the user.

[1575] Step 5: Emotion Recognition with the Emotion Engine

[1576] Input: Facial expression information is input to the emotion engine.

[1577] How it works: The server runs the emotion engine using TensorFlow and PyTorch. The deep learning model analyzes facial expression information and issues emotion labels.

[1578] Data processing: Facial expression information is input into the model as features, and emotion labels (e.g., happiness, sadness, surprise, etc.) are output.

[1579] Output: Emotion labels are generated and sent to the next feedback generation process.

[1580] Step 6: Generate and display feedback

[1581] Input: Emotion labels are input to the feedback generation module.

[1582] Specific behavior: The server generates appropriate feedback for the user based on the analysis results. For example, if the user is confused, feedback containing additional learning materials or help links is generated.

[1583] Data processing: Feedback content is generated and configured in HTML and JSON formats.

[1584] Output: The generated feedback is sent from the server to the device and displayed on the user interface.

[1585] Step 7: Store and use your data

[1586] Input: Emotion labels and feedback are input into the database storage module.

[1587] What it does: The server stores the emotional data and feedback collected during the session in a cloud-based database.

[1588] Data processing: Data is encrypted using encryption techniques such as AES.

[1589] Output: The encrypted data is securely stored in a database for later analysis and service improvement.

[1590] Through the above processing steps, the system can perform real-time emotion analysis of the user and provide feedback.

[1591] (Application example 2)

[1592] 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."

[1593] Conventional content distribution services lack the ability to analyze users' emotions in real time and provide appropriate feedback to improve their viewing experience. As a result, viewers are unable to enjoy appropriate content and a personalized experience is not provided. Furthermore, they lack the ability to ensure the security of users' viewing data and protect their privacy.

[1594] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1595] In this invention, the server includes means for capturing a user's video footage in real time, means for transmitting the captured video footage to the server, means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's terminal and displaying it, means for storing the user's emotional data in a cloud-based database, and, particularly in the case of a content distribution service, means for analyzing the user's emotions while watching and providing appropriate feedback and recommended content in real time.

[1596] This allows for personalized feedback and content recommendations to enhance the user's viewing experience, while data encryption protects user privacy and data security.

[1597] The "means for capturing a user's video footage in real time" refers to a technology that allows a user to obtain video footage in real time through the device's camera and immediately transmit it to the system.

[1598] The "means for transmitting captured video images to a server" is a technique for transmitting captured video data to a remote computer via a communication network such as the Internet.

[1599] "Means of analyzing video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions" refers to technology that analyzes video data using machine learning algorithms and facial detection technology to identify changes in the user's emotional state.

[1600] "Means for generating various feedback based on analysis results" refers to technology that provides users with appropriate information and recommendations in real time based on the results of emotion analysis.

[1601] The "means for transmitting the generated feedback to the user's terminal and displaying it" refers to a technique for transmitting the generated feedback information to the user's device and visually displaying it.

[1602] "Means for storing user emotion data in a cloud-based database" refers to technology for securely storing the results of a user's emotion analysis in remote data storage.

[1603] "A means of analyzing the user's emotions while watching, particularly in content distribution services, and providing appropriate feedback and recommended content in real time" refers to technology that analyzes emotions while the user is watching media such as movies or videos, and instantly provides optimal recommended information.

[1604] MODE FOR CARRYING OUT THE INVENTION

[1605] The present invention is a system that captures a user's video footage in real time and analyzes changes in the user's emotions from that footage. The system of the present invention has the function of analyzing the user's emotions while watching, and providing appropriate feedback and recommended content in real time, particularly in content distribution services.

[1606] System Configuration

[1607] 1. User Interface and Login

[1608] Users log in to the system using their device (e.g., smartphone, PC, smart glasses). After logging in, users can select the content they want to watch.

[1609] 2. Video capture and transmission

[1610] When a user watches content, the device's camera captures video footage, which is then sent to the server in real time.

[1611] 3. Video Analysis and Emotion Recognition

[1612] The server temporarily stores the received video footage and analyzes it using multimodal AI technology (e.g., OpenAI's GPT-4, Google Cloud Vision, Amazon Rekognition) and facial recognition technology (e.g., Microsoft Azure's Face API, DeepFace). As a result of the analysis, the user's emotions (e.g., happiness, sadness, surprise, confusion, etc.) are identified.

[1613] 4. Feedback generation and display

[1614] The server generates various feedback based on the results of the sentiment analysis. This feedback includes comments about the content being viewed and information about recommended content to watch next. The generated feedback is sent to the user's device in real time and displayed.

[1615] 5. Data storage and analysis

[1616] User sentiment data is stored in a cloud-based database (e.g., Google Cloud Firestore, Amazon RDS). This data is encrypted to protect user privacy. The stored data is used for analysis and to improve content delivery services.

[1617] Hardware and Software Used

[1618] Camera devices: Cameras installed in smartphones, smart glasses, and head-mounted displays are used.

[1619] Multimodal AI technology: Uses OpenAI's GPT-4, Google Cloud Vision, and Amazon Rekognition.

[1620] Facial recognition technology: Uses Microsoft Azure's Face API and DeepFace.

[1621] Cloud-based database: Google Cloud Firestore and Amazon RDS are used.

[1622] Specific examples

[1623] For example, imagine a user watching a movie on a video streaming service. At this time, the user's facial expressions are captured in real time by a camera and sent to the server. The server analyzes the video and recognizes emotional changes such as surprise or laughter at a particular scene. Based on this, feedback such as "It looks like you're enjoying this scene! Here are some movies that other users also enjoyed" is displayed.

[1624] Prompt Sentence Examples

[1625] The prompts for the generative AI model to capture specific emotional changes and generate appropriate feedback are as follows:

[1626] Analyze user video footage in real time and classify the user's emotional changes as happy, sad, surprised, confused, etc. Then determine the appropriate feedback and content recommendations to provide next. Use the following video footage data: [Video Data Link]

[1627] In this way, the system of the present invention significantly improves the user's viewing experience, providing personalized feedback and content recommendations in real time. This system, which combines ease of use with high security, adds new value to content distribution services.

[1628] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1629] Specific explanation of processing steps

[1630] Step 1: User Login

[1631] Input: User authentication information (user ID, password)

[1632] How it works: The user enters their authentication information on the device and presses the login button. The device sends the authentication information to the server.

[1633] Data processing: The server compares the received authentication information with the database and performs login authentication.

[1634] Output: Authentication result (success or failure)

[1635] Step 2: Content Selection

[1636] Input: User interface after login (list of content provided)

[1637] What it does: The user selects the content to watch.

[1638] Data processing: The selected content ID is sent to the server.

[1639] Output: Selected content ID

[1640] Step 3: Start video capture

[1641] Input: Content playback start instruction

[1642] How it works: The device's camera begins capturing video footage of the user in real time.

[1643] Data processing: Process the captured video footage as a data stream.

[1644] Output: Real-time video data

[1645] Step 4: Sending video to the server

[1646] Input: Real-time video data

[1647] Operation: The device sends captured video to the server.

[1648] Data processing: Compress the video data and send it using network protocols.

[1649] Output: Video data sent to the server

[1650] Step 5: Temporarily save the video data

[1651] Input: Video data sent to the server

[1652] Operation: The server stores the received video data in a storage device for short-term storage.

[1653] Data processing: Temporarily save the video data in an appropriate format.

[1654] Output: Temporarily saved video data

[1655] Step 6: Sentiment Analysis

[1656] Input: Temporarily saved video data

[1657] How it works: The server analyzes the video data using multimodal AI technology and facial recognition technology.

[1658] Data processing: Facial recognition is performed for each video frame, emotion labels are assigned, and emotional changes are identified using an AI model.

[1659] Output: Analysis results (user emotion data)

[1660] Step 7: Feedback Generation

[1661] Input: Analysis results (user emotion data)

[1662] Operation: The server generates feedback and recommended content for the content being viewed based on the emotional data.

[1663] Data processing: Feedback sentences are created using a generative AI model (e.g., GPT-4) based on emotion data.

[1664] Output: Generated feedback

[1665] Step 8: Submit and view feedback

[1666] Input: Generated feedback

[1667] Operation: The server sends the generated feedback to the user's device.

[1668] Data processing: The feedback data is converted into an appropriate format and sent using the network protocol.

[1669] Output: Feedback displayed on the terminal

[1670] Step 9: Storing Emotion Data

[1671] Input: Analysis results (user emotion data)

[1672] How it works: The server stores user emotion data in a cloud-based database.

[1673] Data processing: Emotional data is encrypted and stored securely in a database.

[1674] Output: Saved emotion data

[1675] Prompt Sentence Examples

[1676] "Prompt sentence for GPT-4"

[1677] Analyze user video footage in real time and classify the user's emotional changes as happy, sad, surprised, confused, etc. Then determine the appropriate feedback and content recommendations to provide next. Use the following video footage data: [Video Data Link]

[1678] 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.

[1679] 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.

[1680] 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.

[1681] [Fourth embodiment]

[1682] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1683] 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.

[1684] 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).

[1685] 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.

[1686] 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.

[1687] 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).

[1688] 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. 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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."

[1695] The system of the present invention improves the user experience by capturing a user's video footage in real time and analyzing changes in the user's emotions from the video footage. This system is composed of the following means and operations.

[1696] System Configuration

[1697] 1. User Interface

[1698] The terminal provides a user interface that allows users to access and operate the system, including functions such as starting video capture, selecting a session, and checking feedback.

[1699] 2. Video capture and transmission

[1700] A user logs into the system and begins a session.

[1701] The device captures the user's video in real time and sends it to the server. The video capture is performed using the device's camera device.

[1702] 3. Video reception and analysis

[1703] The server receives the video image transmitted from the terminal.

[1704] The server analyzes video footage using multimodal AI and facial recognition technology, identifying the user's facial expressions and gestures and detecting changes in the user's emotions in real time.

[1705] 4. Feedback generation and display

[1706] The server generates feedback based on the analysis, which is tailored to the user's situation and may include additional educational material or help links if the user is confused, for example.

[1707] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[1708] 5. Data storage and utilization

[1709] The server stores the user's emotional data in a cloud-based database, which is encrypted to protect user privacy and data security.

[1710] Service providers analyze the stored data and plan improvements to their services and products. The data can be used in education, healthcare, entertainment, and product development to provide users with personalized experiences.

[1711] Specific examples

[1712] Specific examples of online education

[1713] In an online education platform, the system functions as follows:

[1714] 1. User Registration and Login

[1715] A user registers and logs in to the education platform.

[1716] The terminal sends authentication information to the server, and the server performs authentication.

[1717] 2. Starting the lesson and capturing video

[1718] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[1719] 3. Video Analysis

[1720] The server receives the video footage and performs analysis: it detects the user's facial expressions and identifies emotions such as interest and confusion.

[1721] 4. Feedback generation and display

[1722] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[1723] 5. Data storage and utilization

[1724] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[1725] Examples of online therapy

[1726] On the online therapy platform, the system works as follows:

[1727] 1. User Registration and Login

[1728] A user registers and logs in to the therapy platform.

[1729] The terminal sends authentication information to the server, and the server performs authentication.

[1730] 2. Therapy session begins

[1731] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[1732] 3. Video Analysis

[1733] The server receives the video footage and performs analysis to detect changes in emotions from the user's facial expressions and gestures.

[1734] 4. Feedback generation and display

[1735] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[1736] 5. Data storage and utilization

[1737] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[1738] As a result, the system of the present invention can analyze the user's emotions in real time and provide various feedback, thereby improving the user experience in various fields.

[1739] The processing flow will be explained below.

[1740] Step 1:

[1741] The user accesses the Kimochi Scope website or app and fills in the required information on the new registration form.

[1742] Step 2:

[1743] The device sends the entered information (name, email address, password, etc.) to the server.

[1744] Step 3:

[1745] The server stores the received information in its database and completes the new user's registration.

[1746] Step 4:

[1747] The user enters their registered email address and password on the login screen.

[1748] Step 5:

[1749] The terminal transmits the entered authentication information to the server.

[1750] Step 6:

[1751] The server checks the database and, if authentication is successful, generates a session ID and sends it to the terminal.

[1752] Step 7:

[1753] Users select from a range of online education, therapy, entertainment, product testing and other sessions.

[1754] Step 8:

[1755] The terminal transmits the selected session information to the server.

[1756] Step 9:

[1757] The device activates the camera and captures live video footage of the user.

[1758] Step 10:

[1759] The video captured by the device is streamed to the server in real time.

[1760] Step 11:

[1761] The server receives the video image transmitted from the terminal.

[1762] Step 12:

[1763] The server temporarily stores the received video and prepares it for processing.

[1764] Step 13:

[1765] The server analyzes the stored video data using multimodal AI technology and facial recognition technology.

[1766] Step 14:

[1767] The server identifies the user's facial expressions and gestures and evaluates the changes in real time.

[1768] Step 15:

[1769] The server quantifies the emotional state and classifies it into categories (e.g., happy, sad, confused, etc.).

[1770] Step 16:

[1771] The server generates feedback data (e.g., "The user is confused") based on the analysis results.

[1772] Step 17:

[1773] The server transmits the generated feedback data to the terminal.

[1774] Step 18:

[1775] Based on the feedback data received by the terminal, appropriate messages and materials are displayed on the user interface.

[1776] Step 19:

[1777] The user reviews the displayed feedback and materials and decides on their next course of action.

[1778] Step 20:

[1779] The server stores the user's emotional data during the session in a cloud-based database.

[1780] Step 21:

[1781] The server encrypts data at rest for data security and privacy.

[1782] Step 22:

[1783] Service providers analyze the stored data and develop improvements to their services and products.

[1784] Step 23:

[1785] Service providers can use data to tailor online education curricula, therapy plans, entertainment content, and more to improve user experience.

[1786] Example 1

[1787] 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."

[1788] The present invention relates to a system that captures a user's video footage in real time, analyzes the footage, and detects changes in the user's emotions. Conventional systems have difficulty accurately capturing a user's emotions in real time, and have not sufficiently improved the user experience. Furthermore, there are many challenges in storing and utilizing emotional data and protecting privacy. Therefore, there is a need for a system that can significantly improve the user experience by accurately analyzing a user's emotions in real time and effectively providing feedback based on the results.

[1789] 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.

[1790] In this invention, the server includes means for capturing a user's video image in real time, means for transmitting the captured video image to an information processing device, means for analyzing the video image received by the information processing device using multimodal artificial intelligence technology and face recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's output device and displaying it, and means for storing the user's emotion data in a network-based storage device, thereby enabling the user's emotions to be accurately analyzed in real time and appropriate feedback to be provided promptly.

[1791] "User" refers to any individual or entity that uses the System.

[1792] "Video Footage" refers to visual data captured through a camera device.

[1793] "Real-time" refers to the temporal characteristic of being processed immediately without delay.

[1794] "Information processing device" refers to a computer system for analyzing and processing received data.

[1795] "Multimodal AI technology" refers to AI technology that combines and evaluates multiple different data formats (e.g., video, audio, text).

[1796] "Facial recognition technology" refers to technology that detects a person's face from video captured using a camera device and analyzes its features.

[1797] "Changes in emotion" refers to changes in the emotional state detected based on changes in the user's facial expressions and gestures.

[1798] "Feedback" refers to information, instructions, or advice provided to the user based on the analysis results.

[1799] "Output device" refers to a device (e.g., a display or speaker) for providing visual or audible feedback to a user.

[1800] "Network-based storage" refers to a remotely located data storage device that is accessible over a network (e.g., cloud storage).

[1801] "Data encryption" refers to a technology that protects the confidentiality of data by converting it into a format that cannot be understood by third parties during transmission or storage.

[1802] "Education" refers to activities aimed at imparting knowledge and skills and promoting learning.

[1803] "Medical care" refers to diagnostic, therapeutic and preventive activities aimed at maintaining and restoring human health.

[1804] "Leisure" refers to any activity that people undertake for enjoyment or relaxation.

[1805] "Product development" refers to the process of planning, designing, and producing new products and services.

[1806] The present invention is a system that captures a user's video footage in real time and analyzes changes in the user's emotions from the footage. This system is characterized by the configuration and operation described below.

[1807] System Configuration

[1808] 1. User Interface

[1809] The terminal provides a user interface through which the user can access and operate the system, including functions such as starting a video capture, selecting a session, and viewing feedback.

[1810] 2. Video capture and transmission

[1811] A user logs into the system and begins a session.

[1812] The terminal uses a camera device to capture video images of the user in real time and transmits them to an information processing device (server).

[1813] 3. Video reception and analysis

[1814] The server receives the video image transmitted from the terminal.

[1815] The server analyzes video footage using multimodal artificial intelligence and facial recognition technology, identifying the user's facial expressions and gestures and detecting changes in the user's emotions in real time.

[1816] 4. Feedback generation and display

[1817] The server generates feedback based on the analysis, which is tailored to the user's situation and may include additional educational materials or help links if the user is confused.

[1818] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[1819] 5. Data storage and utilization

[1820] The server stores the user's emotional data in a network-based storage device, and the data is encrypted to protect the user's privacy and data security.

[1821] Service providers analyze the stored data to plan improvements to their services and products, and the data can be used in education, healthcare, entertainment, and product development to provide users with a personalized experience.

[1822] Technical details

[1823] Camera device: The device's built-in front camera or an externally connected camera device is used.

[1824] Multimodal artificial intelligence technology: Machine learning models using TensorFlow and PyTorch are used, and libraries such as OpenCV and Dlib are used for face recognition technology.

[1825] Network-based storage: Use Amazon RDS, Google Cloud Firestore, etc. to store and analyze data.

[1826] Specific examples

[1827] Specific examples of online education

[1828] 1. User Registration and Login

[1829] A user registers and logs in to the education platform.

[1830] The terminal sends authentication information to the server, and the server performs authentication.

[1831] 2. Starting the lesson and capturing video

[1832] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[1833] 3. Video Analysis

[1834] The server receives the video footage and performs analysis: it detects the user's facial expressions and identifies emotions such as interest and confusion.

[1835] 4. Feedback generation and display

[1836] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[1837] 5. Data storage and utilization

[1838] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[1839] Examples of online therapy

[1840] 1. User Registration and Login

[1841] A user registers and logs in to the therapy platform.

[1842] The terminal sends authentication information to the server, and the server performs authentication.

[1843] 2. Therapy session begins

[1844] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[1845] 3. Video Analysis

[1846] The server receives the video footage and performs analysis to detect changes in emotions from the user's facial expressions and gestures.

[1847] 4. Feedback generation and display

[1848] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[1849] 5. Data storage and utilization

[1850] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[1851] Prompt Sentence Examples

[1852] Example prompt:

[1853] "I want to design a system that captures a user's facial expressions in real time through a camera and analyzes their emotional changes. What should I use?"

[1854] Example output:

[1855] It is recommended to use a webcam or external camera as the camera device. Machine learning models using TensorFlow or PyTorch are useful for facial expression analysis, and libraries such as OpenCV or Dlib are useful for face recognition. For data storage and analysis, use a cloud-based database such as Amazon RDS or Google Cloud Firestore.

[1856] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1857] Step 1:

[1858] User Registration and Login

[1859] Input: Information entered by the user in the sign-up form (name, email address, password)

[1860] Output: Authentication information generated by the server

[1861] Specific operation: The user enters information into the new registration form on the web browser and clicks the "Register" button. The device sends the information to the server as a POST request. The server stores the received information in a database and generates and returns authentication information.

[1862] Step 2:

[1863] Start video capture

[1864] Input: Instruction to start video capture selected by the user after logging in

[1865] Output: Captured video data

[1866] Specific operation: When the user clicks the start video capture button, the device activates the camera device using JavaScript and starts capturing video in real time. The captured video is passed directly to the next step.

[1867] Step 3:

[1868] Video transmission and reception

[1869] Input: Captured video footage data

[1870] Output: Video data received by the server

[1871] Specific operation: The device sends captured video frame by frame to the server using WebSocket or HTTP streaming. The server receives this data in real time and puts it into an analysis queue.

[1872] Step 4:

[1873] Video Analysis

[1874] Input: Received video data

[1875] Output: Analyzed emotion change data

[1876] How it works: The server uses OpenCV and TensorFlow libraries to analyze the user's facial expressions and gestures for each frame. In particular, it detects expressions such as smiles and confusion, and identifies emotional changes based on them. The identified emotional change data is generated and passed to the next step.

[1877] Step 5:

[1878] Feedback Generation

[1879] Input: Analyzed emotion change data

[1880] Output: Generated feedback data

[1881] How it works: Based on the analysis results, the server creates appropriate feedback using Python scripts or generative AI models (e.g., GPT). The feedback data is converted to JSON format and sent to the device.

[1882] Step 6:

[1883] Feedback Display

[1884] Input: Generated feedback data

[1885] Output: A feedback message that is displayed on the user's terminal.

[1886] Specific behavior: When the device receives feedback data from the server, it uses JavaScript and HTML to display appropriate messages and content on the interface. The user checks the displayed feedback and decides on the next action.

[1887] Step 7:

[1888] Data storage and utilization

[1889] Input: Analyzed emotion change data and generated feedback data

[1890] Output: Data stored on a network-based storage device

[1891] How it works: The server stores the analysis results and feedback in a database. This storage is encrypted to protect the confidentiality of the data. The stored data is later analyzed by the service provider using analysis tools and used to plan and implement improvements to services and products.

[1892] (Application example 1)

[1893] 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."

[1894] While conventional user experience improvement systems generate appropriate feedback based on user sentiment analysis, they have not been adequately applied to customer service and guidance in brick-and-mortar stores. As a result, customers often become confused or dissatisfied in-store, making improving customer satisfaction a challenge. Furthermore, it is difficult to introduce technology that provides guidance and services tailored to individual customers in real time, making it difficult to improve the efficiency of store operations.

[1895] 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.

[1896] In this invention, the server includes means for capturing video footage of users in real time, means for transmitting the captured video footage to the server, means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's terminal and displaying it, means for saving user emotion data in a cloud-based database, and means for capturing video footage of customers and providing in-store guidance and services based on the analysis results. This makes it possible to analyze customer emotions in real stores in real time and provide optimal guidance and services based on the analysis results.

[1897] "User video footage" refers to video data captured by a user in real time.

[1898] "Means for capturing in real time" refers to a system that uses a photographing device to obtain video data instantly.

[1899] "Means for transmitting to a server" refers to a method for transferring captured video data to an external computing device via a network.

[1900] "Multimodal AI technology" refers to artificial intelligence technology that combines and analyzes multiple data inputs (e.g., video, audio, text).

[1901] "Facial recognition technology" is a technology for detecting and identifying facial features from video data.

[1902] "Changes in user's emotions" refers to the transitions in the user's emotional state indicated by the user's facial expressions and behavior detected from the video.

[1903] "Means for generating various feedback" refers to methods for generating responses and information according to the analysis results and providing them to users.

[1904] "User terminal" refers to an electronic device that a user uses to directly operate or display information.

[1905] A "cloud-based database" is online storage that is accessed and managed over the internet.

[1906] A "store visitor" is a customer who visits a physical store.

[1907] "Means for providing guidance and services in-store" refers to methods for providing appropriate information and support to customers in-store in real time.

[1908] MODE FOR CARRYING OUT THE INVENTION

[1909] The system of the present invention aims to improve the customer experience in physical stores based on a program configured as follows. The configuration and processing procedure will be described in detail below.

[1910] System Configuration

[1911] Hardware:

[1912] 1. A smartphone or in-store camera is used as the terminal, which can capture video footage of customers in real time.

[1913] 2. Customer service robots will be used to provide guidance and services within the store, such as providing voice guidance and displaying information on screens.

[1914] software:

[1915] 1. OpenCV is used for video capture and pre-processing, which allows for real-time video capture.

[1916] 2. EmotionRecognizer (emotion analysis library) is used to analyze changes in users' emotions. This library uses multimodal AI technology and facial recognition technology to identify the emotions of customers.

[1917] 3. REST APIs are used to send and receive video footage and analysis results, as well as generate and receive feedback.

[1918] Program processing

[1919] 1. The device captures video footage of customers in real time. This role is played by smartphones or in-store cameras.

[1920] 2. The captured video data is sent to the server, which receives it and analyzes it.

[1921] 3. The server uses OpenCV and EmotionRecognizer to analyze the video data and identify changes in the customers' emotions.

[1922] 4. Based on the analysis results, the server generates appropriate feedback. For example, if a customer is confused, the server may provide guidance through a customer service robot, asking, "Is there anything I can help you with?"

[1923] 5. Feedback is sent to the device in real time and displayed on the customer service robot or smartphone, allowing the store to provide appropriate guidance and services to customers.

[1924] Specific examples

[1925] Example 1:

[1926] For example, if a customer is confused and unable to find a particular product in a store, a smartphone camera captures their facial expression. EmotionRecognizer analyzes the confusion, and the server sends a message to a customer service robot saying, "Are you having trouble?" The customer service robot then plays the message aloud and guides the customer to the product.

[1927] Example 2:

[1928] When a customer shows a satisfied expression, the capture device also captures that expression and the server analyzes it. Based on the analysis results, the customer service robot will say to the customer, "Thank you. We look forward to seeing you again."

[1929] In this way, appropriate feedback and services can be provided based on real-time analysis of customers' emotions.

[1930] Prompt Sentence Examples

[1931] An example prompt phrase might take the following form:

[1932] When a customer enters the store, a camera captures the video. The captured video is analyzed in real time to identify the customer's emotional state. Depending on the identified emotion, for example, if the customer is confused, the customer service robot will ask, "Is there anything I can help you with?" If the customer is satisfied, it will display, "Thank you. We look forward to seeing you again."

[1933] The above is an embodiment of the present invention, and this system will improve the customer experience in physical stores.

[1934] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1935] Step 1:

[1936] When a user enters a store, the device (smartphone or in-store camera) captures video footage in real time. Video data of the customer is obtained as input, and real-time video footage is generated as output. Specifically, the device's camera device is turned on and begins capturing video data.

[1937] Step 2:

[1938] The captured video is sent from the device to the server. The input is real-time video, and the output is uploading video data to the server. Specifically, the device sends video data to the server via the network.

[1939] Step 3:

[1940] The server analyzes the received video using multimodal AI and facial recognition technologies. The input is the video sent to the server, and the output is data on the user's emotional state. Specifically, the server preprocesses the video using OpenCV and then analyzes changes in emotions using EmotionRecognizer.

[1941] Step 4:

[1942] Based on the analysis results, the server generates optimal feedback. The input is data on the user's emotional state, and the output is the generated feedback. Specifically, the server generates prompts based on the analysis data and constructs appropriate messages and guidance.

[1943] Step 5:

[1944] The generated feedback is sent from the server to the terminal, which then displays it or provides audio guidance. The input is the generated feedback, and the output is the information presented to the user. Specifically, the terminal displays the feedback by voice or on a display, and provides guidance and services to customers.

[1945] Step 6:

[1946] The server stores the user's emotional data in a cloud-based database. The input is the analyzed emotional data, and the output is saved in the database. Specifically, the server properly encrypts the emotional data and uploads it to cloud storage.

[1947] Step 7:

[1948] Customer service robots and in-store displays provide appropriate service to customers based on the displayed feedback. The input is the generated feedback, and the output is the actual service provided. Specifically, the robot will provide voice guidance or the display will display guidance information to alleviate customer confusion.

[1949] 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.

[1950] The system of the present invention captures a user's video footage in real time and analyzes changes in the user's emotions from the video footage. In this process, an emotion engine is used to perform detailed classification and recognition of emotions, thereby improving the quality of the user experience. This system is composed of the following means and operations.

[1951] System Configuration

[1952] 1. User Interface

[1953] The terminal provides a user interface that allows the user to access and operate the system, including functions such as starting video capture, selecting a session, and checking feedback.

[1954] 2. Video capture and transmission

[1955] A user logs into the system and begins a session.

[1956] The device captures the user's video in real time and sends it to the server. The video capture is performed using the device's camera device.

[1957] 3. Video reception and analysis

[1958] The server receives the video image transmitted from the terminal.

[1959] The server temporarily stores the received video and prepares it for processing.

[1960] The server analyzes the stored video data using multimodal AI technology and facial recognition technology to identify the user's facial expressions and gestures.

[1961] 4. Emotion Recognition by Emotion Engine

[1962] The server uses an emotion engine to classify the user's emotions in detail based on the analysis results, specifically into emotion categories such as happiness, sadness, surprise, and confusion.

[1963] The emotion engine continuously learns from the user's emotion data to improve the accuracy of emotion recognition.

[1964] 5. Feedback Generation and Display

[1965] The server generates feedback based on the emotion engine's analysis, which is tailored to the user's state and may include additional educational materials or help links if the user is confused, for example.

[1966] The server sends the generated feedback to the terminal, which displays it on the user interface. The user checks the displayed feedback and decides on the next action to take.

[1967] 6. Data storage and utilization

[1968] The server stores the user's emotional data in a cloud-based database, which is encrypted to protect user privacy and data security.

[1969] Service providers analyze the stored data and plan improvements to their services and products. The data can be used in education, healthcare, entertainment, and product development to provide users with personalized experiences.

[1970] Specific examples

[1971] Specific examples of online education

[1972] In an online education platform, the system functions as follows:

[1973] 1. User Registration and Login

[1974] A user registers and logs in to the education platform.

[1975] The terminal sends authentication information to the server, and the server performs authentication.

[1976] 2. Starting the lesson and capturing video

[1977] The user selects a class and starts video capture. The device captures the video and sends it to the server.

[1978] 3. Video Analysis and Emotion Recognition

[1979] The server receives the video footage and analyzes the user's facial expressions using multimodal AI and facial recognition technologies. The server then uses an emotion engine to classify the user's emotions in detail.

[1980] 4. Feedback generation and display

[1981] The server generates a link to the supplementary learning material based on the analysis results and sends it to the terminal, which displays the feedback and the user can view the supplementary learning material.

[1982] 5. Data storage and utilization

[1983] The server stores the emotional data of users during class in a database, and the educational institution analyzes this data to improve the content of the class.

[1984] Examples of online therapy

[1985] On the online therapy platform, the system works as follows:

[1986] 1. User Registration and Login

[1987] A user registers and logs in to the therapy platform.

[1988] The terminal sends authentication information to the server, and the server performs authentication.

[1989] 2. Therapy session begins

[1990] The user starts a therapy session. The device starts video capture and sends the video footage to the server.

[1991] 3. Video Analysis and Emotion Recognition

[1992] The server receives the video footage and analyzes the user's facial expressions and gestures using multimodal AI and facial recognition technology, then uses an emotion engine to classify the user's emotions in detail.

[1993] 4. Feedback generation and display

[1994] The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[1995] 5. Data storage and utilization

[1996] The server stores emotional data from the session in a database, which the therapist can use to plan the next session and provide more effective treatment.

[1997] This allows the system of the present invention to integrate an emotion engine and perform detailed analysis of user emotions, thereby providing personalized, high-quality experiences in various fields.

[1998] The processing flow will be explained below.

[1999] Step 1:

[2000] The user accesses the Kimochi Scope website or app and fills in the required information on the new registration form.

[2001] Step 2:

[2002] The device sends the entered information (name, email address, password, etc.) to the server.

[2003] Step 3:

[2004] The server stores the received information in its database and completes the new user's registration.

[2005] Step 4:

[2006] The user enters their registered email address and password on the login screen.

[2007] Step 5:

[2008] The terminal transmits the entered authentication information to the server.

[2009] Step 6:

[2010] The server checks the database and, if authentication is successful, generates a session ID and sends it to the terminal.

[2011] Step 7:

[2012] The user selects one of the following sessions: online education, therapy, entertainment, etc.

[2013] Step 8:

[2014] The terminal transmits the selected session information to the server.

[2015] Step 9:

[2016] The device activates the camera and captures live video footage of the user.

[2017] Step 10:

[2018] The video captured by the device is streamed to the server in real time.

[2019] Step 11:

[2020] The server receives the video image transmitted from the terminal.

[2021] Step 12:

[2022] The server temporarily stores the received video data and prepares it for processing.

[2023] Step 13:

[2024] The server analyzes the stored video data using multimodal AI technology and facial recognition technology.

[2025] Step 14:

[2026] The server identifies the user's facial expressions and gestures and evaluates the changes in real time.

[2027] Step 15:

[2028] The server uses an emotion engine to classify the emotional state in detail and divide it into categories (e.g., happy, sad, confused, etc.).

[2029] Step 16:

[2030] The emotion engine learns the user's emotion data and improves the accuracy of emotion recognition.

[2031] Step 17:

[2032] The server generates feedback data (e.g., "The user is confused") based on the analysis results.

[2033] Step 18:

[2034] The server transmits the generated feedback data to the terminal.

[2035] Step 19:

[2036] Based on the feedback data received by the terminal, appropriate messages and materials are displayed on the user interface.

[2037] Step 20:

[2038] The user reviews the displayed feedback and materials and decides on their next course of action.

[2039] Step 21:

[2040] The server stores the user's emotional data during the session in a cloud-based database.

[2041] Step 22:

[2042] The server encrypts data at rest for data security and privacy.

[2043] Step 23:

[2044] Service providers analyze the stored data and develop improvements to their services and products.

[2045] Step 24:

[2046] Service providers can use data to tailor online education curricula, therapy plans, entertainment content, and more to improve user experience.

[2047] Example 2

[2048] 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."

[2049] Current online platforms lack systems that can identify users' emotions in real time and provide feedback based on those emotions. This results in a uniform user experience, making it difficult to respond to individual needs, and improving user satisfaction is a challenge. Protecting the privacy and security of collected emotion data is also a major challenge.

[2050] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for temporarily storing video footage, a means for identifying the user's facial expressions and gestures using multimodal AI technology and face recognition technology, and a means for classifying the user's emotions in detail using an emotion engine based on the analysis results. This makes it possible to analyze the user's emotions in real time and provide feedback tailored to individual needs. Furthermore, emotion data is encrypted and securely stored in a cloud-based database, ensuring privacy and data security.

[2051] "User" refers to any individual or entity that uses the System.

[2052] "Video footage" refers to real-time moving images of a user.

[2053] "Means of capturing in real time" refers to devices or programs that instantly acquire current video and digitally process it on the spot.

[2054] A "server" refers to a computer system that processes, stores, and manages data over a network.

[2055] "Multimodal AI technology" refers to artificial intelligence technology that simultaneously analyzes multiple different data formats (e.g., video, audio, text) and integrates the results.

[2056] "Facial recognition technology" refers to technology that detects facial features from video footage and identifies individuals and facial expressions.

[2057] An "emotion engine" refers to an algorithm or program that analyzes data such as a user's facial expressions and gestures, and classifies and recognizes their emotional state.

[2058] "Feedback" refers to information and suggested actions provided to the user based on the system's analysis results.

[2059] A "cloud-based database" is a data storage system accessible via the internet that stores and manages data on a cloud service.

[2060] "Data encryption" refers to a technology that converts data into a format that cannot be deciphered by third parties, thereby increasing data security.

[2061] "Privacy" refers to the state in which a user's personal information and activities are prevented from being known to third parties without permission.

[2062] "Data security" refers to a set of measures and techniques for protecting the integrity, confidentiality, and availability of data.

[2063] "Education" refers to the activity of imparting knowledge and skills to people and promoting growth through learning.

[2064] "Healthcare" refers to a range of medical and health-related services aimed at maintaining health and preventing and treating disease.

[2065] "Entertainment" refers to activities and content that provide people with fun and joy.

[2066] "Product development" refers to the entire process of planning, designing, and realizing new products and services.

[2067] MODE FOR CARRYING OUT THE INVENTION

[2068] The system of this invention captures a user's video footage in real time and analyzes changes in the user's emotions from the video footage. The system is composed of the following main components:

[2069] 1. User Interface

[2070] The terminal provides a user interface that allows users to access and operate the system. The user interface includes functions such as starting video capture, selecting a session, and checking feedback. This interface is implemented using HTML, CSS, JavaScript, etc.

[2071] 2. User login and session initiation

[2072] The user logs into the system and enters their account information. Once the login information is entered, the device sends this information to the server, which performs authentication. Once authentication is complete, the user can select and start a video capture session. The authentication process uses standard authentication technologies such as OAuth.

[2073] 3. Video capture and transmission

[2074] When a user starts a session, the device's camera captures the user's video in real time, and the captured video is encoded by the device and then sent to the server using the H.264 encoding format and the RTSP protocol.

[2075] 4. Receiving and temporarily storing video

[2076] The server receives the video footage sent from the device. The received footage is temporarily stored in the server's storage. The stored data is used for analysis in subsequent processing steps. The storage uses a large-capacity HDD or SSD.

[2077] 5. Video Analysis and Facial Expression Recognition

[2078] The server then applies multimodal AI and facial recognition technology to the stored video data. Specifically, it applies face detection algorithms using libraries such as OpenCV and Dlib to analyze the movement and relative positions of facial features. This process uses Haar cascade classifiers and DNN-based face detection models.

[2079] 6. Emotion Recognition by Emotion Engine

[2080] The server further analyzes the results of the facial expression recognition using an emotion engine. The emotion engine uses a deep learning model (e.g., CNN or RNN) to classify emotions. Frameworks such as TensorFlow or PyTorch are used. The trained model analyzes the features of facial features and outputs an emotion label (e.g., happiness, sadness, surprise, etc.).

[2081] 7. Generating and Displaying Feedback

[2082] The server generates appropriate feedback for the user based on the emotion recognition results of the emotion engine. For example, if the user is confused, feedback including supplementary learning materials and help links will be generated. This feedback is sent from the server to the device, where it is displayed on the user interface. NLP technology and conditional branching algorithms are used to generate the feedback.

[2083] 8. Data storage and utilization

[2084] The server stores the emotional data collected during the session in a cloud-based database. The data is encrypted and uses AES encryption technology to protect user privacy and data security. This data can be used in a variety of fields, including education, healthcare, entertainment, and product development.

[2085] Specific examples

[2086] Specific examples of online education

[2087] In an online education platform, the system works as follows:

[2088] 1. The user registers and logs in to the educational platform.

[2089] 2. The device sends authentication information to the server, and the server performs authentication.

[2090] 3. The user selects a lesson and starts video capture. The device captures the video and sends it to the server.

[2091] 4. The server receives the video footage and analyzes the user's facial expressions using multimodal AI technology and facial recognition technology. The server then uses an emotion engine to classify the user's emotions in detail.

[2092] 5. The server generates a link to the supplementary learning material based on the analysis results and sends it to the device. The device displays the feedback and the user can view the supplementary learning material.

[2093] 6. The server stores the user's emotional data during class in a database. The educational institution analyzes this data and improves the content of the class.

[2094] Examples of online therapy

[2095] On the online therapy platform, this system works as follows:

[2096] 1. The user registers and logs in to the therapy platform.

[2097] 2. The device sends authentication information to the server, and the server performs authentication.

[2098] 3. The user starts a therapy session. The device starts capturing video and sending it to the server.

[2099] 4. The server receives the video footage and analyzes the user's facial expressions and gestures using multimodal AI and facial recognition technologies. The server then uses an emotion engine to classify the user's emotions in detail.

[2100] 5. The server generates appropriate feedback based on the analysis results and provides it to the therapist, who then adjusts the therapy method based on the feedback.

[2101] 6. The server stores the emotional data from the session in a database. The therapist uses this data to plan the next session and provide more effective treatment.

[2102] Example prompts for generative AI models

[2103] "Please tell me the specific processing steps for video analysis in this system."

[2104] "Please explain in detail how the emotion engine classifies the user's emotions."

[2105] This allows users to understand the specific operations and processes and learn how the system achieves emotion recognition.

[2106] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2107] Step 1: User Login

[2108] Input: The user enters their authentication information (username, password).

[2109] What happens: The authentication information provided by the user is sent from the device to the server.

[2110] Data processing: The server checks the received authentication information against the registration information in its database.

[2111] Output: If authentication is successful, the server generates a session ID and sends it to the device. If authentication fails, an error message is returned.

[2112] Step 2: Start video capture

[2113] Input: The user presses the Start Video Capture button to begin the session.

[2114] Specific behavior: The device's camera device will start up and begin capturing video footage.

[2115] Data processing: The captured video is encoded in H.264 format.

[2116] Output: The encoded video data is sent to the server in real time.

[2117] Step 3: Receiving and temporarily saving the video

[2118] Input: Captured video data is sent to the server.

[2119] Specific operation: The server temporarily stores the received video data in storage.

[2120] Data processing: Video data is kept in storage and prepared for analysis.

[2121] Output: Stored video data for subsequent analysis process.

[2122] Step 4: Video analysis and facial expression recognition

[2123] Input: The stored video data is passed to the server's analysis module.

[2124] Specific operation: The server uses OpenCV and Dlib to detect the user's face from the video frame.

[2125] Data processing: The detected facial landmarks (eyes, mouth, nose, etc.) are identified and facial expressions are analyzed based on them.

[2126] Output: The analysis results in the generation of facial expression information of the user.

[2127] Step 5: Emotion Recognition with the Emotion Engine

[2128] Input: Facial expression information is input to the emotion engine.

[2129] How it works: The server runs the emotion engine using TensorFlow and PyTorch. The deep learning model analyzes facial expression information and issues emotion labels.

[2130] Data processing: Facial expression information is input into the model as features, and emotion labels (e.g., happiness, sadness, surprise, etc.) are output.

[2131] Output: Emotion labels are generated and sent to the next feedback generation process.

[2132] Step 6: Generate and display feedback

[2133] Input: Emotion labels are input to the feedback generation module.

[2134] Specific behavior: The server generates appropriate feedback for the user based on the analysis results. For example, if the user is confused, feedback containing additional learning materials or help links is generated.

[2135] Data processing: Feedback content is generated and configured in HTML and JSON formats.

[2136] Output: The generated feedback is sent from the server to the device and displayed on the user interface.

[2137] Step 7: Store and use your data

[2138] Input: Emotion labels and feedback are input into the database storage module.

[2139] What it does: The server stores the emotional data and feedback collected during the session in a cloud-based database.

[2140] Data processing: Data is encrypted using encryption techniques such as AES.

[2141] Output: The encrypted data is securely stored in a database for later analysis and service improvement.

[2142] Through the above processing steps, the system can perform real-time emotion analysis of the user and provide feedback.

[2143] (Application example 2)

[2144] 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."

[2145] Conventional content distribution services lack the ability to analyze users' emotions in real time and provide appropriate feedback to improve their viewing experience. As a result, viewers are unable to enjoy appropriate content and a personalized experience is not provided. Furthermore, they lack the ability to ensure the security of users' viewing data and protect their privacy.

[2146] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2147] In this invention, the server includes means for capturing a user's video footage in real time, means for transmitting the captured video footage to the server, means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions, means for generating various feedback based on the analysis results, means for transmitting the generated feedback to the user's terminal and displaying it, means for storing the user's emotional data in a cloud-based database, and, particularly in the case of a content distribution service, means for analyzing the user's emotions while watching and providing appropriate feedback and recommended content in real time.

[2148] This allows for personalized feedback and content recommendations to enhance the user's viewing experience, while data encryption protects user privacy and data security.

[2149] The "means for capturing a user's video footage in real time" refers to a technology that allows a user to obtain video footage in real time through the device's camera and immediately transmit it to the system.

[2150] The "means for transmitting captured video images to a server" is a technique for transmitting captured video data to a remote computer via a communication network such as the Internet.

[2151] "Means of analyzing video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions" refers to technology that analyzes video data using machine learning algorithms and facial detection technology to identify changes in the user's emotional state.

[2152] "Means for generating various feedback based on analysis results" refers to technology that provides users with appropriate information and recommendations in real time based on the results of emotion analysis.

[2153] The "means for transmitting the generated feedback to the user's terminal and displaying it" refers to a technique for transmitting the generated feedback information to the user's device and visually displaying it.

[2154] "Means for storing user emotion data in a cloud-based database" refers to technology for securely storing the results of a user's emotion analysis in remote data storage.

[2155] "A means of analyzing the user's emotions while watching, particularly in content distribution services, and providing appropriate feedback and recommended content in real time" refers to technology that analyzes emotions while the user is watching media such as movies or videos, and instantly provides optimal recommended information.

[2156] MODE FOR CARRYING OUT THE INVENTION

[2157] The present invention is a system that captures a user's video footage in real time and analyzes changes in the user's emotions from that footage. The system of the present invention has the function of analyzing the user's emotions while watching, and providing appropriate feedback and recommended content in real time, particularly in content distribution services.

[2158] System Configuration

[2159] 1. User Interface and Login

[2160] Users log in to the system using their device (e.g., smartphone, PC, smart glasses). After logging in, users can select the content they want to watch.

[2161] 2. Video capture and transmission

[2162] When a user watches content, the device's camera captures video footage, which is then sent to the server in real time.

[2163] 3. Video Analysis and Emotion Recognition

[2164] The server temporarily stores the received video footage and analyzes it using multimodal AI technology (e.g., OpenAI's GPT-4, Google Cloud Vision, Amazon Rekognition) and facial recognition technology (e.g., Microsoft Azure's Face API, DeepFace). As a result of the analysis, the user's emotions (e.g., happiness, sadness, surprise, confusion, etc.) are identified.

[2165] 4. Feedback generation and display

[2166] The server generates various feedback based on the results of the sentiment analysis. This feedback includes comments about the content being viewed and information about recommended content to watch next. The generated feedback is sent to the user's device in real time and displayed.

[2167] 5. Data storage and analysis

[2168] User sentiment data is stored in a cloud-based database (e.g., Google Cloud Firestore, Amazon RDS). This data is encrypted to protect user privacy. The stored data is used for analysis and to improve content delivery services.

[2169] Hardware and Software Used

[2170] Camera devices: Cameras installed in smartphones, smart glasses, and head-mounted displays are used.

[2171] Multimodal AI technology: Uses OpenAI's GPT-4, Google Cloud Vision, and Amazon Rekognition.

[2172] Facial recognition technology: Uses Microsoft Azure's Face API and DeepFace.

[2173] Cloud-based database: Google Cloud Firestore and Amazon RDS are used.

[2174] Specific examples

[2175] For example, imagine a user watching a movie on a video streaming service. At this time, the user's facial expressions are captured in real time by a camera and sent to the server. The server analyzes the video and recognizes emotional changes such as surprise or laughter at a particular scene. Based on this, feedback such as "It looks like you're enjoying this scene! Here are some movies that other users also enjoyed" is displayed.

[2176] Prompt Sentence Examples

[2177] The prompts for the generative AI model to capture specific emotional changes and generate appropriate feedback are as follows:

[2178] Analyze user video footage in real time and classify the user's emotional changes as happy, sad, surprised, confused, etc. Then determine the appropriate feedback and content recommendations to provide next. Use the following video footage data: [Video Data Link]

[2179] In this way, the system of the present invention significantly improves the user's viewing experience, providing personalized feedback and content recommendations in real time. This system, which combines ease of use with high security, adds new value to content distribution services.

[2180] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2181] Specific explanation of processing steps

[2182] Step 1: User Login

[2183] Input: User authentication information (user ID, password)

[2184] How it works: The user enters their authentication information on the device and presses the login button. The device sends the authentication information to the server.

[2185] Data processing: The server compares the received authentication information with the database and performs login authentication.

[2186] Output: Authentication result (success or failure)

[2187] Step 2: Content Selection

[2188] Input: User interface after login (list of content provided)

[2189] What it does: The user selects the content to watch.

[2190] Data processing: The selected content ID is sent to the server.

[2191] Output: Selected content ID

[2192] Step 3: Start video capture

[2193] Input: Content playback start instruction

[2194] How it works: The device's camera begins capturing video footage of the user in real time.

[2195] Data processing: Process the captured video footage as a data stream.

[2196] Output: Real-time video data

[2197] Step 4: Sending video to the server

[2198] Input: Real-time video data

[2199] Operation: The device sends captured video to the server.

[2200] Data processing: Compress the video data and send it using network protocols.

[2201] Output: Video data sent to the server

[2202] Step 5: Temporarily save the video data

[2203] Input: Video data sent to the server

[2204] Operation: The server stores the received video data in a storage device for short-term storage.

[2205] Data processing: Temporarily save the video data in an appropriate format.

[2206] Output: Temporarily saved video data

[2207] Step 6: Sentiment Analysis

[2208] Input: Temporarily saved video data

[2209] How it works: The server analyzes the video data using multimodal AI technology and facial recognition technology.

[2210] Data processing: Facial recognition is performed for each video frame, emotion labels are assigned, and emotional changes are identified using an AI model.

[2211] Output: Analysis results (user emotion data)

[2212] Step 7: Feedback Generation

[2213] Input: Analysis results (user emotion data)

[2214] Operation: The server generates feedback and recommended content for the content being viewed based on the emotional data.

[2215] Data processing: Feedback sentences are created using a generative AI model (e.g., GPT-4) based on emotion data.

[2216] Output: Generated feedback

[2217] Step 8: Submit and view feedback

[2218] Input: Generated feedback

[2219] Operation: The server sends the generated feedback to the user's device.

[2220] Data processing: The feedback data is converted into an appropriate format and sent using the network protocol.

[2221] Output: Feedback displayed on the terminal

[2222] Step 9: Storing Emotion Data

[2223] Input: Analysis results (user emotion data)

[2224] How it works: The server stores user emotion data in a cloud-based database.

[2225] Data processing: Emotional data is encrypted and stored securely in a database.

[2226] Output: Saved emotion data

[2227] Prompt Sentence Examples

[2228] "Prompt sentence for GPT-4"

[2229] Analyze user video footage in real time and classify the user's emotional changes as happy, sad, surprised, confused, etc. Then determine the appropriate feedback and content recommendations to provide next. Use the following video footage data: [Video Data Link]

[2230] 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.

[2231] 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.

[2232] 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.

[2233] 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.

[2234] 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.

[2235] 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.

[2236] 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).

[2237] 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.

[2238] 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."

[2239] 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.

[2240] 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).

[2241] 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.

[2242] 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.

[2243] 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.

[2244] 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.

[2245] 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.

[2246] 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.

[2247] 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.

[2248] 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.

[2249] 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.

[2250] 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.

[2251] The following is further disclosed regarding the above embodiment.

[2252] (Claim 1)

[2253] means for capturing video footage of a user in real time;

[2254] means for transmitting the captured video footage to a server;

[2255] A means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions;

[2256] A means for generating various feedbacks based on the analysis results;

[2257] means for transmitting the generated feedback to a user's terminal and displaying the feedback;

[2258] means for storing the user's emotional data in a cloud-based database;

[2259] A system including:

[2260] (Claim 2)

[2261] 10. The system of claim 1, further comprising means for using the user's emotional data to provide a personalized user experience in education, healthcare, entertainment, or product development.

[2262] (Claim 3)

[2263] 10. The system of claim 1, further comprising data encryption means for protecting user privacy and data security.

[2264] "Example 1"

[2265] (Claim 1)

[2266] means for capturing video footage of a user in real time;

[2267] means for transmitting the captured video image to an information processing device;

[2268] a means for analyzing the video image received by the information processing device using multimodal artificial intelligence technology and face recognition technology to detect changes in the user's emotions;

[2269] A means for generating various feedbacks based on the analysis results;

[2270] means for transmitting and displaying the generated feedback to a user output device;

[2271] means for storing the user's emotional data in a network-based storage device;

[2272] A system including:

[2273] (Claim 2)

[2274] 10. The system of claim 1, further comprising means for using the user's emotional data to provide a personalized user experience in education, medicine, entertainment, or product development.

[2275] (Claim 3)

[2276] 10. The system of claim 1, further comprising data encryption means for protecting user privacy and data security.

[2277] "Application Example 1"

[2278] (Claim 1)

[2279] means for capturing video footage of a user in real time;

[2280] means for transmitting the captured video footage to a server;

[2281] A means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions;

[2282] A means for generating various feedbacks based on the analysis results;

[2283] means for transmitting the generated feedback to a user's terminal and displaying the feedback;

[2284] means for storing the user's emotional data in a cloud-based database;

[2285] A means for capturing video footage of customers and providing guidance and services within the store based on the analysis results;

[2286] A system including:

[2287] (Claim 2)

[2288] 10. The system of claim 1, further comprising means for using the user's emotional data to provide a personalized user experience in education, healthcare, entertainment, or product development.

[2289] (Claim 3)

[2290] 10. The system of claim 1, further comprising data encryption means for protecting user privacy and data security.

[2291] "Example 2: Combining Emotion Engines"

[2292] (Claim 1)

[2293] means for capturing video footage of a user in real time;

[2294] means for transmitting the captured video footage to a server;

[2295] a means for temporarily storing the video footage received by the server;

[2296] The server analyzes the stored video footage using multimodal AI technology and facial recognition technology to identify the user's facial expressions and gestures;

[2297] A means for classifying the user's emotions in detail using an emotion engine based on the analysis results;

[2298] A means for generating various feedbacks based on the analysis results;

[2299] means for transmitting the generated feedback to a user's terminal and displaying the feedback;

[2300] means for storing the user's emotional data in a cloud-based database;

[2301] A system including:

[2302] (Claim 2)

[2303] 10. The system of claim 1, further comprising means for using the user's emotional data to provide a personalized user experience in education, healthcare, entertainment, or product development.

[2304] (Claim 3)

[2305] 10. The system of claim 1, further comprising data encryption means for protecting user privacy and data security.

[2306] "Application example 2 when combining emotion engines"

[2307] (Claim 1)

[2308] means for capturing video footage of a user in real time;

[2309] means for transmitting the captured video footage to a server;

[2310] A means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes ...

Claims

1. means for capturing video footage of a user in real time; means for transmitting the captured video footage to a server; A means for analyzing the video footage received by the server using multimodal AI technology and facial recognition technology to detect changes in the user's emotions; A means for generating various feedbacks based on the analysis results; means for transmitting the generated feedback to a user's terminal and displaying the feedback; means for storing the user's emotional data in a cloud-based database; A system including:

2. The system of claim 1 , further comprising means for using the user's emotional data to provide a personalized user experience in education, healthcare, entertainment, or product development.

3. 10. The system of claim 1, further comprising data encryption means for protecting user privacy and data security.

Images