system

Abstract

Provide a system. The system comprises: a camera that continuously records video of the interior of a room and transmits the video to a server; A server stores the recorded video; A means for a user to request an investigation into a lost item; a server receiving the search request and generating an image of the item being searched for by an image generating means; A means for the server to search for matching scenes from past recording data based on the generated image; A means for the server to estimate the location of the searched item based on the search results; a means for the server to notify the user of the estimated location information; A system including:

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] Losing things is common in everyday life, and there is a need for efficient ways to find lost items. However, current technology relies mainly on simple tracking devices and manual verification, and does not provide large-scale surveillance or automatic item detection technology. This creates the problem of time and effort required to find lost items. The present invention aims to solve these problems and provide technology for efficiently finding lost items. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means: A system including a means for a camera to continuously record video of a room and transmit the video to a server, a means for the server to save the recorded video, a means for a user to make a search request for an item they are looking for, a means for the server to receive the search request and generate an image of the item they are looking for using an image generation means, a means for the server to search for matching scenes from past recorded data based on the generated image, a means for the server to estimate the location of the item they are looking for based on the search results, and a means for the server to notify the user of the estimated location information, thereby enabling the user to efficiently locate the item they are looking for.

[0006] A "camera" is a device that captures video and outputs it as recorded data.

[0007] A "server" is a computer system that stores, processes, and distributes data over a network.

[0008] A "user" is a person or organization that uses the system to request an investigation into a lost item.

[0009] A "search request" is a request made by a user to the system to find something they are looking for.

[0010] "Image Generation Method" means artificial intelligence technology or software that generates images based on specific text inputs and parameters.

[0011] "Image recognition means" is technology or software for recognizing and identifying specific objects or scenes within an image.

[0012] "Recorded data" refers to a data file of video information captured by a camera.

[0013] "Natural language processing" is technology or software for understanding and analyzing human language.

[0014] A "search tool" is a technology or software used to search for and extract specific information from data.

[0015] An "estimation tool" is a technique or software that predicts new information or outcomes based on existing information.

[0016] A "notification means" is a technology or software for conveying information from the system to the user. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system implemented using a server and a user terminal that cooperate with a camera permanently installed in a room. This system allows a user to efficiently find an object they are looking for. Detailed embodiments of this system are described below.

[0039] System Configuration

[0040] 1. Camera

[0041] The cameras are installed in various locations in the room and record video 24 hours a day, and the recorded video is sent to a server in real time.

[0042] 2. Server

[0043] The server has the following functions:

[0044] Video data storage: Records video data sent from the camera along with date and time information.

[0045] Database: Manages recorded video data so that it can be searched efficiently.

[0046] Image generation: It has an image generation engine that generates an image of the item being searched for based on the user's search request.

[0047] Image Recognition: It has an image recognition engine to search for scenes that match the generated image in the recorded data.

[0048] Data analysis: A function that analyzes users' survey requests using natural language processing and utilizes the results.

[0049] Estimation: A feature that estimates the current location of an item based on its last known location.

[0050] Notification: A function to send estimated information to the user's device.

[0051] 3. User Device

[0052] The user terminal is a smartphone, PC, etc., and has the following functions:

[0053] Survey request: Provides an interface that allows users to easily input survey requests.

[0054] Notification reception: A function that receives notifications from the server and displays them to the user.

[0055] Program Processing

[0056] 1. Acquiring camera footage

[0057] The server receives video data in real time transmitted from a camera installed in the room.

[0058] The server adds date and time information to the received video data and stores it in a database.

[0059] 2. Request for lost items

[0060] Users can submit an investigation request using a dedicated application on their smartphone or PC, for example by entering "Find the red key."

[0061] The terminal sends the input to the server.

[0062] 3. Image generation of the searched item

[0063] The server analyzes the received investigation request using a natural language processing engine and extracts the characteristics of the item being searched for.

[0064] The server then calls an image generation engine (e.g., a generative model) based on the features to generate a specific image of the item being searched for.

[0065] 4. Searching for recorded data

[0066] The server uses the generated image to search for past recorded data.

[0067] The server uses an image recognition engine to extract the relevant scenes.

[0068] 5. Estimating the location of lost items

[0069] The server identifies the last known location of the item being searched for based on the time and location information of the searched scene.

[0070] The server uses this information to estimate the current location.

[0071] 6. Notification

[0072] The server generates a message informing the user of the result of the estimation, including specific information such as "The red key is on the table in the living room."

[0073] The terminal receives the notification from the server and displays it to the user.

[0074] Specific examples

[0075] Example 1: Searching for a key

[0076] 1. The user enters "Find the red key" into the dedicated app.

[0077] 2. The terminal sends the input information to the server.

[0078] 3. The server analyzes the "red key" using a natural language processing engine and generates an image using an image generation engine.

[0079] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[0080] 5. The server estimates the current location based on the time and location of that frame.

[0081] 6. The server generates a notification that says, "The red key is on the living room table," and sends it to the device.

[0082] 7. The device receives the notification and displays it to the user.

[0083] In this way, the user can efficiently locate the item they are looking for.

[0084] The processing flow will be explained below.

[0085] Step 1:

[0086] The camera continuously records video of the room 24 hours a day and transmits the recorded data to a server in real time.

[0087] Step 2:

[0088] The server receives the video data sent from the camera, adds date and time information, and stores it in a database. The stored video data is kept for a certain period of time (for example, one week).

[0089] Step 3:

[0090] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[0091] Step 4:

[0092] The device receives the user's search request and sends it to the server. The communication is via API and includes the necessary information (details of the item being searched).

[0093] Step 5:

[0094] The server analyzes the received search request using a natural language processing (NLP) engine, and extracts specific characteristics of the item from the analysis results.

[0095] Step 6:

[0096] The server inputs the analyzed features into an image generation engine (e.g., a generative model) to generate a specific image of the object being searched for (in this case, a red key). This generated image is used for further processing.

[0097] Step 7:

[0098] The server searches for past recorded data using the images generated by the image generation engine. First, it filters data from a specific period (e.g., the most recent week) from the entire recorded data.

[0099] Step 8:

[0100] The server then feeds the filtered video data into an image recognition engine to find scenes that match the generated image, which then extracts frames that show the object being searched for.

[0101] Step 9:

[0102] The server identifies the last known location of the item based on the time and location information of the video frame extracted by the image recognition engine.

[0103] Step 10:

[0104] The server uses the identified information to estimate the current location of the item, using an algorithm that takes into account the passage of time and the object's movement patterns.

[0105] Step 11:

[0106] The server generates a message to notify the user of the estimated location information, for example, a specific message such as "The red key is on the table in the living room."

[0107] Step 12:

[0108] The device receives the notification from the server and displays the content in the user's application, allowing the user to check the location of the item they are looking for based on the displayed information.

[0109] Following these steps, users can efficiently find what they are looking for.

[0110] Example 1

[0111] 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."

[0112] In today's busy lifestyles, losing things indoors is a frequent occurrence, and searching for them can take up a lot of time and effort. To address this issue, a system that allows users to efficiently find lost items is needed, but existing technologies have limitations in accuracy and convenience. In particular, improvements are needed in areas such as video data search, analysis of investigation requests using natural language, and image generation of lost items.

[0113] 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.

[0114] In this invention, the server includes means for a camera to continuously record video of a room and transmit the video to an information processing device, means for the information processing device to save the recorded video, means for a user to make a request to investigate an item they are looking for, means for the information processing device to receive the investigation request and generate an image of the item they are looking for using an image generation means, means for the information processing device to search for a matching scene from past recorded data based on the generated image, means for the information processing device to estimate the location of the item they are looking for based on the search results, and means for the information processing device to notify the user of the estimated location information, thereby enabling the user to efficiently find the item they are looking for.

[0115] A "camera" is a device that continuously records video of the interior of a room and transmits the video data to an information processing device in real time.

[0116] An "information processing device" is a device that stores received video data, processes investigation requests from users, generates images of items being searched for, searches recorded data, estimates their location, and notifies users.

[0117] "Video data" refers to indoor video recorded by a camera, and includes time and date information.

[0118] "User" refers to a person who uses a dedicated application to input a survey request and receive notifications from the system.

[0119] A "research request" refers to a request entered by a user to find a particular item.

[0120] "Image generation means" refers to a technology that generates a concrete image of the item being searched for based on the characteristics of the item obtained from the investigation request.

[0121] "Image recognition means" refers to a technology that searches for matching scenes from past recorded data based on the generated image.

[0122] "Natural language processing means" refers to technology that analyzes a user's search request and generates an image of the item being searched for based on the analysis results.

[0123] "Inference" refers to the process of determining the location of a searched item based on search results and predicting the appropriate location.

[0124] "Notification" refers to an act of conveying estimated location information from an information processing device to a user.

[0125] A "scene" refers to a frame or video section that represents a specific time or place in recorded data.

[0126] The present invention is a system implemented using an information processing device and a user terminal that cooperate with a camera permanently installed in a room. This system allows a user to efficiently find an item they are looking for. Detailed embodiments of this system are described below.

[0127] System Configuration

[0128] 1. Camera

[0129] The cameras are installed in various locations in the room and record images 24 hours a day, and the recorded images are sent to an information processing device in real time.

[0130] Example of use: Network cameras (e.g. Hikvision or Axis cameras)

[0131] 2. Information Processing Device

[0132] The information processing device has the following functions.

[0133] Video data storage: Video data sent from the camera is recorded along with date and time information. Database software (e.g., MySQL (registered trademark) or PostgreSQL) is used.

[0134] Database management: Manage recorded video data so that it can be searched efficiently.

[0135] Image generation: It has an image generation engine (e.g., DALL-E or Stable Diffusion) to generate an image of the item being searched for based on the user's investigation request.

[0136] Image Recognition: It has an image recognition engine (e.g., OpenCV or YOLOv5) to search for scenes that match the generated image from the recorded data.

[0137] Data analysis: It has the ability to analyze users' survey requests using natural language processing tools (e.g., GPT-3 (registered trademark) or BERT) and use the results.

[0138] Estimation: Has the ability to estimate the current location of the item based on the last known location of the item.

[0139] Notification: Has the function of sending estimated information to the user's terminal.

[0140] 3. User Device

[0141] The user terminal is a smartphone, PC, etc., and has the following functions:

[0142] Survey request: Provide an interface that allows users to easily input survey requests using an application (e.g., iOS app, Android app, web app).

[0143] Notification reception: Has the function of receiving notifications from the information processing device and displaying them to the user.

[0144] Examples of specific examples and prompts

[0145] Example 1: Searching for a key

[0146] 1. The user enters "Find the red key" into a dedicated app (smartphone app or web app).

[0147] 2. The terminal transmits the input contents to the information processing device.

[0148] 3. The information processing device analyzes the "red key" using a natural language processing engine (e.g., GPT-3 or BERT) and generates an image using an image generation engine (e.g., DALL-E or Stable Diffusion).

[0149] 4. The information processing device uses an image recognition engine (e.g., OpenCV or YOLOv5) to search for the relevant scene from the recorded data and extract the frame in which the "red key" appears.

[0150] 5. The information processing device estimates the current location based on the time and location of the frame.

[0151] 6. The information processing device generates a notification that says, "The red key is on the table in the living room," and sends it to the terminal.

[0152] 7. The device receives the notification and displays it to the user.

[0153] Example prompt sentence:

[0154] Prompt for the natural language processing engine:

[0155] text

[0156] User input: "Find the red key"

[0157] Analyze this request and extract the characteristics of the item being sought.

[0158] Prompt for image generation engine:

[0159] text

[0160] Search Feature: Red Key

[0161] Based on this feature, generate an image of the red key.

[0162] This allows the system to efficiently find what the user is looking for.

[0163] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0164] Step 1: Acquire camera footage

[0165] Input: Cameras are installed at various locations in the room, record images in real time, and send them to an information processing device.

[0166] Server operation: The server sets the IP address and streaming URL of each camera and receives video data using the RTSP protocol or HTTP protocol.

[0167] Data processing: The server adds time and date metadata to the received video data.

[0168] Output: The video data is stored in a database along with date and time information.

[0169] Step 2: Request a lost item investigation

[0170] Input: The user inputs an investigation request such as "Find the red key" using a dedicated application on their smartphone or PC.

[0171] What the device does: The device receives the text entered by the user and sends it to the server via an API request (e.g., an HTTP POST request).

[0172] Output: The survey request is sent to the server.

[0173] Step 3: Analyze the survey request and generate an image

[0174] Input: The server receives a survey request from the user.

[0175] Server operation: The server uses a natural language processing engine (e.g., GPT-3 or BERT) to extract the keyword "red key."

[0176] Data processing: The server sends the extracted keywords as prompts to the image generation engine (e.g., DALL-E or Stable Diffusion).

[0177] Output: The image generation engine generates an image of the "red key" which is returned to the server.

[0178] Step 4: Search for recording data

[0179] Input: The server receives the generated image.

[0180] Server operation: The server uses an image recognition engine (e.g., OpenCV or YOLOv5) to search for scenes that match the generated image from the video data in the database.

[0181] Data processing: An image recognition engine compares each frame of video data to identify matching scenes.

[0182] Output: The time and location information of the matching scenes is returned to the server.

[0183] Step 5: Estimate the location of the item

[0184] Input: The server receives the time and location information of the matching scene.

[0185] Server operation: The server uses historical data and AI algorithms (e.g., recurrent neural networks) to analyze the movement patterns of the items being searched for.

[0186] Data processing: The server calculates the current estimated location based on past location data.

[0187] Output: The estimated current location of the searched item is identified.

[0188] Step 6: Notification

[0189] Input: The server receives the estimated current location of the object.

[0190] Server Action: The server generates a notification message for the user, containing specific information such as "The red key is on the table in the living room."

[0191] Output: A notification message is sent to the terminal.

[0192] Terminal behavior: The terminal receives the notification message and displays it to the user, either as a pop-up notification or a banner notification.

[0193] (Application example 1)

[0194] 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."

[0195] In large facilities such as logistics centers, it is crucial for workers to be able to quickly and accurately locate goods and materials. However, in environments with large areas and many products, finding what is needed can take a lot of time and effort. Current systems lack sufficient visual instructions and real-time information to quickly identify the location of items, so further improvements are needed.

[0196] 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.

[0197] In this invention, the server includes: a camera for continuously recording video of a room and transmitting the video to the server; a server for storing the recorded video; a user's request for an investigation into a lost item; a server for receiving the investigation request and generating an image of the lost item using an image generating means; a server for searching for a matching scene from past recorded data based on the generated image; a server for estimating the location of the lost item based on the search result; a server for notifying the user of the estimated location information; a smart device for displaying visual instructions on the smart device worn by the user; and an augmented reality means for highlighting an object within the user's field of view based on the visual instructions. This allows the user to quickly and accurately locate the lost item through the smart device, significantly improving work efficiency.

[0198] A "camera" is a device that continuously records video of the room and transmits it to a server.

[0199] The "server" is a central computer system that stores recorded video and generates images of items searched for based on user requests for investigation, as well as searches from past recorded data.

[0200] A "user" is a person or terminal user who requests a search for a lost item and receives notifications from the system.

[0201] "Image generation means" refers to the function of the server to generate a concrete image of the item being searched for based on a search request.

[0202] "Recorded data" refers to past video information captured by a camera and stored on a server.

[0203] "Image recognition means" is a technology for searching past recorded data for scenes that match the generated image.

[0204] The "location estimation means" is a function that estimates the current location of the item being searched for based on the search results.

[0205] The "notification means" is a function that transmits estimated location information to the user.

[0206] A "smart device" is a device worn by a user that can receive visual instructions from a system. Specific examples include smart glasses.

[0207] "Augmented reality tools" are techniques that visually highlight objects within a user's field of view.

[0208] The present invention is a system for efficiently finding lost items or products in a logistics center. This system is composed of a camera, a server, a smart device, etc.

[0209] System Configuration

[0210] 1. Camera

[0211] Cameras are installed in various locations within the distribution center, continuously recording images from inside the center and sending them to a server. It is desirable for the cameras to be high-resolution and capable of monitoring a wide area.

[0212] 2. Server

[0213] The server has the following main functions:

[0214] Recording data storage: Video data sent from the camera is recorded with date and time information, and the video data is stored in a database for efficient searching.

[0215] Image generation: Based on the search request, a generative AI model is used to generate a specific image of the searched item. For example, if a user types "Look for product A," the generative AI model will generate an image of product A.

[0216] Image Recognition: Searches for scenes that match the generated image in the recorded data. Using an image recognition engine, it extracts relevant scenes from past data.

[0217] Natural language processing: Understanding the user's research request and triggering the image generation engine based on the results. This process uses natural language processing models such as BERT.

[0218] Location estimation: Based on the time and location information of the searched scene, the last known location of the item is identified and its current location is estimated.

[0219] Notification: The estimation results are sent to the user's device, providing specific information such as "Product A is located fifth on shelf A."

[0220] 3. Smart Devices

[0221] Smart devices include smart glasses, smartphones, etc., which have the following functions:

[0222] User interface: Provide a voice and touch input interface that allows users to easily input survey requests.

[0223] Displaying visual instructions: Visually displaying the inference results sent from the server, for example by using the AR functionality of smart glasses to highlight objects within the user's field of view.

[0224] Notification reception: Receive notifications from the server in real time and display them to the user.

[0225] Specific examples

[0226] For example, suppose a user working in a logistics center is wearing smart glasses. If the user voice-inputs, "Look for product A," the smart glasses recognize the voice and send it to the server. The server analyzes the search request using a natural language processing engine and generates an image of product A using a generative AI model. The server then uses an image recognition engine to search for scenes in which product A was recorded in the camera's video data and estimate its current location. The resulting information, "Product A is located fifth on shelf A," is generated and sent to the smart glasses. Using the smart glasses' AR function, this information is displayed within the user's field of view, with product A highlighted in a specific location.

[0227] Prompt Sentence Examples

[0228] User: "Looking for product A"

[0229] This system enables logistics center workers to quickly and accurately find the items they are looking for, significantly improving work efficiency.

[0230] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0231] Step 1:

[0232] The user inputs "Search for product A" through voice or text input via smart glasses or a smartphone. This input is sent to the server via the user interface (input: voice or text, output: data sent to the server).

[0233] Step 2:

[0234] The server analyzes the received user survey request using a natural language processing engine. In the analysis process, for example, a BERT model is used to extract the intent of the survey request and important keywords (input: survey request data, output: analysis result data).

[0235] Step 3:

[0236] The server uses a generative AI model based on the analysis results obtained by the natural language processing engine to generate a concrete image of Product A. The generated image data is saved on the server (input: analysis result data, output: generated image data).

[0237] Step 4:

[0238] The server uses an image recognition engine to search for scenes that match the image of product A generated from past recorded data. This process uses a machine learning model (for example, YOLO or OpenCV functions) (input: generated image data, recorded data, output: matching scene data).

[0239] Step 5:

[0240] The server identifies the last known location of product A based on the time and location information of the matching scene data, and estimates its current location. The estimation uses an algorithm that analyzes movement patterns within a specified range (input: matching scene data, output: estimated location data).

[0241] Step 6:

[0242] The server generates a message notifying the user of the estimated location information and sends it to the smart device worn by the user. The generated message includes the specific location (e.g., "Product A is located fifth on shelf A") (Input: estimated location data, Output: notification message).

[0243] Step 7:

[0244] The smart device receives the notification message from the server and displays it on the user's screen as a visual indication. It also uses AR functionality to highlight objects within the user's field of view. In this way, the user can confirm the location of Item A in real time (Input: notification message, Output: visual display and highlighting).

[0245] This allows users to quickly and accurately find the products and materials they need, significantly improving the work efficiency of logistics centers.

[0246] 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.

[0247] The present invention is a search system that uses a camera, server, and user terminal combined with an emotion engine. The entire system aims to realize more human-centered interaction by recognizing the user's emotions, thereby enabling efficient discovery of searched items and improving the user experience. Detailed embodiments of the system are described below.

[0248] System Configuration

[0249] 1. Camera

[0250] The cameras are installed in various locations in the room and record video 24 hours a day, and the recorded video is sent to a server in real time.

[0251] 2. Server

[0252] The server has the following functions:

[0253] Video data storage: Records video data sent from the camera along with date and time information.

[0254] Database: Manages recorded video data so that it can be searched efficiently.

[0255] Image generation: It has an image generation engine that generates an image of the item being searched for based on the user's search request.

[0256] Image Recognition: It has an image recognition engine to search for scenes that match the generated image in the recorded data.

[0257] Data analysis: A function that analyzes users' survey requests using natural language processing and utilizes the results.

[0258] Estimation: A feature that estimates the current location of an item based on its last known location.

[0259] Emotion Engine: A feature that recognizes the emotion a user expresses when requesting a survey and tailors responses accordingly.

[0260] Notification: A function that sends estimated information to the user's device. The notification content is customized based on emotional information.

[0261] 3. User Device

[0262] The user terminal is a smartphone, PC, etc., and has the following functions:

[0263] Survey request: Provides an interface that allows users to easily input survey requests.

[0264] Emotion recognition: Detects emotions from the user's facial expressions and voice and sends that information to the server.

[0265] Notification reception: A function that receives notifications from the server and displays them to the user.

[0266] Program Processing

[0267] 1. Acquiring camera footage

[0268] The server receives video data in real time transmitted from a camera installed in the room.

[0269] The server adds date and time information to the received video data and stores it in a database.

[0270] 2. Request for lost items

[0271] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[0272] The terminal recognizes the user's emotions from their facial expressions and voice, and sends a survey request including the emotion information to the server.

[0273] 3. Image generation of the searched item

[0274] The server analyzes the received investigation request using a natural language processing engine and extracts the characteristics of the item being searched for.

[0275] The server then invokes an image generation engine (e.g., a generative model) based on the features to generate a specific image of the object being searched for (in this case, a red key).

[0276] 4. Searching for recorded data

[0277] The server uses the generated image to search for past recorded data.

[0278] The server uses an image recognition engine to extract the relevant scenes.

[0279] 5. Estimating the location of lost items

[0280] The server identifies the location where the item was last seen based on the time and location information of the searched scene.

[0281] The server uses this information to estimate the current location.

[0282] 6. Notification

[0283] The server then changes the content of the notification message appropriately based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, the server will notify them with a message such as, "Please stay calm. The red key is on the table in the living room."

[0284] The terminal receives the notification from the server and displays the content in the user's application.

[0285] Specific examples

[0286] Example 1: Searching for a key

[0287] 1. The user types "Find the red key" into the dedicated app. At the same time, the user's facial expressions and voice are analyzed by the emotion recognition engine to detect stress or impatience.

[0288] 2. The device sends the input content and emotion information to the server.

[0289] 3. The server analyzes the "red key" using a natural language processing engine and generates an image using an image generation engine.

[0290] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[0291] 5. The server estimates the current location based on the time and location of that frame.

[0292] 6. The server generates a notification message that takes into account the user's emotional information: "Please stay calm. The red key is on the table in the living room." and sends it to the device.

[0293] 7. The device receives the notification and displays it to the user.

[0294] This system allows users to find what they are looking for more safely and efficiently.

[0295] The processing flow will be explained below.

[0296] Step 1:

[0297] The camera continuously records video of the room 24 hours a day and transmits the recorded data to a server in real time.

[0298] Step 2:

[0299] The server receives the video data sent from the camera, adds date and time information, and stores it in a database. The stored video data is kept for a certain period of time (for example, one week).

[0300] Step 3:

[0301] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[0302] Step 4:

[0303] The device analyzes the user's facial expressions and voice to recognize their emotions, and then sends a survey request including that emotional information to the server. For example, if the user is feeling anxious, that emotional information will also be sent.

[0304] Step 5:

[0305] The server analyzes the received investigation request using a natural language processing (NLP) engine and extracts the characteristics of the item being searched for: the "red key."

[0306] Step 6:

[0307] The server calls an image generation engine based on the extracted features to generate a concrete image of the "red key."

[0308] Step 7:

[0309] The server searches past recorded data based on the generated "red key" image. First, it filters recorded data from a specific period (for example, the past week).

[0310] Step 8:

[0311] The server then inputs the filtered video data into an image recognition engine to find scenes that match the generated image, and the image recognition engine extracts frames that show the "red key."

[0312] Step 9:

[0313] The server determines the last known location of the item being searched for (the red key) based on the time and location information of the extracted video frame.

[0314] Step 10:

[0315] The server uses the location and time information to estimate the current location of the red key, using an algorithm that takes into account the passage of time and patterns of movement of objects.

[0316] Step 11:

[0317] The server then changes the content of the notification message appropriately based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, the server will notify them with a message such as, "Please stay calm. The red key is on the table in the living room."

[0318] Step 12:

[0319] The device receives the notification sent from the server and displays the content to the user within the application, allowing the user to check the location of the item they are looking for based on the notification content.

[0320] This process flow allows users to find what they are looking for efficiently and safely.

[0321] Example 2

[0322] 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."

[0323] Conventional object-finding systems have had the problem of making it difficult for users to quickly and efficiently find lost items. In particular, the system's response is often insufficient when users have difficulty clearly imagining the characteristics of the object they are looking for or when they are in an emotional state. Furthermore, there is a lack of a way to quickly search for an object from past video recording data, making it difficult to accurately estimate the location of the object. This results in a decrease in user satisfaction.

[0324] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0325] In this invention, the server includes: means for a camera to continuously record video of a room and transmit the video to the data processing device; means for the data processing device to save the recorded video; means for a user to request an investigation into an item they are looking for; means for the data processing device to receive the investigation request and generate an image of the item they are looking for using an image generation means; means for the data processing device to search for a matching scene from past recorded data based on the generated image; means for the data processing device to estimate the location of the item they are looking for based on the search results; means for the data processing device to notify the user of the estimated location information; and means for detecting emotional information from the user and generating a notification message according to the emotional information. This enables the user to find the item they are looking for quickly and efficiently, and an improved user experience can be expected by providing an appropriate response according to the user's emotional state.

[0326] A "camera" is a device that continuously records video of a room and transmits the captured video data to a server or data processing device.

[0327] The "data processing device" is a processing device that stores the video data transmitted from the camera and performs various processes based on the investigation request from the user.

[0328] A "request to find a lost item" is an operation or content in which a user inputs information about a lost item and requests the system to identify its location.

[0329] The "image generation means" is a means for analyzing the characteristics of the item being searched for based on the investigation request and generating a specific image of the item from those characteristics.

[0330] "Recorded data" refers to video data captured by a camera and stored in a data processing device.

[0331] A "matching scene" is a scene in the recorded data that matches the generated image.

[0332] "Means for estimating location" refers to means for estimating the last known location and current location of a searched item based on search results.

[0333] The "means for notifying" is a means for transmitting estimated location information to the user's terminal and notifying the user of this.

[0334] "Emotion information" is emotional data obtained from the user's facial expressions, voice, etc.

[0335] A "notification message" is a message that contains information about the item being searched for or other response content that is generated in response to the user's emotions.

[0336] The present invention is a search system that uses a camera, a data processing device, and a user terminal combined with an emotion engine. The entire system aims to realize more human-centered interaction by recognizing the user's emotions, thereby enabling efficient discovery of the searched item and improving the user experience. Detailed embodiments of the system are described below.

[0337] System Configuration

[0338] 1. Camera

[0339] The cameras are installed in various locations in the room and record video 24 hours a day. The recorded video is sent to a data processing device in real time. Specifically, the video stream is sent using the TCP / IP protocol, and the data processing device encodes the video data using a library such as FFmpeg.

[0340] 2. Data Processing Device

[0341] The data processing device has the following functions:

[0342] Video data storage: Video data sent from the camera is recorded along with date and time information. For storage, an SQL-based database (e.g., MySQL) or a NoSQL database (e.g., MongoDB) is used.

[0343] Database: Manages recorded video data so that it can be searched efficiently.

[0344] Image generation: It has an image generation method (e.g., DALL-E or Stable Diffusion) to generate an image of the searched item based on the user's investigation request.

[0345] Image Recognition: It has an image recognition method (e.g., YOLOv5) to search for scenes that match the generated image from the recorded data.

[0346] Data analysis: A function that analyzes user survey requests using natural language processing tools (e.g., BERT) and uses the results.

[0347] Estimation: A feature that estimates the current location of an item based on its last known location.

[0348] Emotion engine: A feature that recognizes the emotion a user expresses when requesting a survey and tailors responses accordingly (e.g., OpenFace or Microsoft® Azure® Face API).

[0349] Notification: A function that sends estimated information to the user's device. The notification content is customized based on emotional information.

[0350] 3. User Device

[0351] The user terminal is a smartphone, PC, etc., and has the following functions:

[0352] Survey request: We provide an interface that allows users to easily input survey requests. This uses cameras and microphones to detect emotions from facial expressions and voice.

[0353] Emotion recognition: Detecting emotions from the user's facial expressions and voice and sending that information to a data processing device.

[0354] Notification reception: A function to receive notifications from the data processing device and display them to the user.

[0355] Specific examples

[0356] Example 1: Searching for a key

[0357] 1. The user opens the app and types in "Find the red key." At the same time, the user's facial expressions and voice are captured by the camera and microphone and analyzed by the emotion recognition engine. For example, it may be determined that the user is anxious.

[0358] 2. The terminal transmits the input content and emotional information to the data processing device.

[0359] 3. The data processing device analyzes the "red key" using a natural language processing engine and generates a concrete image using an image generation means (e.g., DALL-E).

[0360] 4. Based on the generated image, the data processing device uses image recognition means (e.g., YOLOv5) to extract scenes containing the red key from past recorded data.

[0361] 5. The data processing device estimates the current location based on the time and location information of the detected scene. For example, the last place where the red key was seen may be identified as "on the table in the living room."

[0362] 6. The data processing device, taking into account that the user is in a hurry, generates a notification message saying "Please stay calm. The red key is on the table in the living room" and sends it to the terminal.

[0363] 7. The device receives the notification and displays its contents to the user.

[0364] This system allows users to find what they are looking for more safely and efficiently.

[0365] Example prompt sentence:

[0366] "I'm looking for the red key. I'm getting impatient."

[0367] "Generate an image of a red key."

[0368] "Please tell me where and when the red key was last seen."

[0369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0370] Step 1: Acquire camera footage

[0371] The server receives video data in real time from a camera installed in the room. The input is the video stream from the camera, and the output is the encoded video data.

[0372] The server adds date and time information to the received video data and stores it in a database. Specifically, it encodes the video data using a library such as FFmpeg and adds date and time metadata. This data is then stored in an SQL-based database (e.g., MySQL) or a NoSQL database (e.g., MongoDB).

[0373] Step 2: Request for lost item investigation

[0374] The user uses a dedicated app on their device (smartphone or PC) to input a search request for an item they are looking for, such as "Find the red key." The input is the search request entered by the user in a text field, and the output is the search request data sent to the server.

[0375] The device's camera and microphone capture the user's facial expressions and voice, and an emotion recognition engine (e.g., OpenFace or Microsoft Azure Face API) extracts emotional data. Specifically, the emotion recognition engine analyzes facial and vocal characteristics to detect the user's emotional state.

[0376] The device formats the survey request text and extracted emotion data into JSON format and sends it to the server via an HTTP POST request.

[0377] Step 3: Create an image of what you're looking for

[0378] The server analyzes the received search request using a natural language processing engine (e.g., BERT) and extracts features of the searched item. The input is the text of the search request, and the output is feature data of the searched item. Specifically, it extracts key phrases using a natural language processing library (e.g., Hugging Face transformers).

[0379] The server uses an image generation engine (e.g., DALL-E or Stable Diffusion) based on the features to generate a specific image of the searched item. The input is the feature data of the searched item, and the output is the generated image. An example of a prompt sentence: "Generate an image of a red key."

[0380] Step 4: Search for recording data

[0381] The server uses the generated images to search past recorded data. The input is the generated images, and the output is the search results for matching scenes.

[0382] The server uses an image recognition engine (e.g., YOLOv5) to extract the relevant scenes. Specifically, the YOLOv5 model performs image recognition processing on each video frame to detect matching scenes.

[0383] Step 5: Estimate the location of the item

[0384] The server identifies the location where the item was last seen based on the time and location information of the searched scene. The input is the matching scene information, and the output is the estimated location information. Specifically, it retrieves the entry where the red key was last seen from the database and uses a rule-based algorithm to estimate its current location.

[0385] Step 6: Notification

[0386] The server appropriately changes the content of the notification message based on the user's emotional information recognized by the emotion engine. The input is emotional information and estimated location information, and the output is a customized notification message. In concrete terms, the emotion engine is used to analyze the user's emotional state and generate an appropriate notification message. An example of a prompt sentence: "Please stay calm. The red key is on the table in the living room."

[0387] The device receives notifications from the server and displays the contents in the user's application. The input is the notification message from the server, and the output is the notification display to the user. Specifically, the notification message is displayed in the notification center of the dedicated application or as a pop-up notification.

[0388] This allows users to find what they are looking for quickly and efficiently, and is expected to improve the user experience by providing appropriate responses based on emotions.

[0389] (Application example 2)

[0390] 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."

[0391] While there are existing systems for efficiently finding lost items, few systems can recognize and respond to the user's emotions. Furthermore, if the user is feeling anxious or stressed, appropriate assistance is not provided, resulting in a poor user experience. Conventional systems generate notification content without considering the user's emotional state, resulting in low user satisfaction. To solve this problem, a system that recognizes the user's emotions and responds appropriately based on them is needed.

[0392] 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.

[0393] In this invention, the server includes means for a camera to continuously record video of a room and transmit the video to the server, means for the server to save the recorded video, means for a user to request an investigation into an item they are looking for, means for the server to receive the investigation request and generate an image of the item they are looking for using an image generation means, means for the server to search for a matching scene from past recorded data based on the generated image, means for the server to estimate the location of the item they are looking for based on the search result, means for recognizing the user's emotion and adjusting notification content based on the emotion information, and means for the server to notify the user of the estimated location information. This enables appropriate notification and support based on the user's emotion, improving the user experience.

[0394] A "camera" is a device that continuously records video of a specific location or object and transmits it to a server.

[0395] The "server" is a computer system that stores the images sent from the camera, processes the user's investigation request, generates an image of the item being searched for, and searches for recorded data based on that image.

[0396] A "user terminal" is a device that allows a user to request an investigation into a lost item and has the function of receiving the investigation results. This includes smartphones and personal computers.

[0397] The "image generation means" is a technology that allows the server to generate a concrete image of the item being searched for based on the user's search request. For example, it uses a generative model.

[0398] "Image recognition means" is a technology that allows the server to search for scenes that match the generated image from the recorded data. For example, it uses an image analysis engine such as OpenCV.

[0399] "Natural language processing means" is a technology that analyzes a user's search request and extracts the characteristics of the item based on that analysis. For example, Google (registered trademark) Cloud Natural Language API is an example of this.

[0400] "Emotion recognition means" refers to a technology that detects emotions by analyzing the user's facial expressions and voice and reflects that information in the notification content. For example, an emotion recognition engine is an example of this.

[0401] The "notification means" is a technology that notifies the user terminal of the content based on the predetermined information generated by the server. The content of the notification is adjusted based on the user's emotions.

[0402] The "means for estimating the location of a lost item" is a technology in which the server determines the time and place in which the lost item was captured based on the search results of image recognition, and estimates its current location.

[0403] The present invention is a system for finding things that uses emotion recognition technology and is configured by combining a camera, a server, and a user terminal. The detailed configuration of this system is described below.

[0404] System Configuration

[0405] 1. Camera

[0406] The camera continuously records video of a specific location or room and transmits it to a server in real time. High-resolution cameras are used to accurately capture video details.

[0407] 2. Server

[0408] A server is a computer system that has the following functions:

[0409] Video data storage: Video data sent from the camera is stored in a database along with date and time information.

[0410] Database: Efficiently search and manage video data.

[0411] Image generation: Using a generative model, a concrete image of the item being searched for is created based on the user's research request.

[0412] Image recognition: Using an image analysis engine such as OpenCV, it searches for scenes that match images generated from past recorded data.

[0413] Emotion Recognition: Using an emotion recognition engine, we analyze and recognize the user's emotional information and process it on the server side.

[0414] Natural language processing: Analyze user survey requests using Google Cloud Natural Language API, etc.

[0415] Location estimation of lost items: Estimates the current location based on the time and location information of search results.

[0416] Notification: Customize notification content based on the user's emotions and send information to the user's device.

[0417] 3. User Device

[0418] The user terminal is a smartphone or personal computer and has the following functions:

[0419] Interface: Provide an interface that allows users to easily input search requests for items they are looking for.

[0420] Emotion recognition: Analyzes the user's facial expressions and voice to detect emotional information and send it to the server.

[0421] Receive notifications: Receive notifications from the server and display them to the user.

[0422] System Operation

[0423] 1. The user launches the app and speaks or texts, "I'm looking for a miniature blue car."

[0424] 2. The user device analyzes the user's facial expressions and voice to obtain emotional information.

[0425] 3. The device sends the input information and emotion information to the server.

[0426] 4. The server uses natural language processing to identify a "miniature blue car" and generates an image using a generative model.

[0427] 5. The server uses an image analysis engine to search for the relevant scene in the recorded data.

[0428] 6. The server estimates the location of the item based on the search results and notifies the user's device of this information.

[0429] 7. The server customizes the notification content based on the emotional information. For example, if a user is feeling anxious, the server sends a message such as, "Don't worry. The miniature blue car is in the toy section on the second floor."

[0430] 8. The user device receives the notification and displays it to the user.

[0431] Specific examples

[0432] Example 1: Finding a key

[0433] 1. The user launches the app and types in "Find the red key." At the same time, the emotion recognition engine analyzes the user's facial expressions and voice to detect stress or impatience.

[0434] 2. The device sends the input content and emotion information to the server.

[0435] 3. The server analyzes the "red key" using natural language processing and generates an image using an image generation engine.

[0436] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[0437] 5. The server estimates the current location based on the time and location of that frame.

[0438] 6. The server generates a notification message that takes into account the user's emotional information: "Please stay calm. The red key is on the table in the living room." and sends it to the device.

[0439] 7. The device receives the notification and displays it to the user.

[0440] Prompt Sentence Examples

[0441] text

[0442] User: When a user is looking for a miniature blue car in a store but cannot find it, he / she types "find a miniature blue car" into the app. At the same time, the camera detects that the customer is anxious. The server processes this, estimates the location of the item, and responds with a relaxed message.

[0443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0444] Step 1:

[0445] The user launches the app and enters a survey request.

[0446] The user launches a dedicated application on their smartphone or personal computer and inputs a search request for the item they are looking for by voice or text. For example, they might input, "I'm looking for a miniature blue car." At this time, the application also analyzes the user's facial expressions and voice to obtain emotional information.

[0447] Input: User survey request (voice or text), facial expression and voice data

[0448] Output: Investigation request details and emotional information

[0449] Step 2:

[0450] The device sends the investigation request and emotional information to the server.

[0451] The terminal transmits the survey request entered by the user and the analyzed emotional information to the server. At this time, the data is encrypted before transmission.

[0452] Input: Investigation request details and emotional information

[0453] Output: Survey request details and emotional information sent to the server

[0454] Step 3:

[0455] The server analyzes the investigation request.

[0456] The server analyzes the received search request using a natural language processing engine (such as Google Cloud Natural Language API) and extracts the characteristics of the item being searched for. In this case, the characteristics of a "miniature blue car" are extracted.

[0457] Input: Survey request details

[0458] Output: Extracted features of the searched item

[0459] Step 4:

[0460] The server generates an image of the item using an image generation engine.

[0461] The server uses a generative model (AI model) to generate a specific image of the item based on the extracted features of the item. For example, an image of a specific "miniature blue car" is generated.

[0462] Input: Extracted features of the object being searched for

[0463] Output: Generated image of the object being searched

[0464] Step 5:

[0465] The server searches for recorded data using an image recognition engine

[0466] The server uses the generated image to search past recorded data using an image recognition engine (such as OpenCV) and extracts matching scenes.

[0467] Input: Generated image of the object to be found

[0468] Output: Matching scenes with their time and location information

[0469] Step 6:

[0470] The server estimates the location of the item

[0471] The server estimates the current location of the item based on the time and location information of the search results. In this case, it estimates the specific location of the "miniature blue car."

[0472] Input: Time and location information of the matching scene

[0473] Output: Estimated current location of the item

[0474] Step 7:

[0475] The server customizes the notification content based on emotion information.

[0476] The server considers the user's emotional information and adjusts the notification content appropriately. For example, if the user is feeling anxious, it generates a relaxing message such as, "Don't worry. The miniature blue car is in the toy section on the second floor."

[0477] Input: Current location of the item being searched for, user's emotion information

[0478] Output: Customized notification content

[0479] Step 8:

[0480] The server notifies the user device of the estimated location information.

[0481] The server sends the customized notification content to the user terminal, and the terminal displays the received notification to the user.

[0482] Input: Customized notification content

[0483] Output: Information sent to the user's device

[0484] These steps allow users to find what they are looking for efficiently and provide a more pleasant experience by receiving appropriate notifications based on their emotions.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] [Second embodiment]

[0489] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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).

[0495] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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."

[0501] The present invention is a system implemented using a server and a user terminal that cooperate with a camera permanently installed in a room. This system allows a user to efficiently find an object they are looking for. Detailed embodiments of this system are described below.

[0502] System Configuration

[0503] 1. Camera

[0504] The cameras are installed in various locations in the room and record video 24 hours a day, and the recorded video is sent to a server in real time.

[0505] 2. Server

[0506] The server has the following functions:

[0507] Video data storage: Records video data sent from the camera along with date and time information.

[0508] Database: Manages recorded video data so that it can be searched efficiently.

[0509] Image generation: It has an image generation engine that generates an image of the item being searched for based on the user's search request.

[0510] Image Recognition: It has an image recognition engine to search for scenes that match the generated image in the recorded data.

[0511] Data analysis: A function that analyzes users' survey requests using natural language processing and utilizes the results.

[0512] Estimation: A feature that estimates the current location of an item based on its last known location.

[0513] Notification: A function to send estimated information to the user's device.

[0514] 3. User Device

[0515] The user terminal is a smartphone, PC, etc., and has the following functions:

[0516] Survey request: Provides an interface that allows users to easily input survey requests.

[0517] Notification reception: A function that receives notifications from the server and displays them to the user.

[0518] Program Processing

[0519] 1. Acquiring camera footage

[0520] The server receives video data in real time transmitted from a camera installed in the room.

[0521] The server adds date and time information to the received video data and stores it in a database.

[0522] 2. Request for lost items

[0523] Users can submit an investigation request using a dedicated application on their smartphone or PC, for example by entering "Find the red key."

[0524] The terminal sends the input to the server.

[0525] 3. Image generation of the searched item

[0526] The server analyzes the received investigation request using a natural language processing engine and extracts the characteristics of the item being searched for.

[0527] The server then calls an image generation engine (e.g., a generative model) based on the features to generate a specific image of the item being searched for.

[0528] 4. Searching for recorded data

[0529] The server uses the generated image to search for past recorded data.

[0530] The server uses an image recognition engine to extract the relevant scenes.

[0531] 5. Estimating the location of lost items

[0532] The server identifies the last known location of the item being searched for based on the time and location information of the searched scene.

[0533] The server uses this information to estimate the current location.

[0534] 6. Notification

[0535] The server generates a message informing the user of the result of the estimation, including specific information such as "The red key is on the table in the living room."

[0536] The terminal receives the notification from the server and displays it to the user.

[0537] Specific examples

[0538] Example 1: Searching for a key

[0539] 1. The user enters "Find the red key" into the dedicated app.

[0540] 2. The terminal sends the input information to the server.

[0541] 3. The server analyzes the "red key" using a natural language processing engine and generates an image using an image generation engine.

[0542] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[0543] 5. The server estimates the current location based on the time and location of that frame.

[0544] 6. The server generates a notification that says, "The red key is on the living room table," and sends it to the device.

[0545] 7. The device receives the notification and displays it to the user.

[0546] In this way, the user can efficiently locate the item they are looking for.

[0547] The processing flow will be explained below.

[0548] Step 1:

[0549] The camera continuously records video of the room 24 hours a day and transmits the recorded data to a server in real time.

[0550] Step 2:

[0551] The server receives the video data sent from the camera, adds date and time information, and stores it in a database. The stored video data is kept for a certain period of time (for example, one week).

[0552] Step 3:

[0553] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[0554] Step 4:

[0555] The device receives the user's search request and sends it to the server. The communication is via API and includes the necessary information (details of the item being searched).

[0556] Step 5:

[0557] The server analyzes the received search request using a natural language processing (NLP) engine, and extracts specific characteristics of the item from the analysis results.

[0558] Step 6:

[0559] The server inputs the analyzed features into an image generation engine (e.g., a generative model) to generate a specific image of the object being searched for (in this case, a red key). This generated image is used for further processing.

[0560] Step 7:

[0561] The server searches for past recorded data using the images generated by the image generation engine. First, it filters data from a specific period (e.g., the most recent week) from the entire recorded data.

[0562] Step 8:

[0563] The server then feeds the filtered video data into an image recognition engine to find scenes that match the generated image, which then extracts frames that show the object being searched for.

[0564] Step 9:

[0565] The server identifies the last known location of the item based on the time and location information of the video frame extracted by the image recognition engine.

[0566] Step 10:

[0567] The server uses the identified information to estimate the current location of the item, using an algorithm that takes into account the passage of time and the object's movement patterns.

[0568] Step 11:

[0569] The server generates a message to notify the user of the estimated location information, for example, a specific message such as "The red key is on the table in the living room."

[0570] Step 12:

[0571] The device receives the notification from the server and displays the content in the user's application, allowing the user to check the location of the item they are looking for based on the displayed information.

[0572] Following these steps, users can efficiently find what they are looking for.

[0573] Example 1

[0574] 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."

[0575] In today's busy lifestyles, losing things indoors is a frequent occurrence, and searching for them can take up a lot of time and effort. To address this issue, a system that allows users to efficiently find lost items is needed, but existing technologies have limitations in accuracy and convenience. In particular, improvements are needed in areas such as video data search, analysis of investigation requests using natural language, and image generation of lost items.

[0576] 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.

[0577] In this invention, the server includes means for a camera to continuously record video of a room and transmit the video to an information processing device, means for the information processing device to save the recorded video, means for a user to make a request to investigate an item they are looking for, means for the information processing device to receive the investigation request and generate an image of the item they are looking for using an image generation means, means for the information processing device to search for a matching scene from past recorded data based on the generated image, means for the information processing device to estimate the location of the item they are looking for based on the search results, and means for the information processing device to notify the user of the estimated location information, thereby enabling the user to efficiently find the item they are looking for.

[0578] A "camera" is a device that continuously records video of the interior of a room and transmits the video data to an information processing device in real time.

[0579] An "information processing device" is a device that stores received video data, processes investigation requests from users, generates images of items being searched for, searches recorded data, estimates their location, and notifies users.

[0580] "Video data" refers to indoor video recorded by a camera, and includes time and date information.

[0581] "User" refers to a person who uses a dedicated application to input a survey request and receive notifications from the system.

[0582] A "research request" refers to a request entered by a user to find a particular item.

[0583] "Image generation means" refers to a technology that generates a concrete image of the item being searched for based on the characteristics of the item obtained from the investigation request.

[0584] "Image recognition means" refers to a technology that searches for matching scenes from past recorded data based on the generated image.

[0585] "Natural language processing means" refers to technology that analyzes a user's search request and generates an image of the item being searched for based on the analysis results.

[0586] "Inference" refers to the process of determining the location of a searched item based on search results and predicting the appropriate location.

[0587] "Notification" refers to an act of conveying estimated location information from an information processing device to a user.

[0588] A "scene" refers to a frame or video section that represents a specific time or place in recorded data.

[0589] The present invention is a system implemented using an information processing device and a user terminal that cooperate with a camera permanently installed in a room. This system allows a user to efficiently find an item they are looking for. Detailed embodiments of this system are described below.

[0590] System Configuration

[0591] 1. Camera

[0592] The cameras are installed in various locations in the room and record images 24 hours a day, and the recorded images are sent to an information processing device in real time.

[0593] Example of use: Network cameras (e.g. Hikvision or Axis cameras)

[0594] 2. Information Processing Device

[0595] The information processing device has the following functions.

[0596] Video data storage: Video data sent from the camera is recorded along with date and time information. Database software (e.g., MySQL or PostgreSQL) is used.

[0597] Database management: Manage recorded video data so that it can be searched efficiently.

[0598] Image generation: It has an image generation engine (e.g., DALL-E or Stable Diffusion) to generate an image of the item being searched for based on the user's investigation request.

[0599] Image Recognition: It has an image recognition engine (e.g., OpenCV or YOLOv5) to search for scenes that match the generated image from the recorded data.

[0600] Data analysis: It has the ability to analyze user survey requests using natural language processing tools (e.g., GPT-3 and BERT) and use the results.

[0601] Estimation: Has the ability to estimate the current location of the item based on the last known location of the item.

[0602] Notification: Has the function of sending estimated information to the user's terminal.

[0603] 3. User Device

[0604] The user terminal is a smartphone, PC, etc., and has the following functions:

[0605] Survey request: Provide an interface that allows users to easily input survey requests using an application (e.g., iOS app, Android app, web app).

[0606] Notification reception: Has the function of receiving notifications from the information processing device and displaying them to the user.

[0607] Examples of specific examples and prompts

[0608] Example 1: Searching for a key

[0609] 1. The user enters "Find the red key" into a dedicated app (smartphone app or web app).

[0610] 2. The terminal transmits the input contents to the information processing device.

[0611] 3. The information processing device analyzes the "red key" using a natural language processing engine (e.g., GPT-3 or BERT) and generates an image using an image generation engine (e.g., DALL-E or Stable Diffusion).

[0612] 4. The information processing device uses an image recognition engine (e.g., OpenCV or YOLOv5) to search for the relevant scene from the recorded data and extract the frame in which the "red key" appears.

[0613] 5. The information processing device estimates the current location based on the time and location of the frame.

[0614] 6. The information processing device generates a notification that says, "The red key is on the table in the living room," and sends it to the terminal.

[0615] 7. The device receives the notification and displays it to the user.

[0616] Example prompt sentence:

[0617] Prompt for the natural language processing engine:

[0618] text

[0619] User input: "Find the red key"

[0620] Analyze this request and extract the characteristics of the item being sought.

[0621] Prompt for image generation engine:

[0622] text

[0623] Search Feature: Red Key

[0624] Based on this feature, generate an image of the red key.

[0625] This allows the system to efficiently find what the user is looking for.

[0626] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0627] Step 1: Acquire camera footage

[0628] Input: Cameras are installed at various locations in the room, record images in real time, and send them to an information processing device.

[0629] Server operation: The server sets the IP address and streaming URL of each camera and receives video data using the RTSP protocol or HTTP protocol.

[0630] Data processing: The server adds time and date metadata to the received video data.

[0631] Output: The video data is stored in a database along with date and time information.

[0632] Step 2: Request a lost item investigation

[0633] Input: The user inputs an investigation request such as "Find the red key" using a dedicated application on their smartphone or PC.

[0634] What the device does: The device receives the text entered by the user and sends it to the server via an API request (e.g., an HTTP POST request).

[0635] Output: The survey request is sent to the server.

[0636] Step 3: Analyze the survey request and generate an image

[0637] Input: The server receives a survey request from the user.

[0638] Server operation: The server uses a natural language processing engine (e.g., GPT-3 or BERT) to extract the keyword "red key."

[0639] Data processing: The server sends the extracted keywords as prompts to the image generation engine (e.g., DALL-E or Stable Diffusion).

[0640] Output: The image generation engine generates an image of the "red key" which is returned to the server.

[0641] Step 4: Search for recording data

[0642] Input: The server receives the generated image.

[0643] Server operation: The server uses an image recognition engine (e.g., OpenCV or YOLOv5) to search for scenes that match the generated image from the video data in the database.

[0644] Data processing: An image recognition engine compares each frame of video data to identify matching scenes.

[0645] Output: The time and location information of the matching scenes is returned to the server.

[0646] Step 5: Estimate the location of the item

[0647] Input: The server receives the time and location information of the matching scene.

[0648] Server operation: The server uses historical data and AI algorithms (e.g., recurrent neural networks) to analyze the movement patterns of the items being searched for.

[0649] Data processing: The server calculates the current estimated location based on past location data.

[0650] Output: The estimated current location of the searched item is identified.

[0651] Step 6: Notification

[0652] Input: The server receives the estimated current location of the object.

[0653] Server Action: The server generates a notification message for the user, containing specific information such as "The red key is on the table in the living room."

[0654] Output: A notification message is sent to the terminal.

[0655] Terminal behavior: The terminal receives the notification message and displays it to the user, either as a pop-up notification or a banner notification.

[0656] (Application example 1)

[0657] 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."

[0658] In large facilities such as logistics centers, it is crucial for workers to be able to quickly and accurately locate goods and materials. However, in environments with large areas and many products, finding what is needed can take a lot of time and effort. Current systems lack sufficient visual instructions and real-time information to quickly identify the location of items, so further improvements are needed.

[0659] 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.

[0660] In this invention, the server includes: a camera for continuously recording video of a room and transmitting the video to the server; a server for storing the recorded video; a user's request for an investigation into a lost item; a server for receiving the investigation request and generating an image of the lost item using an image generating means; a server for searching for a matching scene from past recorded data based on the generated image; a server for estimating the location of the lost item based on the search result; a server for notifying the user of the estimated location information; a smart device for displaying visual instructions on the smart device worn by the user; and an augmented reality means for highlighting an object within the user's field of view based on the visual instructions. This allows the user to quickly and accurately locate the lost item through the smart device, significantly improving work efficiency.

[0661] A "camera" is a device that continuously records video of the room and transmits it to a server.

[0662] The "server" is a central computer system that stores recorded video and generates images of items searched for based on user requests for investigation, as well as searches from past recorded data.

[0663] A "user" is a person or terminal user who requests a search for a lost item and receives notifications from the system.

[0664] "Image generation means" refers to the function of the server to generate a concrete image of the item being searched for based on a search request.

[0665] "Recorded data" refers to past video information captured by a camera and stored on a server.

[0666] "Image recognition means" is a technology for searching past recorded data for scenes that match the generated image.

[0667] The "location estimation means" is a function that estimates the current location of the item being searched for based on the search results.

[0668] The "notification means" is a function that transmits estimated location information to the user.

[0669] A "smart device" is a device worn by a user that can receive visual instructions from a system. Specific examples include smart glasses.

[0670] "Augmented reality tools" are techniques that visually highlight objects within a user's field of view.

[0671] The present invention is a system for efficiently finding lost items or products in a logistics center. This system is composed of a camera, a server, a smart device, etc.

[0672] System Configuration

[0673] 1. Camera

[0674] Cameras are installed in various locations within the distribution center, continuously recording images from inside the center and sending them to a server. It is desirable for the cameras to be high-resolution and capable of monitoring a wide area.

[0675] 2. Server

[0676] The server has the following main functions:

[0677] Recording data storage: Video data sent from the camera is recorded with date and time information, and the video data is stored in a database for efficient searching.

[0678] Image generation: Based on the search request, a generative AI model is used to generate a specific image of the searched item. For example, if a user types "Look for product A," the generative AI model will generate an image of product A.

[0679] Image Recognition: Searches for scenes that match the generated image in the recorded data. Using an image recognition engine, it extracts relevant scenes from past data.

[0680] Natural language processing: Understanding the user's research request and triggering the image generation engine based on the results. This process uses natural language processing models such as BERT.

[0681] Location estimation: Based on the time and location information of the searched scene, the last known location of the item is identified and its current location is estimated.

[0682] Notification: The estimation results are sent to the user's device, providing specific information such as "Product A is located fifth on shelf A."

[0683] 3. Smart Devices

[0684] Smart devices include smart glasses, smartphones, etc., which have the following functions:

[0685] User interface: Provide a voice and touch input interface that allows users to easily input survey requests.

[0686] Displaying visual instructions: Visually displaying the inference results sent from the server, for example by using the AR functionality of smart glasses to highlight objects within the user's field of view.

[0687] Notification reception: Receive notifications from the server in real time and display them to the user.

[0688] Specific examples

[0689] For example, suppose a user working in a logistics center is wearing smart glasses. If the user voice-inputs, "Look for product A," the smart glasses recognize the voice and send it to the server. The server analyzes the search request using a natural language processing engine and generates an image of product A using a generative AI model. The server then uses an image recognition engine to search for scenes in which product A was recorded in the camera's video data and estimate its current location. The resulting information, "Product A is located fifth on shelf A," is generated and sent to the smart glasses. Using the smart glasses' AR function, this information is displayed within the user's field of view, with product A highlighted in a specific location.

[0690] Prompt Sentence Examples

[0691] User: "Looking for product A"

[0692] This system enables logistics center workers to quickly and accurately find the items they are looking for, significantly improving work efficiency.

[0693] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0694] Step 1:

[0695] The user inputs "Search for product A" through voice or text input via smart glasses or a smartphone. This input is sent to the server via the user interface (input: voice or text, output: data sent to the server).

[0696] Step 2:

[0697] The server analyzes the received user survey request using a natural language processing engine. In the analysis process, for example, a BERT model is used to extract the intent of the survey request and important keywords (input: survey request data, output: analysis result data).

[0698] Step 3:

[0699] The server uses a generative AI model based on the analysis results obtained by the natural language processing engine to generate a concrete image of Product A. The generated image data is saved on the server (input: analysis result data, output: generated image data).

[0700] Step 4:

[0701] The server uses an image recognition engine to search for scenes that match the image of product A generated from past recorded data. This process uses a machine learning model (for example, YOLO or OpenCV functions) (input: generated image data, recorded data, output: matching scene data).

[0702] Step 5:

[0703] The server identifies the last known location of product A based on the time and location information of the matching scene data, and estimates its current location. The estimation uses an algorithm that analyzes movement patterns within a specified range (input: matching scene data, output: estimated location data).

[0704] Step 6:

[0705] The server generates a message notifying the user of the estimated location information and sends it to the smart device worn by the user. The generated message includes the specific location (e.g., "Product A is located fifth on shelf A") (Input: estimated location data, Output: notification message).

[0706] Step 7:

[0707] The smart device receives the notification message from the server and displays it on the user's screen as a visual indication. It also uses AR functionality to highlight objects within the user's field of view. In this way, the user can confirm the location of Item A in real time (Input: notification message, Output: visual display and highlighting).

[0708] This allows users to quickly and accurately find the products and materials they need, significantly improving the work efficiency of logistics centers.

[0709] 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.

[0710] The present invention is a search system that uses a camera, server, and user terminal combined with an emotion engine. The entire system aims to realize more human-centered interaction by recognizing the user's emotions, thereby enabling efficient discovery of searched items and improving the user experience. Detailed embodiments of the system are described below.

[0711] System Configuration

[0712] 1. Camera

[0713] The cameras are installed in various locations in the room and record video 24 hours a day, and the recorded video is sent to a server in real time.

[0714] 2. Server

[0715] The server has the following functions:

[0716] Video data storage: Records video data sent from the camera along with date and time information.

[0717] Database: Manages recorded video data so that it can be searched efficiently.

[0718] Image generation: It has an image generation engine that generates an image of the item being searched for based on the user's search request.

[0719] Image Recognition: It has an image recognition engine to search for scenes that match the generated image in the recorded data.

[0720] Data analysis: A function that analyzes users' survey requests using natural language processing and utilizes the results.

[0721] Estimation: A feature that estimates the current location of an item based on its last known location.

[0722] Emotion Engine: A feature that recognizes the emotion a user expresses when requesting a survey and tailors responses accordingly.

[0723] Notification: A function that sends estimated information to the user's device. The notification content is customized based on emotional information.

[0724] 3. User Device

[0725] The user terminal is a smartphone, PC, etc., and has the following functions:

[0726] Survey request: Provides an interface that allows users to easily input survey requests.

[0727] Emotion recognition: Detects emotions from the user's facial expressions and voice and sends that information to the server.

[0728] Notification reception: A function that receives notifications from the server and displays them to the user.

[0729] Program Processing

[0730] 1. Acquiring camera footage

[0731] The server receives video data in real time transmitted from a camera installed in the room.

[0732] The server adds date and time information to the received video data and stores it in a database.

[0733] 2. Request for lost items

[0734] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[0735] The terminal recognizes the user's emotions from their facial expressions and voice, and sends a survey request including the emotion information to the server.

[0736] 3. Image generation of the searched item

[0737] The server analyzes the received investigation request using a natural language processing engine and extracts the characteristics of the item being searched for.

[0738] The server then invokes an image generation engine (e.g., a generative model) based on the features to generate a specific image of the object being searched for (in this case, a red key).

[0739] 4. Searching for recorded data

[0740] The server uses the generated image to search for past recorded data.

[0741] The server uses an image recognition engine to extract the relevant scenes.

[0742] 5. Estimating the location of lost items

[0743] The server identifies the location where the item was last seen based on the time and location information of the searched scene.

[0744] The server uses this information to estimate the current location.

[0745] 6. Notification

[0746] The server then changes the content of the notification message appropriately based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, the server will notify them with a message such as, "Please stay calm. The red key is on the table in the living room."

[0747] The terminal receives the notification from the server and displays the content in the user's application.

[0748] Specific examples

[0749] Example 1: Searching for a key

[0750] 1. The user types "Find the red key" into the dedicated app. At the same time, the user's facial expressions and voice are analyzed by the emotion recognition engine to detect stress or impatience.

[0751] 2. The device sends the input content and emotion information to the server.

[0752] 3. The server analyzes the "red key" using a natural language processing engine and generates an image using an image generation engine.

[0753] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[0754] 5. The server estimates the current location based on the time and location of that frame.

[0755] 6. The server generates a notification message that takes into account the user's emotional information: "Please stay calm. The red key is on the table in the living room." and sends it to the device.

[0756] 7. The device receives the notification and displays it to the user.

[0757] This system allows users to find what they are looking for more safely and efficiently.

[0758] The processing flow will be explained below.

[0759] Step 1:

[0760] The camera continuously records video of the room 24 hours a day and transmits the recorded data to a server in real time.

[0761] Step 2:

[0762] The server receives the video data sent from the camera, adds date and time information, and stores it in a database. The stored video data is kept for a certain period of time (for example, one week).

[0763] Step 3:

[0764] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[0765] Step 4:

[0766] The device analyzes the user's facial expressions and voice to recognize their emotions, and then sends a survey request including that emotional information to the server. For example, if the user is feeling anxious, that emotional information will also be sent.

[0767] Step 5:

[0768] The server analyzes the received investigation request using a natural language processing (NLP) engine and extracts the characteristics of the item being searched for: the "red key."

[0769] Step 6:

[0770] The server calls an image generation engine based on the extracted features to generate a concrete image of the "red key."

[0771] Step 7:

[0772] The server searches past recorded data based on the generated "red key" image. First, it filters recorded data from a specific period (for example, the past week).

[0773] Step 8:

[0774] The server then inputs the filtered video data into an image recognition engine to find scenes that match the generated image, and the image recognition engine extracts frames that show the "red key."

[0775] Step 9:

[0776] The server determines the last known location of the item being searched for (the red key) based on the time and location information of the extracted video frame.

[0777] Step 10:

[0778] The server uses the location and time information to estimate the current location of the red key, using an algorithm that takes into account the passage of time and patterns of movement of objects.

[0779] Step 11:

[0780] The server then changes the content of the notification message appropriately based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, the server will notify them with a message such as, "Please stay calm. The red key is on the table in the living room."

[0781] Step 12:

[0782] The device receives the notification sent from the server and displays the content to the user within the application, allowing the user to check the location of the item they are looking for based on the notification content.

[0783] This process flow allows users to find what they are looking for efficiently and safely.

[0784] Example 2

[0785] 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."

[0786] Conventional object-finding systems have had the problem of making it difficult for users to quickly and efficiently find lost items. In particular, the system's response is often insufficient when users have difficulty clearly imagining the characteristics of the object they are looking for or when they are in an emotional state. Furthermore, there is a lack of a way to quickly search for an object from past video recording data, making it difficult to accurately estimate the location of the object. This results in a decrease in user satisfaction.

[0787] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0788] In this invention, the server includes: means for a camera to continuously record video of a room and transmit the video to the data processing device; means for the data processing device to save the recorded video; means for a user to request an investigation into an item they are looking for; means for the data processing device to receive the investigation request and generate an image of the item they are looking for using an image generation means; means for the data processing device to search for a matching scene from past recorded data based on the generated image; means for the data processing device to estimate the location of the item they are looking for based on the search results; means for the data processing device to notify the user of the estimated location information; and means for detecting emotional information from the user and generating a notification message according to the emotional information. This enables the user to find the item they are looking for quickly and efficiently, and an improved user experience can be expected by providing an appropriate response according to the user's emotional state.

[0789] A "camera" is a device that continuously records video of a room and transmits the captured video data to a server or data processing device.

[0790] The "data processing device" is a processing device that stores the video data transmitted from the camera and performs various processes based on the investigation request from the user.

[0791] A "request to find a lost item" is an operation or content in which a user inputs information about a lost item and requests the system to identify its location.

[0792] The "image generation means" is a means for analyzing the characteristics of the item being searched for based on the investigation request and generating a specific image of the item from those characteristics.

[0793] "Recorded data" refers to video data captured by a camera and stored in a data processing device.

[0794] A "matching scene" is a scene in the recorded data that matches the generated image.

[0795] "Means for estimating location" refers to means for estimating the last known location and current location of a searched item based on search results.

[0796] The "means for notifying" is a means for transmitting estimated location information to the user's terminal and notifying the user of this.

[0797] "Emotion information" is emotional data obtained from the user's facial expressions, voice, etc.

[0798] A "notification message" is a message that contains information about the item being searched for or other response content that is generated in response to the user's emotions.

[0799] The present invention is a search system that uses a camera, a data processing device, and a user terminal combined with an emotion engine. The entire system aims to realize more human-centered interaction by recognizing the user's emotions, thereby enabling efficient discovery of the searched item and improving the user experience. Detailed embodiments of the system are described below.

[0800] System Configuration

[0801] 1. Camera

[0802] The cameras are installed in various locations in the room and record video 24 hours a day. The recorded video is sent to a data processing device in real time. Specifically, the video stream is sent using the TCP / IP protocol, and the data processing device encodes the video data using a library such as FFmpeg.

[0803] 2. Data Processing Device

[0804] The data processing device has the following functions:

[0805] Video data storage: Video data sent from the camera is recorded along with date and time information. For storage, an SQL-based database (e.g., MySQL) or a NoSQL database (e.g., MongoDB) is used.

[0806] Database: Manages recorded video data so that it can be searched efficiently.

[0807] Image generation: It has an image generation method (e.g., DALL-E or Stable Diffusion) to generate an image of the searched item based on the user's investigation request.

[0808] Image Recognition: It has an image recognition method (e.g., YOLOv5) to search for scenes that match the generated image from the recorded data.

[0809] Data analysis: A function that analyzes user survey requests using natural language processing tools (e.g., BERT) and uses the results.

[0810] Estimation: A feature that estimates the current location of an item based on its last known location.

[0811] Emotion engine: The ability to recognize the emotion a user expresses when requesting a survey and tailor responses accordingly (e.g., OpenFace or Microsoft Azure Face API).

[0812] Notification: A function that sends estimated information to the user's device. The notification content is customized based on emotional information.

[0813] 3. User Device

[0814] The user terminal is a smartphone, PC, etc., and has the following functions:

[0815] Survey request: We provide an interface that allows users to easily input survey requests. This uses cameras and microphones to detect emotions from facial expressions and voice.

[0816] Emotion recognition: Detecting emotions from the user's facial expressions and voice and sending that information to a data processing device.

[0817] Notification reception: A function to receive notifications from the data processing device and display them to the user.

[0818] Specific examples

[0819] Example 1: Searching for a key

[0820] 1. The user opens the app and types in "Find the red key." At the same time, the user's facial expressions and voice are captured by the camera and microphone and analyzed by the emotion recognition engine. For example, it may be determined that the user is anxious.

[0821] 2. The terminal transmits the input content and emotional information to the data processing device.

[0822] 3. The data processing device analyzes the "red key" using a natural language processing engine and generates a concrete image using an image generation means (e.g., DALL-E).

[0823] 4. Based on the generated image, the data processing device uses image recognition means (e.g., YOLOv5) to extract scenes containing the red key from past recorded data.

[0824] 5. The data processing device estimates the current location based on the time and location information of the detected scene. For example, the last place where the red key was seen may be identified as "on the table in the living room."

[0825] 6. The data processing device, taking into account that the user is in a hurry, generates a notification message saying "Please stay calm. The red key is on the table in the living room" and sends it to the terminal.

[0826] 7. The device receives the notification and displays its contents to the user.

[0827] This system allows users to find what they are looking for more safely and efficiently.

[0828] Example prompt sentence:

[0829] "I'm looking for the red key. I'm getting impatient."

[0830] "Generate an image of a red key."

[0831] "Please tell me where and when the red key was last seen."

[0832] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0833] Step 1: Acquire camera footage

[0834] The server receives video data in real time from a camera installed in the room. The input is the video stream from the camera, and the output is the encoded video data.

[0835] The server adds date and time information to the received video data and stores it in a database. Specifically, it encodes the video data using a library such as FFmpeg and adds date and time metadata. This data is then stored in an SQL-based database (e.g., MySQL) or a NoSQL database (e.g., MongoDB).

[0836] Step 2: Request for lost item investigation

[0837] The user uses a dedicated app on their device (smartphone or PC) to input a search request for an item they are looking for, such as "Find the red key." The input is the search request entered by the user in a text field, and the output is the search request data sent to the server.

[0838] The device's camera and microphone capture the user's facial expressions and voice, and an emotion recognition engine (e.g., OpenFace or Microsoft Azure Face API) extracts emotional data. Specifically, the emotion recognition engine analyzes facial and vocal characteristics to detect the user's emotional state.

[0839] The device formats the survey request text and extracted emotion data into JSON format and sends it to the server via an HTTP POST request.

[0840] Step 3: Create an image of what you're looking for

[0841] The server analyzes the received search request using a natural language processing engine (e.g., BERT) and extracts features of the searched item. The input is the text of the search request, and the output is feature data of the searched item. Specifically, it extracts key phrases using a natural language processing library (e.g., Hugging Face transformers).

[0842] The server uses an image generation engine (e.g., DALL-E or Stable Diffusion) based on the features to generate a specific image of the searched item. The input is the feature data of the searched item, and the output is the generated image. An example of a prompt sentence: "Generate an image of a red key."

[0843] Step 4: Search for recording data

[0844] The server uses the generated images to search past recorded data. The input is the generated images, and the output is the search results for matching scenes.

[0845] The server uses an image recognition engine (e.g., YOLOv5) to extract the relevant scenes. Specifically, the YOLOv5 model performs image recognition processing on each video frame to detect matching scenes.

[0846] Step 5: Estimate the location of the item

[0847] The server identifies the location where the item was last seen based on the time and location information of the searched scene. The input is the matching scene information, and the output is the estimated location information. Specifically, it retrieves the entry where the red key was last seen from the database and uses a rule-based algorithm to estimate its current location.

[0848] Step 6: Notification

[0849] The server appropriately changes the content of the notification message based on the user's emotional information recognized by the emotion engine. The input is emotional information and estimated location information, and the output is a customized notification message. In concrete terms, the emotion engine is used to analyze the user's emotional state and generate an appropriate notification message. An example of a prompt sentence: "Please stay calm. The red key is on the table in the living room."

[0850] The device receives notifications from the server and displays the contents in the user's application. The input is the notification message from the server, and the output is the notification display to the user. Specifically, the notification message is displayed in the notification center of the dedicated application or as a pop-up notification.

[0851] This allows users to find what they are looking for quickly and efficiently, and is expected to improve the user experience by providing appropriate responses based on emotions.

[0852] (Application example 2)

[0853] 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."

[0854] While there are existing systems for efficiently finding lost items, few systems can recognize and respond to the user's emotions. Furthermore, if the user is feeling anxious or stressed, appropriate assistance is not provided, resulting in a poor user experience. Conventional systems generate notification content without considering the user's emotional state, resulting in low user satisfaction. To solve this problem, a system that recognizes the user's emotions and responds appropriately based on them is needed.

[0855] 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.

[0856] In this invention, the server includes means for a camera to continuously record video of a room and transmit the video to the server, means for the server to save the recorded video, means for a user to request an investigation into an item they are looking for, means for the server to receive the investigation request and generate an image of the item they are looking for using an image generation means, means for the server to search for a matching scene from past recorded data based on the generated image, means for the server to estimate the location of the item they are looking for based on the search result, means for recognizing the user's emotion and adjusting notification content based on the emotion information, and means for the server to notify the user of the estimated location information. This enables appropriate notification and support based on the user's emotion, improving the user experience.

[0857] A "camera" is a device that continuously records video of a specific location or object and transmits it to a server.

[0858] The "server" is a computer system that stores the images sent from the camera, processes the user's investigation request, generates an image of the item being searched for, and searches for recorded data based on that image.

[0859] A "user terminal" is a device that allows a user to request an investigation into a lost item and has the function of receiving the investigation results. This includes smartphones and personal computers.

[0860] The "image generation means" is a technology that allows the server to generate a concrete image of the item being searched for based on the user's search request. For example, it uses a generative model.

[0861] "Image recognition means" is a technology that allows the server to search for scenes that match the generated image from the recorded data. For example, it uses an image analysis engine such as OpenCV.

[0862] "Natural language processing means" is a technology that analyzes a user's search request and extracts the characteristics of the item based on that analysis. For example, Google Cloud Natural Language API is an example of this.

[0863] "Emotion recognition means" refers to a technology that detects emotions by analyzing the user's facial expressions and voice and reflects that information in the notification content. For example, an emotion recognition engine is an example of this.

[0864] The "notification means" is a technology that notifies the user terminal of the content based on the predetermined information generated by the server. The content of the notification is adjusted based on the user's emotions.

[0865] The "means for estimating the location of a lost item" is a technology in which the server determines the time and place in which the lost item was captured based on the search results of image recognition, and estimates its current location.

[0866] The present invention is a system for finding things that uses emotion recognition technology and is configured by combining a camera, a server, and a user terminal. The detailed configuration of this system is described below.

[0867] System Configuration

[0868] 1. Camera

[0869] The camera continuously records video of a specific location or room and transmits it to a server in real time. High-resolution cameras are used to accurately capture video details.

[0870] 2. Server

[0871] A server is a computer system that has the following functions:

[0872] Video data storage: Video data sent from the camera is stored in a database along with date and time information.

[0873] Database: Efficiently search and manage video data.

[0874] Image generation: Using a generative model, a concrete image of the item being searched for is created based on the user's research request.

[0875] Image recognition: Using an image analysis engine such as OpenCV, it searches for scenes that match images generated from past recorded data.

[0876] Emotion Recognition: Using an emotion recognition engine, we analyze and recognize the user's emotional information and process it on the server side.

[0877] Natural language processing: Analyze user survey requests using Google Cloud Natural Language API, etc.

[0878] Location estimation of lost items: Estimates the current location based on the time and location information of search results.

[0879] Notification: Customize notification content based on the user's emotions and send information to the user's device.

[0880] 3. User Device

[0881] The user terminal is a smartphone or personal computer and has the following functions:

[0882] Interface: Provide an interface that allows users to easily input search requests for items they are looking for.

[0883] Emotion recognition: Analyzes the user's facial expressions and voice to detect emotional information and send it to the server.

[0884] Receive notifications: Receive notifications from the server and display them to the user.

[0885] System Operation

[0886] 1. The user launches the app and speaks or texts, "I'm looking for a miniature blue car."

[0887] 2. The user device analyzes the user's facial expressions and voice to obtain emotional information.

[0888] 3. The device sends the input information and emotion information to the server.

[0889] 4. The server uses natural language processing to identify a "miniature blue car" and generates an image using a generative model.

[0890] 5. The server uses an image analysis engine to search for the relevant scene in the recorded data.

[0891] 6. The server estimates the location of the item based on the search results and notifies the user's device of this information.

[0892] 7. The server customizes the notification content based on the emotional information. For example, if a user is feeling anxious, the server sends a message such as, "Don't worry. The miniature blue car is in the toy section on the second floor."

[0893] 8. The user device receives the notification and displays it to the user.

[0894] Specific examples

[0895] Example 1: Finding a key

[0896] 1. The user launches the app and types in "Find the red key." At the same time, the emotion recognition engine analyzes the user's facial expressions and voice to detect stress or impatience.

[0897] 2. The device sends the input content and emotion information to the server.

[0898] 3. The server analyzes the "red key" using natural language processing and generates an image using an image generation engine.

[0899] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[0900] 5. The server estimates the current location based on the time and location of that frame.

[0901] 6. The server generates a notification message that takes into account the user's emotional information: "Please stay calm. The red key is on the table in the living room." and sends it to the device.

[0902] 7. The device receives the notification and displays it to the user.

[0903] Prompt Sentence Examples

[0904] text

[0905] User: When searching for a miniature blue car in a store but cannot find it, the user types "find a miniature blue car" into the app. At the same time, the camera detects that the customer is anxious. The server processes this, estimates the location of the item, and responds with a relaxed message.

[0906] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0907] Step 1:

[0908] The user launches the app and enters a survey request.

[0909] The user launches a dedicated application on their smartphone or personal computer and inputs a search request for the item they are looking for by voice or text. For example, they might input, "I'm looking for a miniature blue car." At this time, the application also analyzes the user's facial expressions and voice to obtain emotional information.

[0910] Input: User survey request (voice or text), facial expression and voice data

[0911] Output: Investigation request details and emotional information

[0912] Step 2:

[0913] The device sends the investigation request and emotional information to the server.

[0914] The terminal transmits the survey request entered by the user and the analyzed emotional information to the server. At this time, the data is encrypted before transmission.

[0915] Input: Investigation request details and emotional information

[0916] Output: Survey request details and emotional information sent to the server

[0917] Step 3:

[0918] The server analyzes the investigation request.

[0919] The server analyzes the received search request using a natural language processing engine (such as Google Cloud Natural Language API) and extracts the characteristics of the item being searched for. In this case, the characteristics of a "miniature blue car" are extracted.

[0920] Input: Survey request details

[0921] Output: Extracted features of the searched item

[0922] Step 4:

[0923] The server generates an image of the item using an image generation engine.

[0924] The server uses a generative model (AI model) to generate a specific image of the item based on the extracted features of the item. For example, an image of a specific "miniature blue car" is generated.

[0925] Input: Extracted features of the object being searched for

[0926] Output: Generated image of the object being searched

[0927] Step 5:

[0928] The server searches for recorded data using an image recognition engine

[0929] The server uses the generated image to search past recorded data using an image recognition engine (such as OpenCV) and extracts matching scenes.

[0930] Input: Generated image of the object to be found

[0931] Output: Matching scenes with their time and location information

[0932] Step 6:

[0933] The server estimates the location of the item

[0934] The server estimates the current location of the item based on the time and location information of the search results. In this case, it estimates the specific location of the "miniature blue car."

[0935] Input: Time and location information of the matching scene

[0936] Output: Estimated current location of the item

[0937] Step 7:

[0938] The server customizes the notification content based on emotion information.

[0939] The server considers the user's emotional information and adjusts the notification content appropriately. For example, if the user is feeling anxious, it generates a relaxing message such as, "Don't worry. The miniature blue car is in the toy section on the second floor."

[0940] Input: Current location of the item being searched for, user's emotion information

[0941] Output: Customized notification content

[0942] Step 8:

[0943] The server notifies the user device of the estimated location information.

[0944] The server sends the customized notification content to the user terminal, and the terminal displays the received notification to the user.

[0945] Input: Customized notification content

[0946] Output: Information sent to the user's device

[0947] These steps allow users to find what they are looking for efficiently and provide a more pleasant experience by receiving appropriate notifications based on their emotions.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] [Third embodiment]

[0952] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0953] 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.

[0954] 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).

[0955] 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.

[0956] 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.

[0957] 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).

[0958] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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."

[0964] The present invention is a system implemented using a server and a user terminal that cooperate with a camera permanently installed in a room. This system allows a user to efficiently find an object they are looking for. Detailed embodiments of this system are described below.

[0965] System Configuration

[0966] 1. Camera

[0967] The cameras are installed in various locations in the room and record video 24 hours a day, and the recorded video is sent to a server in real time.

[0968] 2. Server

[0969] The server has the following functions:

[0970] Video data storage: Records video data sent from the camera along with date and time information.

[0971] Database: Manages recorded video data so that it can be searched efficiently.

[0972] Image generation: It has an image generation engine that generates an image of the item being searched for based on the user's search request.

[0973] Image Recognition: It has an image recognition engine to search for scenes that match the generated image in the recorded data.

[0974] Data analysis: A function that analyzes users' survey requests using natural language processing and utilizes the results.

[0975] Estimation: A feature that estimates the current location of an item based on its last known location.

[0976] Notification: A function to send estimated information to the user's device.

[0977] 3. User Device

[0978] The user terminal is a smartphone, PC, etc., and has the following functions:

[0979] Survey request: Provides an interface that allows users to easily input survey requests.

[0980] Notification reception: A function that receives notifications from the server and displays them to the user.

[0981] Program Processing

[0982] 1. Acquiring camera footage

[0983] The server receives video data in real time transmitted from a camera installed in the room.

[0984] The server adds date and time information to the received video data and stores it in a database.

[0985] 2. Request for lost items

[0986] Users can submit an investigation request using a dedicated application on their smartphone or PC, for example by entering "Find the red key."

[0987] The terminal sends the input to the server.

[0988] 3. Image generation of the searched item

[0989] The server analyzes the received investigation request using a natural language processing engine and extracts the characteristics of the item being searched for.

[0990] The server then calls an image generation engine (e.g., a generative model) based on the features to generate a specific image of the item being searched for.

[0991] 4. Searching for recorded data

[0992] The server uses the generated image to search for past recorded data.

[0993] The server uses an image recognition engine to extract the relevant scenes.

[0994] 5. Estimating the location of lost items

[0995] The server identifies the last known location of the item being searched for based on the time and location information of the searched scene.

[0996] The server uses this information to estimate the current location.

[0997] 6. Notification

[0998] The server generates a message informing the user of the result of the estimation, including specific information such as "The red key is on the table in the living room."

[0999] The terminal receives the notification from the server and displays it to the user.

[1000] Specific examples

[1001] Example 1: Searching for a key

[1002] 1. The user enters "Find the red key" into the dedicated app.

[1003] 2. The terminal sends the input information to the server.

[1004] 3. The server analyzes the "red key" using a natural language processing engine and generates an image using an image generation engine.

[1005] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[1006] 5. The server estimates the current location based on the time and location of that frame.

[1007] 6. The server generates a notification that says, "The red key is on the living room table," and sends it to the device.

[1008] 7. The device receives the notification and displays it to the user.

[1009] In this way, the user can efficiently locate the item they are looking for.

[1010] The processing flow will be explained below.

[1011] Step 1:

[1012] The camera continuously records video of the room 24 hours a day and transmits the recorded data to a server in real time.

[1013] Step 2:

[1014] The server receives the video data sent from the camera, adds date and time information, and stores it in a database. The stored video data is kept for a certain period of time (for example, one week).

[1015] Step 3:

[1016] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[1017] Step 4:

[1018] The device receives the user's search request and sends it to the server. The communication is via API and includes the necessary information (details of the item being searched).

[1019] Step 5:

[1020] The server analyzes the received search request using a natural language processing (NLP) engine, and extracts specific characteristics of the item from the analysis results.

[1021] Step 6:

[1022] The server inputs the analyzed features into an image generation engine (e.g., a generative model) to generate a specific image of the object being searched for (in this case, a red key). This generated image is used for further processing.

[1023] Step 7:

[1024] The server searches for past recorded data using the images generated by the image generation engine. First, it filters data from a specific period (e.g., the most recent week) from the entire recorded data.

[1025] Step 8:

[1026] The server then feeds the filtered video data into an image recognition engine to find scenes that match the generated image, which then extracts frames that show the object being searched for.

[1027] Step 9:

[1028] The server identifies the last known location of the item based on the time and location information of the video frame extracted by the image recognition engine.

[1029] Step 10:

[1030] The server uses the identified information to estimate the current location of the item, using an algorithm that takes into account the passage of time and the object's movement patterns.

[1031] Step 11:

[1032] The server generates a message to notify the user of the estimated location information, for example, a specific message such as "The red key is on the table in the living room."

[1033] Step 12:

[1034] The device receives the notification from the server and displays the content in the user's application, allowing the user to check the location of the item they are looking for based on the displayed information.

[1035] Following these steps, users can efficiently find what they are looking for.

[1036] Example 1

[1037] 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."

[1038] In today's busy lifestyles, losing things indoors is a frequent occurrence, and searching for them can take up a lot of time and effort. To address this issue, a system that allows users to efficiently find lost items is needed, but existing technologies have limitations in accuracy and convenience. In particular, improvements are needed in areas such as video data search, analysis of investigation requests using natural language, and image generation of lost items.

[1039] 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.

[1040] In this invention, the server includes means for a camera to continuously record video of a room and transmit the video to an information processing device, means for the information processing device to save the recorded video, means for a user to make a request to investigate an item they are looking for, means for the information processing device to receive the investigation request and generate an image of the item they are looking for using an image generation means, means for the information processing device to search for a matching scene from past recorded data based on the generated image, means for the information processing device to estimate the location of the item they are looking for based on the search results, and means for the information processing device to notify the user of the estimated location information, thereby enabling the user to efficiently find the item they are looking for.

[1041] A "camera" is a device that continuously records video of the interior of a room and transmits the video data to an information processing device in real time.

[1042] An "information processing device" is a device that stores received video data, processes investigation requests from users, generates images of items being searched for, searches recorded data, estimates their location, and notifies users.

[1043] "Video data" refers to indoor video recorded by a camera, and includes time and date information.

[1044] "User" refers to a person who uses a dedicated application to input a survey request and receive notifications from the system.

[1045] A "research request" refers to a request entered by a user to find a particular item.

[1046] "Image generation means" refers to a technology that generates a concrete image of the item being searched for based on the characteristics of the item obtained from the investigation request.

[1047] "Image recognition means" refers to a technology that searches for matching scenes from past recorded data based on the generated image.

[1048] "Natural language processing means" refers to technology that analyzes a user's search request and generates an image of the item being searched for based on the analysis results.

[1049] "Inference" refers to the process of determining the location of a searched item based on search results and predicting the appropriate location.

[1050] "Notification" refers to an act of conveying estimated location information from an information processing device to a user.

[1051] A "scene" refers to a frame or video section that represents a specific time or place in recorded data.

[1052] The present invention is a system implemented using an information processing device and a user terminal that cooperate with a camera permanently installed in a room. This system allows a user to efficiently find an item they are looking for. Detailed embodiments of this system are described below.

[1053] System Configuration

[1054] 1. Camera

[1055] The cameras are installed in various locations in the room and record images 24 hours a day, and the recorded images are sent to an information processing device in real time.

[1056] Example of use: Network cameras (e.g. Hikvision or Axis cameras)

[1057] 2. Information Processing Device

[1058] The information processing device has the following functions.

[1059] Video data storage: Video data sent from the camera is recorded along with date and time information. Database software (e.g., MySQL or PostgreSQL) is used.

[1060] Database management: Manage recorded video data so that it can be searched efficiently.

[1061] Image generation: It has an image generation engine (e.g., DALL-E or Stable Diffusion) to generate an image of the item being searched for based on the user's investigation request.

[1062] Image Recognition: It has an image recognition engine (e.g., OpenCV or YOLOv5) to search for scenes that match the generated image from the recorded data.

[1063] Data analysis: It has the ability to analyze user survey requests using natural language processing tools (e.g., GPT-3 and BERT) and use the results.

[1064] Estimation: Has the ability to estimate the current location of the item based on the last known location of the item.

[1065] Notification: Has the function of sending estimated information to the user's terminal.

[1066] 3. User Device

[1067] The user terminal is a smartphone, PC, etc., and has the following functions:

[1068] Survey request: Provide an interface that allows users to easily input survey requests using an application (e.g., iOS app, Android app, web app).

[1069] Notification reception: Has the function of receiving notifications from the information processing device and displaying them to the user.

[1070] Examples of specific examples and prompts

[1071] Example 1: Searching for a key

[1072] 1. The user enters "Find the red key" into a dedicated app (smartphone app or web app).

[1073] 2. The terminal transmits the input contents to the information processing device.

[1074] 3. The information processing device analyzes the "red key" using a natural language processing engine (e.g., GPT-3 or BERT) and generates an image using an image generation engine (e.g., DALL-E or Stable Diffusion).

[1075] 4. The information processing device uses an image recognition engine (e.g., OpenCV or YOLOv5) to search for the relevant scene from the recorded data and extract the frame in which the "red key" appears.

[1076] 5. The information processing device estimates the current location based on the time and location of the frame.

[1077] 6. The information processing device generates a notification that says, "The red key is on the table in the living room," and sends it to the terminal.

[1078] 7. The device receives the notification and displays it to the user.

[1079] Example prompt sentence:

[1080] Prompt for the natural language processing engine:

[1081] text

[1082] User input: "Find the red key"

[1083] Analyze this request and extract the characteristics of the item being sought.

[1084] Prompt for image generation engine:

[1085] text

[1086] Search Feature: Red Key

[1087] Based on this feature, generate an image of the red key.

[1088] This allows the system to efficiently find what the user is looking for.

[1089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1090] Step 1: Acquire camera footage

[1091] Input: Cameras are installed at various locations in the room, record images in real time, and send them to an information processing device.

[1092] Server operation: The server sets the IP address and streaming URL of each camera and receives video data using the RTSP protocol or HTTP protocol.

[1093] Data processing: The server adds time and date metadata to the received video data.

[1094] Output: The video data is stored in a database along with date and time information.

[1095] Step 2: Request a lost item investigation

[1096] Input: The user inputs an investigation request such as "Find the red key" using a dedicated application on their smartphone or PC.

[1097] What the device does: The device receives the text entered by the user and sends it to the server via an API request (e.g., an HTTP POST request).

[1098] Output: The survey request is sent to the server.

[1099] Step 3: Analyze the survey request and generate an image

[1100] Input: The server receives a survey request from the user.

[1101] Server operation: The server uses a natural language processing engine (e.g., GPT-3 or BERT) to extract the keyword "red key."

[1102] Data processing: The server sends the extracted keywords as prompts to the image generation engine (e.g., DALL-E or Stable Diffusion).

[1103] Output: The image generation engine generates an image of the "red key" which is returned to the server.

[1104] Step 4: Search for recording data

[1105] Input: The server receives the generated image.

[1106] Server operation: The server uses an image recognition engine (e.g., OpenCV or YOLOv5) to search for scenes that match the generated image from the video data in the database.

[1107] Data processing: An image recognition engine compares each frame of video data to identify matching scenes.

[1108] Output: The time and location information of the matching scenes is returned to the server.

[1109] Step 5: Estimate the location of the item

[1110] Input: The server receives the time and location information of the matching scene.

[1111] Server operation: The server uses historical data and AI algorithms (e.g., recurrent neural networks) to analyze the movement patterns of the items being searched for.

[1112] Data processing: The server calculates the current estimated location based on past location data.

[1113] Output: The estimated current location of the searched item is identified.

[1114] Step 6: Notification

[1115] Input: The server receives the estimated current location of the object.

[1116] Server Action: The server generates a notification message for the user, containing specific information such as "The red key is on the table in the living room."

[1117] Output: A notification message is sent to the terminal.

[1118] Terminal behavior: The terminal receives the notification message and displays it to the user, either as a pop-up notification or a banner notification.

[1119] (Application example 1)

[1120] 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."

[1121] In large facilities such as logistics centers, it is crucial for workers to be able to quickly and accurately locate goods and materials. However, in environments with large areas and many products, finding what is needed can take a lot of time and effort. Current systems lack sufficient visual instructions and real-time information to quickly identify the location of items, so further improvements are needed.

[1122] 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.

[1123] In this invention, the server includes: a camera for continuously recording video of a room and transmitting the video to the server; a server for storing the recorded video; a user's request for an investigation into a lost item; a server for receiving the investigation request and generating an image of the lost item using an image generating means; a server for searching for a matching scene from past recorded data based on the generated image; a server for estimating the location of the lost item based on the search result; a server for notifying the user of the estimated location information; a smart device for displaying visual instructions on the smart device worn by the user; and an augmented reality means for highlighting an object within the user's field of view based on the visual instructions. This allows the user to quickly and accurately locate the lost item through the smart device, significantly improving work efficiency.

[1124] A "camera" is a device that continuously records video of the room and transmits it to a server.

[1125] The "server" is a central computer system that stores recorded video and generates images of items searched for based on user requests for investigation, as well as searches from past recorded data.

[1126] A "user" is a person or terminal user who requests a search for a lost item and receives notifications from the system.

[1127] "Image generation means" refers to the function of the server to generate a concrete image of the item being searched for based on a search request.

[1128] "Recorded data" refers to past video information captured by a camera and stored on a server.

[1129] "Image recognition means" is a technology for searching past recorded data for scenes that match the generated image.

[1130] The "location estimation means" is a function that estimates the current location of the item being searched for based on the search results.

[1131] The "notification means" is a function that transmits estimated location information to the user.

[1132] A "smart device" is a device worn by a user that can receive visual instructions from a system. Specific examples include smart glasses.

[1133] "Augmented reality tools" are techniques that visually highlight objects within a user's field of view.

[1134] The present invention is a system for efficiently finding lost items or products in a logistics center. This system is composed of a camera, a server, a smart device, etc.

[1135] System Configuration

[1136] 1. Camera

[1137] Cameras are installed in various locations within the distribution center, continuously recording images from inside the center and sending them to a server. It is desirable for the cameras to be high-resolution and capable of monitoring a wide area.

[1138] 2. Server

[1139] The server has the following main functions:

[1140] Recording data storage: Video data sent from the camera is recorded with date and time information, and the video data is stored in a database for efficient searching.

[1141] Image generation: Based on the search request, a generative AI model is used to generate a specific image of the searched item. For example, if a user types "Look for product A," the generative AI model will generate an image of product A.

[1142] Image Recognition: Searches for scenes that match the generated image in the recorded data. Using an image recognition engine, it extracts relevant scenes from past data.

[1143] Natural language processing: Understanding the user's research request and triggering the image generation engine based on the results. This process uses natural language processing models such as BERT.

[1144] Location estimation: Based on the time and location information of the searched scene, the last known location of the item is identified and its current location is estimated.

[1145] Notification: The estimation results are sent to the user's device, providing specific information such as "Product A is located fifth on shelf A."

[1146] 3. Smart Devices

[1147] Smart devices include smart glasses, smartphones, etc., which have the following functions:

[1148] User interface: Provide a voice and touch input interface that allows users to easily input survey requests.

[1149] Displaying visual instructions: Visually displaying the inference results sent from the server, for example by using the AR functionality of smart glasses to highlight objects within the user's field of view.

[1150] Notification reception: Receive notifications from the server in real time and display them to the user.

[1151] Specific examples

[1152] For example, suppose a user working in a logistics center is wearing smart glasses. If the user voice-inputs, "Look for product A," the smart glasses recognize the voice and send it to the server. The server analyzes the search request using a natural language processing engine and generates an image of product A using a generative AI model. The server then uses an image recognition engine to search for scenes in which product A was recorded in the camera's video data and estimate its current location. The resulting information, "Product A is located fifth on shelf A," is generated and sent to the smart glasses. Using the smart glasses' AR function, this information is displayed within the user's field of view, with product A highlighted in a specific location.

[1153] Prompt Sentence Examples

[1154] User: "Looking for product A"

[1155] This system enables logistics center workers to quickly and accurately find the items they are looking for, significantly improving work efficiency.

[1156] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1157] Step 1:

[1158] The user inputs "Search for product A" through voice or text input via smart glasses or a smartphone. This input is sent to the server via the user interface (input: voice or text, output: data sent to the server).

[1159] Step 2:

[1160] The server analyzes the received user survey request using a natural language processing engine. In the analysis process, for example, a BERT model is used to extract the intent of the survey request and important keywords (input: survey request data, output: analysis result data).

[1161] Step 3:

[1162] The server uses a generative AI model based on the analysis results obtained by the natural language processing engine to generate a concrete image of Product A. The generated image data is saved on the server (input: analysis result data, output: generated image data).

[1163] Step 4:

[1164] The server uses an image recognition engine to search for scenes that match the image of product A generated from past recorded data. This process uses a machine learning model (for example, YOLO or OpenCV functions) (input: generated image data, recorded data, output: matching scene data).

[1165] Step 5:

[1166] The server identifies the last known location of product A based on the time and location information of the matching scene data, and estimates its current location. The estimation uses an algorithm that analyzes movement patterns within a specified range (input: matching scene data, output: estimated location data).

[1167] Step 6:

[1168] The server generates a message notifying the user of the estimated location information and sends it to the smart device worn by the user. The generated message includes the specific location (e.g., "Product A is located fifth on shelf A") (Input: estimated location data, Output: notification message).

[1169] Step 7:

[1170] The smart device receives the notification message from the server and displays it on the user's screen as a visual indication. It also uses AR functionality to highlight objects within the user's field of view. In this way, the user can confirm the location of Item A in real time (Input: notification message, Output: visual display and highlighting).

[1171] This allows users to quickly and accurately find the products and materials they need, significantly improving the work efficiency of logistics centers.

[1172] 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.

[1173] The present invention is a search system that uses a camera, server, and user terminal combined with an emotion engine. The entire system aims to realize more human-centered interaction by recognizing the user's emotions, thereby enabling efficient discovery of searched items and improving the user experience. Detailed embodiments of the system are described below.

[1174] System Configuration

[1175] 1. Camera

[1176] The cameras are installed in various locations in the room and record video 24 hours a day, and the recorded video is sent to a server in real time.

[1177] 2. Server

[1178] The server has the following functions:

[1179] Video data storage: Records video data sent from the camera along with date and time information.

[1180] Database: Manages recorded video data so that it can be searched efficiently.

[1181] Image generation: It has an image generation engine that generates an image of the item being searched for based on the user's search request.

[1182] Image Recognition: It has an image recognition engine to search for scenes that match the generated image in the recorded data.

[1183] Data analysis: A function that analyzes users' survey requests using natural language processing and utilizes the results.

[1184] Estimation: A feature that estimates the current location of an item based on its last known location.

[1185] Emotion Engine: A feature that recognizes the emotion a user expresses when requesting a survey and tailors responses accordingly.

[1186] Notification: A function that sends estimated information to the user's device. The notification content is customized based on emotional information.

[1187] 3. User Device

[1188] The user terminal is a smartphone, PC, etc., and has the following functions:

[1189] Survey request: Provides an interface that allows users to easily input survey requests.

[1190] Emotion recognition: Detects emotions from the user's facial expressions and voice and sends that information to the server.

[1191] Notification reception: A function that receives notifications from the server and displays them to the user.

[1192] Program Processing

[1193] 1. Acquiring camera footage

[1194] The server receives video data in real time transmitted from a camera installed in the room.

[1195] The server adds date and time information to the received video data and stores it in a database.

[1196] 2. Request for lost items

[1197] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[1198] The terminal recognizes the user's emotions from their facial expressions and voice, and sends a survey request including the emotion information to the server.

[1199] 3. Image generation of the searched item

[1200] The server analyzes the received investigation request using a natural language processing engine and extracts the characteristics of the item being searched for.

[1201] The server then invokes an image generation engine (e.g., a generative model) based on the features to generate a specific image of the object being searched for (in this case, a red key).

[1202] 4. Searching for recorded data

[1203] The server uses the generated image to search for past recorded data.

[1204] The server uses an image recognition engine to extract the relevant scenes.

[1205] 5. Estimating the location of lost items

[1206] The server identifies the location where the item was last seen based on the time and location information of the searched scene.

[1207] The server uses this information to estimate the current location.

[1208] 6. Notification

[1209] The server then changes the content of the notification message appropriately based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, the server will notify them with a message such as, "Please stay calm. The red key is on the table in the living room."

[1210] The terminal receives the notification from the server and displays the content in the user's application.

[1211] Specific examples

[1212] Example 1: Searching for a key

[1213] 1. The user types "Find the red key" into the dedicated app. At the same time, the user's facial expressions and voice are analyzed by the emotion recognition engine to detect stress or impatience.

[1214] 2. The device sends the input content and emotion information to the server.

[1215] 3. The server analyzes the "red key" using a natural language processing engine and generates an image using an image generation engine.

[1216] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[1217] 5. The server estimates the current location based on the time and location of that frame.

[1218] 6. The server generates a notification message that takes into account the user's emotional information: "Please stay calm. The red key is on the table in the living room." and sends it to the device.

[1219] 7. The device receives the notification and displays it to the user.

[1220] This system allows users to find what they are looking for more safely and efficiently.

[1221] The processing flow will be explained below.

[1222] Step 1:

[1223] The camera continuously records video of the room 24 hours a day and transmits the recorded data to a server in real time.

[1224] Step 2:

[1225] The server receives the video data sent from the camera, adds date and time information, and stores it in a database. The stored video data is kept for a certain period of time (for example, one week).

[1226] Step 3:

[1227] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[1228] Step 4:

[1229] The device analyzes the user's facial expressions and voice to recognize their emotions, and then sends a survey request including that emotional information to the server. For example, if the user is feeling anxious, that emotional information will also be sent.

[1230] Step 5:

[1231] The server analyzes the received investigation request using a natural language processing (NLP) engine and extracts the characteristics of the item being searched for: the "red key."

[1232] Step 6:

[1233] The server calls an image generation engine based on the extracted features to generate a concrete image of the "red key."

[1234] Step 7:

[1235] The server searches past recorded data based on the generated "red key" image. First, it filters recorded data from a specific period (for example, the past week).

[1236] Step 8:

[1237] The server then inputs the filtered video data into an image recognition engine to find scenes that match the generated image, and the image recognition engine extracts frames that show the "red key."

[1238] Step 9:

[1239] The server determines the last known location of the item being searched for (the red key) based on the time and location information of the extracted video frame.

[1240] Step 10:

[1241] The server uses the location and time information to estimate the current location of the red key, using an algorithm that takes into account the passage of time and patterns of movement of objects.

[1242] Step 11:

[1243] The server then changes the content of the notification message appropriately based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, the server will notify them with a message such as, "Please stay calm. The red key is on the table in the living room."

[1244] Step 12:

[1245] The device receives the notification sent from the server and displays the content to the user within the application, allowing the user to check the location of the item they are looking for based on the notification content.

[1246] This process flow allows users to find what they are looking for efficiently and safely.

[1247] Example 2

[1248] 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."

[1249] Conventional object-finding systems have had the problem of making it difficult for users to quickly and efficiently find lost items. In particular, the system's response is often insufficient when users have difficulty clearly imagining the characteristics of the object they are looking for or when they are in an emotional state. Furthermore, there is a lack of a way to quickly search for an object from past video recording data, making it difficult to accurately estimate the location of the object. This results in a decrease in user satisfaction.

[1250] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1251] In this invention, the server includes: means for a camera to continuously record video of a room and transmit the video to the data processing device; means for the data processing device to save the recorded video; means for a user to request an investigation into an item they are looking for; means for the data processing device to receive the investigation request and generate an image of the item they are looking for using an image generation means; means for the data processing device to search for a matching scene from past recorded data based on the generated image; means for the data processing device to estimate the location of the item they are looking for based on the search results; means for the data processing device to notify the user of the estimated location information; and means for detecting emotional information from the user and generating a notification message according to the emotional information. This enables the user to find the item they are looking for quickly and efficiently, and an improved user experience can be expected by providing an appropriate response according to the user's emotional state.

[1252] A "camera" is a device that continuously records video of a room and transmits the captured video data to a server or data processing device.

[1253] The "data processing device" is a processing device that stores the video data transmitted from the camera and performs various processes based on the investigation request from the user.

[1254] A "request to find a lost item" is an operation or content in which a user inputs information about a lost item and requests the system to identify its location.

[1255] The "image generation means" is a means for analyzing the characteristics of the item being searched for based on the investigation request and generating a specific image of the item from those characteristics.

[1256] "Recorded data" refers to video data captured by a camera and stored in a data processing device.

[1257] A "matching scene" is a scene in the recorded data that matches the generated image.

[1258] "Means for estimating location" refers to means for estimating the last known location and current location of a searched item based on search results.

[1259] The "means for notifying" is a means for transmitting estimated location information to the user's terminal and notifying the user of this.

[1260] "Emotion information" is emotional data obtained from the user's facial expressions, voice, etc.

[1261] A "notification message" is a message that contains information about the item being searched for or other response content that is generated in response to the user's emotions.

[1262] The present invention is a search system that uses a camera, a data processing device, and a user terminal combined with an emotion engine. The entire system aims to realize more human-centered interaction by recognizing the user's emotions, thereby enabling efficient discovery of the searched item and improving the user experience. Detailed embodiments of the system are described below.

[1263] System Configuration

[1264] 1. Camera

[1265] The cameras are installed in various locations in the room and record video 24 hours a day. The recorded video is sent to a data processing device in real time. Specifically, the video stream is sent using the TCP / IP protocol, and the data processing device encodes the video data using a library such as FFmpeg.

[1266] 2. Data Processing Device

[1267] The data processing device has the following functions:

[1268] Video data storage: Video data sent from the camera is recorded along with date and time information. For storage, an SQL-based database (e.g., MySQL) or a NoSQL database (e.g., MongoDB) is used.

[1269] Database: Manages recorded video data so that it can be searched efficiently.

[1270] Image generation: It has an image generation method (e.g., DALL-E or Stable Diffusion) to generate an image of the searched item based on the user's investigation request.

[1271] Image Recognition: It has an image recognition method (e.g., YOLOv5) to search for scenes that match the generated image from the recorded data.

[1272] Data analysis: A function that analyzes user survey requests using natural language processing tools (e.g., BERT) and uses the results.

[1273] Estimation: A feature that estimates the current location of an item based on its last known location.

[1274] Emotion engine: The ability to recognize the emotion a user expresses when requesting a survey and tailor responses accordingly (e.g., OpenFace or Microsoft Azure Face API).

[1275] Notification: A function that sends estimated information to the user's device. The notification content is customized based on emotional information.

[1276] 3. User Device

[1277] The user terminal is a smartphone, PC, etc., and has the following functions:

[1278] Survey request: We provide an interface that allows users to easily input survey requests. This uses cameras and microphones to detect emotions from facial expressions and voice.

[1279] Emotion recognition: Detecting emotions from the user's facial expressions and voice and sending that information to a data processing device.

[1280] Notification reception: A function to receive notifications from the data processing device and display them to the user.

[1281] Specific examples

[1282] Example 1: Searching for a key

[1283] 1. The user opens the app and types in "Find the red key." At the same time, the user's facial expressions and voice are captured by the camera and microphone and analyzed by the emotion recognition engine. For example, it may be determined that the user is anxious.

[1284] 2. The terminal transmits the input content and emotional information to the data processing device.

[1285] 3. The data processing device analyzes the "red key" using a natural language processing engine and generates a concrete image using an image generation means (e.g., DALL-E).

[1286] 4. Based on the generated image, the data processing device uses image recognition means (e.g., YOLOv5) to extract scenes containing the red key from past recorded data.

[1287] 5. The data processing device estimates the current location based on the time and location information of the detected scene. For example, the last place where the red key was seen may be identified as "on the table in the living room."

[1288] 6. The data processing device, taking into account that the user is in a hurry, generates a notification message saying "Please stay calm. The red key is on the table in the living room" and sends it to the terminal.

[1289] 7. The device receives the notification and displays its contents to the user.

[1290] This system allows users to find what they are looking for more safely and efficiently.

[1291] Example prompt sentence:

[1292] "I'm looking for the red key. I'm getting impatient."

[1293] "Generate an image of a red key."

[1294] "Please tell me where and when the red key was last seen."

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

[1296] Step 1: Acquire camera footage

[1297] The server receives video data in real time from a camera installed in the room. The input is the video stream from the camera, and the output is the encoded video data.

[1298] The server adds date and time information to the received video data and stores it in a database. Specifically, it encodes the video data using a library such as FFmpeg and adds date and time metadata. This data is then stored in an SQL-based database (e.g., MySQL) or a NoSQL database (e.g., MongoDB).

[1299] Step 2: Request for lost item investigation

[1300] The user uses a dedicated app on their device (smartphone or PC) to input a search request for an item they are looking for, such as "Find the red key." The input is the search request entered by the user in a text field, and the output is the search request data sent to the server.

[1301] The device's camera and microphone capture the user's facial expressions and voice, and an emotion recognition engine (e.g., OpenFace or Microsoft Azure Face API) extracts emotional data. Specifically, the emotion recognition engine analyzes facial and vocal characteristics to detect the user's emotional state.

[1302] The device formats the survey request text and extracted emotion data into JSON format and sends it to the server via an HTTP POST request.

[1303] Step 3: Create an image of what you're looking for

[1304] The server analyzes the received search request using a natural language processing engine (e.g., BERT) and extracts features of the searched item. The input is the text of the search request, and the output is feature data of the searched item. Specifically, it extracts key phrases using a natural language processing library (e.g., Hugging Face transformers).

[1305] The server uses an image generation engine (e.g., DALL-E or Stable Diffusion) based on the features to generate a specific image of the searched item. The input is the feature data of the searched item, and the output is the generated image. An example of a prompt sentence: "Generate an image of a red key."

[1306] Step 4: Search for recording data

[1307] The server uses the generated images to search past recorded data. The input is the generated images, and the output is the search results for matching scenes.

[1308] The server uses an image recognition engine (e.g., YOLOv5) to extract the relevant scenes. Specifically, the YOLOv5 model performs image recognition processing on each video frame to detect matching scenes.

[1309] Step 5: Estimate the location of the item

[1310] The server identifies the location where the item was last seen based on the time and location information of the searched scene. The input is the matching scene information, and the output is the estimated location information. Specifically, it retrieves the entry where the red key was last seen from the database and uses a rule-based algorithm to estimate its current location.

[1311] Step 6: Notification

[1312] The server appropriately changes the content of the notification message based on the user's emotional information recognized by the emotion engine. The input is emotional information and estimated location information, and the output is a customized notification message. In concrete terms, the emotion engine is used to analyze the user's emotional state and generate an appropriate notification message. An example of a prompt sentence: "Please stay calm. The red key is on the table in the living room."

[1313] The device receives notifications from the server and displays the contents in the user's application. The input is the notification message from the server, and the output is the notification display to the user. Specifically, the notification message is displayed in the notification center of the dedicated application or as a pop-up notification.

[1314] This allows users to find what they are looking for quickly and efficiently, and is expected to improve the user experience by providing appropriate responses based on emotions.

[1315] (Application example 2)

[1316] 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."

[1317] While there are existing systems for efficiently finding lost items, few systems can recognize and respond to the user's emotions. Furthermore, if the user is feeling anxious or stressed, appropriate assistance is not provided, resulting in a poor user experience. Conventional systems generate notification content without considering the user's emotional state, resulting in low user satisfaction. To solve this problem, a system that recognizes the user's emotions and responds appropriately based on them is needed.

[1318] 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.

[1319] In this invention, the server includes means for a camera to continuously record video of a room and transmit the video to the server, means for the server to save the recorded video, means for a user to request an investigation into an item they are looking for, means for the server to receive the investigation request and generate an image of the item they are looking for using an image generation means, means for the server to search for a matching scene from past recorded data based on the generated image, means for the server to estimate the location of the item they are looking for based on the search result, means for recognizing the user's emotion and adjusting notification content based on the emotion information, and means for the server to notify the user of the estimated location information. This enables appropriate notification and support based on the user's emotion, improving the user experience.

[1320] A "camera" is a device that continuously records video of a specific location or object and transmits it to a server.

[1321] The "server" is a computer system that stores the images sent from the camera, processes the user's investigation request, generates an image of the item being searched for, and searches for recorded data based on that image.

[1322] A "user terminal" is a device that allows a user to request an investigation into a lost item and has the function of receiving the investigation results. This includes smartphones and personal computers.

[1323] The "image generation means" is a technology that allows the server to generate a concrete image of the item being searched for based on the user's search request. For example, it uses a generative model.

[1324] "Image recognition means" is a technology that allows the server to search for scenes that match the generated image from the recorded data. For example, it uses an image analysis engine such as OpenCV.

[1325] "Natural language processing means" is a technology that analyzes a user's search request and extracts the characteristics of the item based on that analysis. For example, Google Cloud Natural Language API is an example of this.

[1326] "Emotion recognition means" refers to a technology that detects emotions by analyzing the user's facial expressions and voice and reflects that information in the notification content. For example, an emotion recognition engine is an example of this.

[1327] The "notification means" is a technology that notifies the user terminal of the content based on the predetermined information generated by the server. The content of the notification is adjusted based on the user's emotions.

[1328] The "means for estimating the location of a lost item" is a technology in which the server determines the time and place in which the lost item was captured based on the search results of image recognition, and estimates its current location.

[1329] The present invention is a system for finding things that uses emotion recognition technology and is configured by combining a camera, a server, and a user terminal. The detailed configuration of this system is described below.

[1330] System Configuration

[1331] 1. Camera

[1332] The camera continuously records video of a specific location or room and transmits it to a server in real time. High-resolution cameras are used to accurately capture video details.

[1333] 2. Server

[1334] A server is a computer system that has the following functions:

[1335] Video data storage: Video data sent from the camera is stored in a database along with date and time information.

[1336] Database: Efficiently search and manage video data.

[1337] Image generation: Using a generative model, a concrete image of the item being searched for is created based on the user's research request.

[1338] Image recognition: Using an image analysis engine such as OpenCV, it searches for scenes that match images generated from past recorded data.

[1339] Emotion Recognition: Using an emotion recognition engine, we analyze and recognize the user's emotional information and process it on the server side.

[1340] Natural language processing: Analyze user survey requests using Google Cloud Natural Language API, etc.

[1341] Location estimation of lost items: Estimates the current location based on the time and location information of search results.

[1342] Notification: Customize notification content based on the user's emotions and send information to the user's device.

[1343] 3. User Device

[1344] The user terminal is a smartphone or personal computer and has the following functions:

[1345] Interface: Provide an interface that allows users to easily input search requests for items they are looking for.

[1346] Emotion recognition: Analyzes the user's facial expressions and voice to detect emotional information and send it to the server.

[1347] Receive notifications: Receive notifications from the server and display them to the user.

[1348] System Operation

[1349] 1. The user launches the app and speaks or texts, "I'm looking for a miniature blue car."

[1350] 2. The user device analyzes the user's facial expressions and voice to obtain emotional information.

[1351] 3. The device sends the input information and emotion information to the server.

[1352] 4. The server uses natural language processing to identify a "miniature blue car" and generates an image using a generative model.

[1353] 5. The server uses an image analysis engine to search for the relevant scene in the recorded data.

[1354] 6. The server estimates the location of the item based on the search results and notifies the user's device of this information.

[1355] 7. The server customizes the notification content based on the emotional information. For example, if a user is feeling anxious, the server sends a message such as, "Don't worry. The miniature blue car is in the toy section on the second floor."

[1356] 8. The user device receives the notification and displays it to the user.

[1357] Specific examples

[1358] Example 1: Finding a key

[1359] 1. The user launches the app and types in "Find the red key." At the same time, the emotion recognition engine analyzes the user's facial expressions and voice to detect stress or impatience.

[1360] 2. The device sends the input content and emotion information to the server.

[1361] 3. The server analyzes the "red key" using natural language processing and generates an image using an image generation engine.

[1362] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[1363] 5. The server estimates the current location based on the time and location of that frame.

[1364] 6. The server generates a notification message that takes into account the user's emotional information: "Please stay calm. The red key is on the table in the living room." and sends it to the device.

[1365] 7. The device receives the notification and displays it to the user.

[1366] Prompt Sentence Examples

[1367] text

[1368] User: When searching for a miniature blue car in a store but cannot find it, the user types "find a miniature blue car" into the app. At the same time, the camera detects that the customer is anxious. The server processes this, estimates the location of the item, and responds with a relaxed message.

[1369] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1370] Step 1:

[1371] The user launches the app and enters a survey request.

[1372] The user launches a dedicated application on their smartphone or personal computer and inputs a search request for the item they are looking for by voice or text. For example, they might input, "I'm looking for a miniature blue car." At this time, the application also analyzes the user's facial expressions and voice to obtain emotional information.

[1373] Input: User survey request (voice or text), facial expression and voice data

[1374] Output: Investigation request details and emotional information

[1375] Step 2:

[1376] The device sends the investigation request and emotional information to the server.

[1377] The terminal transmits the survey request entered by the user and the analyzed emotional information to the server. At this time, the data is encrypted before transmission.

[1378] Input: Investigation request details and emotional information

[1379] Output: Survey request details and emotional information sent to the server

[1380] Step 3:

[1381] The server analyzes the investigation request.

[1382] The server analyzes the received search request using a natural language processing engine (such as Google Cloud Natural Language API) and extracts the characteristics of the item being searched for. In this case, the characteristics of a "miniature blue car" are extracted.

[1383] Input: Survey request details

[1384] Output: Extracted features of the searched item

[1385] Step 4:

[1386] The server generates an image of the item using an image generation engine.

[1387] The server uses a generative model (AI model) to generate a specific image of the item based on the extracted features of the item. For example, an image of a specific "miniature blue car" is generated.

[1388] Input: Extracted features of the object being searched for

[1389] Output: Generated image of the object being searched

[1390] Step 5:

[1391] The server searches for recorded data using an image recognition engine

[1392] The server uses the generated image to search past recorded data using an image recognition engine (such as OpenCV) and extracts matching scenes.

[1393] Input: Generated image of the object to be found

[1394] Output: Matching scenes with their time and location information

[1395] Step 6:

[1396] The server estimates the location of the item

[1397] The server estimates the current location of the item based on the time and location information of the search results. In this case, it estimates the specific location of the "miniature blue car."

[1398] Input: Time and location information of the matching scene

[1399] Output: Estimated current location of the item

[1400] Step 7:

[1401] The server customizes the notification content based on emotion information.

[1402] The server considers the user's emotional information and adjusts the notification content appropriately. For example, if the user is feeling anxious, it generates a relaxing message such as, "Don't worry. The miniature blue car is in the toy section on the second floor."

[1403] Input: Current location of the item being searched for, user's emotion information

[1404] Output: Customized notification content

[1405] Step 8:

[1406] The server notifies the user device of the estimated location information.

[1407] The server sends the customized notification content to the user terminal, and the terminal displays the received notification to the user.

[1408] Input: Customized notification content

[1409] Output: Information sent to the user's device

[1410] These steps allow users to find what they are looking for efficiently and provide a more pleasant experience by receiving appropriate notifications based on their emotions.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] [Fourth embodiment]

[1415] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1416] 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.

[1417] 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).

[1418] 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.

[1419] 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.

[1420] 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).

[1421] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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."

[1428] The present invention is a system implemented using a server and a user terminal that cooperate with a camera permanently installed in a room. This system allows a user to efficiently find an object they are looking for. Detailed embodiments of this system are described below.

[1429] System Configuration

[1430] 1. Camera

[1431] The cameras are installed in various locations in the room and record video 24 hours a day, and the recorded video is sent to a server in real time.

[1432] 2. Server

[1433] The server has the following functions:

[1434] Video data storage: Records video data sent from the camera along with date and time information.

[1435] Database: Manages recorded video data so that it can be searched efficiently.

[1436] Image generation: It has an image generation engine that generates an image of the item being searched for based on the user's search request.

[1437] Image Recognition: It has an image recognition engine to search for scenes that match the generated image in the recorded data.

[1438] Data analysis: A function that analyzes users' survey requests using natural language processing and utilizes the results.

[1439] Estimation: A feature that estimates the current location of an item based on its last known location.

[1440] Notification: A function to send estimated information to the user's device.

[1441] 3. User Device

[1442] The user terminal is a smartphone, PC, etc., and has the following functions:

[1443] Survey request: Provides an interface that allows users to easily input survey requests.

[1444] Notification reception: A function that receives notifications from the server and displays them to the user.

[1445] Program Processing

[1446] 1. Acquiring camera footage

[1447] The server receives video data in real time transmitted from a camera installed in the room.

[1448] The server adds date and time information to the received video data and stores it in a database.

[1449] 2. Request for lost items

[1450] Users can submit an investigation request using a dedicated application on their smartphone or PC, for example by entering "Find the red key."

[1451] The terminal sends the input to the server.

[1452] 3. Image generation of the searched item

[1453] The server analyzes the received investigation request using a natural language processing engine and extracts the characteristics of the item being searched for.

[1454] The server then calls an image generation engine (e.g., a generative model) based on the features to generate a specific image of the item being searched for.

[1455] 4. Searching for recorded data

[1456] The server uses the generated image to search for past recorded data.

[1457] The server uses an image recognition engine to extract the relevant scenes.

[1458] 5. Estimating the location of lost items

[1459] The server identifies the last known location of the item being searched for based on the time and location information of the searched scene.

[1460] The server uses this information to estimate the current location.

[1461] 6. Notification

[1462] The server generates a message informing the user of the result of the estimation, including specific information such as "The red key is on the table in the living room."

[1463] The terminal receives the notification from the server and displays it to the user.

[1464] Specific examples

[1465] Example 1: Searching for a key

[1466] 1. The user enters "Find the red key" into the dedicated app.

[1467] 2. The terminal sends the input information to the server.

[1468] 3. The server analyzes the "red key" using a natural language processing engine and generates an image using an image generation engine.

[1469] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[1470] 5. The server estimates the current location based on the time and location of that frame.

[1471] 6. The server generates a notification that says, "The red key is on the living room table," and sends it to the device.

[1472] 7. The device receives the notification and displays it to the user.

[1473] In this way, the user can efficiently locate the item they are looking for.

[1474] The processing flow will be explained below.

[1475] Step 1:

[1476] The camera continuously records video of the room 24 hours a day and transmits the recorded data to a server in real time.

[1477] Step 2:

[1478] The server receives the video data sent from the camera, adds date and time information, and stores it in a database. The stored video data is kept for a certain period of time (for example, one week).

[1479] Step 3:

[1480] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[1481] Step 4:

[1482] The device receives the user's search request and sends it to the server. The communication is via API and includes the necessary information (details of the item being searched).

[1483] Step 5:

[1484] The server analyzes the received search request using a natural language processing (NLP) engine, and extracts specific characteristics of the item from the analysis results.

[1485] Step 6:

[1486] The server inputs the analyzed features into an image generation engine (e.g., a generative model) to generate a specific image of the object being searched for (in this case, a red key). This generated image is used for further processing.

[1487] Step 7:

[1488] The server searches for past recorded data using the images generated by the image generation engine. First, it filters data from a specific period (e.g., the most recent week) from the entire recorded data.

[1489] Step 8:

[1490] The server then feeds the filtered video data into an image recognition engine to find scenes that match the generated image, which then extracts frames that show the object being searched for.

[1491] Step 9:

[1492] The server identifies the last known location of the item based on the time and location information of the video frame extracted by the image recognition engine.

[1493] Step 10:

[1494] The server uses the identified information to estimate the current location of the item, using an algorithm that takes into account the passage of time and the object's movement patterns.

[1495] Step 11:

[1496] The server generates a message to notify the user of the estimated location information, for example, a specific message such as "The red key is on the table in the living room."

[1497] Step 12:

[1498] The device receives the notification from the server and displays the content in the user's application, allowing the user to check the location of the item they are looking for based on the displayed information.

[1499] Following these steps, users can efficiently find what they are looking for.

[1500] Example 1

[1501] 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."

[1502] In today's busy lifestyles, losing things indoors is a frequent occurrence, and searching for them can take up a lot of time and effort. To address this issue, a system that allows users to efficiently find lost items is needed, but existing technologies have limitations in accuracy and convenience. In particular, improvements are needed in areas such as video data search, analysis of investigation requests using natural language, and image generation of lost items.

[1503] 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.

[1504] In this invention, the server includes means for a camera to continuously record video of a room and transmit the video to an information processing device, means for the information processing device to save the recorded video, means for a user to make a request to investigate an item they are looking for, means for the information processing device to receive the investigation request and generate an image of the item they are looking for using an image generation means, means for the information processing device to search for a matching scene from past recorded data based on the generated image, means for the information processing device to estimate the location of the item they are looking for based on the search results, and means for the information processing device to notify the user of the estimated location information, thereby enabling the user to efficiently find the item they are looking for.

[1505] A "camera" is a device that continuously records video of the interior of a room and transmits the video data to an information processing device in real time.

[1506] An "information processing device" is a device that stores received video data, processes investigation requests from users, generates images of items being searched for, searches recorded data, estimates their location, and notifies users.

[1507] "Video data" refers to indoor video recorded by a camera, and includes time and date information.

[1508] "User" refers to a person who uses a dedicated application to input a survey request and receive notifications from the system.

[1509] A "research request" refers to a request entered by a user to find a particular item.

[1510] "Image generation means" refers to a technology that generates a concrete image of the item being searched for based on the characteristics of the item obtained from the investigation request.

[1511] "Image recognition means" refers to a technology that searches for matching scenes from past recorded data based on the generated image.

[1512] "Natural language processing means" refers to technology that analyzes a user's search request and generates an image of the item being searched for based on the analysis results.

[1513] "Inference" refers to the process of determining the location of a searched item based on search results and predicting the appropriate location.

[1514] "Notification" refers to an act of conveying estimated location information from an information processing device to a user.

[1515] A "scene" refers to a frame or video section that represents a specific time or place in recorded data.

[1516] The present invention is a system implemented using an information processing device and a user terminal that cooperate with a camera permanently installed in a room. This system allows a user to efficiently find an item they are looking for. Detailed embodiments of this system are described below.

[1517] System Configuration

[1518] 1. Camera

[1519] The cameras are installed in various locations in the room and record images 24 hours a day, and the recorded images are sent to an information processing device in real time.

[1520] Example of use: Network cameras (e.g. Hikvision or Axis cameras)

[1521] 2. Information Processing Device

[1522] The information processing device has the following functions.

[1523] Video data storage: Video data sent from the camera is recorded along with date and time information. Database software (e.g., MySQL or PostgreSQL) is used.

[1524] Database management: Manage recorded video data so that it can be searched efficiently.

[1525] Image generation: It has an image generation engine (e.g., DALL-E or Stable Diffusion) to generate an image of the item being searched for based on the user's investigation request.

[1526] Image Recognition: It has an image recognition engine (e.g., OpenCV or YOLOv5) to search for scenes that match the generated image from the recorded data.

[1527] Data analysis: It has the ability to analyze user survey requests using natural language processing tools (e.g., GPT-3 and BERT) and use the results.

[1528] Estimation: Has the ability to estimate the current location of the item based on the last known location of the item.

[1529] Notification: Has the function of sending estimated information to the user's terminal.

[1530] 3. User Device

[1531] The user terminal is a smartphone, PC, etc., and has the following functions:

[1532] Survey request: Provide an interface that allows users to easily input survey requests using an application (e.g., iOS app, Android app, web app).

[1533] Notification reception: Has the function of receiving notifications from the information processing device and displaying them to the user.

[1534] Examples of specific examples and prompts

[1535] Example 1: Searching for a key

[1536] 1. The user enters "Find the red key" into a dedicated app (smartphone app or web app).

[1537] 2. The terminal transmits the input contents to the information processing device.

[1538] 3. The information processing device analyzes the "red key" using a natural language processing engine (e.g., GPT-3 or BERT) and generates an image using an image generation engine (e.g., DALL-E or Stable Diffusion).

[1539] 4. The information processing device uses an image recognition engine (e.g., OpenCV or YOLOv5) to search for the relevant scene from the recorded data and extract the frame in which the "red key" appears.

[1540] 5. The information processing device estimates the current location based on the time and location of the frame.

[1541] 6. The information processing device generates a notification that says, "The red key is on the table in the living room," and sends it to the terminal.

[1542] 7. The device receives the notification and displays it to the user.

[1543] Example prompt sentence:

[1544] Prompt for the natural language processing engine:

[1545] text

[1546] User input: "Find the red key"

[1547] Analyze this request and extract the characteristics of the item being sought.

[1548] Prompt for image generation engine:

[1549] text

[1550] Search Feature: Red Key

[1551] Based on this feature, generate an image of the red key.

[1552] This allows the system to efficiently find what the user is looking for.

[1553] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1554] Step 1: Acquire camera footage

[1555] Input: Cameras are installed at various locations in the room, record images in real time, and send them to an information processing device.

[1556] Server operation: The server sets the IP address and streaming URL of each camera and receives video data using the RTSP protocol or HTTP protocol.

[1557] Data processing: The server adds time and date metadata to the received video data.

[1558] Output: The video data is stored in a database along with date and time information.

[1559] Step 2: Request a lost item investigation

[1560] Input: The user inputs an investigation request such as "Find the red key" using a dedicated application on their smartphone or PC.

[1561] What the device does: The device receives the text entered by the user and sends it to the server via an API request (e.g., an HTTP POST request).

[1562] Output: The survey request is sent to the server.

[1563] Step 3: Analyze the survey request and generate an image

[1564] Input: The server receives a survey request from the user.

[1565] Server operation: The server uses a natural language processing engine (e.g., GPT-3 or BERT) to extract the keyword "red key."

[1566] Data processing: The server sends the extracted keywords as prompts to the image generation engine (e.g., DALL-E or Stable Diffusion).

[1567] Output: The image generation engine generates an image of the "red key" which is returned to the server.

[1568] Step 4: Search for recording data

[1569] Input: The server receives the generated image.

[1570] Server operation: The server uses an image recognition engine (e.g., OpenCV or YOLOv5) to search for scenes that match the generated image from the video data in the database.

[1571] Data processing: An image recognition engine compares each frame of video data to identify matching scenes.

[1572] Output: The time and location information of the matching scenes is returned to the server.

[1573] Step 5: Estimate the location of the item

[1574] Input: The server receives the time and location information of the matching scene.

[1575] Server operation: The server uses historical data and AI algorithms (e.g., recurrent neural networks) to analyze the movement patterns of the items being searched for.

[1576] Data processing: The server calculates the current estimated location based on past location data.

[1577] Output: The estimated current location of the searched item is identified.

[1578] Step 6: Notification

[1579] Input: The server receives the estimated current location of the object.

[1580] Server Action: The server generates a notification message for the user, containing specific information such as "The red key is on the table in the living room."

[1581] Output: A notification message is sent to the terminal.

[1582] Terminal behavior: The terminal receives the notification message and displays it to the user, either as a pop-up notification or a banner notification.

[1583] (Application example 1)

[1584] 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."

[1585] In large facilities such as logistics centers, it is crucial for workers to be able to quickly and accurately locate goods and materials. However, in environments with large areas and many products, finding what is needed can take a lot of time and effort. Current systems lack sufficient visual instructions and real-time information to quickly identify the location of items, so further improvements are needed.

[1586] 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.

[1587] In this invention, the server includes: a camera for continuously recording video of a room and transmitting the video to the server; a server for storing the recorded video; a user's request for an investigation into a lost item; a server for receiving the investigation request and generating an image of the lost item using an image generating means; a server for searching for a matching scene from past recorded data based on the generated image; a server for estimating the location of the lost item based on the search result; a server for notifying the user of the estimated location information; a smart device for displaying visual instructions on the smart device worn by the user; and an augmented reality means for highlighting an object within the user's field of view based on the visual instructions. This allows the user to quickly and accurately locate the lost item through the smart device, significantly improving work efficiency.

[1588] A "camera" is a device that continuously records video of the room and transmits it to a server.

[1589] The "server" is a central computer system that stores recorded video and generates images of items searched for based on user requests for investigation, as well as searches from past recorded data.

[1590] A "user" is a person or terminal user who requests a search for a lost item and receives notifications from the system.

[1591] "Image generation means" refers to the function of the server to generate a concrete image of the item being searched for based on a search request.

[1592] "Recorded data" refers to past video information captured by a camera and stored on a server.

[1593] "Image recognition means" is a technology for searching past recorded data for scenes that match the generated image.

[1594] The "location estimation means" is a function that estimates the current location of the item being searched for based on the search results.

[1595] The "notification means" is a function that transmits estimated location information to the user.

[1596] A "smart device" is a device worn by a user that can receive visual instructions from a system. Specific examples include smart glasses.

[1597] "Augmented reality tools" are techniques that visually highlight objects within a user's field of view.

[1598] The present invention is a system for efficiently finding lost items or products in a logistics center. This system is composed of a camera, a server, a smart device, etc.

[1599] System Configuration

[1600] 1. Camera

[1601] Cameras are installed in various locations within the distribution center, continuously recording images from inside the center and sending them to a server. It is desirable for the cameras to be high-resolution and capable of monitoring a wide area.

[1602] 2. Server

[1603] The server has the following main functions:

[1604] Recording data storage: Video data sent from the camera is recorded with date and time information, and the video data is stored in a database for efficient searching.

[1605] Image generation: Based on the search request, a generative AI model is used to generate a specific image of the searched item. For example, if a user types "Look for product A," the generative AI model will generate an image of product A.

[1606] Image Recognition: Searches for scenes that match the generated image in the recorded data. Using an image recognition engine, it extracts relevant scenes from past data.

[1607] Natural language processing: Understanding the user's research request and triggering the image generation engine based on the results. This process uses natural language processing models such as BERT.

[1608] Location estimation: Based on the time and location information of the searched scene, the last known location of the item is identified and its current location is estimated.

[1609] Notification: The estimation results are sent to the user's device, providing specific information such as "Product A is located fifth on shelf A."

[1610] 3. Smart Devices

[1611] Smart devices include smart glasses, smartphones, etc., which have the following functions:

[1612] User interface: Provide a voice and touch input interface that allows users to easily input survey requests.

[1613] Displaying visual instructions: Visually displaying the inference results sent from the server, for example by using the AR functionality of smart glasses to highlight objects within the user's field of view.

[1614] Notification reception: Receive notifications from the server in real time and display them to the user.

[1615] Specific examples

[1616] For example, suppose a user working in a logistics center is wearing smart glasses. If the user voice-inputs, "Look for product A," the smart glasses recognize the voice and send it to the server. The server analyzes the search request using a natural language processing engine and generates an image of product A using a generative AI model. The server then uses an image recognition engine to search for scenes in which product A was recorded in the camera's video data and estimate its current location. The resulting information, "Product A is located fifth on shelf A," is generated and sent to the smart glasses. Using the smart glasses' AR function, this information is displayed within the user's field of view, with product A highlighted in a specific location.

[1617] Prompt Sentence Examples

[1618] User: "Looking for product A"

[1619] This system enables logistics center workers to quickly and accurately find the items they are looking for, significantly improving work efficiency.

[1620] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1621] Step 1:

[1622] The user inputs "Search for product A" through voice or text input via smart glasses or a smartphone. This input is sent to the server via the user interface (input: voice or text, output: data sent to the server).

[1623] Step 2:

[1624] The server analyzes the received user survey request using a natural language processing engine. In the analysis process, for example, a BERT model is used to extract the intent of the survey request and important keywords (input: survey request data, output: analysis result data).

[1625] Step 3:

[1626] The server uses a generative AI model based on the analysis results obtained by the natural language processing engine to generate a concrete image of Product A. The generated image data is saved on the server (input: analysis result data, output: generated image data).

[1627] Step 4:

[1628] The server uses an image recognition engine to search for scenes that match the image of product A generated from past recorded data. This process uses a machine learning model (for example, YOLO or OpenCV functions) (input: generated image data, recorded data, output: matching scene data).

[1629] Step 5:

[1630] The server identifies the last known location of product A based on the time and location information of the matching scene data, and estimates its current location. The estimation uses an algorithm that analyzes movement patterns within a specified range (input: matching scene data, output: estimated location data).

[1631] Step 6:

[1632] The server generates a message notifying the user of the estimated location information and sends it to the smart device worn by the user. The generated message includes the specific location (e.g., "Product A is located fifth on shelf A") (Input: estimated location data, Output: notification message).

[1633] Step 7:

[1634] The smart device receives the notification message from the server and displays it on the user's screen as a visual indication. It also uses AR functionality to highlight objects within the user's field of view. In this way, the user can confirm the location of Item A in real time (Input: notification message, Output: visual display and highlighting).

[1635] This allows users to quickly and accurately find the products and materials they need, significantly improving the work efficiency of logistics centers.

[1636] 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.

[1637] The present invention is a search system that uses a camera, server, and user terminal combined with an emotion engine. The entire system aims to realize more human-centered interaction by recognizing the user's emotions, thereby enabling efficient discovery of searched items and improving the user experience. Detailed embodiments of the system are described below.

[1638] System Configuration

[1639] 1. Camera

[1640] The cameras are installed in various locations in the room and record video 24 hours a day, and the recorded video is sent to a server in real time.

[1641] 2. Server

[1642] The server has the following functions:

[1643] Video data storage: Records video data sent from the camera along with date and time information.

[1644] Database: Manages recorded video data so that it can be searched efficiently.

[1645] Image generation: It has an image generation engine that generates an image of the item being searched for based on the user's search request.

[1646] Image Recognition: It has an image recognition engine to search for scenes that match the generated image in the recorded data.

[1647] Data analysis: A function that analyzes users' survey requests using natural language processing and utilizes the results.

[1648] Estimation: A feature that estimates the current location of an item based on its last known location.

[1649] Emotion Engine: A feature that recognizes the emotion a user expresses when requesting a survey and tailors responses accordingly.

[1650] Notification: A function that sends estimated information to the user's device. The notification content is customized based on emotional information.

[1651] 3. User Device

[1652] The user terminal is a smartphone, PC, etc., and has the following functions:

[1653] Survey request: Provides an interface that allows users to easily input survey requests.

[1654] Emotion recognition: Detects emotions from the user's facial expressions and voice and sends that information to the server.

[1655] Notification reception: A function that receives notifications from the server and displays them to the user.

[1656] Program Processing

[1657] 1. Acquiring camera footage

[1658] The server receives video data in real time transmitted from a camera installed in the room.

[1659] The server adds date and time information to the received video data and stores it in a database.

[1660] 2. Request for lost items

[1661] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[1662] The terminal recognizes the user's emotions from their facial expressions and voice, and sends a survey request including the emotion information to the server.

[1663] 3. Image generation of the searched item

[1664] The server analyzes the received investigation request using a natural language processing engine and extracts the characteristics of the item being searched for.

[1665] The server then invokes an image generation engine (e.g., a generative model) based on the features to generate a specific image of the object being searched for (in this case, a red key).

[1666] 4. Searching for recorded data

[1667] The server uses the generated image to search for past recorded data.

[1668] The server uses an image recognition engine to extract the relevant scenes.

[1669] 5. Estimating the location of lost items

[1670] The server identifies the location where the item was last seen based on the time and location information of the searched scene.

[1671] The server uses this information to estimate the current location.

[1672] 6. Notification

[1673] The server then changes the content of the notification message appropriately based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, the server will notify them with a message such as, "Please stay calm. The red key is on the table in the living room."

[1674] The terminal receives the notification from the server and displays the content in the user's application.

[1675] Specific examples

[1676] Example 1: Searching for a key

[1677] 1. The user types "Find the red key" into the dedicated app. At the same time, the user's facial expressions and voice are analyzed by the emotion recognition engine to detect stress or impatience.

[1678] 2. The device sends the input content and emotion information to the server.

[1679] 3. The server analyzes the "red key" using a natural language processing engine and generates an image using an image generation engine.

[1680] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[1681] 5. The server estimates the current location based on the time and location of that frame.

[1682] 6. The server generates a notification message that takes into account the user's emotional information: "Please stay calm. The red key is on the table in the living room." and sends it to the device.

[1683] 7. The device receives the notification and displays it to the user.

[1684] This system allows users to find what they are looking for more safely and efficiently.

[1685] The processing flow will be explained below.

[1686] Step 1:

[1687] The camera continuously records video of the room 24 hours a day and transmits the recorded data to a server in real time.

[1688] Step 2:

[1689] The server receives the video data sent from the camera, adds date and time information, and stores it in a database. The stored video data is kept for a certain period of time (for example, one week).

[1690] Step 3:

[1691] Users use a dedicated application on their own device (smartphone or PC) to input a search request for a lost item, such as "Find the red key."

[1692] Step 4:

[1693] The device analyzes the user's facial expressions and voice to recognize their emotions, and then sends a survey request including that emotional information to the server. For example, if the user is feeling anxious, that emotional information will also be sent.

[1694] Step 5:

[1695] The server analyzes the received investigation request using a natural language processing (NLP) engine and extracts the characteristics of the item being searched for: the "red key."

[1696] Step 6:

[1697] The server calls an image generation engine based on the extracted features to generate a concrete image of the "red key."

[1698] Step 7:

[1699] The server searches past recorded data based on the generated "red key" image. First, it filters recorded data from a specific period (for example, the past week).

[1700] Step 8:

[1701] The server then inputs the filtered video data into an image recognition engine to find scenes that match the generated image, and the image recognition engine extracts frames that show the "red key."

[1702] Step 9:

[1703] The server determines the last known location of the item being searched for (the red key) based on the time and location information of the extracted video frame.

[1704] Step 10:

[1705] The server uses the location and time information to estimate the current location of the red key, using an algorithm that takes into account the passage of time and patterns of movement of objects.

[1706] Step 11:

[1707] The server then changes the content of the notification message appropriately based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, the server will notify them with a message such as, "Please stay calm. The red key is on the table in the living room."

[1708] Step 12:

[1709] The device receives the notification sent from the server and displays the content to the user within the application, allowing the user to check the location of the item they are looking for based on the notification content.

[1710] This process flow allows users to find what they are looking for efficiently and safely.

[1711] Example 2

[1712] 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."

[1713] Conventional object-finding systems have had the problem of making it difficult for users to quickly and efficiently find lost items. In particular, the system's response is often insufficient when users have difficulty clearly imagining the characteristics of the object they are looking for or when they are in an emotional state. Furthermore, there is a lack of a way to quickly search for an object from past video recording data, making it difficult to accurately estimate the location of the object. This results in a decrease in user satisfaction.

[1714] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1715] In this invention, the server includes: means for a camera to continuously record video of a room and transmit the video to the data processing device; means for the data processing device to save the recorded video; means for a user to request an investigation into an item they are looking for; means for the data processing device to receive the investigation request and generate an image of the item they are looking for using an image generation means; means for the data processing device to search for a matching scene from past recorded data based on the generated image; means for the data processing device to estimate the location of the item they are looking for based on the search results; means for the data processing device to notify the user of the estimated location information; and means for detecting emotional information from the user and generating a notification message according to the emotional information. This enables the user to find the item they are looking for quickly and efficiently, and an improved user experience can be expected by providing an appropriate response according to the user's emotional state.

[1716] A "camera" is a device that continuously records video of a room and transmits the captured video data to a server or data processing device.

[1717] The "data processing device" is a processing device that stores the video data transmitted from the camera and performs various processes based on the investigation request from the user.

[1718] A "request to find a lost item" is an operation or content in which a user inputs information about a lost item and requests the system to identify its location.

[1719] The "image generation means" is a means for analyzing the characteristics of the item being searched for based on the investigation request and generating a specific image of the item from those characteristics.

[1720] "Recorded data" refers to video data captured by a camera and stored in a data processing device.

[1721] A "matching scene" is a scene in the recorded data that matches the generated image.

[1722] "Means for estimating location" refers to means for estimating the last known location and current location of a searched item based on search results.

[1723] The "means for notifying" is a means for transmitting estimated location information to the user's terminal and notifying the user of this.

[1724] "Emotion information" is emotional data obtained from the user's facial expressions, voice, etc.

[1725] A "notification message" is a message that contains information about the item being searched for or other response content that is generated in response to the user's emotions.

[1726] The present invention is a search system that uses a camera, a data processing device, and a user terminal combined with an emotion engine. The entire system aims to realize more human-centered interaction by recognizing the user's emotions, thereby enabling efficient discovery of the searched item and improving the user experience. Detailed embodiments of the system are described below.

[1727] System Configuration

[1728] 1. Camera

[1729] The cameras are installed in various locations in the room and record video 24 hours a day. The recorded video is sent to a data processing device in real time. Specifically, the video stream is sent using the TCP / IP protocol, and the data processing device encodes the video data using a library such as FFmpeg.

[1730] 2. Data Processing Device

[1731] The data processing device has the following functions:

[1732] Video data storage: Video data sent from the camera is recorded along with date and time information. For storage, an SQL-based database (e.g., MySQL) or a NoSQL database (e.g., MongoDB) is used.

[1733] Database: Manages recorded video data so that it can be searched efficiently.

[1734] Image generation: It has an image generation method (e.g., DALL-E or Stable Diffusion) to generate an image of the searched item based on the user's investigation request.

[1735] Image Recognition: It has an image recognition method (e.g., YOLOv5) to search for scenes that match the generated image from the recorded data.

[1736] Data analysis: A function that analyzes user survey requests using natural language processing tools (e.g., BERT) and uses the results.

[1737] Estimation: A feature that estimates the current location of an item based on its last known location.

[1738] Emotion engine: The ability to recognize the emotion a user expresses when requesting a survey and tailor responses accordingly (e.g., OpenFace or Microsoft Azure Face API).

[1739] Notification: A function that sends estimated information to the user's device. The notification content is customized based on emotional information.

[1740] 3. User Device

[1741] The user terminal is a smartphone, PC, etc., and has the following functions:

[1742] Survey request: We provide an interface that allows users to easily input survey requests. This uses cameras and microphones to detect emotions from facial expressions and voice.

[1743] Emotion recognition: Detecting emotions from the user's facial expressions and voice and sending that information to a data processing device.

[1744] Notification reception: A function to receive notifications from the data processing device and display them to the user.

[1745] Specific examples

[1746] Example 1: Searching for a key

[1747] 1. The user opens the app and types in "Find the red key." At the same time, the user's facial expressions and voice are captured by the camera and microphone and analyzed by the emotion recognition engine. For example, it may be determined that the user is anxious.

[1748] 2. The terminal transmits the input content and emotional information to the data processing device.

[1749] 3. The data processing device analyzes the "red key" using a natural language processing engine and generates a concrete image using an image generation means (e.g., DALL-E).

[1750] 4. Based on the generated image, the data processing device uses image recognition means (e.g., YOLOv5) to extract scenes containing the red key from past recorded data.

[1751] 5. The data processing device estimates the current location based on the time and location information of the detected scene. For example, the last place where the red key was seen may be identified as "on the table in the living room."

[1752] 6. The data processing device, taking into account that the user is in a hurry, generates a notification message saying "Please stay calm. The red key is on the table in the living room" and sends it to the terminal.

[1753] 7. The device receives the notification and displays its contents to the user.

[1754] This system allows users to find what they are looking for more safely and efficiently.

[1755] Example prompt sentence:

[1756] "I'm looking for the red key. I'm getting impatient."

[1757] "Generate an image of a red key."

[1758] "Please tell me where and when the red key was last seen."

[1759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1760] Step 1: Acquire camera footage

[1761] The server receives video data in real time from a camera installed in the room. The input is the video stream from the camera, and the output is the encoded video data.

[1762] The server adds date and time information to the received video data and stores it in a database. Specifically, it encodes the video data using a library such as FFmpeg and adds date and time metadata. This data is then stored in an SQL-based database (e.g., MySQL) or a NoSQL database (e.g., MongoDB).

[1763] Step 2: Request for lost item investigation

[1764] The user uses a dedicated app on their device (smartphone or PC) to input a search request for an item they are looking for, such as "Find the red key." The input is the search request entered by the user in a text field, and the output is the search request data sent to the server.

[1765] The device's camera and microphone capture the user's facial expressions and voice, and an emotion recognition engine (e.g., OpenFace or Microsoft Azure Face API) extracts emotional data. Specifically, the emotion recognition engine analyzes facial and vocal characteristics to detect the user's emotional state.

[1766] The device formats the survey request text and extracted emotion data into JSON format and sends it to the server via an HTTP POST request.

[1767] Step 3: Create an image of what you're looking for

[1768] The server analyzes the received search request using a natural language processing engine (e.g., BERT) and extracts features of the searched item. The input is the text of the search request, and the output is feature data of the searched item. Specifically, it extracts key phrases using a natural language processing library (e.g., Hugging Face transformers).

[1769] The server uses an image generation engine (e.g., DALL-E or Stable Diffusion) based on the features to generate a specific image of the searched item. The input is the feature data of the searched item, and the output is the generated image. An example of a prompt sentence: "Generate an image of a red key."

[1770] Step 4: Search for recording data

[1771] The server uses the generated images to search past recorded data. The input is the generated images, and the output is the search results for matching scenes.

[1772] The server uses an image recognition engine (e.g., YOLOv5) to extract the relevant scenes. Specifically, the YOLOv5 model performs image recognition processing on each video frame to detect matching scenes.

[1773] Step 5: Estimate the location of the item

[1774] The server identifies the location where the item was last seen based on the time and location information of the searched scene. The input is the matching scene information, and the output is the estimated location information. Specifically, it retrieves the entry where the red key was last seen from the database and uses a rule-based algorithm to estimate its current location.

[1775] Step 6: Notification

[1776] The server appropriately changes the content of the notification message based on the user's emotional information recognized by the emotion engine. The input is emotional information and estimated location information, and the output is a customized notification message. In concrete terms, the emotion engine is used to analyze the user's emotional state and generate an appropriate notification message. An example of a prompt sentence: "Please stay calm. The red key is on the table in the living room."

[1777] The device receives notifications from the server and displays the contents in the user's application. The input is the notification message from the server, and the output is the notification display to the user. Specifically, the notification message is displayed in the notification center of the dedicated application or as a pop-up notification.

[1778] This allows users to find what they are looking for quickly and efficiently, and is expected to improve the user experience by providing appropriate responses based on emotions.

[1779] (Application example 2)

[1780] 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."

[1781] While there are existing systems for efficiently finding lost items, few systems can recognize and respond to the user's emotions. Furthermore, if the user is feeling anxious or stressed, appropriate assistance is not provided, resulting in a poor user experience. Conventional systems generate notification content without considering the user's emotional state, resulting in low user satisfaction. To solve this problem, a system that recognizes the user's emotions and responds appropriately based on them is needed.

[1782] 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.

[1783] In this invention, the server includes means for a camera to continuously record video of a room and transmit the video to the server, means for the server to save the recorded video, means for a user to request an investigation into an item they are looking for, means for the server to receive the investigation request and generate an image of the item they are looking for using an image generation means, means for the server to search for a matching scene from past recorded data based on the generated image, means for the server to estimate the location of the item they are looking for based on the search result, means for recognizing the user's emotion and adjusting notification content based on the emotion information, and means for the server to notify the user of the estimated location information. This enables appropriate notification and support based on the user's emotion, improving the user experience.

[1784] A "camera" is a device that continuously records video of a specific location or object and transmits it to a server.

[1785] The "server" is a computer system that stores the images sent from the camera, processes the user's investigation request, generates an image of the item being searched for, and searches for recorded data based on that image.

[1786] A "user terminal" is a device that allows a user to request an investigation into a lost item and has the function of receiving the investigation results. This includes smartphones and personal computers.

[1787] The "image generation means" is a technology that allows the server to generate a concrete image of the item being searched for based on the user's search request. For example, it uses a generative model.

[1788] "Image recognition means" is a technology that allows the server to search for scenes that match the generated image from the recorded data. For example, it uses an image analysis engine such as OpenCV.

[1789] "Natural language processing means" is a technology that analyzes a user's search request and extracts the characteristics of the item based on that analysis. For example, Google Cloud Natural Language API is an example of this.

[1790] "Emotion recognition means" refers to a technology that detects emotions by analyzing the user's facial expressions and voice and reflects that information in the notification content. For example, an emotion recognition engine is an example of this.

[1791] The "notification means" is a technology that notifies the user terminal of the content based on the predetermined information generated by the server. The content of the notification is adjusted based on the user's emotions.

[1792] The "means for estimating the location of a lost item" is a technology in which the server determines the time and place in which the lost item was captured based on the search results of image recognition, and estimates its current location.

[1793] The present invention is a system for finding things that uses emotion recognition technology and is configured by combining a camera, a server, and a user terminal. The detailed configuration of this system is described below.

[1794] System Configuration

[1795] 1. Camera

[1796] The camera continuously records video of a specific location or room and transmits it to a server in real time. High-resolution cameras are used to accurately capture video details.

[1797] 2. Server

[1798] A server is a computer system that has the following functions:

[1799] Video data storage: Video data sent from the camera is stored in a database along with date and time information.

[1800] Database: Efficiently search and manage video data.

[1801] Image generation: Using a generative model, a concrete image of the item being searched for is created based on the user's research request.

[1802] Image recognition: Using an image analysis engine such as OpenCV, it searches for scenes that match images generated from past recorded data.

[1803] Emotion Recognition: Using an emotion recognition engine, we analyze and recognize the user's emotional information and process it on the server side.

[1804] Natural language processing: Analyze user survey requests using Google Cloud Natural Language API, etc.

[1805] Location estimation of lost items: Estimates the current location based on the time and location information of search results.

[1806] Notification: Customize notification content based on the user's emotions and send information to the user's device.

[1807] 3. User Device

[1808] The user terminal is a smartphone or personal computer and has the following functions:

[1809] Interface: Provide an interface that allows users to easily input search requests for items they are looking for.

[1810] Emotion recognition: Analyzes the user's facial expressions and voice to detect emotional information and send it to the server.

[1811] Receive notifications: Receive notifications from the server and display them to the user.

[1812] System Operation

[1813] 1. The user launches the app and speaks or texts, "I'm looking for a miniature blue car."

[1814] 2. The user device analyzes the user's facial expressions and voice to obtain emotional information.

[1815] 3. The device sends the input information and emotion information to the server.

[1816] 4. The server uses natural language processing to identify a "miniature blue car" and generates an image using a generative model.

[1817] 5. The server uses an image analysis engine to search for the relevant scene in the recorded data.

[1818] 6. The server estimates the location of the item based on the search results and notifies the user's device of this information.

[1819] 7. The server customizes the notification content based on the emotional information. For example, if a user is feeling anxious, the server sends a message such as, "Don't worry. The miniature blue car is in the toy section on the second floor."

[1820] 8. The user device receives the notification and displays it to the user.

[1821] Specific examples

[1822] Example 1: Finding a key

[1823] 1. The user launches the app and types in "Find the red key." At the same time, the emotion recognition engine analyzes the user's facial expressions and voice to detect stress or impatience.

[1824] 2. The device sends the input content and emotion information to the server.

[1825] 3. The server analyzes the "red key" using natural language processing and generates an image using an image generation engine.

[1826] 4. The server uses an image recognition engine to search for the relevant scene in the recorded data and extracts frames that show the red key.

[1827] 5. The server estimates the current location based on the time and location of that frame.

[1828] 6. The server generates a notification message that takes into account the user's emotional information: "Please stay calm. The red key is on the table in the living room." and sends it to the device.

[1829] 7. The device receives the notification and displays it to the user.

[1830] Prompt Sentence Examples

[1831] text

[1832] User: When searching for a miniature blue car in a store but cannot find it, the user types "find a miniature blue car" into the app. At the same time, the camera detects that the customer is anxious. The server processes this, estimates the location of the item, and responds with a relaxed message.

[1833] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1834] Step 1:

[1835] The user launches the app and enters a survey request.

[1836] The user launches a dedicated application on their smartphone or personal computer and inputs a search request for the item they are looking for by voice or text. For example, they might input, "I'm looking for a miniature blue car." At this time, the application also analyzes the user's facial expressions and voice to obtain emotional information.

[1837] Input: User survey request (voice or text), facial expression and voice data

[1838] Output: Investigation request details and emotional information

[1839] Step 2:

[1840] The device sends the investigation request and emotional information to the server.

[1841] The terminal transmits the survey request entered by the user and the analyzed emotional information to the server. At this time, the data is encrypted before transmission.

[1842] Input: Investigation request details and emotional information

[1843] Output: Survey request details and emotional information sent to the server

[1844] Step 3:

[1845] The server analyzes the investigation request.

[1846] The server analyzes the received search request using a natural language processing engine (such as Google Cloud Natural Language API) and extracts the characteristics of the item being searched for. In this case, the characteristics of a "miniature blue car" are extracted.

[1847] Input: Survey request details

[1848] Output: Extracted features of the searched item

[1849] Step 4:

[1850] The server generates an image of the item using an image generation engine.

[1851] The server uses a generative model (AI model) to generate a specific image of the item based on the extracted features of the item. For example, an image of a specific "miniature blue car" is generated.

[1852] Input: Extracted features of the object being searched for

[1853] Output: Generated image of the object being searched

[1854] Step 5:

[1855] The server searches for recorded data using an image recognition engine

[1856] The server uses the generated image to search past recorded data using an image recognition engine (such as OpenCV) and extracts matching scenes.

[1857] Input: Generated image of the object to be found

[1858] Output: Matching scenes with their time and location information

[1859] Step 6:

[1860] The server estimates the location of the item

[1861] The server estimates the current location of the item based on the time and location information of the search results. In this case, it estimates the specific location of the "miniature blue car."

[1862] Input: Time and location information of the matching scene

[1863] Output: Estimated current location of the item

[1864] Step 7:

[1865] The server customizes the notification content based on emotion information.

[1866] The server considers the user's emotional information and adjusts the notification content appropriately. For example, if the user is feeling anxious, it generates a relaxing message such as, "Don't worry. The miniature blue car is in the toy section on the second floor."

[1867] Input: Current location of the item being searched for, user's emotion information

[1868] Output: Customized notification content

[1869] Step 8:

[1870] The server notifies the user device of the estimated location information.

[1871] The server sends the customized notification content to the user terminal, and the terminal displays the received notification to the user.

[1872] Input: Customized notification content

[1873] Output: Information sent to the user's device

[1874] These steps allow users to find what they are looking for efficiently and provide a more pleasant experience by receiving appropriate notifications based on their emotions.

[1875] 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.

[1876] 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.

[1877] 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.

[1878] 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.

[1879] 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.

[1880] 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.

[1881] 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).

[1882] 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.

[1883] 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."

[1884] 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.

[1885] 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).

[1886] 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.

[1887] 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.

[1888] 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.

[1889] 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.

[1890] 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.

[1891] 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.

[1892] 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.

[1893] 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.

[1894] 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.

[1895] 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.

[1896] The following is further disclosed regarding the above embodiment.

[1897] (Claim 1)

[1898] A camera continuously records video of the room and transmits the video to a server;

[1899] A server stores the recorded video;

[1900] A means for a user to request an investigation into a lost item;

[1901] a server receiving the search request and generating an image of the item being searched for by an image generating means;

[1902] A means for the server to search for matching scenes from past recording data based on the generated image;

[1903] A means for the server to estimate the location of the searched item based on the search results;

[1904] a means for the server to notify the user of the estimated location information;

[1905] A system including:

[1906] (Claim 2)

[1907] 10. The system of claim 1, wherein image recognition means is used to search for scenes that match images generated from previously recorded data.

[1908] (Claim 3)

[1909] 2. The system according to claim 1, wherein the user's search request is analyzed using natural language processing means, and an image of the item being searched for is generated by image generation means based on the analysis results.

[1910] "Example 1"

[1911] (Claim 1)

[1912] A means for continuously recording video of the interior of a room using a camera and transmitting the video to an information processing device;

[1913] a means for storing the recorded video in the information processing device;

[1914] A means for a user to request an investigation into a lost item;

[1915] a means for receiving the search request by an information processing device and generating an image of the item being searched for by an image generating means;

[1916] A means for searching for a matching scene from past recording data based on the image generated by the information processing device;

[1917] A means for the information processing device to estimate the location of the item being searched for based on the search results;

[1918] a means for notifying a user of the estimated location information by the information processing device;

[1919] A system including:

[1920] (Claim 2)

[1921] 10. The system of claim 1, wherein image recognition means is used to search for scenes that match images generated from previously recorded data.

[1922] (Claim 3)

[1923] 2. The system according to claim 1, wherein the user's search request is analyzed using natural language processing means, and an image of the item being searched for is generated by image generation means based on the analysis results.

[1924] "Application Example 1"

[1925] (Claim 1)

[1926] A camera continuously records video of the room and transmits the video to a server;

[1927] A server stores the recorded video;

[1928] A means for a user to request an investigation into a lost item;

[1929] a server receiving the search request and generating an image of the item being searched for by an image generating means;

[1930] A means for the server to search for matching scenes from past recording data based on the generated image;

[1931] A means for the server to estimate the location of the searched item based on the search results;

[1932] a means for the server to notify the user of the estimated location information;

[1933] means for displaying visual instructions on a smart device worn by a user;

[1934] an augmented reality means for highlighting objects in the user's field of view based on visual cues;

[1935] A system including:

[1936] (Claim 2)

[1937] 10. The system of claim 1, wherein image recognition means is used to search for scenes that match images generated from previously recorded data.

[1938] (Claim 3)

[1939] 2. The system according to claim 1, wherein the user's search request is analyzed using natural language processing means, and an image of the item being searched for is generated by image generation means based on the analysis results.

[1940] "Example 2: Combining Emotion Engines"

[1941] (Claim 1)

[1942] A camera continuously records video of the room and transmits the video to a data processing device;

[1943] a means for storing the recorded video in the data processing device;

[1944] A means for a user to request an investigation into a lost item;

[1945] a means for receiving the search request by a data processing device and generating an image of the item being searched for by an image generating means;

[1946] A means for searching for a matching scene from past recorded data based on the image generated by the data processing device;

[1947] A means for the data processing device to estimate the location of the searched item based on the search results;

[1948] a means for notifying a user of the estimated location information by the data processing device;

[1949] means for detecting emotion information of a user and generating a notification message according to the emotion;

[1950] A system including:

[1951] (Claim 2)

[1952] 10. The system of claim 1, wherein image recognition means is used to search for scenes that match images generated from previously recorded data.

[1953] (Claim 3)

[1954] 2. The system according to claim 1, wherein the user's search request is analyzed using natural language processing means, and an image of the item being searched for is generated using image generation means based on the analysis results.

[1955] "Application example 2 when combining emotion engines"

[1956] (Claim 1)

[1957] A camera continuously records video of the room and transmits the video to a server;

[1958] A server stores the recorded video;

[1959] A means for a user to request an investigation into a lost item;

[1960] a server receiving the search request and generating an image of the item being searched for by an image generating means;

[1961] A means for the server to search for matching scenes from past recording data based on the generated image;

[1962] A means for the server to estimate the location of the searched item based on the search results;

[1963] A means for recognizing a user's emotion and adjusting notification content based on the emotion information;

[1964] a means for the server to notify the user of the estimated location information;

[1965] A system including:

[1966] (Claim 2)

[1967] 10. The system of claim 1, wherein image recognition means is used to search for scenes that match images generated from previously recorded data.

[1968] (Claim 3)

[1969] 2. The system according to claim 1, wherein the user's search request is analyzed using natural language processing means, and an image of the item being searched for is generated by image generation means based on the analysis results.

[1970] (Claim 4)

[1971] 2. The system according to claim 1, further comprising means for detecting a user's emotion by analyzing the user's facial expression and voice, and generating a notification including the emotion information. [Explanation of symbols]

[1972] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A camera continuously records video of the room and transmits the video to a server; A server stores the recorded video; A means for a user to request an investigation into a lost item; a server receiving the search request and generating an image of the item being searched for by an image generating means; A means for the server to search for matching scenes from past recording data based on the generated image; A means for the server to estimate the location of the searched item based on the search results; a means for the server to notify the user of the estimated location information; A system including:

2. 10. The system of claim 1, wherein image recognition means is used to search for scenes that match images generated from previously recorded data.

3. 2. The system according to claim 1, wherein the user's search request is analyzed using natural language processing means, and an image of the item being searched for is generated by image generation means based on the analysis result.

Images