system

Abstract

Provide a system. The method includes: a means for a user to scan a food item with an image capture device; means for transmitting image data of food captured by the image capture device to a server; A means for the server to analyze the transmitted image data and identify the food; means for the server to retrieve origin information and nutritional information for the identified food from a database; means for transmitting the retrieved information back to the image capture device; means for displaying the information returned by the image capture device in augmented reality; 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] Today's consumers have an increasing need for more detailed information about the foods they purchase. However, due to a lack of ways to instantly and visually understand food origins and nutritional information, there is a need for a fast and easy way to provide this information. Furthermore, existing methods have difficulty in providing information effectively. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes means for a user to scan food with an image capture device, means for transmitting image data of the food captured by the image capture device to a server, means for the server to analyze the transmitted image data and identify the food, means for the server to search a database for origin and nutritional information about the identified food, means for returning the retrieved information to the image capture device, and means for displaying the information returned by the image capture device in augmented reality, thereby allowing a user to visually obtain detailed information about the scanned food in real time.

[0006] "User" refers to the entity that operates the image capture device to scan food items.

[0007] "Image capture device" refers to a device that has the ability to capture images, such as a camera.

[0008] "Food" refers to an object that a user scans with an image capture device to learn about its origin and nutritional information.

[0009] "Image data" refers to a digitally stored image of a food item captured by an image capture device.

[0010] "Server" refers to a computer system that receives image data sent from a terminal and performs analysis and database search.

[0011] "Image recognition algorithm" refers to a program that includes machine learning and pattern recognition technology to identify food from transmitted image data.

[0012] "Database" refers to an information management system that accumulates and stores searchable information on the origin and nutritional information of food.

[0013] "Origin information" refers to information that indicates where the food came from, such as where it was produced and how it was produced.

[0014] "Nutrition information" refers to information that indicates the nutritional components and amounts contained in food.

[0015] "Augmented reality" refers to the technology of overlaying digital information onto the real world, including the means to display additional information on the real world seen by the user through a camera. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] To implement the present invention, a system is required that allows a user to scan food using an image capture device and obtain information about the food in real time. A detailed description of specific embodiments of the present invention is provided below.

[0038] Food scanning and image transmission

[0039] A user scans a food item using an image capture device such as a smartphone or tablet. For example, if the user scans an apple, they use the camera function to capture an image of the apple. The device saves this image and makes a request to send the image data to a server.

[0040] Image analysis and food identification

[0041] The server receives the image data sent from the device. The received image data is analyzed using an image recognition algorithm to identify the food. In this case, the server uses the image recognition algorithm to identify the food as an apple.

[0042] Food information search

[0043] The server searches the database for information corresponding to the food (apple) it has recognized. Specifically, it obtains information about the apple's origin (e.g., "Central Asia") and nutritional information (e.g., "rich in vitamin C and dietary fiber").

[0044] Information transmission and AR display

[0045] The server retrieves information from the database, formats it appropriately, and sends it to the device. The device then analyzes the information and generates content for the AR display. For example, it could overlay text information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on an apple. The device then displays this information in real time on the camera view and presents it to the user.

[0046] Specific examples

[0047] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple, then sends it to a server. The server uses an image recognition algorithm to identify the apple and searches a database for the apple's origin and nutritional information. The search results are sent back to the device, which displays the received information as AR content. The user can view the origin and nutritional information displayed on the apple through the screen.

[0048] In this way, the system based on the present invention allows users to easily scan food and obtain detailed information about it in real time. The terminal and server work together to provide users with effective information.

[0049] The processing flow will be explained below.

[0050] Specific processing steps of the program

[0051] Step 1:

[0052] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a picture.

[0053] Step 2:

[0054] The image capture device stores the image data of the scanned apple, typically in local storage or temporary memory.

[0055] Step 3:

[0056] The device sends the captured image data to the server by making an HTTP POST request and including the image data as a payload.

[0057] Step 4:

[0058] The server receives the image data sent from the terminal, confirms that the image has been sent, and temporarily stores the data.

[0059] Step 5:

[0060] The server then runs the image data through an image recognition algorithm, using machine learning models and pattern recognition techniques to analyze the object in the image and identify it as an apple.

[0061] Step 6:

[0062] The server searches the database for origin and nutritional information for the identified food (apple). For example, it retrieves information such as "originating from Central Asia" and "rich in vitamin C and dietary fiber."

[0063] Step 7:

[0064] The server formats the search results and returns them to the device in the appropriate format, typically in JSON format.

[0065] Step 8:

[0066] The terminal analyzes the data received from the server, and identifies the origin and nutritional information that needs to be displayed.

[0067] Step 9:

[0068] The device generates the content for the AR display, specifically the text and graphics that are overlaid on the camera view.

[0069] Step 10:

[0070] The device displays AR content in real time on the camera view. When the user looks at the apple through their smartphone, information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" is superimposed on the apple.

[0071] By following these steps, users can easily scan food items and visually obtain detailed information about them in real time.

[0072] Example 1

[0073] 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."

[0074] In today's world, there is a demand for instant access and understanding of detailed information about food. However, conventional methods require users to individually look up information about foods, which is time-consuming and laborious. In addition, there are limited means for easily displaying food information visually. This means that users need time to select and understand foods, making it difficult to efficiently obtain information.

[0075] 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.

[0076] In this invention, the server includes means for a user to scan an object with an image capture device, means for transmitting image data of the object captured by the image capture device to a processing device, means for the processing device to analyze the transmitted image data and identify the object, means for the processing device to search a database for detailed information about the identified object, means for returning the searched information to the image capture device, and means for presenting the returned information by the image capture device in an expanded display, thereby enabling a user to easily obtain detailed information about food and visually understand it.

[0077] "User" refers to a person who uses the system to scan food or other objects to obtain detailed information.

[0078] An "image acquisition device" is a device equipped with a camera function, including a smartphone or tablet, that is used to acquire image data of an object.

[0079] An "object" is anything that a user scans with an image capture device to obtain detailed information about, such as food or an object.

[0080] The "processing device" is a central processing unit that receives image data sent from the image acquisition device and performs analysis and database searches, and is usually a high-performance computer such as a server.

[0081] "Detailed information" refers to data including information that the user wants to obtain, such as information about the origin, nutritional information, and characteristics of the subject.

[0082] "Augmented display" refers to a method of using AR (augmented reality) technology to overlay information on the screen of an image capture device.

[0083] An "image analysis algorithm" is a computational method and program for performing image recognition, and is an algorithm used to identify objects.

[0084] To implement this invention, a system is required that allows a user to scan an object (e.g., food) using an image capture device and acquire information about it in real time. This system is composed of a user, a terminal, and a server, and these components work together to provide information efficiently.

[0085] The user scans the object with the image capture device

[0086] A user scans an object using an image capture device such as a smartphone or tablet. Specifically, the user captures an image of the object using the camera function. For example, when a user wants to scan an apple, they launch the camera app on their smartphone, focus on the apple, and press the capture button.

[0087] The device sends the captured image to the server.

[0088] The device saves the captured image data and generates and executes a request to send it to a server. The software used for this includes the camera app on a smartphone or tablet, as well as a dedicated application. The image data is sent to the server via an HTTP request.

[0089] The server analyzes the image and identifies the subject

[0090] The server receives the image data sent from the device and identifies the object (e.g., an apple) using an image analysis algorithm. The software used here includes image recognition algorithms such as TENSORFLOW (registered trademark) and OpenCV. The server reads the image file, inputs it into an image recognition model, and identifies the object.

[0091] The server searches the database for the relevant information

[0092] The server retrieves detailed information (e.g., origin information, nutritional information) about the identified subject from a database. The software used here includes an SQL query and a database management system (e.g., MySQL®). The server runs the SQL query against the database to retrieve data corresponding to the subject.

[0093] The server sends the information to the device

[0094] The server formats the retrieved information into an appropriate format (e.g., JSON) and sends it to the device. The server then converts the retrieved data into JSON format and sends it back to the device as an HTTP response, allowing the device to easily analyze the returned information.

[0095] The device generates the information as an AR display and presents it to the user.

[0096] The device parses the JSON data received from the server and extracts the data to display. The software used includes a JSON parser and an AR application (e.g., ARKit or ARCore). The device uses the extracted information to generate AR display content and present it to the user. Specifically, the device overlays text such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on the camera view.

[0097] Specific examples of operation

[0098] When a user scans an apple with their smartphone camera, the device captures and saves an image of the apple, then sends the image file to the server. The server uses an image recognition algorithm to identify the apple and retrieves detailed information about the apple from a database. The server formats the information into JSON format and sends it to the device as an HTTP response. The device then analyzes the received JSON data, extracts the necessary information, generates AR display content, and presents it to the user. In this way, the user can visually confirm detailed information about the apple in real time.

[0099] Example prompts for generative AI models

[0100] The following prompt sentences can be used to provide input to the generative AI model:

[0101] Describe a system that scans images of food and displays its nutritional information and origin in real time. Break it down into the following processing steps:

[0102] 1. The user scans the object with the image capture device.

[0103] 2. The device captures the image and sends it to the server

[0104] 3. The server analyzes the image and identifies the target

[0105] 4. The server searches the database for the relevant information

[0106] 5. The server sends the information to the device

[0107] 6. The device generates the information as an AR display and presents it to the user.

[0108] In this way, the system based on the present invention allows users to easily scan an object and obtain detailed information about it in real time. The terminal and server work together to provide effective information provision.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1:

[0111] User scans food

[0112] Input: User takes a picture of food using the camera.

[0113] Specific actions: The user launches the smartphone camera app, scans the apple, focuses, and presses the capture button.

[0114] Output: The captured image data (e.g., an image of an apple).

[0115] Step 2:

[0116] The device captures and saves the image

[0117] Input: Image data sent from the camera app.

[0118] Specific operation: The device saves the captured image file (e.g., apple.jpg) in the specified directory in the storage.

[0119] Output: Image file saved on your device.

[0120] Step 3:

[0121] The device sends the image data to the server.

[0122] Input: A saved image file (e.g. apple.jpg).

[0123] Specific operation: The device obtains the path to the image file, generates an HTTP request, and sends the image data to the server.

[0124] Output: Image data sent to the server.

[0125] Step 4:

[0126] The server receives the image data.

[0127] Input: HTTP request from the device (including image data).

[0128] Specific operation: The server receives the HTTP request and saves the image data in the analysis directory.

[0129] Output: Image file saved on the server.

[0130] Step 5:

[0131] The server analyzes the image and identifies the food.

[0132] Input: Image file stored on the server.

[0133] Specific operation: The server analyzes the image data using image analysis algorithms such as TensorFlow or OpenCV, and identifies the specific food item (e.g., apple).

[0134] Output: The type of food identified (e.g., apple).

[0135] Step 6:

[0136] The server searches the database for food information

[0137] Input: The type of food identified (e.g., apple).

[0138] What happens: The server runs an SQL query against the database to find the origin and nutritional information of the apple.

[0139] Output: Food information retrieved from the database (e.g., origin and nutritional information for an apple).

[0140] Step 7:

[0141] The server sends the information to the device

[0142] Input: Food information retrieved from a database (e.g., origin and nutritional information for an apple).

[0143] Specific operation: The server formats the food information it obtains into JSON format and sends it to the terminal as an HTTP response.

[0144] Output: Food information in JSON data format sent to the terminal.

[0145] Step 8:

[0146] The device analyzes the received data and generates an AR display.

[0147] Input: Food information received from the server in JSON data format.

[0148] What it does: The device uses a JSON parser to parse the data and extract the necessary information (e.g., the origin and nutritional information of the apple), then uses ARKit or ARCore to generate AR content that corresponds to the camera view.

[0149] Output: AR content displayed in the camera view (e.g. text information "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" overlaid on an apple).

[0150] In this way, each processing step works together in a series of steps to provide detailed visual information about the food to the user. Through the customer interface, the user can easily scan the food and obtain detailed information in real time.

[0151] (Application example 1)

[0152] 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."

[0153] Previously, when purchasing food in a physical store, users had to read the product packaging to obtain detailed product information. However, there were limited ways to check information not listed on the packaging or more detailed background information. For example, it was difficult to display information about the food's origin or nutritional information, or even real-time information in a physical store. There was also a lack of ways to present product information visually in an easy-to-understand manner. This resulted in users being unable to obtain sufficient information when making food choices, resulting in a decrease in satisfaction.

[0154] 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.

[0155] In this invention, the server includes means for a user to scan food with an image capture device, means for transmitting image data of the food captured by the image capture device to the server, means for the server to analyze the transmitted image data and identify the food, means for the server to search a database for origin information and nutritional information about the identified food, means for returning the searched information to the image capture device, means for the image capture device to display the returned information in augmented reality, means for scanning products using smart glasses in a physical store, and means for presenting the food information displayed on the smart glasses in real time, thereby enabling a user to intuitively obtain detailed information about food simply by scanning the food in a physical store.

[0156] "User" refers to an individual who uses the system to obtain food information.

[0157] "Image capture device" refers to a device, such as a smartphone, tablet, or smart glasses with a camera function, that captures images of food and transmits them to a server.

[0158] "Food" refers to edible products that can be scanned and captured using the system.

[0159] "Server" refers to a computing device that analyzes the food image data sent from the image capture device, retrieves information from a database, and returns it.

[0160] "Image data" refers to images of food captured with an image capture device.

[0161] "Analysis" refers to the process of identifying the type of food based on the transmitted image data.

[0162] "Identification" refers to the process of recognizing a particular food item from the analyzed image data.

[0163] "Origin information" refers to information about the region or origin from which a food product comes.

[0164] "Nutrition information" refers to information about the nutritional components contained in food and their effects.

[0165] "Database" refers to a collection of information that stores and makes searchable information about food origins and nutritional information.

[0166] "Search" refers to the process by which the server retrieves information about a specified food item from the database.

[0167] "Return" refers to the process by which the server sends the acquired information back to the image capture device.

[0168] "Augmented reality" refers to a technology that overlays computer-generated information onto your physical field of vision.

[0169] A "physical store" refers to a facility that sells food and other items in a real physical space.

[0170] "Smart glasses" refers to a glasses-type device that can display information in the field of view using augmented reality technology.

[0171] "Real-time" refers to a state in which information is provided almost immediately in response to a user's operation.

[0172] To implement the present invention, a system must be built that allows a user to scan food using an image capture device and capture the food information in real time. Specifically, this is accomplished by the following steps:

[0173] 1. Use of image acquisition equipment

[0174] Users scan food items in physical stores using an image capture device (such as a smartphone, tablet, or smart glasses), and the image data of the scanned food items is stored on the device.

[0175] 2. Sending image data to the server

[0176] The device sends the stored image data of the food to a server using the device's internal communication module and internet connection.

[0177] 3. Image analysis on the server

[0178] The server analyzes the received image data, using image recognition algorithms (e.g., TensorFlow or OpenCV) to identify the type of food.

[0179] 4. Searching for food information

[0180] The server searches the database for the corresponding origin and nutritional information for the identified food products. The server uses a database management system such as MySQL or MongoDB.

[0181] 5. Returning information to your device

[0182] The server converts the search results into an appropriate format, such as JSON, and sends them back to the device.

[0183] 6. Augmented Reality Information Display

[0184] Based on the received information, the device displays detailed information about the food in augmented reality on the smart glasses display, allowing users to visually check the food's background information and nutritional content in real time.

[0185] Hardware and Software

[0186] Hardware: Smartphones, tablets, smart glasses (such as Google® Glass® or Microsoft® HoloLens®)

[0187] Software: Python, OpenCV, Requests library, database management system (MySQL, MongoDB)

[0188] Specific examples

[0189] For example, when a user chooses an apple in a physical store, they put on the smart glasses and scan the apple. The image data of the scanned apple is sent from the device to a server. The server uses an image recognition algorithm to identify the apple and search a database for information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber." The search results are then sent back to the device and displayed in real time in the user's field of vision through the smart glasses.

[0190] Prompt Sentence Examples

[0191] "Please tell me the origin and nutritional information of the apples."

[0192] This allows users to easily obtain detailed information about food products in physical stores.

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] Users use an image capture device (e.g., smartphone, tablet, smart glasses) to scan food items in a physical store.

[0196] Input: Food images captured with smart glasses or a smartphone camera

[0197] What it does: Point the image capture device's camera at the food and tap the camera button to take a picture, or, in the case of smart glasses, use a voice command.

[0198] Step 2:

[0199] The device sends the captured images to a server, using a communication module and an internet connection.

[0200] Input: Image data of the food you photographed

[0201] Output: Image data sent to the server

[0202] What happens: The device sends image data to the server in an HTTP POST request, over an Internet connection.

[0203] Step 3:

[0204] The server analyzes the received image data and identifies the food using an image recognition algorithm.

[0205] Input: Image data sent from the device

[0206] Output: The type of food identified (e.g., apple)

[0207] What it does: The server runs Python and TensorFlow image recognition models to analyze the image data it receives, and returns the type of food it identifies.

[0208] Step 4:

[0209] The server retrieves origin and nutritional information for the identified food products from a database.

[0210] Input: Identified food type (e.g. apple)

[0211] Output: Origin and nutritional information about the food

[0212] Specific operation: The server executes an SQL query against a database managed by MySQL or MongoDB to retrieve food information. For example, the query "SELECT FROM food_info WHERE name="apple"" is executed.

[0213] Step 5:

[0214] The server converts the acquired information into an appropriate format, such as JSON, and sends it back to the device.

[0215] Input: Food information retrieved from the database

[0216] Output: Food information returned to the device

[0217] Specific operation: The server serializes the acquired information in JSON format and sends it to the terminal as an HTTP response.

[0218] Step 6:

[0219] Based on the information received, the device displays food information in augmented reality on the smart glasses display.

[0220] Input: Food information received from the server

[0221] Output: Augmented reality food information displayed on smart glasses

[0222] Specific operation: The device analyzes the received information and uses the smart glasses' AR display module to overlay information on the origin and nutritional information on the food.

[0223] These steps allow users to easily obtain detailed information about food products and visually check them in-store.

[0224] 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.

[0225] The present invention combines a system that allows a user to scan food using an image capture device and acquire information about the food in real time with an emotion engine that recognizes the user's emotions. Hereinafter, specific embodiments of the present invention will be described in detail.

[0226] Food scanning and image transmission

[0227] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a photo. The device saves the image and executes a request to send the image data to a server.

[0228] Image analysis and food identification

[0229] The server receives the image data sent from the device. The received image data is analyzed using an image recognition algorithm to identify the food item. For example, the server uses the image recognition algorithm to identify the food item as an apple.

[0230] Food information search

[0231] The server searches the database for information corresponding to the food (apple) it has recognized. Specifically, it obtains information about the apple's origin (e.g., "Central Asia") and nutritional information (e.g., "rich in vitamin C and dietary fiber").

[0232] Information transmission and AR display

[0233] The server appropriately formats the information retrieved from the database and sends it to the device. The device then analyzes the received information and generates content for AR display. At this time, the emotion engine analyzes the user's facial expression data and recognizes their emotion. For example, if the user has a surprised expression, the AR display will emphasize the information that matches their emotion. Specifically, not only will the information "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" be displayed above the apple, but the color and font size will also be adjusted according to the emotion.

[0234] Specific examples

[0235] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple, then sends it to a server. The server uses an image recognition algorithm to identify the apple and searches a database for information about the apple's origin and nutritional value. The search results are sent back to the device, which then displays the received information as AR content. At this time, an emotion engine analyzes the user's facial expression and recognizes, for example, that the user is looking surprised. Based on that emotion, the AR-displayed information is more emphasized. The user not only sees the origin and nutritional value displayed on the apple on the screen, but also experiences the information dynamically changing to match their own emotions.

[0236] In this way, the present invention allows users to simply scan food items, visually obtain detailed information about them in real time, and then use an emotion engine to interactively present information according to the user's emotions. Cooperation between the terminal, server, and emotion engine makes it possible to provide users with more effective information.

[0237] The processing flow will be explained below.

[0238] Specific processing steps of the program

[0239] Step 1:

[0240] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a picture.

[0241] Step 2:

[0242] The image capture device stores the image data of the scanned apple, typically in local storage or temporary memory.

[0243] Step 3:

[0244] The device sends the captured image data to the server by making an HTTP POST request and including the image data as a payload.

[0245] Step 4:

[0246] The server receives the image data sent from the terminal, confirms that the image has been sent, and temporarily stores the data.

[0247] Step 5:

[0248] The server then runs the image data through an image recognition algorithm, using machine learning models and pattern recognition techniques to analyze the object in the image and identify it as an apple.

[0249] Step 6:

[0250] The server searches the database for origin and nutritional information for the identified food (apple). For example, it retrieves information such as "originating from Central Asia" and "rich in vitamin C and dietary fiber."

[0251] Step 7:

[0252] The server formats the search results and returns them to the device in the appropriate format, typically in JSON format.

[0253] Step 8:

[0254] The terminal analyzes the data received from the server, and identifies the origin and nutritional information that needs to be displayed.

[0255] Step 9:

[0256] The emotion engine captures the user's facial expressions through the device's camera, and facial data is collected as the user scans the apple.

[0257] Step 10:

[0258] The emotion engine analyzes the collected facial expression data to identify the user's emotion, for example, recognizing that the user has a surprised expression.

[0259] Step 11:

[0260] The device generates content for the AR display, and the displayed information is enhanced based on the user's emotions. For example, if the user has a surprised expression, the font size of nutrition information will be increased.

[0261] Step 12:

[0262] The device displays AR content in real time on the camera view. When the user looks at the apple through their smartphone, information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" is displayed on top of the apple, with emphasis based on the user's emotions.

[0263] By following these steps, users can easily scan food items and visually obtain detailed information about them in real time, and interactive information presentation based on the user's emotions is also possible.

[0264] Example 2

[0265] 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."

[0266] Conventional food identification systems have the problem that they do not provide interactive information according to the user's emotions when obtaining detailed information about a food. Furthermore, the system does not dynamically change information based on the user's emotions, which hinders the user experience. Furthermore, there is no efficient coordination between real-time information processing and display.

[0267] 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.

[0268] In this invention, the server includes a means for analyzing image data to identify foods, a means for searching a database for origin and nutritional information about the identified foods, and a means for returning the searched information to the image capture device. This allows a user to obtain detailed information about foods scanned using the image capture device in real time. Furthermore, by adding a means for an emotion engine to analyze the user's facial expression data to recognize emotions and dynamically change the information displayed in augmented reality according to the emotions, interactive information presentation according to the user's emotions can be provided.

[0269] "User" refers to the individual who operates the system to scan food items and obtain information.

[0270] "Image capture device" refers to an electronic device, such as a smartphone or tablet, used to capture images of food.

[0271] A "server" refers to a computer system that receives image data sent from a terminal via a network, analyzes it, and provides information.

[0272] "Image data" refers to an image file of food that a user scans with an image capture device.

[0273] "Image recognition algorithm" refers to a computational method for analyzing transmitted image data and identifying the type of food.

[0274] A "database" refers to a collection of data that stores information on the origin and nutritional information of food products.

[0275] "Augmented reality" refers to a technology that overlays computer-generated information onto real-world images.

[0276] An "emotion engine" refers to software that analyzes a user's facial expression data and recognizes their emotions.

[0277] "Facial expression data" refers to information data that indicates the facial expression of the user.

[0278] "Dynamic change" refers to changing the display content in real time according to the user's emotions.

[0279] The present invention combines a system that allows users to scan food using an image capture device and acquire information about the food in real time with an emotion engine that recognizes the user's emotions. The purpose of this system is to provide detailed information about food to users intuitively and interactively.

[0280] Hardware and Software Configuration

[0281] This system uses the following hardware and software:

[0282] Image acquisition device: A device with a camera function, such as a smartphone or tablet.

[0283] Server: A computer system for data analysis and database searches. It uses libraries such as TensorFlow and OpenCV for image recognition.

[0284] Database: Data storage containing food origin and nutritional information.

[0285] Emotion engine: Uses Microsoft Azure's (registered trademark) Emotion API and Google Cloud Vision's facial recognition function.

[0286] System Operation

[0287] 1. User-initiated scan

[0288] The user activates the camera of the image capture device and scans the food item. For example, the user focuses on an apple and takes a photo with their smartphone. This image data is then stored on the device.

[0289] 2. Sending image data

[0290] The device sends the stored image data to the server using an HTTP POST request to a specific API endpoint, along with any necessary metadata (e.g., user ID, timestamp, etc.).

[0291] 3. Analysis of image data

[0292] The server analyzes the received image data and identifies the type of food using image recognition algorithms such as ResNet. The server then uses Python's OpenCV and TensorFlow to perform image analysis and identify that the food is an apple.

[0293] 4. Database Search

[0294] The server searches the database for information about the identified food, specifically, using SQL queries to retrieve the food's origin and nutritional information.

[0295] 5. Transmission of Information

[0296] The server formats the information retrieved from the database and returns it to the device in JSON format, including information on the apple's origin and nutritional information.

[0297] 6. Augmented Reality Display

[0298] The device then parses the received JSON data and displays it as AR content, using the AR Kit and AR Core to overlay information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on top of the food.

[0299] 7. Sentiment analysis and display adjustment

[0300] The device's built-in camera captures the user's facial expressions, which are then analyzed by the emotion engine. Based on the analysis results, the device adjusts the color and font size of the information displayed in AR. For example, if the user has a surprised expression, the information will be displayed more prominently.

[0301] Specific examples

[0302] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple and sends it to a server. The server uses an image recognition algorithm to identify the apple and search for related information in a database. The search results are sent back to the device, which displays the received information as AR content. At this time, an emotion engine analyzes the user's facial expression and recognizes surprise. Based on that emotion, the AR-displayed information is more emphasized. Through this process, the user not only sees information about the apple's origin and nutritional value, but also experiences how the information dynamically changes to match their own emotions.

[0303] Prompt Sentence Examples

[0304] Examples of prompts for generative AI models include:

[0305] "Create a system in which a user scans an apple with their smartphone camera. The system sends the image data to a server, which analyzes the image and searches a database for food information. It also uses an emotion recognition engine to adjust the displayed information based on the user's emotions."

[0306] In this way, the present invention allows users to simply scan food items, visually obtain detailed information about them in real time, and then uses an emotion engine to provide interactive information according to the user's emotions. Cooperation between the terminal, server, and emotion engine makes it possible to provide more effective information to users.

[0307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0308] Step 1:

[0309] The user activates the camera of the image capture device, focuses on the food, and takes a photo. This action saves the image data to the device. Specifically, the user activates the camera of their smartphone, takes a photo of an apple, and saves it as an image file.

[0310] Input: Food image

[0311] Output: Image file (image data saved on the device)

[0312] Step 2:

[0313] The device sends the saved image data to the server. Specifically, the device uses an HTTP POST request to send the saved image data along with metadata such as the user ID and timestamp to the server.

[0314] Input: Image file, user ID, timestamp

[0315] Output: HTTP request sent to the server

[0316] Step 3:

[0317] The server analyzes the received image data. Specifically, it uses an image recognition algorithm (e.g., ResNet) to identify the type of food. It then decodes the image data using Python's OpenCV or TensorFlow library to identify the apple.

[0318] Input: Image file

[0319] Output: Identified food item (e.g. apple)

[0320] Step 4:

[0321] The server searches the database for information about the identified food, specifically, the server retrieves the origin and nutritional information about the food from the database using an SQL query.

[0322] Input: Identified food item (e.g. apple)

[0323] Output: Food origin and nutritional information

[0324] Step 5:

[0325] The server formats the information retrieved from the database and sends it to the terminal. Specifically, the server formats the information retrieved in JSON format and returns it to the terminal as an HTTP response.

[0326] Input: Food origin and nutritional information

[0327] Output: JSON format data (sent to the terminal)

[0328] Step 6:

[0329] The device analyzes the received JSON data and displays it as augmented reality (AR) content. Specifically, the device uses the AR Kit or AR Core to overlay information on the food.

[0330] Input: JSON format data

[0331] Output: AR display (information displayed overlaid on food)

[0332] Step 7:

[0333] The emotion engine analyzes the user's facial expression data and recognizes their emotions. Specifically, the device's camera captures the user's facial expressions, and the data is analyzed by the emotion engine (for example, Microsoft Azure's Emotion API or Google Cloud Vision).

[0334] Input: User's facial expression data

[0335] Output: Recognized emotion (e.g., surprise)

[0336] Step 8:

[0337] The device dynamically changes the information displayed in AR depending on the recognized emotion. Specifically, the device adjusts the color and font size of the displayed information based on the recognized emotion. For example, if the user has a surprised expression, the information is displayed in a more emphasized form.

[0338] Input: Recognized emotion

[0339] Output: Dynamically adjusted AR display

[0340] (Application example 2)

[0341] 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."

[0342] Conventional product information acquisition systems simply provide static information about products and are unable to present dynamic, interactive information tailored to the user's interests and emotions. This limits the user experience and can make product information provision ineffective, especially in physical stores. Furthermore, with typical systems, the process from scanning products to providing information is not performed in real time, making it difficult to quickly acquire information.

[0343] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to scan an item with an image acquisition device; means for transmitting image data of the item acquired by the image acquisition device to the server; means for the server to analyze the transmitted image data and identify the item; means for the server to search a database for origin information and ingredient information about the identified item; means for returning the searched information to the image acquisition device; means for displaying the information returned by the image acquisition device in augmented reality; and means for an emotion engine to analyze the user's facial expression and adjust the augmented reality display according to the emotion. This allows the user to visually obtain detailed information about the item in real time and receive interactive and dynamic information presentation tailored to their emotions.

[0344] An "image capture device" is a device with a camera function that scans an item and captures image data.

[0345] A "server" is a computer system that receives image data via a network and performs analysis and information retrieval.

[0346] An "article" is an item that a user scans, such as food or merchandise.

[0347] "Image data" is a digital representation of visual information of an item captured by an image capture device.

[0348] An "image recognition algorithm" is a program that analyzes transmitted image data and identifies objects and features contained in the image.

[0349] "Provenance information" is background information about where an item was produced and how it was made.

[0350] "Ingredient information" is data such as nutrients and chemical components contained in an item.

[0351] A "database" is a system that accumulates data related to products, such as origin and ingredient information, and manages it in a searchable format.

[0352] Augmented reality (AR) is a technology that overlays digital information onto the real world.

[0353] An "emotion engine" is an algorithm or program that analyzes a user's facial expressions and recognizes their emotional state.

[0354] "Analyzing facial expressions" refers to the process of analyzing images of a user's face captured by an image capture device and identifying emotions from the facial expressions.

[0355] "Interactive information presentation" is a form of information display that dynamically changes according to the user's emotions and reactions.

[0356] The present invention combines a system in which a user scans an item using an image capture device and captures the information in real time with an emotion engine that recognizes the user's emotions.

[0357] System configuration

[0358] The system mainly consists of the following components:

[0359] Image capture device (smartphone, smart glasses, etc.): Equipped with a camera function, it scans items and captures image data.

[0360] Server: Receives image data via the network and performs analysis and information searches.

[0361] Image recognition algorithms (e.g., TensorFlow, OpenCV): Analyze image data and identify items.

[0362] Database: Data related to products, such as origin and ingredient information, is stored and managed in a searchable format.

[0363] Emotion engine (e.g., Emotion Recognition API): Analyzes the user's facial expressions and recognizes their emotional state.

[0364] Augmented reality (AR) display: A technology that overlays digital information onto the real world.

[0365] Program processing explanation

[0366] 1. Image Acquisition:

[0367] A user scans an item using an image capture device (such as a smartphone or smart glasses), for example, a user finds an apple in a store and scans it with the camera.

[0368] 2. Image transmission and analysis:

[0369] The image capture device sends the captured image data to a server, which then analyzes the received image data using an image recognition algorithm (TensorFlow, OpenCV) to identify the object. For example, the server identifies the object in the image as an apple.

[0370] 3. Product information search:

[0371] The server searches the database for the origin and ingredient information of the identified item, for example, for the origin and nutritional information of "apple."

[0372] 4. Facial Expression Analysis and Emotion Recognition:

[0373] The front camera of the image capture device captures the user's facial expression and analyzes it using the emotion engine (Emotion Recognition API). For example, it can recognize that the user has a surprised expression.

[0374] 5. Information transmission and AR display:

[0375] The server returns the retrieved information to the image capture device, which then displays the returned information in AR. The display content is adjusted according to the user's emotions. For example, if a user is surprised, the information is displayed in a pop-up format in a larger font.

[0376] Specific examples

[0377] The user scans an apple with their smartphone camera. The device captures an image of the apple and sends it to the server. The server uses an image recognition algorithm to identify the apple and searches a database for information about the apple's origin and nutritional value. The server returns the search results to the device, which then displays the received information as AR content. At this time, the emotion engine analyzes the user's facial expression and recognizes, for example, that the user has a surprised expression. Based on this emotion, the information is displayed in a more emphasized form.

[0378] Example prompt sentence:

[0379] "Capture an image of an apple with your smartphone camera. The app then sends it to the server to retrieve the item information. If the user is surprised, the information will be highlighted in a pop-up."

[0380] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0381] Step 1:

[0382] A user scans an object using an image capture device (such as a smartphone or smart glasses). The input is the object to be scanned (e.g., an apple), and the output is the captured image data. Specifically, the user activates the camera, focuses on the object, and presses the capture button.

[0383] Step 2:

[0384] The image data acquired by the terminal is sent to the server. The input is the image data acquired in step 1, and the output is the image data sent to the server. Specifically, the terminal sends the image data to the server via the Internet as an HTTP request.

[0385] Step 3:

[0386] The server analyzes the image data sent and identifies the item. The input is the image data received by the server, and the output is identification information that the item is an apple. Specifically, the server uses an image recognition algorithm (TensorFlow or OpenCV) to analyze and identify the item in the image.

[0387] Step 4:

[0388] The server searches the database for origin and ingredient information about the identified item. The input is the identified item's information (apple), and the output is the item's origin information (e.g., origin in Central Asia) and ingredient information (e.g., vitamin C, dietary fiber). Specifically, the server queries the database system (e.g., MySQL or PostgreSQL) to retrieve the relevant information.

[0389] Step 5:

[0390] The device captures the user's facial expression using the front camera and sends it to the server. The input is image data of the user's facial expression, and the output is image data of the facial expression sent to the server. Specifically, the device sends the facial expression data to the server via the Internet as an HTTP request.

[0391] Step 6:

[0392] The server analyzes the facial expression data and recognizes the user's emotions. The input is the facial expression data received by the server, and the output is the recognized emotional information (for example, surprise). Specifically, the server uses an emotion engine (Emotion Recognition API) to analyze the facial expression data and identify the user's emotions.

[0393] Step 7:

[0394] The server integrates product information and the user's emotional information and sends it back to the terminal. The input is product information (origin information, ingredient information) and emotional information (surprise, etc.), and the output is the integrated information. Specifically, the server organizes the information into an appropriate format and sends it back to the terminal via the Internet.

[0395] Step 8:

[0396] The device analyzes the information it receives and displays it as augmented reality (AR). The input is the integrated information returned from the server, and the output is the AR content displayed on the screen. Specifically, the device visualizes the information using an AR library (e.g., ARKit or ARCore) and displays it overlaid on the user's screen.

[0397] For example, a user scans an apple with their smartphone camera, and the system acquires, processes, and displays the information. The prompt above, "Please capture an image of the apple with your smartphone camera. Then, the app will send it to the server to acquire the item information. If the user is surprised, the information will be highlighted in a pop-up format." is based on this sequence.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Second embodiment]

[0402] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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."

[0414] To implement the present invention, a system is required that allows a user to scan food using an image capture device and obtain information about the food in real time. A detailed description of specific embodiments of the present invention is provided below.

[0415] Food scanning and image transmission

[0416] A user scans a food item using an image capture device such as a smartphone or tablet. For example, if the user scans an apple, they use the camera function to capture an image of the apple. The device saves this image and makes a request to send the image data to a server.

[0417] Image analysis and food identification

[0418] The server receives the image data sent from the device. The received image data is analyzed using an image recognition algorithm to identify the food. In this case, the server uses the image recognition algorithm to identify the food as an apple.

[0419] Food information search

[0420] The server searches the database for information corresponding to the food (apple) it has recognized. Specifically, it obtains information about the apple's origin (e.g., "Central Asia") and nutritional information (e.g., "rich in vitamin C and dietary fiber").

[0421] Information transmission and AR display

[0422] The server retrieves information from the database, formats it appropriately, and sends it to the device. The device then analyzes the information and generates content for the AR display. For example, it could overlay text information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on an apple. The device then displays this information in real time on the camera view and presents it to the user.

[0423] Specific examples

[0424] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple, then sends it to a server. The server uses an image recognition algorithm to identify the apple and searches a database for the apple's origin and nutritional information. The search results are sent back to the device, which displays the received information as AR content. The user can view the origin and nutritional information displayed on the apple through the screen.

[0425] In this way, the system based on the present invention allows users to easily scan food and obtain detailed information about it in real time. The terminal and server work together to provide users with effective information.

[0426] The processing flow will be explained below.

[0427] Specific processing steps of the program

[0428] Step 1:

[0429] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a picture.

[0430] Step 2:

[0431] The image capture device stores the image data of the scanned apple, typically in local storage or temporary memory.

[0432] Step 3:

[0433] The device sends the captured image data to the server by making an HTTP POST request and including the image data as a payload.

[0434] Step 4:

[0435] The server receives the image data sent from the terminal, confirms that the image has been sent, and temporarily stores the data.

[0436] Step 5:

[0437] The server then runs the image data through an image recognition algorithm, using machine learning models and pattern recognition techniques to analyze the object in the image and identify it as an apple.

[0438] Step 6:

[0439] The server searches the database for origin and nutritional information for the identified food (apple). For example, it retrieves information such as "originating from Central Asia" and "rich in vitamin C and dietary fiber."

[0440] Step 7:

[0441] The server formats the search results and returns them to the device in the appropriate format, typically in JSON format.

[0442] Step 8:

[0443] The terminal analyzes the data received from the server, and identifies the origin and nutritional information that needs to be displayed.

[0444] Step 9:

[0445] The device generates the content for the AR display, specifically the text and graphics that are overlaid on the camera view.

[0446] Step 10:

[0447] The device displays AR content in real time on the camera view. When the user looks at the apple through their smartphone, information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" is superimposed on the apple.

[0448] By following these steps, users can easily scan food items and visually obtain detailed information about them in real time.

[0449] Example 1

[0450] 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."

[0451] In today's world, there is a demand for instant access and understanding of detailed information about food. However, conventional methods require users to individually look up information about foods, which is time-consuming and laborious. In addition, there are limited means for easily displaying food information visually. This means that users need time to select and understand foods, making it difficult to efficiently obtain information.

[0452] 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.

[0453] In this invention, the server includes means for a user to scan an object with an image capture device, means for transmitting image data of the object captured by the image capture device to a processing device, means for the processing device to analyze the transmitted image data and identify the object, means for the processing device to search a database for detailed information about the identified object, means for returning the searched information to the image capture device, and means for presenting the returned information by the image capture device in an expanded display, thereby enabling a user to easily obtain detailed information about food and visually understand it.

[0454] "User" refers to a person who uses the system to scan food or other objects to obtain detailed information.

[0455] An "image acquisition device" is a device equipped with a camera function, including a smartphone or tablet, that is used to acquire image data of an object.

[0456] An "object" is anything that a user scans with an image capture device to obtain detailed information about, such as food or an object.

[0457] The "processing device" is a central processing unit that receives image data sent from the image acquisition device and performs analysis and database searches, and is usually a high-performance computer such as a server.

[0458] "Detailed information" refers to data including information that the user wants to obtain, such as information about the origin, nutritional information, and characteristics of the subject.

[0459] "Augmented display" refers to a method of using AR (augmented reality) technology to overlay information on the screen of an image capture device.

[0460] An "image analysis algorithm" is a computational method and program for performing image recognition, and is an algorithm used to identify objects.

[0461] To implement this invention, a system is required that allows a user to scan an object (e.g., food) using an image capture device and acquire information about it in real time. This system is composed of a user, a terminal, and a server, and these components work together to provide information efficiently.

[0462] The user scans the object with the image capture device

[0463] A user scans an object using an image capture device such as a smartphone or tablet. Specifically, the user captures an image of the object using the camera function. For example, when a user wants to scan an apple, they launch the camera app on their smartphone, focus on the apple, and press the capture button.

[0464] The device sends the captured image to the server.

[0465] The device saves the captured image data and generates and executes a request to send it to a server. The software used for this includes the camera app on a smartphone or tablet, as well as a dedicated application. The image data is sent to the server via an HTTP request.

[0466] The server analyzes the image and identifies the subject

[0467] The server receives the image data sent from the device and identifies the object (e.g., an apple) using an image analysis algorithm. The software used here includes image recognition algorithms such as TensorFlow and OpenCV. The server reads the image file, inputs it into the image recognition model, and identifies the object.

[0468] The server searches the database for the relevant information

[0469] The server retrieves detailed information about the identified subjects (e.g., origin information, nutritional information) from a database. The software used here includes an SQL query and a database management system (e.g., MySQL). The server executes SQL queries against the database to retrieve data corresponding to the subjects.

[0470] The server sends the information to the device

[0471] The server formats the retrieved information into an appropriate format (e.g., JSON) and sends it to the device. The server then converts the retrieved data into JSON format and sends it back to the device as an HTTP response, allowing the device to easily analyze the returned information.

[0472] The device generates the information as an AR display and presents it to the user.

[0473] The device parses the JSON data received from the server and extracts the data to display. The software used includes a JSON parser and an AR application (e.g., ARKit or ARCore). The device uses the extracted information to generate AR display content and present it to the user. Specifically, the device overlays text such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on the camera view.

[0474] Specific examples of operation

[0475] When a user scans an apple with their smartphone camera, the device captures and saves an image of the apple, then sends the image file to the server. The server uses an image recognition algorithm to identify the apple and retrieves detailed information about the apple from a database. The server formats the information into JSON format and sends it to the device as an HTTP response. The device then analyzes the received JSON data, extracts the necessary information, generates AR display content, and presents it to the user. In this way, the user can visually confirm detailed information about the apple in real time.

[0476] Example prompts for generative AI models

[0477] The following prompt sentences can be used to provide input to the generative AI model:

[0478] Describe a system that scans images of food and displays its nutritional information and origin in real time. Break it down into the following processing steps:

[0479] 1. The user scans the object with the image capture device.

[0480] 2. The device captures the image and sends it to the server

[0481] 3. The server analyzes the image and identifies the target

[0482] 4. The server searches the database for the relevant information

[0483] 5. The server sends the information to the device

[0484] 6. The device generates the information as an AR display and presents it to the user.

[0485] In this way, the system based on the present invention allows users to easily scan an object and obtain detailed information about it in real time. The terminal and server work together to provide effective information provision.

[0486] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0487] Step 1:

[0488] User scans food

[0489] Input: User takes a picture of food using the camera.

[0490] Specific actions: The user launches the smartphone camera app, scans the apple, focuses, and presses the capture button.

[0491] Output: The captured image data (e.g., an image of an apple).

[0492] Step 2:

[0493] The device captures and saves the image

[0494] Input: Image data sent from the camera app.

[0495] Specific operation: The device saves the captured image file (e.g., apple.jpg) in the specified directory in the storage.

[0496] Output: Image file saved on your device.

[0497] Step 3:

[0498] The device sends the image data to the server.

[0499] Input: A saved image file (e.g. apple.jpg).

[0500] Specific operation: The device obtains the path to the image file, generates an HTTP request, and sends the image data to the server.

[0501] Output: Image data sent to the server.

[0502] Step 4:

[0503] The server receives the image data.

[0504] Input: HTTP request from the device (including image data).

[0505] Specific operation: The server receives the HTTP request and saves the image data in the analysis directory.

[0506] Output: Image file saved on the server.

[0507] Step 5:

[0508] The server analyzes the image and identifies the food.

[0509] Input: Image file stored on the server.

[0510] Specific operation: The server analyzes the image data using image analysis algorithms such as TensorFlow or OpenCV, and identifies the specific food item (e.g., apple).

[0511] Output: The type of food identified (e.g., apple).

[0512] Step 6:

[0513] The server searches the database for food information

[0514] Input: The type of food identified (e.g., apple).

[0515] What happens: The server runs an SQL query against the database to find the origin and nutritional information of the apple.

[0516] Output: Food information retrieved from the database (e.g., origin and nutritional information for an apple).

[0517] Step 7:

[0518] The server sends the information to the device

[0519] Input: Food information retrieved from a database (e.g., origin and nutritional information for an apple).

[0520] Specific operation: The server formats the food information it obtains into JSON format and sends it to the terminal as an HTTP response.

[0521] Output: Food information in JSON data format sent to the terminal.

[0522] Step 8:

[0523] The device analyzes the received data and generates an AR display.

[0524] Input: Food information received from the server in JSON data format.

[0525] What it does: The device uses a JSON parser to parse the data and extract the necessary information (e.g., the origin and nutritional information of the apple), then uses ARKit or ARCore to generate AR content that corresponds to the camera view.

[0526] Output: AR content displayed in the camera view (e.g. text information "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" overlaid on an apple).

[0527] In this way, each processing step works together in a series of steps to provide detailed visual information about the food to the user. Through the customer interface, the user can easily scan the food and obtain detailed information in real time.

[0528] (Application example 1)

[0529] 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."

[0530] Previously, when purchasing food in a physical store, users had to read the product packaging to obtain detailed product information. However, there were limited ways to check information not listed on the packaging or more detailed background information. For example, it was difficult to display information about the food's origin or nutritional information, or even real-time information in a physical store. There was also a lack of ways to present product information visually in an easy-to-understand manner. This resulted in users being unable to obtain sufficient information when making food choices, resulting in a decrease in satisfaction.

[0531] 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.

[0532] In this invention, the server includes means for a user to scan food with an image capture device, means for transmitting image data of the food captured by the image capture device to the server, means for the server to analyze the transmitted image data and identify the food, means for the server to search a database for origin information and nutritional information about the identified food, means for returning the searched information to the image capture device, means for the image capture device to display the returned information in augmented reality, means for scanning products using smart glasses in a physical store, and means for presenting the food information displayed on the smart glasses in real time, thereby enabling a user to intuitively obtain detailed information about food simply by scanning the food in a physical store.

[0533] "User" refers to an individual who uses the system to obtain food information.

[0534] "Image capture device" refers to a device, such as a smartphone, tablet, or smart glasses with a camera function, that captures images of food and transmits them to a server.

[0535] "Food" refers to edible products that can be scanned and captured using the system.

[0536] "Server" refers to a computing device that analyzes the food image data sent from the image capture device, retrieves information from a database, and returns it.

[0537] "Image data" refers to images of food captured with an image capture device.

[0538] "Analysis" refers to the process of identifying the type of food based on the transmitted image data.

[0539] "Identification" refers to the process of recognizing a particular food item from the analyzed image data.

[0540] "Origin information" refers to information about the region or origin from which a food product comes.

[0541] "Nutrition information" refers to information about the nutritional components contained in food and their effects.

[0542] "Database" refers to a collection of information that stores and makes searchable information about food origins and nutritional information.

[0543] "Search" refers to the process by which the server retrieves information about a specified food item from the database.

[0544] "Return" refers to the process by which the server sends the acquired information back to the image capture device.

[0545] "Augmented reality" refers to a technology that overlays computer-generated information onto your physical field of vision.

[0546] A "physical store" refers to a facility that sells food and other items in a real physical space.

[0547] "Smart glasses" refers to a glasses-type device that can display information in the field of view using augmented reality technology.

[0548] "Real-time" refers to a state in which information is provided almost immediately in response to a user's operation.

[0549] To implement the present invention, a system must be built that allows a user to scan food using an image capture device and capture the food information in real time. Specifically, this is accomplished by the following steps:

[0550] 1. Use of image acquisition equipment

[0551] Users scan food items in physical stores using an image capture device (such as a smartphone, tablet, or smart glasses), and the image data of the scanned food items is stored on the device.

[0552] 2. Sending image data to the server

[0553] The device sends the stored image data of the food to a server using the device's internal communication module and internet connection.

[0554] 3. Image analysis on the server

[0555] The server analyzes the received image data, using image recognition algorithms (e.g., TensorFlow or OpenCV) to identify the type of food.

[0556] 4. Searching for food information

[0557] The server searches the database for the corresponding origin and nutritional information for the identified food products. The server uses a database management system such as MySQL or MongoDB.

[0558] 5. Returning information to your device

[0559] The server converts the search results into an appropriate format, such as JSON, and sends them back to the device.

[0560] 6. Augmented Reality Information Display

[0561] Based on the received information, the device displays detailed information about the food in augmented reality on the smart glasses display, allowing users to visually check the food's background information and nutritional content in real time.

[0562] Hardware and Software

[0563] Hardware: Smartphones, tablets, smart glasses (such as Google Glass or Microsoft HoloLens)

[0564] Software: Python, OpenCV, Requests library, database management system (MySQL, MongoDB)

[0565] Specific examples

[0566] For example, when a user chooses an apple in a physical store, they put on the smart glasses and scan the apple. The image data of the scanned apple is sent from the device to a server. The server uses an image recognition algorithm to identify the apple and search a database for information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber." The search results are then sent back to the device and displayed in real time in the user's field of vision through the smart glasses.

[0567] Prompt Sentence Examples

[0568] "Please tell me the origin and nutritional information of the apples."

[0569] This allows users to easily obtain detailed information about food products in physical stores.

[0570] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0571] Step 1:

[0572] Users use an image capture device (e.g., smartphone, tablet, smart glasses) to scan food items in a physical store.

[0573] Input: Food images captured with smart glasses or a smartphone camera

[0574] What it does: Point the image capture device's camera at the food and tap the camera button to take a picture, or, in the case of smart glasses, use a voice command.

[0575] Step 2:

[0576] The device sends the captured images to a server, using a communication module and an internet connection.

[0577] Input: Image data of the food you photographed

[0578] Output: Image data sent to the server

[0579] What happens: The device sends image data to the server in an HTTP POST request, over an Internet connection.

[0580] Step 3:

[0581] The server analyzes the received image data and identifies the food using an image recognition algorithm.

[0582] Input: Image data sent from the device

[0583] Output: The type of food identified (e.g., apple)

[0584] What it does: The server runs Python and TensorFlow image recognition models to analyze the image data it receives, and returns the type of food it identifies.

[0585] Step 4:

[0586] The server retrieves origin and nutritional information for the identified food products from a database.

[0587] Input: Identified food type (e.g. apple)

[0588] Output: Origin and nutritional information about the food

[0589] Specific operation: The server executes an SQL query against a database managed by MySQL or MongoDB to retrieve food information. For example, the query "SELECT FROM food_info WHERE name="apple"" is executed.

[0590] Step 5:

[0591] The server converts the acquired information into an appropriate format, such as JSON, and sends it back to the device.

[0592] Input: Food information retrieved from the database

[0593] Output: Food information returned to the device

[0594] Specific operation: The server serializes the acquired information in JSON format and sends it to the terminal as an HTTP response.

[0595] Step 6:

[0596] Based on the information received, the device displays food information in augmented reality on the smart glasses display.

[0597] Input: Food information received from the server

[0598] Output: Augmented reality food information displayed on smart glasses

[0599] Specific operation: The device analyzes the received information and uses the smart glasses' AR display module to overlay information on the origin and nutritional information on the food.

[0600] These steps allow users to easily obtain detailed information about food products and visually check them in-store.

[0601] 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.

[0602] The present invention combines a system that allows a user to scan food using an image capture device and acquire information about the food in real time with an emotion engine that recognizes the user's emotions. Hereinafter, specific embodiments of the present invention will be described in detail.

[0603] Food scanning and image transmission

[0604] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a photo. The device saves the image and executes a request to send the image data to a server.

[0605] Image analysis and food identification

[0606] The server receives the image data sent from the device. The received image data is analyzed using an image recognition algorithm to identify the food item. For example, the server uses the image recognition algorithm to identify the food item as an apple.

[0607] Food information search

[0608] The server searches the database for information corresponding to the food (apple) it has recognized. Specifically, it obtains information about the apple's origin (e.g., "Central Asia") and nutritional information (e.g., "rich in vitamin C and dietary fiber").

[0609] Information transmission and AR display

[0610] The server appropriately formats the information retrieved from the database and sends it to the device. The device then analyzes the received information and generates content for AR display. At this time, the emotion engine analyzes the user's facial expression data and recognizes their emotion. For example, if the user has a surprised expression, the AR display will emphasize the information that matches their emotion. Specifically, not only will the information "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" be displayed above the apple, but the color and font size will also be adjusted according to the emotion.

[0611] Specific examples

[0612] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple, then sends it to a server. The server uses an image recognition algorithm to identify the apple and searches a database for information about the apple's origin and nutritional value. The search results are sent back to the device, which then displays the received information as AR content. At this time, an emotion engine analyzes the user's facial expression and recognizes, for example, that the user is looking surprised. Based on that emotion, the AR-displayed information is more emphasized. The user not only sees the origin and nutritional value displayed on the apple on the screen, but also experiences the information dynamically changing to match their own emotions.

[0613] In this way, the present invention allows users to simply scan food items, visually obtain detailed information about them in real time, and then use an emotion engine to interactively present information according to the user's emotions. Cooperation between the terminal, server, and emotion engine makes it possible to provide users with more effective information.

[0614] The processing flow will be explained below.

[0615] Specific processing steps of the program

[0616] Step 1:

[0617] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a picture.

[0618] Step 2:

[0619] The image capture device stores the image data of the scanned apple, typically in local storage or temporary memory.

[0620] Step 3:

[0621] The device sends the captured image data to the server by making an HTTP POST request and including the image data as a payload.

[0622] Step 4:

[0623] The server receives the image data sent from the terminal, confirms that the image has been sent, and temporarily stores the data.

[0624] Step 5:

[0625] The server then runs the image data through an image recognition algorithm, using machine learning models and pattern recognition techniques to analyze the object in the image and identify it as an apple.

[0626] Step 6:

[0627] The server searches the database for origin and nutritional information for the identified food (apple). For example, it retrieves information such as "originating from Central Asia" and "rich in vitamin C and dietary fiber."

[0628] Step 7:

[0629] The server formats the search results and returns them to the device in the appropriate format, typically in JSON format.

[0630] Step 8:

[0631] The terminal analyzes the data received from the server, and identifies the origin and nutritional information that needs to be displayed.

[0632] Step 9:

[0633] The emotion engine captures the user's facial expressions through the device's camera, and facial data is collected as the user scans the apple.

[0634] Step 10:

[0635] The emotion engine analyzes the collected facial expression data to identify the user's emotion, for example, recognizing that the user has a surprised expression.

[0636] Step 11:

[0637] The device generates content for the AR display, and the displayed information is enhanced based on the user's emotions. For example, if the user has a surprised expression, the font size of nutrition information will be increased.

[0638] Step 12:

[0639] The device displays AR content in real time on the camera view. When the user looks at the apple through their smartphone, information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" is displayed on top of the apple, with emphasis based on the user's emotions.

[0640] By following these steps, users can easily scan food items and visually obtain detailed information about them in real time, and interactive information presentation based on the user's emotions is also possible.

[0641] Example 2

[0642] 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."

[0643] Conventional food identification systems have the problem that they do not provide interactive information according to the user's emotions when obtaining detailed information about a food. Furthermore, the system does not dynamically change information based on the user's emotions, which hinders the user experience. Furthermore, there is no efficient coordination between real-time information processing and display.

[0644] 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.

[0645] In this invention, the server includes a means for analyzing image data to identify foods, a means for searching a database for origin and nutritional information about the identified foods, and a means for returning the searched information to the image capture device. This allows a user to obtain detailed information about foods scanned using the image capture device in real time. Furthermore, by adding a means for an emotion engine to analyze the user's facial expression data to recognize emotions and dynamically change the information displayed in augmented reality according to the emotions, interactive information presentation according to the user's emotions can be provided.

[0646] "User" refers to the individual who operates the system to scan food items and obtain information.

[0647] "Image capture device" refers to an electronic device, such as a smartphone or tablet, used to capture images of food.

[0648] A "server" refers to a computer system that receives image data sent from a terminal via a network, analyzes it, and provides information.

[0649] "Image data" refers to an image file of food that a user scans with an image capture device.

[0650] "Image recognition algorithm" refers to a computational method for analyzing transmitted image data and identifying the type of food.

[0651] A "database" refers to a collection of data that stores information on the origin and nutritional information of food products.

[0652] "Augmented reality" refers to a technology that overlays computer-generated information onto real-world images.

[0653] An "emotion engine" refers to software that analyzes a user's facial expression data and recognizes their emotions.

[0654] "Facial expression data" refers to information data that indicates the facial expression of the user.

[0655] "Dynamic change" refers to changing the display content in real time according to the user's emotions.

[0656] The present invention combines a system that allows users to scan food using an image capture device and acquire information about the food in real time with an emotion engine that recognizes the user's emotions. The purpose of this system is to provide detailed information about food to users intuitively and interactively.

[0657] Hardware and Software Configuration

[0658] This system uses the following hardware and software:

[0659] Image acquisition device: A device with a camera function, such as a smartphone or tablet.

[0660] Server: A computer system for data analysis and database searches. It uses libraries such as TensorFlow and OpenCV for image recognition.

[0661] Database: Data storage containing food origin and nutritional information.

[0662] Emotion engine: Uses Microsoft Azure's Emotion API and Google Cloud Vision's facial recognition capabilities.

[0663] System Operation

[0664] 1. User-initiated scan

[0665] The user activates the camera of the image capture device and scans the food item. For example, the user focuses on an apple and takes a photo with their smartphone. This image data is then stored on the device.

[0666] 2. Sending image data

[0667] The device sends the stored image data to the server using an HTTP POST request to a specific API endpoint, along with any necessary metadata (e.g., user ID, timestamp, etc.).

[0668] 3. Analysis of image data

[0669] The server analyzes the received image data and identifies the type of food using image recognition algorithms such as ResNet. The server then uses Python's OpenCV and TensorFlow to perform image analysis and identify that the food is an apple.

[0670] 4. Database Search

[0671] The server searches the database for information about the identified food, specifically, using SQL queries to retrieve the food's origin and nutritional information.

[0672] 5. Transmission of Information

[0673] The server formats the information retrieved from the database and returns it to the device in JSON format, including information on the apple's origin and nutritional information.

[0674] 6. Augmented Reality Display

[0675] The device then parses the received JSON data and displays it as AR content, using the AR Kit and AR Core to overlay information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on top of the food.

[0676] 7. Sentiment analysis and display adjustment

[0677] The device's built-in camera captures the user's facial expressions, which are then analyzed by the emotion engine. Based on the analysis results, the device adjusts the color and font size of the information displayed in AR. For example, if the user has a surprised expression, the information will be displayed more prominently.

[0678] Specific examples

[0679] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple and sends it to a server. The server uses an image recognition algorithm to identify the apple and search for related information in a database. The search results are sent back to the device, which displays the received information as AR content. At this time, an emotion engine analyzes the user's facial expression and recognizes surprise. Based on that emotion, the AR-displayed information is more emphasized. Through this process, the user not only sees information about the apple's origin and nutritional value, but also experiences how the information dynamically changes to match their own emotions.

[0680] Prompt Sentence Examples

[0681] Examples of prompts for generative AI models include:

[0682] "Create a system in which a user scans an apple with their smartphone camera. The system sends the image data to a server, which analyzes the image and searches a database for food information. It also uses an emotion recognition engine to adjust the displayed information based on the user's emotions."

[0683] In this way, the present invention allows users to simply scan food items, visually obtain detailed information about them in real time, and then uses an emotion engine to provide interactive information according to the user's emotions. Cooperation between the terminal, server, and emotion engine makes it possible to provide more effective information to users.

[0684] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0685] Step 1:

[0686] The user activates the camera of the image capture device, focuses on the food, and takes a photo. This action saves the image data to the device. Specifically, the user activates the camera of their smartphone, takes a photo of an apple, and saves it as an image file.

[0687] Input: Food image

[0688] Output: Image file (image data saved on the device)

[0689] Step 2:

[0690] The device sends the saved image data to the server. Specifically, the device uses an HTTP POST request to send the saved image data along with metadata such as the user ID and timestamp to the server.

[0691] Input: Image file, user ID, timestamp

[0692] Output: HTTP request sent to the server

[0693] Step 3:

[0694] The server analyzes the received image data. Specifically, it uses an image recognition algorithm (e.g., ResNet) to identify the type of food. It then decodes the image data using Python's OpenCV or TensorFlow library to identify the apple.

[0695] Input: Image file

[0696] Output: Identified food item (e.g. apple)

[0697] Step 4:

[0698] The server searches the database for information about the identified food, specifically, the server retrieves the origin and nutritional information about the food from the database using an SQL query.

[0699] Input: Identified food item (e.g. apple)

[0700] Output: Food origin and nutritional information

[0701] Step 5:

[0702] The server formats the information retrieved from the database and sends it to the terminal. Specifically, the server formats the information retrieved in JSON format and returns it to the terminal as an HTTP response.

[0703] Input: Food origin and nutritional information

[0704] Output: JSON format data (sent to the terminal)

[0705] Step 6:

[0706] The device analyzes the received JSON data and displays it as augmented reality (AR) content. Specifically, the device uses the AR Kit or AR Core to overlay information on the food.

[0707] Input: JSON format data

[0708] Output: AR display (information displayed overlaid on food)

[0709] Step 7:

[0710] The emotion engine analyzes the user's facial expression data and recognizes their emotions. Specifically, the device's camera captures the user's facial expressions, and the data is analyzed by the emotion engine (for example, Microsoft Azure's Emotion API or Google Cloud Vision).

[0711] Input: User's facial expression data

[0712] Output: Recognized emotion (e.g., surprise)

[0713] Step 8:

[0714] The device dynamically changes the information displayed in AR depending on the recognized emotion. Specifically, the device adjusts the color and font size of the displayed information based on the recognized emotion. For example, if the user has a surprised expression, the information is displayed in a more emphasized form.

[0715] Input: Recognized emotion

[0716] Output: Dynamically adjusted AR display

[0717] (Application example 2)

[0718] 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."

[0719] Conventional product information acquisition systems simply provide static information about products and are unable to present dynamic, interactive information tailored to the user's interests and emotions. This limits the user experience and can make product information provision ineffective, especially in physical stores. Furthermore, with typical systems, the process from scanning products to providing information is not performed in real time, making it difficult to quickly acquire information.

[0720] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to scan an item with an image acquisition device; means for transmitting image data of the item acquired by the image acquisition device to the server; means for the server to analyze the transmitted image data and identify the item; means for the server to search a database for origin information and ingredient information about the identified item; means for returning the searched information to the image acquisition device; means for displaying the information returned by the image acquisition device in augmented reality; and means for an emotion engine to analyze the user's facial expression and adjust the augmented reality display according to the emotion. This allows the user to visually obtain detailed information about the item in real time and receive interactive and dynamic information presentation tailored to their emotions.

[0721] An "image capture device" is a device with a camera function that scans an item and captures image data.

[0722] A "server" is a computer system that receives image data via a network and performs analysis and information retrieval.

[0723] An "article" is an item that a user scans, such as food or merchandise.

[0724] "Image data" is a digital representation of visual information of an item captured by an image capture device.

[0725] An "image recognition algorithm" is a program that analyzes transmitted image data and identifies objects and features contained in the image.

[0726] "Provenance information" is background information about where an item was produced and how it was made.

[0727] "Ingredient information" is data such as nutrients and chemical components contained in an item.

[0728] A "database" is a system that accumulates data related to products, such as origin and ingredient information, and manages it in a searchable format.

[0729] Augmented reality (AR) is a technology that overlays digital information onto the real world.

[0730] An "emotion engine" is an algorithm or program that analyzes a user's facial expressions and recognizes their emotional state.

[0731] "Analyzing facial expressions" refers to the process of analyzing images of a user's face captured by an image capture device and identifying emotions from the facial expressions.

[0732] "Interactive information presentation" is a form of information display that dynamically changes according to the user's emotions and reactions.

[0733] The present invention combines a system in which a user scans an item using an image capture device and captures the information in real time with an emotion engine that recognizes the user's emotions.

[0734] System configuration

[0735] The system mainly consists of the following components:

[0736] Image capture device (smartphone, smart glasses, etc.): Equipped with a camera function, it scans items and captures image data.

[0737] Server: Receives image data via the network and performs analysis and information searches.

[0738] Image recognition algorithms (e.g., TensorFlow, OpenCV): Analyze image data and identify items.

[0739] Database: Data related to products, such as origin and ingredient information, is stored and managed in a searchable format.

[0740] Emotion engine (e.g., Emotion Recognition API): Analyzes the user's facial expressions and recognizes their emotional state.

[0741] Augmented reality (AR) display: A technology that overlays digital information onto the real world.

[0742] Program processing explanation

[0743] 1. Image Acquisition:

[0744] A user scans an item using an image capture device (such as a smartphone or smart glasses), for example, a user finds an apple in a store and scans it with the camera.

[0745] 2. Image transmission and analysis:

[0746] The image capture device sends the captured image data to a server, which then analyzes the received image data using an image recognition algorithm (TensorFlow, OpenCV) to identify the object. For example, the server identifies the object in the image as an apple.

[0747] 3. Product information search:

[0748] The server searches the database for the origin and ingredient information of the identified item, for example, for the origin and nutritional information of "apple."

[0749] 4. Facial Expression Analysis and Emotion Recognition:

[0750] The front camera of the image capture device captures the user's facial expression and analyzes it using the emotion engine (Emotion Recognition API). For example, it can recognize that the user has a surprised expression.

[0751] 5. Information transmission and AR display:

[0752] The server returns the retrieved information to the image capture device, which then displays the returned information in AR. The display content is adjusted according to the user's emotions. For example, if a user is surprised, the information is displayed in a pop-up format in a larger font.

[0753] Specific examples

[0754] The user scans an apple with their smartphone camera. The device captures an image of the apple and sends it to the server. The server uses an image recognition algorithm to identify the apple and searches a database for information about the apple's origin and nutritional value. The server returns the search results to the device, which then displays the received information as AR content. At this time, the emotion engine analyzes the user's facial expression and recognizes, for example, that the user has a surprised expression. Based on this emotion, the information is displayed in a more emphasized form.

[0755] Example prompt sentence:

[0756] "Capture an image of an apple with your smartphone camera. The app then sends it to the server to retrieve the item information. If the user is surprised, the information will be highlighted in a pop-up."

[0757] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0758] Step 1:

[0759] A user scans an object using an image capture device (such as a smartphone or smart glasses). The input is the object to be scanned (e.g., an apple), and the output is the captured image data. Specifically, the user activates the camera, focuses on the object, and presses the capture button.

[0760] Step 2:

[0761] The image data acquired by the terminal is sent to the server. The input is the image data acquired in step 1, and the output is the image data sent to the server. Specifically, the terminal sends the image data to the server via the Internet as an HTTP request.

[0762] Step 3:

[0763] The server analyzes the image data sent and identifies the item. The input is the image data received by the server, and the output is identification information that the item is an apple. Specifically, the server uses an image recognition algorithm (TensorFlow or OpenCV) to analyze and identify the item in the image.

[0764] Step 4:

[0765] The server searches the database for origin and ingredient information about the identified item. The input is the identified item's information (apple), and the output is the item's origin information (e.g., origin in Central Asia) and ingredient information (e.g., vitamin C, dietary fiber). Specifically, the server queries the database system (e.g., MySQL or PostgreSQL) to retrieve the relevant information.

[0766] Step 5:

[0767] The device captures the user's facial expression using the front camera and sends it to the server. The input is image data of the user's facial expression, and the output is image data of the facial expression sent to the server. Specifically, the device sends the facial expression data to the server via the Internet as an HTTP request.

[0768] Step 6:

[0769] The server analyzes the facial expression data and recognizes the user's emotions. The input is the facial expression data received by the server, and the output is the recognized emotional information (for example, surprise). Specifically, the server uses an emotion engine (Emotion Recognition API) to analyze the facial expression data and identify the user's emotions.

[0770] Step 7:

[0771] The server integrates product information and the user's emotional information and sends it back to the terminal. The input is product information (origin information, ingredient information) and emotional information (surprise, etc.), and the output is the integrated information. Specifically, the server organizes the information into an appropriate format and sends it back to the terminal via the Internet.

[0772] Step 8:

[0773] The device analyzes the information it receives and displays it as augmented reality (AR). The input is the integrated information returned from the server, and the output is the AR content displayed on the screen. Specifically, the device visualizes the information using an AR library (e.g., ARKit or ARCore) and displays it overlaid on the user's screen.

[0774] For example, a user scans an apple with their smartphone camera, and the system acquires, processes, and displays the information. The prompt above, "Please capture an image of the apple with your smartphone camera. Then, the app will send it to the server to acquire the item information. If the user is surprised, the information will be highlighted in a pop-up format." is based on this sequence.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] [Third embodiment]

[0779] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0780] 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.

[0781] 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).

[0782] 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.

[0783] 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.

[0784] 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).

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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."

[0791] To implement the present invention, a system is required that allows a user to scan food using an image capture device and obtain information about the food in real time. A detailed description of specific embodiments of the present invention is provided below.

[0792] Food scanning and image transmission

[0793] A user scans a food item using an image capture device such as a smartphone or tablet. For example, if the user scans an apple, they use the camera function to capture an image of the apple. The device saves this image and makes a request to send the image data to a server.

[0794] Image analysis and food identification

[0795] The server receives the image data sent from the device. The received image data is analyzed using an image recognition algorithm to identify the food. In this case, the server uses the image recognition algorithm to identify the food as an apple.

[0796] Food information search

[0797] The server searches the database for information corresponding to the food (apple) it has recognized. Specifically, it obtains information about the apple's origin (e.g., "Central Asia") and nutritional information (e.g., "rich in vitamin C and dietary fiber").

[0798] Information transmission and AR display

[0799] The server retrieves information from the database, formats it appropriately, and sends it to the device. The device then analyzes the information and generates content for the AR display. For example, it could overlay text information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on an apple. The device then displays this information in real time on the camera view and presents it to the user.

[0800] Specific examples

[0801] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple, then sends it to a server. The server uses an image recognition algorithm to identify the apple and searches a database for the apple's origin and nutritional information. The search results are sent back to the device, which displays the received information as AR content. The user can view the origin and nutritional information displayed on the apple through the screen.

[0802] In this way, the system based on the present invention allows users to easily scan food and obtain detailed information about it in real time. The terminal and server work together to provide users with effective information.

[0803] The processing flow will be explained below.

[0804] Specific processing steps of the program

[0805] Step 1:

[0806] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a picture.

[0807] Step 2:

[0808] The image capture device stores the image data of the scanned apple, typically in local storage or temporary memory.

[0809] Step 3:

[0810] The device sends the captured image data to the server by making an HTTP POST request and including the image data as a payload.

[0811] Step 4:

[0812] The server receives the image data sent from the terminal, confirms that the image has been sent, and temporarily stores the data.

[0813] Step 5:

[0814] The server then runs the image data through an image recognition algorithm, using machine learning models and pattern recognition techniques to analyze the object in the image and identify it as an apple.

[0815] Step 6:

[0816] The server searches the database for origin and nutritional information for the identified food (apple). For example, it retrieves information such as "originating from Central Asia" and "rich in vitamin C and dietary fiber."

[0817] Step 7:

[0818] The server formats the search results and returns them to the device in the appropriate format, typically in JSON format.

[0819] Step 8:

[0820] The terminal analyzes the data received from the server, and identifies the origin and nutritional information that needs to be displayed.

[0821] Step 9:

[0822] The device generates the content for the AR display, specifically the text and graphics that are overlaid on the camera view.

[0823] Step 10:

[0824] The device displays AR content in real time on the camera view. When the user looks at the apple through their smartphone, information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" is superimposed on the apple.

[0825] By following these steps, users can easily scan food items and visually obtain detailed information about them in real time.

[0826] Example 1

[0827] 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."

[0828] In today's world, there is a demand for instant access and understanding of detailed information about food. However, conventional methods require users to individually look up information about foods, which is time-consuming and laborious. In addition, there are limited means for easily displaying food information visually. This means that users need time to select and understand foods, making it difficult to efficiently obtain information.

[0829] 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.

[0830] In this invention, the server includes means for a user to scan an object with an image capture device, means for transmitting image data of the object captured by the image capture device to a processing device, means for the processing device to analyze the transmitted image data and identify the object, means for the processing device to search a database for detailed information about the identified object, means for returning the searched information to the image capture device, and means for presenting the returned information by the image capture device in an expanded display, thereby enabling a user to easily obtain detailed information about food and visually understand it.

[0831] "User" refers to a person who uses the system to scan food or other objects to obtain detailed information.

[0832] An "image acquisition device" is a device equipped with a camera function, including a smartphone or tablet, that is used to acquire image data of an object.

[0833] An "object" is anything that a user scans with an image capture device to obtain detailed information about, such as food or an object.

[0834] The "processing device" is a central processing unit that receives image data sent from the image acquisition device and performs analysis and database searches, and is usually a high-performance computer such as a server.

[0835] "Detailed information" refers to data including information that the user wants to obtain, such as information about the origin, nutritional information, and characteristics of the subject.

[0836] "Augmented display" refers to a method of using AR (augmented reality) technology to overlay information on the screen of an image capture device.

[0837] An "image analysis algorithm" is a computational method and program for performing image recognition, and is an algorithm used to identify objects.

[0838] To implement this invention, a system is required that allows a user to scan an object (e.g., food) using an image capture device and acquire information about it in real time. This system is composed of a user, a terminal, and a server, and these components work together to provide information efficiently.

[0839] The user scans the object with the image capture device

[0840] A user scans an object using an image capture device such as a smartphone or tablet. Specifically, the user captures an image of the object using the camera function. For example, when a user wants to scan an apple, they launch the camera app on their smartphone, focus on the apple, and press the capture button.

[0841] The device sends the captured image to the server.

[0842] The device saves the captured image data and generates and executes a request to send it to a server. The software used for this includes the camera app on a smartphone or tablet, as well as a dedicated application. The image data is sent to the server via an HTTP request.

[0843] The server analyzes the image and identifies the subject

[0844] The server receives the image data sent from the device and identifies the object (e.g., an apple) using an image analysis algorithm. The software used here includes image recognition algorithms such as TensorFlow and OpenCV. The server reads the image file, inputs it into the image recognition model, and identifies the object.

[0845] The server searches the database for the relevant information

[0846] The server retrieves detailed information about the identified subjects (e.g., origin information, nutritional information) from a database. The software used here includes an SQL query and a database management system (e.g., MySQL). The server executes SQL queries against the database to retrieve data corresponding to the subjects.

[0847] The server sends the information to the device

[0848] The server formats the retrieved information into an appropriate format (e.g., JSON) and sends it to the device. The server then converts the retrieved data into JSON format and sends it back to the device as an HTTP response, allowing the device to easily analyze the returned information.

[0849] The device generates the information as an AR display and presents it to the user.

[0850] The device parses the JSON data received from the server and extracts the data to display. The software used includes a JSON parser and an AR application (e.g., ARKit or ARCore). The device uses the extracted information to generate AR display content and present it to the user. Specifically, the device overlays text such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on the camera view.

[0851] Specific examples of operation

[0852] When a user scans an apple with their smartphone camera, the device captures and saves an image of the apple, then sends the image file to the server. The server uses an image recognition algorithm to identify the apple and retrieves detailed information about the apple from a database. The server formats the information into JSON format and sends it to the device as an HTTP response. The device then analyzes the received JSON data, extracts the necessary information, generates AR display content, and presents it to the user. In this way, the user can visually confirm detailed information about the apple in real time.

[0853] Example prompts for generative AI models

[0854] The following prompt sentences can be used to provide input to the generative AI model:

[0855] Describe a system that scans images of food and displays its nutritional information and origin in real time. Break it down into the following processing steps:

[0856] 1. The user scans the object with the image capture device.

[0857] 2. The device captures the image and sends it to the server

[0858] 3. The server analyzes the image and identifies the target

[0859] 4. The server searches the database for the relevant information

[0860] 5. The server sends the information to the device

[0861] 6. The device generates the information as an AR display and presents it to the user.

[0862] In this way, the system based on the present invention allows users to easily scan an object and obtain detailed information about it in real time. The terminal and server work together to provide effective information provision.

[0863] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0864] Step 1:

[0865] User scans food

[0866] Input: User takes a picture of food using the camera.

[0867] Specific actions: The user launches the smartphone camera app, scans the apple, focuses, and presses the capture button.

[0868] Output: The captured image data (e.g., an image of an apple).

[0869] Step 2:

[0870] The device captures and saves the image

[0871] Input: Image data sent from the camera app.

[0872] Specific operation: The device saves the captured image file (e.g., apple.jpg) in the specified directory in the storage.

[0873] Output: Image file saved on your device.

[0874] Step 3:

[0875] The device sends the image data to the server.

[0876] Input: A saved image file (e.g. apple.jpg).

[0877] Specific operation: The device obtains the path to the image file, generates an HTTP request, and sends the image data to the server.

[0878] Output: Image data sent to the server.

[0879] Step 4:

[0880] The server receives the image data.

[0881] Input: HTTP request from the device (including image data).

[0882] Specific operation: The server receives the HTTP request and saves the image data in the analysis directory.

[0883] Output: Image file saved on the server.

[0884] Step 5:

[0885] The server analyzes the image and identifies the food.

[0886] Input: Image file stored on the server.

[0887] Specific operation: The server analyzes the image data using image analysis algorithms such as TensorFlow or OpenCV, and identifies the specific food item (e.g., apple).

[0888] Output: The type of food identified (e.g., apple).

[0889] Step 6:

[0890] The server searches the database for food information

[0891] Input: The type of food identified (e.g., apple).

[0892] What happens: The server runs an SQL query against the database to find the origin and nutritional information of the apple.

[0893] Output: Food information retrieved from the database (e.g., origin and nutritional information for an apple).

[0894] Step 7:

[0895] The server sends the information to the device

[0896] Input: Food information retrieved from a database (e.g., origin and nutritional information for an apple).

[0897] Specific operation: The server formats the food information it obtains into JSON format and sends it to the terminal as an HTTP response.

[0898] Output: Food information in JSON data format sent to the terminal.

[0899] Step 8:

[0900] The device analyzes the received data and generates an AR display.

[0901] Input: Food information received from the server in JSON data format.

[0902] What it does: The device uses a JSON parser to parse the data and extract the necessary information (e.g., the origin and nutritional information of the apple), then uses ARKit or ARCore to generate AR content that corresponds to the camera view.

[0903] Output: AR content displayed in the camera view (e.g. text information "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" overlaid on an apple).

[0904] In this way, each processing step works together in a series of steps to provide detailed visual information about the food to the user. Through the customer interface, the user can easily scan the food and obtain detailed information in real time.

[0905] (Application example 1)

[0906] 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."

[0907] Previously, when purchasing food in a physical store, users had to read the product packaging to obtain detailed product information. However, there were limited ways to check information not listed on the packaging or more detailed background information. For example, it was difficult to display information about the food's origin or nutritional information, or even real-time information in a physical store. There was also a lack of ways to present product information visually in an easy-to-understand manner. This resulted in users being unable to obtain sufficient information when making food choices, resulting in a decrease in satisfaction.

[0908] 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.

[0909] In this invention, the server includes means for a user to scan food with an image capture device, means for transmitting image data of the food captured by the image capture device to the server, means for the server to analyze the transmitted image data and identify the food, means for the server to search a database for origin information and nutritional information about the identified food, means for returning the searched information to the image capture device, means for the image capture device to display the returned information in augmented reality, means for scanning products using smart glasses in a physical store, and means for presenting the food information displayed on the smart glasses in real time, thereby enabling a user to intuitively obtain detailed information about food simply by scanning the food in a physical store.

[0910] "User" refers to an individual who uses the system to obtain food information.

[0911] "Image capture device" refers to a device, such as a smartphone, tablet, or smart glasses with a camera function, that captures images of food and transmits them to a server.

[0912] "Food" refers to edible products that can be scanned and captured using the system.

[0913] "Server" refers to a computing device that analyzes the food image data sent from the image capture device, retrieves information from a database, and returns it.

[0914] "Image data" refers to images of food captured with an image capture device.

[0915] "Analysis" refers to the process of identifying the type of food based on the transmitted image data.

[0916] "Identification" refers to the process of recognizing a particular food item from the analyzed image data.

[0917] "Origin information" refers to information about the region or origin from which a food product comes.

[0918] "Nutrition information" refers to information about the nutritional components contained in food and their effects.

[0919] "Database" refers to a collection of information that stores and makes searchable information about food origins and nutritional information.

[0920] "Search" refers to the process by which the server retrieves information about a specified food item from the database.

[0921] "Return" refers to the process by which the server sends the acquired information back to the image capture device.

[0922] "Augmented reality" refers to a technology that overlays computer-generated information onto your physical field of vision.

[0923] A "physical store" refers to a facility that sells food and other items in a real physical space.

[0924] "Smart glasses" refers to a glasses-type device that can display information in the field of view using augmented reality technology.

[0925] "Real-time" refers to a state in which information is provided almost immediately in response to a user's operation.

[0926] To implement the present invention, a system must be built that allows a user to scan food using an image capture device and capture the food information in real time. Specifically, this is accomplished by the following steps:

[0927] 1. Use of image acquisition equipment

[0928] Users scan food items in physical stores using an image capture device (such as a smartphone, tablet, or smart glasses), and the image data of the scanned food items is stored on the device.

[0929] 2. Sending image data to the server

[0930] The device sends the stored image data of the food to a server using the device's internal communication module and internet connection.

[0931] 3. Image analysis on the server

[0932] The server analyzes the received image data, using image recognition algorithms (e.g., TensorFlow or OpenCV) to identify the type of food.

[0933] 4. Searching for food information

[0934] The server searches the database for the corresponding origin and nutritional information for the identified food products. The server uses a database management system such as MySQL or MongoDB.

[0935] 5. Returning information to your device

[0936] The server converts the search results into an appropriate format, such as JSON, and sends them back to the device.

[0937] 6. Augmented Reality Information Display

[0938] Based on the received information, the device displays detailed information about the food in augmented reality on the smart glasses display, allowing users to visually check the food's background information and nutritional content in real time.

[0939] Hardware and Software

[0940] Hardware: Smartphones, tablets, smart glasses (such as Google Glass or Microsoft HoloLens)

[0941] Software: Python, OpenCV, Requests library, database management system (MySQL, MongoDB)

[0942] Specific examples

[0943] For example, when a user chooses an apple in a physical store, they put on the smart glasses and scan the apple. The image data of the scanned apple is sent from the device to a server. The server uses an image recognition algorithm to identify the apple and search a database for information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber." The search results are then sent back to the device and displayed in real time in the user's field of vision through the smart glasses.

[0944] Prompt Sentence Examples

[0945] "Please tell me the origin and nutritional information of the apples."

[0946] This allows users to easily obtain detailed information about food products in physical stores.

[0947] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0948] Step 1:

[0949] Users use an image capture device (e.g., smartphone, tablet, smart glasses) to scan food items in a physical store.

[0950] Input: Food images captured with smart glasses or a smartphone camera

[0951] What it does: Point the image capture device's camera at the food and tap the camera button to take a picture, or, in the case of smart glasses, use a voice command.

[0952] Step 2:

[0953] The device sends the captured images to a server, using a communication module and an internet connection.

[0954] Input: Image data of the food you photographed

[0955] Output: Image data sent to the server

[0956] What happens: The device sends image data to the server in an HTTP POST request, over an Internet connection.

[0957] Step 3:

[0958] The server analyzes the received image data and identifies the food using an image recognition algorithm.

[0959] Input: Image data sent from the device

[0960] Output: The type of food identified (e.g., apple)

[0961] What it does: The server runs Python and TensorFlow image recognition models to analyze the image data it receives, and returns the type of food it identifies.

[0962] Step 4:

[0963] The server retrieves origin and nutritional information for the identified food products from a database.

[0964] Input: Identified food type (e.g. apple)

[0965] Output: Origin and nutritional information about the food

[0966] Specific operation: The server executes an SQL query against a database managed by MySQL or MongoDB to retrieve food information. For example, the query "SELECT FROM food_info WHERE name="apple"" is executed.

[0967] Step 5:

[0968] The server converts the acquired information into an appropriate format, such as JSON, and sends it back to the device.

[0969] Input: Food information retrieved from the database

[0970] Output: Food information returned to the device

[0971] Specific operation: The server serializes the acquired information in JSON format and sends it to the terminal as an HTTP response.

[0972] Step 6:

[0973] Based on the information received, the device displays food information in augmented reality on the smart glasses display.

[0974] Input: Food information received from the server

[0975] Output: Augmented reality food information displayed on smart glasses

[0976] Specific operation: The device analyzes the received information and uses the smart glasses' AR display module to overlay information on the origin and nutritional information on the food.

[0977] These steps allow users to easily obtain detailed information about food products and visually check them in-store.

[0978] 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.

[0979] The present invention combines a system that allows a user to scan food using an image capture device and acquire information about the food in real time with an emotion engine that recognizes the user's emotions. Hereinafter, specific embodiments of the present invention will be described in detail.

[0980] Food scanning and image transmission

[0981] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a photo. The device saves the image and executes a request to send the image data to a server.

[0982] Image analysis and food identification

[0983] The server receives the image data sent from the device. The received image data is analyzed using an image recognition algorithm to identify the food item. For example, the server uses the image recognition algorithm to identify the food item as an apple.

[0984] Food information search

[0985] The server searches the database for information corresponding to the food (apple) it has recognized. Specifically, it obtains information about the apple's origin (e.g., "Central Asia") and nutritional information (e.g., "rich in vitamin C and dietary fiber").

[0986] Information transmission and AR display

[0987] The server appropriately formats the information retrieved from the database and sends it to the device. The device then analyzes the received information and generates content for AR display. At this time, the emotion engine analyzes the user's facial expression data and recognizes their emotion. For example, if the user has a surprised expression, the AR display will emphasize the information that matches their emotion. Specifically, not only will the information "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" be displayed above the apple, but the color and font size will also be adjusted according to the emotion.

[0988] Specific examples

[0989] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple, then sends it to a server. The server uses an image recognition algorithm to identify the apple and searches a database for information about the apple's origin and nutritional value. The search results are sent back to the device, which then displays the received information as AR content. At this time, an emotion engine analyzes the user's facial expression and recognizes, for example, that the user is looking surprised. Based on that emotion, the AR-displayed information is more emphasized. The user not only sees the origin and nutritional value displayed on the apple on the screen, but also experiences the information dynamically changing to match their own emotions.

[0990] In this way, the present invention allows users to simply scan food items, visually obtain detailed information about them in real time, and then use an emotion engine to interactively present information according to the user's emotions. Cooperation between the terminal, server, and emotion engine makes it possible to provide users with more effective information.

[0991] The processing flow will be explained below.

[0992] Specific processing steps of the program

[0993] Step 1:

[0994] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a picture.

[0995] Step 2:

[0996] The image capture device stores the image data of the scanned apple, typically in local storage or temporary memory.

[0997] Step 3:

[0998] The device sends the captured image data to the server by making an HTTP POST request and including the image data as a payload.

[0999] Step 4:

[1000] The server receives the image data sent from the terminal, confirms that the image has been sent, and temporarily stores the data.

[1001] Step 5:

[1002] The server then runs the image data through an image recognition algorithm, using machine learning models and pattern recognition techniques to analyze the object in the image and identify it as an apple.

[1003] Step 6:

[1004] The server searches the database for origin and nutritional information for the identified food (apple). For example, it retrieves information such as "originating from Central Asia" and "rich in vitamin C and dietary fiber."

[1005] Step 7:

[1006] The server formats the search results and returns them to the device in the appropriate format, typically in JSON format.

[1007] Step 8:

[1008] The terminal analyzes the data received from the server, and identifies the origin and nutritional information that needs to be displayed.

[1009] Step 9:

[1010] The emotion engine captures the user's facial expressions through the device's camera, and facial data is collected as the user scans the apple.

[1011] Step 10:

[1012] The emotion engine analyzes the collected facial expression data to identify the user's emotion, for example, recognizing that the user has a surprised expression.

[1013] Step 11:

[1014] The device generates content for the AR display, and the displayed information is enhanced based on the user's emotions. For example, if the user has a surprised expression, the font size of nutrition information will be increased.

[1015] Step 12:

[1016] The device displays AR content in real time on the camera view. When the user looks at the apple through their smartphone, information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" is displayed on top of the apple, with emphasis based on the user's emotions.

[1017] By following these steps, users can easily scan food items and visually obtain detailed information about them in real time, and interactive information presentation based on the user's emotions is also possible.

[1018] Example 2

[1019] 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."

[1020] Conventional food identification systems have the problem that they do not provide interactive information according to the user's emotions when obtaining detailed information about a food. Furthermore, the system does not dynamically change information based on the user's emotions, which hinders the user experience. Furthermore, there is no efficient coordination between real-time information processing and display.

[1021] 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.

[1022] In this invention, the server includes a means for analyzing image data to identify foods, a means for searching a database for origin and nutritional information about the identified foods, and a means for returning the searched information to the image capture device. This allows a user to obtain detailed information about foods scanned using the image capture device in real time. Furthermore, by adding a means for an emotion engine to analyze the user's facial expression data to recognize emotions and dynamically change the information displayed in augmented reality according to the emotions, interactive information presentation according to the user's emotions can be provided.

[1023] "User" refers to the individual who operates the system to scan food items and obtain information.

[1024] "Image capture device" refers to an electronic device, such as a smartphone or tablet, used to capture images of food.

[1025] A "server" refers to a computer system that receives image data sent from a terminal via a network, analyzes it, and provides information.

[1026] "Image data" refers to an image file of food that a user scans with an image capture device.

[1027] "Image recognition algorithm" refers to a computational method for analyzing transmitted image data and identifying the type of food.

[1028] A "database" refers to a collection of data that stores information on the origin and nutritional information of food products.

[1029] "Augmented reality" refers to a technology that overlays computer-generated information onto real-world images.

[1030] An "emotion engine" refers to software that analyzes a user's facial expression data and recognizes their emotions.

[1031] "Facial expression data" refers to information data that indicates the facial expression of the user.

[1032] "Dynamic change" refers to changing the display content in real time according to the user's emotions.

[1033] The present invention combines a system that allows users to scan food using an image capture device and acquire information about the food in real time with an emotion engine that recognizes the user's emotions. The purpose of this system is to provide detailed information about food to users intuitively and interactively.

[1034] Hardware and Software Configuration

[1035] This system uses the following hardware and software:

[1036] Image acquisition device: A device with a camera function, such as a smartphone or tablet.

[1037] Server: A computer system for data analysis and database searches. It uses libraries such as TensorFlow and OpenCV for image recognition.

[1038] Database: Data storage containing food origin and nutritional information.

[1039] Emotion engine: Uses Microsoft Azure's Emotion API and Google Cloud Vision's facial recognition capabilities.

[1040] System Operation

[1041] 1. User-initiated scan

[1042] The user activates the camera of the image capture device and scans the food item. For example, the user focuses on an apple and takes a photo with their smartphone. This image data is then stored on the device.

[1043] 2. Sending image data

[1044] The device sends the stored image data to the server using an HTTP POST request to a specific API endpoint, along with any necessary metadata (e.g., user ID, timestamp, etc.).

[1045] 3. Analysis of image data

[1046] The server analyzes the received image data and identifies the type of food using image recognition algorithms such as ResNet. The server then uses Python's OpenCV and TensorFlow to perform image analysis and identify that the food is an apple.

[1047] 4. Database Search

[1048] The server searches the database for information about the identified food, specifically, using SQL queries to retrieve the food's origin and nutritional information.

[1049] 5. Transmission of Information

[1050] The server formats the information retrieved from the database and returns it to the device in JSON format, including information on the apple's origin and nutritional information.

[1051] 6. Augmented Reality Display

[1052] The device then parses the received JSON data and displays it as AR content, using the AR Kit and AR Core to overlay information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on top of the food.

[1053] 7. Sentiment analysis and display adjustment

[1054] The device's built-in camera captures the user's facial expressions, which are then analyzed by the emotion engine. Based on the analysis results, the device adjusts the color and font size of the information displayed in AR. For example, if the user has a surprised expression, the information will be displayed more prominently.

[1055] Specific examples

[1056] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple and sends it to a server. The server uses an image recognition algorithm to identify the apple and search for related information in a database. The search results are sent back to the device, which displays the received information as AR content. At this time, an emotion engine analyzes the user's facial expression and recognizes surprise. Based on that emotion, the AR-displayed information is more emphasized. Through this process, the user not only sees information about the apple's origin and nutritional value, but also experiences how the information dynamically changes to match their own emotions.

[1057] Prompt Sentence Examples

[1058] Examples of prompts for generative AI models include:

[1059] "Create a system in which a user scans an apple with their smartphone camera. The system sends the image data to a server, which analyzes the image and searches a database for food information. It also uses an emotion recognition engine to adjust the displayed information based on the user's emotions."

[1060] In this way, the present invention allows users to simply scan food items, visually obtain detailed information about them in real time, and then uses an emotion engine to provide interactive information according to the user's emotions. Cooperation between the terminal, server, and emotion engine makes it possible to provide more effective information to users.

[1061] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1062] Step 1:

[1063] The user activates the camera of the image capture device, focuses on the food, and takes a photo. This action saves the image data to the device. Specifically, the user activates the camera of their smartphone, takes a photo of an apple, and saves it as an image file.

[1064] Input: Food image

[1065] Output: Image file (image data saved on the device)

[1066] Step 2:

[1067] The device sends the saved image data to the server. Specifically, the device uses an HTTP POST request to send the saved image data along with metadata such as the user ID and timestamp to the server.

[1068] Input: Image file, user ID, timestamp

[1069] Output: HTTP request sent to the server

[1070] Step 3:

[1071] The server analyzes the received image data. Specifically, it uses an image recognition algorithm (e.g., ResNet) to identify the type of food. It then decodes the image data using Python's OpenCV or TensorFlow library to identify the apple.

[1072] Input: Image file

[1073] Output: Identified food item (e.g. apple)

[1074] Step 4:

[1075] The server searches the database for information about the identified food, specifically, the server retrieves the origin and nutritional information about the food from the database using an SQL query.

[1076] Input: Identified food item (e.g. apple)

[1077] Output: Food origin and nutritional information

[1078] Step 5:

[1079] The server formats the information retrieved from the database and sends it to the terminal. Specifically, the server formats the information retrieved in JSON format and returns it to the terminal as an HTTP response.

[1080] Input: Food origin and nutritional information

[1081] Output: JSON format data (sent to the terminal)

[1082] Step 6:

[1083] The device analyzes the received JSON data and displays it as augmented reality (AR) content. Specifically, the device uses the AR Kit or AR Core to overlay information on the food.

[1084] Input: JSON format data

[1085] Output: AR display (information displayed overlaid on food)

[1086] Step 7:

[1087] The emotion engine analyzes the user's facial expression data and recognizes their emotions. Specifically, the device's camera captures the user's facial expressions, and the data is analyzed by the emotion engine (for example, Microsoft Azure's Emotion API or Google Cloud Vision).

[1088] Input: User's facial expression data

[1089] Output: Recognized emotion (e.g., surprise)

[1090] Step 8:

[1091] The device dynamically changes the information displayed in AR depending on the recognized emotion. Specifically, the device adjusts the color and font size of the displayed information based on the recognized emotion. For example, if the user has a surprised expression, the information is displayed in a more emphasized form.

[1092] Input: Recognized emotion

[1093] Output: Dynamically adjusted AR display

[1094] (Application example 2)

[1095] 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."

[1096] Conventional product information acquisition systems simply provide static information about products and are unable to present dynamic, interactive information tailored to the user's interests and emotions. This limits the user experience and can make product information provision ineffective, especially in physical stores. Furthermore, with typical systems, the process from scanning products to providing information is not performed in real time, making it difficult to quickly acquire information.

[1097] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to scan an item with an image acquisition device; means for transmitting image data of the item acquired by the image acquisition device to the server; means for the server to analyze the transmitted image data and identify the item; means for the server to search a database for origin information and ingredient information about the identified item; means for returning the searched information to the image acquisition device; means for displaying the information returned by the image acquisition device in augmented reality; and means for an emotion engine to analyze the user's facial expression and adjust the augmented reality display according to the emotion. This allows the user to visually obtain detailed information about the item in real time and receive interactive and dynamic information presentation tailored to their emotions.

[1098] An "image capture device" is a device with a camera function that scans an item and captures image data.

[1099] A "server" is a computer system that receives image data via a network and performs analysis and information retrieval.

[1100] An "article" is an item that a user scans, such as food or merchandise.

[1101] "Image data" is a digital representation of visual information of an item captured by an image capture device.

[1102] An "image recognition algorithm" is a program that analyzes transmitted image data and identifies objects and features contained in the image.

[1103] "Provenance information" is background information about where an item was produced and how it was made.

[1104] "Ingredient information" is data such as nutrients and chemical components contained in an item.

[1105] A "database" is a system that accumulates data related to products, such as origin and ingredient information, and manages it in a searchable format.

[1106] Augmented reality (AR) is a technology that overlays digital information onto the real world.

[1107] An "emotion engine" is an algorithm or program that analyzes a user's facial expressions and recognizes their emotional state.

[1108] "Analyzing facial expressions" refers to the process of analyzing images of a user's face captured by an image capture device and identifying emotions from the facial expressions.

[1109] "Interactive information presentation" is a form of information display that dynamically changes according to the user's emotions and reactions.

[1110] The present invention combines a system in which a user scans an item using an image capture device and captures the information in real time with an emotion engine that recognizes the user's emotions.

[1111] System configuration

[1112] The system mainly consists of the following components:

[1113] Image capture device (smartphone, smart glasses, etc.): Equipped with a camera function, it scans items and captures image data.

[1114] Server: Receives image data via the network and performs analysis and information searches.

[1115] Image recognition algorithms (e.g., TensorFlow, OpenCV): Analyze image data and identify items.

[1116] Database: Data related to products, such as origin and ingredient information, is stored and managed in a searchable format.

[1117] Emotion engine (e.g., Emotion Recognition API): Analyzes the user's facial expressions and recognizes their emotional state.

[1118] Augmented reality (AR) display: A technology that overlays digital information onto the real world.

[1119] Program processing explanation

[1120] 1. Image Acquisition:

[1121] A user scans an item using an image capture device (such as a smartphone or smart glasses), for example, a user finds an apple in a store and scans it with the camera.

[1122] 2. Image transmission and analysis:

[1123] The image capture device sends the captured image data to a server, which then analyzes the received image data using an image recognition algorithm (TensorFlow, OpenCV) to identify the object. For example, the server identifies the object in the image as an apple.

[1124] 3. Product information search:

[1125] The server searches the database for the origin and ingredient information of the identified item, for example, for the origin and nutritional information of "apple."

[1126] 4. Facial Expression Analysis and Emotion Recognition:

[1127] The front camera of the image capture device captures the user's facial expression and analyzes it using the emotion engine (Emotion Recognition API). For example, it can recognize that the user has a surprised expression.

[1128] 5. Information transmission and AR display:

[1129] The server returns the retrieved information to the image capture device, which then displays the returned information in AR. The display content is adjusted according to the user's emotions. For example, if a user is surprised, the information is displayed in a pop-up format in a larger font.

[1130] Specific examples

[1131] The user scans an apple with their smartphone camera. The device captures an image of the apple and sends it to the server. The server uses an image recognition algorithm to identify the apple and searches a database for information about the apple's origin and nutritional value. The server returns the search results to the device, which then displays the received information as AR content. At this time, the emotion engine analyzes the user's facial expression and recognizes, for example, that the user has a surprised expression. Based on this emotion, the information is displayed in a more emphasized form.

[1132] Example prompt sentence:

[1133] "Capture an image of an apple with your smartphone camera. The app then sends it to the server to retrieve the item information. If the user is surprised, the information will be highlighted in a pop-up."

[1134] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1135] Step 1:

[1136] A user scans an object using an image capture device (such as a smartphone or smart glasses). The input is the object to be scanned (e.g., an apple), and the output is the captured image data. Specifically, the user activates the camera, focuses on the object, and presses the capture button.

[1137] Step 2:

[1138] The image data acquired by the terminal is sent to the server. The input is the image data acquired in step 1, and the output is the image data sent to the server. Specifically, the terminal sends the image data to the server via the Internet as an HTTP request.

[1139] Step 3:

[1140] The server analyzes the image data sent and identifies the item. The input is the image data received by the server, and the output is identification information that the item is an apple. Specifically, the server uses an image recognition algorithm (TensorFlow or OpenCV) to analyze and identify the item in the image.

[1141] Step 4:

[1142] The server searches the database for origin and ingredient information about the identified item. The input is the identified item's information (apple), and the output is the item's origin information (e.g., origin in Central Asia) and ingredient information (e.g., vitamin C, dietary fiber). Specifically, the server queries the database system (e.g., MySQL or PostgreSQL) to retrieve the relevant information.

[1143] Step 5:

[1144] The device captures the user's facial expression using the front camera and sends it to the server. The input is image data of the user's facial expression, and the output is image data of the facial expression sent to the server. Specifically, the device sends the facial expression data to the server via the Internet as an HTTP request.

[1145] Step 6:

[1146] The server analyzes the facial expression data and recognizes the user's emotions. The input is the facial expression data received by the server, and the output is the recognized emotional information (for example, surprise). Specifically, the server uses an emotion engine (Emotion Recognition API) to analyze the facial expression data and identify the user's emotions.

[1147] Step 7:

[1148] The server integrates product information and the user's emotional information and sends it back to the terminal. The input is product information (origin information, ingredient information) and emotional information (surprise, etc.), and the output is the integrated information. Specifically, the server organizes the information into an appropriate format and sends it back to the terminal via the Internet.

[1149] Step 8:

[1150] The device analyzes the information it receives and displays it as augmented reality (AR). The input is the integrated information returned from the server, and the output is the AR content displayed on the screen. Specifically, the device visualizes the information using an AR library (e.g., ARKit or ARCore) and displays it overlaid on the user's screen.

[1151] For example, a user scans an apple with their smartphone camera, and the system acquires, processes, and displays the information. The prompt above, "Please capture an image of the apple with your smartphone camera. Then, the app will send it to the server to acquire the item information. If the user is surprised, the information will be highlighted in a pop-up format." is based on this sequence.

[1152] 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.

[1153] 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.

[1154] 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.

[1155] [Fourth embodiment]

[1156] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1157] 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.

[1158] 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).

[1159] 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.

[1160] 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.

[1161] 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).

[1162] 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.

[1163] 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.

[1164] 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.

[1165] 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.

[1166] 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.

[1167] 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.

[1168] 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."

[1169] To implement the present invention, a system is required that allows a user to scan food using an image capture device and obtain information about the food in real time. A detailed description of specific embodiments of the present invention is provided below.

[1170] Food scanning and image transmission

[1171] A user scans a food item using an image capture device such as a smartphone or tablet. For example, if the user scans an apple, they use the camera function to capture an image of the apple. The device saves this image and makes a request to send the image data to a server.

[1172] Image analysis and food identification

[1173] The server receives the image data sent from the device. The received image data is analyzed using an image recognition algorithm to identify the food. In this case, the server uses the image recognition algorithm to identify the food as an apple.

[1174] Food information search

[1175] The server searches the database for information corresponding to the food (apple) it has recognized. Specifically, it obtains information about the apple's origin (e.g., "Central Asia") and nutritional information (e.g., "rich in vitamin C and dietary fiber").

[1176] Information transmission and AR display

[1177] The server retrieves information from the database, formats it appropriately, and sends it to the device. The device then analyzes the information and generates content for the AR display. For example, it could overlay text information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on an apple. The device then displays this information in real time on the camera view and presents it to the user.

[1178] Specific examples

[1179] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple, then sends it to a server. The server uses an image recognition algorithm to identify the apple and searches a database for the apple's origin and nutritional information. The search results are sent back to the device, which displays the received information as AR content. The user can view the origin and nutritional information displayed on the apple through the screen.

[1180] In this way, the system based on the present invention allows users to easily scan food and obtain detailed information about it in real time. The terminal and server work together to provide users with effective information.

[1181] The processing flow will be explained below.

[1182] Specific processing steps of the program

[1183] Step 1:

[1184] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a picture.

[1185] Step 2:

[1186] The image capture device stores the image data of the scanned apple, typically in local storage or temporary memory.

[1187] Step 3:

[1188] The device sends the captured image data to the server by making an HTTP POST request and including the image data as a payload.

[1189] Step 4:

[1190] The server receives the image data sent from the terminal, confirms that the image has been sent, and temporarily stores the data.

[1191] Step 5:

[1192] The server then runs the image data through an image recognition algorithm, using machine learning models and pattern recognition techniques to analyze the object in the image and identify it as an apple.

[1193] Step 6:

[1194] The server searches the database for origin and nutritional information for the identified food (apple). For example, it retrieves information such as "originating from Central Asia" and "rich in vitamin C and dietary fiber."

[1195] Step 7:

[1196] The server formats the search results and returns them to the device in the appropriate format, typically in JSON format.

[1197] Step 8:

[1198] The terminal analyzes the data received from the server, and identifies the origin and nutritional information that needs to be displayed.

[1199] Step 9:

[1200] The device generates the content for the AR display, specifically the text and graphics that are overlaid on the camera view.

[1201] Step 10:

[1202] The device displays AR content in real time on the camera view. When the user looks at the apple through their smartphone, information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" is superimposed on the apple.

[1203] By following these steps, users can easily scan food items and visually obtain detailed information about them in real time.

[1204] Example 1

[1205] 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."

[1206] In today's world, there is a demand for instant access and understanding of detailed information about food. However, conventional methods require users to individually look up information about foods, which is time-consuming and laborious. In addition, there are limited means for easily displaying food information visually. This means that users need time to select and understand foods, making it difficult to efficiently obtain information.

[1207] 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.

[1208] In this invention, the server includes means for a user to scan an object with an image capture device, means for transmitting image data of the object captured by the image capture device to a processing device, means for the processing device to analyze the transmitted image data and identify the object, means for the processing device to search a database for detailed information about the identified object, means for returning the searched information to the image capture device, and means for presenting the returned information by the image capture device in an expanded display, thereby enabling a user to easily obtain detailed information about food and visually understand it.

[1209] "User" refers to a person who uses the system to scan food or other objects to obtain detailed information.

[1210] An "image acquisition device" is a device equipped with a camera function, including a smartphone or tablet, that is used to acquire image data of an object.

[1211] An "object" is anything that a user scans with an image capture device to obtain detailed information about, such as food or an object.

[1212] The "processing device" is a central processing unit that receives image data sent from the image acquisition device and performs analysis and database searches, and is usually a high-performance computer such as a server.

[1213] "Detailed information" refers to data including information that the user wants to obtain, such as information about the origin, nutritional information, and characteristics of the subject.

[1214] "Augmented display" refers to a method of using AR (augmented reality) technology to overlay information on the screen of an image capture device.

[1215] An "image analysis algorithm" is a computational method and program for performing image recognition, and is an algorithm used to identify objects.

[1216] To implement this invention, a system is required that allows a user to scan an object (e.g., food) using an image capture device and acquire information about it in real time. This system is composed of a user, a terminal, and a server, and these components work together to provide information efficiently.

[1217] The user scans the object with the image capture device

[1218] A user scans an object using an image capture device such as a smartphone or tablet. Specifically, the user captures an image of the object using the camera function. For example, when a user wants to scan an apple, they launch the camera app on their smartphone, focus on the apple, and press the capture button.

[1219] The device sends the captured image to the server.

[1220] The device saves the captured image data and generates and executes a request to send it to a server. The software used for this includes the camera app on a smartphone or tablet, as well as a dedicated application. The image data is sent to the server via an HTTP request.

[1221] The server analyzes the image and identifies the subject

[1222] The server receives the image data sent from the device and identifies the object (e.g., an apple) using an image analysis algorithm. The software used here includes image recognition algorithms such as TensorFlow and OpenCV. The server reads the image file, inputs it into the image recognition model, and identifies the object.

[1223] The server searches the database for the relevant information

[1224] The server retrieves detailed information about the identified subjects (e.g., origin information, nutritional information) from a database. The software used here includes an SQL query and a database management system (e.g., MySQL). The server executes SQL queries against the database to retrieve data corresponding to the subjects.

[1225] The server sends the information to the device

[1226] The server formats the retrieved information into an appropriate format (e.g., JSON) and sends it to the device. The server then converts the retrieved data into JSON format and sends it back to the device as an HTTP response, allowing the device to easily analyze the returned information.

[1227] The device generates the information as an AR display and presents it to the user.

[1228] The device parses the JSON data received from the server and extracts the data to display. The software used includes a JSON parser and an AR application (e.g., ARKit or ARCore). The device uses the extracted information to generate AR display content and present it to the user. Specifically, the device overlays text such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on the camera view.

[1229] Specific examples of operation

[1230] When a user scans an apple with their smartphone camera, the device captures and saves an image of the apple, then sends the image file to the server. The server uses an image recognition algorithm to identify the apple and retrieves detailed information about the apple from a database. The server formats the information into JSON format and sends it to the device as an HTTP response. The device then analyzes the received JSON data, extracts the necessary information, generates AR display content, and presents it to the user. In this way, the user can visually confirm detailed information about the apple in real time.

[1231] Example prompts for generative AI models

[1232] The following prompt sentences can be used to provide input to the generative AI model:

[1233] Describe a system that scans images of food and displays its nutritional information and origin in real time. Break it down into the following processing steps:

[1234] 1. The user scans the object with the image capture device.

[1235] 2. The device captures the image and sends it to the server

[1236] 3. The server analyzes the image and identifies the target

[1237] 4. The server searches the database for the relevant information

[1238] 5. The server sends the information to the device

[1239] 6. The device generates the information as an AR display and presents it to the user.

[1240] In this way, the system based on the present invention allows users to easily scan an object and obtain detailed information about it in real time. The terminal and server work together to provide effective information provision.

[1241] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1242] Step 1:

[1243] User scans food

[1244] Input: User takes a picture of food using the camera.

[1245] Specific actions: The user launches the smartphone camera app, scans the apple, focuses, and presses the capture button.

[1246] Output: The captured image data (e.g., an image of an apple).

[1247] Step 2:

[1248] The device captures and saves the image

[1249] Input: Image data sent from the camera app.

[1250] Specific operation: The device saves the captured image file (e.g., apple.jpg) in the specified directory in the storage.

[1251] Output: Image file saved on your device.

[1252] Step 3:

[1253] The device sends the image data to the server.

[1254] Input: A saved image file (e.g. apple.jpg).

[1255] Specific operation: The device obtains the path to the image file, generates an HTTP request, and sends the image data to the server.

[1256] Output: Image data sent to the server.

[1257] Step 4:

[1258] The server receives the image data.

[1259] Input: HTTP request from the device (including image data).

[1260] Specific operation: The server receives the HTTP request and saves the image data in the analysis directory.

[1261] Output: Image file saved on the server.

[1262] Step 5:

[1263] The server analyzes the image and identifies the food.

[1264] Input: Image file stored on the server.

[1265] Specific operation: The server analyzes the image data using image analysis algorithms such as TensorFlow or OpenCV, and identifies the specific food item (e.g., apple).

[1266] Output: The type of food identified (e.g., apple).

[1267] Step 6:

[1268] The server searches the database for food information

[1269] Input: The type of food identified (e.g., apple).

[1270] What happens: The server runs an SQL query against the database to find the origin and nutritional information of the apple.

[1271] Output: Food information retrieved from the database (e.g., origin and nutritional information for an apple).

[1272] Step 7:

[1273] The server sends the information to the device

[1274] Input: Food information retrieved from a database (e.g., origin and nutritional information for an apple).

[1275] Specific operation: The server formats the food information it obtains into JSON format and sends it to the terminal as an HTTP response.

[1276] Output: Food information in JSON data format sent to the terminal.

[1277] Step 8:

[1278] The device analyzes the received data and generates an AR display.

[1279] Input: Food information received from the server in JSON data format.

[1280] What it does: The device uses a JSON parser to parse the data and extract the necessary information (e.g., the origin and nutritional information of the apple), then uses ARKit or ARCore to generate AR content that corresponds to the camera view.

[1281] Output: AR content displayed in the camera view (e.g. text information "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" overlaid on an apple).

[1282] In this way, each processing step works together in a series of steps to provide detailed visual information about the food to the user. Through the customer interface, the user can easily scan the food and obtain detailed information in real time.

[1283] (Application example 1)

[1284] 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."

[1285] Previously, when purchasing food in a physical store, users had to read the product packaging to obtain detailed product information. However, there were limited ways to check information not listed on the packaging or more detailed background information. For example, it was difficult to display information about the food's origin or nutritional information, or even real-time information in a physical store. There was also a lack of ways to present product information visually in an easy-to-understand manner. This resulted in users being unable to obtain sufficient information when making food choices, resulting in a decrease in satisfaction.

[1286] 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.

[1287] In this invention, the server includes means for a user to scan food with an image capture device, means for transmitting image data of the food captured by the image capture device to the server, means for the server to analyze the transmitted image data and identify the food, means for the server to search a database for origin information and nutritional information about the identified food, means for returning the searched information to the image capture device, means for the image capture device to display the returned information in augmented reality, means for scanning products using smart glasses in a physical store, and means for presenting the food information displayed on the smart glasses in real time, thereby enabling a user to intuitively obtain detailed information about food simply by scanning the food in a physical store.

[1288] "User" refers to an individual who uses the system to obtain food information.

[1289] "Image capture device" refers to a device, such as a smartphone, tablet, or smart glasses with a camera function, that captures images of food and transmits them to a server.

[1290] "Food" refers to edible products that can be scanned and captured using the system.

[1291] "Server" refers to a computing device that analyzes the food image data sent from the image capture device, retrieves information from a database, and returns it.

[1292] "Image data" refers to images of food captured with an image capture device.

[1293] "Analysis" refers to the process of identifying the type of food based on the transmitted image data.

[1294] "Identification" refers to the process of recognizing a particular food item from the analyzed image data.

[1295] "Origin information" refers to information about the region or origin from which a food product comes.

[1296] "Nutrition information" refers to information about the nutritional components contained in food and their effects.

[1297] "Database" refers to a collection of information that stores and makes searchable information about food origins and nutritional information.

[1298] "Search" refers to the process by which the server retrieves information about a specified food item from the database.

[1299] "Return" refers to the process by which the server sends the acquired information back to the image capture device.

[1300] "Augmented reality" refers to a technology that overlays computer-generated information onto your physical field of vision.

[1301] A "physical store" refers to a facility that sells food and other items in a real physical space.

[1302] "Smart glasses" refers to a glasses-type device that can display information in the field of view using augmented reality technology.

[1303] "Real-time" refers to a state in which information is provided almost immediately in response to a user's operation.

[1304] To implement the present invention, a system must be built that allows a user to scan food using an image capture device and capture the food information in real time. Specifically, this is accomplished by the following steps:

[1305] 1. Use of image acquisition equipment

[1306] Users scan food items in physical stores using an image capture device (such as a smartphone, tablet, or smart glasses), and the image data of the scanned food items is stored on the device.

[1307] 2. Sending image data to the server

[1308] The device sends the stored image data of the food to a server using the device's internal communication module and internet connection.

[1309] 3. Image analysis on the server

[1310] The server analyzes the received image data, using image recognition algorithms (e.g., TensorFlow or OpenCV) to identify the type of food.

[1311] 4. Searching for food information

[1312] The server searches the database for the corresponding origin and nutritional information for the identified food products. The server uses a database management system such as MySQL or MongoDB.

[1313] 5. Returning information to your device

[1314] The server converts the search results into an appropriate format, such as JSON, and sends them back to the device.

[1315] 6. Augmented Reality Information Display

[1316] Based on the received information, the device displays detailed information about the food in augmented reality on the smart glasses display, allowing users to visually check the food's background information and nutritional content in real time.

[1317] Hardware and Software

[1318] Hardware: Smartphones, tablets, smart glasses (such as Google Glass or Microsoft HoloLens)

[1319] Software: Python, OpenCV, Requests library, database management system (MySQL, MongoDB)

[1320] Specific examples

[1321] For example, when a user chooses an apple in a physical store, they put on the smart glasses and scan the apple. The image data of the scanned apple is sent from the device to a server. The server uses an image recognition algorithm to identify the apple and search a database for information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber." The search results are then sent back to the device and displayed in real time in the user's field of vision through the smart glasses.

[1322] Prompt Sentence Examples

[1323] "Please tell me the origin and nutritional information of the apples."

[1324] This allows users to easily obtain detailed information about food products in physical stores.

[1325] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1326] Step 1:

[1327] Users use an image capture device (e.g., smartphone, tablet, smart glasses) to scan food items in a physical store.

[1328] Input: Food images captured with smart glasses or a smartphone camera

[1329] What it does: Point the image capture device's camera at the food and tap the camera button to take a picture, or, in the case of smart glasses, use a voice command.

[1330] Step 2:

[1331] The device sends the captured images to a server, using a communication module and an internet connection.

[1332] Input: Image data of the food you photographed

[1333] Output: Image data sent to the server

[1334] What happens: The device sends image data to the server in an HTTP POST request, over an Internet connection.

[1335] Step 3:

[1336] The server analyzes the received image data and identifies the food using an image recognition algorithm.

[1337] Input: Image data sent from the device

[1338] Output: The type of food identified (e.g., apple)

[1339] What it does: The server runs Python and TensorFlow image recognition models to analyze the image data it receives, and returns the type of food it identifies.

[1340] Step 4:

[1341] The server retrieves origin and nutritional information for the identified food products from a database.

[1342] Input: Identified food type (e.g. apple)

[1343] Output: Origin and nutritional information about the food

[1344] Specific operation: The server executes an SQL query against a database managed by MySQL or MongoDB to retrieve food information. For example, the query "SELECT FROM food_info WHERE name="apple"" is executed.

[1345] Step 5:

[1346] The server converts the acquired information into an appropriate format, such as JSON, and sends it back to the device.

[1347] Input: Food information retrieved from the database

[1348] Output: Food information returned to the device

[1349] Specific operation: The server serializes the acquired information in JSON format and sends it to the terminal as an HTTP response.

[1350] Step 6:

[1351] Based on the information received, the device displays food information in augmented reality on the smart glasses display.

[1352] Input: Food information received from the server

[1353] Output: Augmented reality food information displayed on smart glasses

[1354] Specific operation: The device analyzes the received information and uses the smart glasses' AR display module to overlay information on the origin and nutritional information on the food.

[1355] These steps allow users to easily obtain detailed information about food products and visually check them in-store.

[1356] 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.

[1357] The present invention combines a system that allows a user to scan food using an image capture device and acquire information about the food in real time with an emotion engine that recognizes the user's emotions. Hereinafter, specific embodiments of the present invention will be described in detail.

[1358] Food scanning and image transmission

[1359] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a photo. The device saves the image and executes a request to send the image data to a server.

[1360] Image analysis and food identification

[1361] The server receives the image data sent from the device. The received image data is analyzed using an image recognition algorithm to identify the food item. For example, the server uses the image recognition algorithm to identify the food item as an apple.

[1362] Food information search

[1363] The server searches the database for information corresponding to the food (apple) it has recognized. Specifically, it obtains information about the apple's origin (e.g., "Central Asia") and nutritional information (e.g., "rich in vitamin C and dietary fiber").

[1364] Information transmission and AR display

[1365] The server appropriately formats the information retrieved from the database and sends it to the device. The device then analyzes the received information and generates content for AR display. At this time, the emotion engine analyzes the user's facial expression data and recognizes their emotion. For example, if the user has a surprised expression, the AR display will emphasize the information that matches their emotion. Specifically, not only will the information "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" be displayed above the apple, but the color and font size will also be adjusted according to the emotion.

[1366] Specific examples

[1367] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple, then sends it to a server. The server uses an image recognition algorithm to identify the apple and searches a database for information about the apple's origin and nutritional value. The search results are sent back to the device, which then displays the received information as AR content. At this time, an emotion engine analyzes the user's facial expression and recognizes, for example, that the user is looking surprised. Based on that emotion, the AR-displayed information is more emphasized. The user not only sees the origin and nutritional value displayed on the apple on the screen, but also experiences the information dynamically changing to match their own emotions.

[1368] In this way, the present invention allows users to simply scan food items, visually obtain detailed information about them in real time, and then use an emotion engine to interactively present information according to the user's emotions. Cooperation between the terminal, server, and emotion engine makes it possible to provide users with more effective information.

[1369] The processing flow will be explained below.

[1370] Specific processing steps of the program

[1371] Step 1:

[1372] A user scans a food item using an image capture device such as a smartphone or tablet. For example, the user activates the camera, focuses on an apple, and takes a picture.

[1373] Step 2:

[1374] The image capture device stores the image data of the scanned apple, typically in local storage or temporary memory.

[1375] Step 3:

[1376] The device sends the captured image data to the server by making an HTTP POST request and including the image data as a payload.

[1377] Step 4:

[1378] The server receives the image data sent from the terminal, confirms that the image has been sent, and temporarily stores the data.

[1379] Step 5:

[1380] The server then runs the image data through an image recognition algorithm, using machine learning models and pattern recognition techniques to analyze the object in the image and identify it as an apple.

[1381] Step 6:

[1382] The server searches the database for origin and nutritional information for the identified food (apple). For example, it retrieves information such as "originating from Central Asia" and "rich in vitamin C and dietary fiber."

[1383] Step 7:

[1384] The server formats the search results and returns them to the device in the appropriate format, typically in JSON format.

[1385] Step 8:

[1386] The terminal analyzes the data received from the server, and identifies the origin and nutritional information that needs to be displayed.

[1387] Step 9:

[1388] The emotion engine captures the user's facial expressions through the device's camera, and facial data is collected as the user scans the apple.

[1389] Step 10:

[1390] The emotion engine analyzes the collected facial expression data to identify the user's emotion, for example, recognizing that the user has a surprised expression.

[1391] Step 11:

[1392] The device generates content for the AR display, and the displayed information is enhanced based on the user's emotions. For example, if the user has a surprised expression, the font size of nutrition information will be increased.

[1393] Step 12:

[1394] The device displays AR content in real time on the camera view. When the user looks at the apple through their smartphone, information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" is displayed on top of the apple, with emphasis based on the user's emotions.

[1395] By following these steps, users can easily scan food items and visually obtain detailed information about them in real time, and interactive information presentation based on the user's emotions is also possible.

[1396] Example 2

[1397] 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."

[1398] Conventional food identification systems have the problem that they do not provide interactive information according to the user's emotions when obtaining detailed information about a food. Furthermore, the system does not dynamically change information based on the user's emotions, which hinders the user experience. Furthermore, there is no efficient coordination between real-time information processing and display.

[1399] 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.

[1400] In this invention, the server includes a means for analyzing image data to identify foods, a means for searching a database for origin and nutritional information about the identified foods, and a means for returning the searched information to the image capture device. This allows a user to obtain detailed information about foods scanned using the image capture device in real time. Furthermore, by adding a means for an emotion engine to analyze the user's facial expression data to recognize emotions and dynamically change the information displayed in augmented reality according to the emotions, interactive information presentation according to the user's emotions can be provided.

[1401] "User" refers to the individual who operates the system to scan food items and obtain information.

[1402] "Image capture device" refers to an electronic device, such as a smartphone or tablet, used to capture images of food.

[1403] A "server" refers to a computer system that receives image data sent from a terminal via a network, analyzes it, and provides information.

[1404] "Image data" refers to an image file of food that a user scans with an image capture device.

[1405] "Image recognition algorithm" refers to a computational method for analyzing transmitted image data and identifying the type of food.

[1406] A "database" refers to a collection of data that stores information on the origin and nutritional information of food products.

[1407] "Augmented reality" refers to a technology that overlays computer-generated information onto real-world images.

[1408] An "emotion engine" refers to software that analyzes a user's facial expression data and recognizes their emotions.

[1409] "Facial expression data" refers to information data that indicates the facial expression of the user.

[1410] "Dynamic change" refers to changing the display content in real time according to the user's emotions.

[1411] The present invention combines a system that allows users to scan food using an image capture device and acquire information about the food in real time with an emotion engine that recognizes the user's emotions. The purpose of this system is to provide detailed information about food to users intuitively and interactively.

[1412] Hardware and Software Configuration

[1413] This system uses the following hardware and software:

[1414] Image acquisition device: A device with a camera function, such as a smartphone or tablet.

[1415] Server: A computer system for data analysis and database searches. It uses libraries such as TensorFlow and OpenCV for image recognition.

[1416] Database: Data storage containing food origin and nutritional information.

[1417] Emotion engine: Uses Microsoft Azure's Emotion API and Google Cloud Vision's facial recognition capabilities.

[1418] System Operation

[1419] 1. User-initiated scan

[1420] The user activates the camera of the image capture device and scans the food item. For example, the user focuses on an apple and takes a photo with their smartphone. This image data is then stored on the device.

[1421] 2. Sending image data

[1422] The device sends the stored image data to the server using an HTTP POST request to a specific API endpoint, along with any necessary metadata (e.g., user ID, timestamp, etc.).

[1423] 3. Analysis of image data

[1424] The server analyzes the received image data and identifies the type of food using image recognition algorithms such as ResNet. The server then uses Python's OpenCV and TensorFlow to perform image analysis and identify that the food is an apple.

[1425] 4. Database Search

[1426] The server searches the database for information about the identified food, specifically, using SQL queries to retrieve the food's origin and nutritional information.

[1427] 5. Transmission of Information

[1428] The server formats the information retrieved from the database and returns it to the device in JSON format, including information on the apple's origin and nutritional information.

[1429] 6. Augmented Reality Display

[1430] The device then parses the received JSON data and displays it as AR content, using the AR Kit and AR Core to overlay information such as "Origin: Central Asia" and "Nutrition: Vitamin C, dietary fiber" on top of the food.

[1431] 7. Sentiment analysis and display adjustment

[1432] The device's built-in camera captures the user's facial expressions, which are then analyzed by the emotion engine. Based on the analysis results, the device adjusts the color and font size of the information displayed in AR. For example, if the user has a surprised expression, the information will be displayed more prominently.

[1433] Specific examples

[1434] The user scans an apple with their smartphone camera. The device captures and saves an image of the apple and sends it to a server. The server uses an image recognition algorithm to identify the apple and search for related information in a database. The search results are sent back to the device, which displays the received information as AR content. At this time, an emotion engine analyzes the user's facial expression and recognizes surprise. Based on that emotion, the AR-displayed information is more emphasized. Through this process, the user not only sees information about the apple's origin and nutritional value, but also experiences how the information dynamically changes to match their own emotions.

[1435] Prompt Sentence Examples

[1436] Examples of prompts for generative AI models include:

[1437] "Create a system in which a user scans an apple with their smartphone camera. The system sends the image data to a server, which analyzes the image and searches a database for food information. It also uses an emotion recognition engine to adjust the displayed information based on the user's emotions."

[1438] In this way, the present invention allows users to simply scan food items, visually obtain detailed information about them in real time, and then uses an emotion engine to provide interactive information according to the user's emotions. Cooperation between the terminal, server, and emotion engine makes it possible to provide more effective information to users.

[1439] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1440] Step 1:

[1441] The user activates the camera of the image capture device, focuses on the food, and takes a photo. This action saves the image data to the device. Specifically, the user activates the camera of their smartphone, takes a photo of an apple, and saves it as an image file.

[1442] Input: Food image

[1443] Output: Image file (image data saved on the device)

[1444] Step 2:

[1445] The device sends the saved image data to the server. Specifically, the device uses an HTTP POST request to send the saved image data along with metadata such as the user ID and timestamp to the server.

[1446] Input: Image file, user ID, timestamp

[1447] Output: HTTP request sent to the server

[1448] Step 3:

[1449] The server analyzes the received image data. Specifically, it uses an image recognition algorithm (e.g., ResNet) to identify the type of food. It then decodes the image data using Python's OpenCV or TensorFlow library to identify the apple.

[1450] Input: Image file

[1451] Output: Identified food item (e.g. apple)

[1452] Step 4:

[1453] The server searches the database for information about the identified food, specifically, the server retrieves the origin and nutritional information about the food from the database using an SQL query.

[1454] Input: Identified food item (e.g. apple)

[1455] Output: Food origin and nutritional information

[1456] Step 5:

[1457] The server formats the information retrieved from the database and sends it to the terminal. Specifically, the server formats the information retrieved in JSON format and returns it to the terminal as an HTTP response.

[1458] Input: Food origin and nutritional information

[1459] Output: JSON format data (sent to the terminal)

[1460] Step 6:

[1461] The device analyzes the received JSON data and displays it as augmented reality (AR) content. Specifically, the device uses the AR Kit or AR Core to overlay information on the food.

[1462] Input: JSON format data

[1463] Output: AR display (information displayed overlaid on food)

[1464] Step 7:

[1465] The emotion engine analyzes the user's facial expression data and recognizes their emotions. Specifically, the device's camera captures the user's facial expressions, and the data is analyzed by the emotion engine (for example, Microsoft Azure's Emotion API or Google Cloud Vision).

[1466] Input: User's facial expression data

[1467] Output: Recognized emotion (e.g., surprise)

[1468] Step 8:

[1469] The device dynamically changes the information displayed in AR depending on the recognized emotion. Specifically, the device adjusts the color and font size of the displayed information based on the recognized emotion. For example, if the user has a surprised expression, the information is displayed in a more emphasized form.

[1470] Input: Recognized emotion

[1471] Output: Dynamically adjusted AR display

[1472] (Application example 2)

[1473] 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."

[1474] Conventional product information acquisition systems simply provide static information about products and are unable to present dynamic, interactive information tailored to the user's interests and emotions. This limits the user experience and can make product information provision ineffective, especially in physical stores. Furthermore, with typical systems, the process from scanning products to providing information is not performed in real time, making it difficult to quickly acquire information.

[1475] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to scan an item with an image acquisition device; means for transmitting image data of the item acquired by the image acquisition device to the server; means for the server to analyze the transmitted image data and identify the item; means for the server to search a database for origin information and ingredient information about the identified item; means for returning the searched information to the image acquisition device; means for displaying the information returned by the image acquisition device in augmented reality; and means for an emotion engine to analyze the user's facial expression and adjust the augmented reality display according to the emotion. This allows the user to visually obtain detailed information about the item in real time and receive interactive and dynamic information presentation tailored to their emotions.

[1476] An "image capture device" is a device with a camera function that scans an item and captures image data.

[1477] A "server" is a computer system that receives image data via a network and performs analysis and information retrieval.

[1478] An "article" is an item that a user scans, such as food or merchandise.

[1479] "Image data" is a digital representation of visual information of an item captured by an image capture device.

[1480] An "image recognition algorithm" is a program that analyzes transmitted image data and identifies objects and features contained in the image.

[1481] "Provenance information" is background information about where an item was produced and how it was made.

[1482] "Ingredient information" is data such as nutrients and chemical components contained in an item.

[1483] A "database" is a system that accumulates data related to products, such as origin and ingredient information, and manages it in a searchable format.

[1484] Augmented reality (AR) is a technology that overlays digital information onto the real world.

[1485] An "emotion engine" is an algorithm or program that analyzes a user's facial expressions and recognizes their emotional state.

[1486] "Analyzing facial expressions" refers to the process of analyzing images of a user's face captured by an image capture device and identifying emotions from the facial expressions.

[1487] "Interactive information presentation" is a form of information display that dynamically changes according to the user's emotions and reactions.

[1488] The present invention combines a system in which a user scans an item using an image capture device and captures the information in real time with an emotion engine that recognizes the user's emotions.

[1489] System configuration

[1490] The system mainly consists of the following components:

[1491] Image capture device (smartphone, smart glasses, etc.): Equipped with a camera function, it scans items and captures image data.

[1492] Server: Receives image data via the network and performs analysis and information searches.

[1493] Image recognition algorithms (e.g., TensorFlow, OpenCV): Analyze image data and identify items.

[1494] Database: Data related to products, such as origin and ingredient information, is stored and managed in a searchable format.

[1495] Emotion engine (e.g., Emotion Recognition API): Analyzes the user's facial expressions and recognizes their emotional state.

[1496] Augmented reality (AR) display: A technology that overlays digital information onto the real world.

[1497] Program processing explanation

[1498] 1. Image Acquisition:

[1499] A user scans an item using an image capture device (such as a smartphone or smart glasses), for example, a user finds an apple in a store and scans it with the camera.

[1500] 2. Image transmission and analysis:

[1501] The image capture device sends the captured image data to a server, which then analyzes the received image data using an image recognition algorithm (TensorFlow, OpenCV) to identify the object. For example, the server identifies the object in the image as an apple.

[1502] 3. Product information search:

[1503] The server searches the database for the origin and ingredient information of the identified item, for example, for the origin and nutritional information of "apple."

[1504] 4. Facial Expression Analysis and Emotion Recognition:

[1505] The front camera of the image capture device captures the user's facial expression and analyzes it using the emotion engine (Emotion Recognition API). For example, it can recognize that the user has a surprised expression.

[1506] 5. Information transmission and AR display:

[1507] The server returns the retrieved information to the image capture device, which then displays the returned information in AR. The display content is adjusted according to the user's emotions. For example, if a user is surprised, the information is displayed in a pop-up format in a larger font.

[1508] Specific examples

[1509] The user scans an apple with their smartphone camera. The device captures an image of the apple and sends it to the server. The server uses an image recognition algorithm to identify the apple and searches a database for information about the apple's origin and nutritional value. The server returns the search results to the device, which then displays the received information as AR content. At this time, the emotion engine analyzes the user's facial expression and recognizes, for example, that the user has a surprised expression. Based on this emotion, the information is displayed in a more emphasized form.

[1510] Example prompt sentence:

[1511] "Capture an image of an apple with your smartphone camera. The app then sends it to the server to retrieve the item information. If the user is surprised, the information will be highlighted in a pop-up."

[1512] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1513] Step 1:

[1514] A user scans an object using an image capture device (such as a smartphone or smart glasses). The input is the object to be scanned (e.g., an apple), and the output is the captured image data. Specifically, the user activates the camera, focuses on the object, and presses the capture button.

[1515] Step 2:

[1516] The image data acquired by the terminal is sent to the server. The input is the image data acquired in step 1, and the output is the image data sent to the server. Specifically, the terminal sends the image data to the server via the Internet as an HTTP request.

[1517] Step 3:

[1518] The server analyzes the image data sent and identifies the item. The input is the image data received by the server, and the output is identification information that the item is an apple. Specifically, the server uses an image recognition algorithm (TensorFlow or OpenCV) to analyze and identify the item in the image.

[1519] Step 4:

[1520] The server searches the database for origin and ingredient information about the identified item. The input is the identified item's information (apple), and the output is the item's origin information (e.g., origin in Central Asia) and ingredient information (e.g., vitamin C, dietary fiber). Specifically, the server queries the database system (e.g., MySQL or PostgreSQL) to retrieve the relevant information.

[1521] Step 5:

[1522] The device captures the user's facial expression using the front camera and sends it to the server. The input is image data of the user's facial expression, and the output is image data of the facial expression sent to the server. Specifically, the device sends the facial expression data to the server via the Internet as an HTTP request.

[1523] Step 6:

[1524] The server analyzes the facial expression data and recognizes the user's emotions. The input is the facial expression data received by the server, and the output is the recognized emotional information (for example, surprise). Specifically, the server uses an emotion engine (Emotion Recognition API) to analyze the facial expression data and identify the user's emotions.

[1525] Step 7:

[1526] The server integrates product information and the user's emotional information and sends it back to the terminal. The input is product information (origin information, ingredient information) and emotional information (surprise, etc.), and the output is the integrated information. Specifically, the server organizes the information into an appropriate format and sends it back to the terminal via the Internet.

[1527] Step 8:

[1528] The device analyzes the information it receives and displays it as augmented reality (AR). The input is the integrated information returned from the server, and the output is the AR content displayed on the screen. Specifically, the device visualizes the information using an AR library (e.g., ARKit or ARCore) and displays it overlaid on the user's screen.

[1529] For example, a user scans an apple with their smartphone camera, and the system acquires, processes, and displays the information. The prompt above, "Please capture an image of the apple with your smartphone camera. Then, the app will send it to the server to acquire the item information. If the user is surprised, the information will be highlighted in a pop-up format." is based on this sequence.

[1530] 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.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion 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.

[1535] 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.

[1536] 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).

[1537] 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.

[1538] 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."

[1539] 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.

[1540] 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).

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another 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.

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] The following is further disclosed regarding the above embodiment.

[1552] (Claim 1)

[1553] means for a user to scan a food item with an image capture device;

[1554] means for transmitting image data of food captured by the image capture device to a server;

[1555] A means for the server to analyze the transmitted image data and identify the food;

[1556] means for the server to retrieve origin information and nutritional information for the identified food from a database;

[1557] means for transmitting the retrieved information back to the image capture device;

[1558] means for displaying the information returned by the image capture device in augmented reality;

[1559] A system including:

[1560] (Claim 2)

[1561] 10. The system of claim 1, wherein the image capture device comprises a camera device.

[1562] (Claim 3)

[1563] 10. The system of claim 1, wherein the server utilizes an image recognition algorithm to identify the food item.

[1564] "Example 1"

[1565] (Claim 1)

[1566] means for a user to scan an object with an image capture device;

[1567] means for transmitting image data of the object acquired by the image acquisition device to a processing device;

[1568] means for the processing device to analyze the transmitted image data and identify the object;

[1569] means for the processing device to retrieve detailed information about the identified object from a database;

[1570] means for transmitting the retrieved information back to the image capture device;

[1571] means for presenting the information returned by the image capture device in an augmented display;

[1572] A system including:

[1573] (Claim 2)

[1574] 10. The system of claim 1, wherein the image capture device comprises an imaging device.

[1575] (Claim 3)

[1576] 10. The system of claim 1, wherein the processing unit utilizes an image analysis algorithm to identify the object.

[1577] "Application Example 1"

[1578] (Claim 1)

[1579] means for a user to scan a food item with an image capture device;

[1580] means for transmitting image data of food captured by the image capture device to a server;

[1581] A means for the server to analyze the transmitted image data and identify the food;

[1582] means for the server to retrieve origin information and nutritional information for the identified food from a database;

[1583] means for transmitting the retrieved information back to the image capture device;

[1584] means for displaying the information returned by the image capture device in augmented reality;

[1585] a means for scanning products in a physical store using smart glasses;

[1586] a means for presenting real-time food information displayed on the smart glasses;

[1587] A system including:

[1588] (Claim 2)

[1589] 10. The system of claim 1, wherein the image capture device comprises a camera device.

[1590] (Claim 3)

[1591] 10. The system of claim 1, wherein the server utilizes an image recognition algorithm to identify the food item.

[1592] "Example 2: Combining Emotion Engines"

[1593] (Claim 1)

[1594] means for a user to scan a food item with an image capture device;

[1595] means for transmitting image data of food captured by the image capture device to a server;

[1596] A means for the server to analyze the transmitted image data and identify the food;

[1597] means for the server to retrieve origin information and nutritional information for the identified food from a database;

[1598] means for transmitting the retrieved information back to the image capture device;

[1599] means for displaying the information returned by the image capture device in augmented reality;

[1600] A means for the emotion engine to analyze the user's facial expression data and recognize emotions;

[1601] A means for dynamically changing information displayed in augmented reality according to emotions;

[1602] A system including:

[1603] (Claim 2)

[1604] 10. The system of claim 1, wherein the image capture device comprises a camera device.

[1605] (Claim 3)

[1606] 10. The system of claim 1, wherein the server utilizes an image recognition algorithm to identify the food item.

[1607] "Application example 2 when combining emotion engines"

[1608] (Claim 1)

[1609] means for a user to scan an item with an image capture device;

[1610] means for transmitting image data of an article acquired by the image acquisition device to a server;

[1611] A means for the server to analyze the transmitted image data and identify the item;

[1612] means for the server to retrieve origin and ingredient information for the identified item from a database;

[1613] means for transmitting the retrieved information back to the image capture device;

[1614] means for displaying the information returned by the image capture device in augmented reality;

[1615] a means for the emotion engine to analyze the user's facial expression and adjust the augmented reality display according to the emotion;

[1616] A system including:

[1617] (Claim 2)

[1618] 10. The system of claim 1, wherein the image capture device comprises a camera device.

[1619] (Claim 3)

[1620] 10. The system of claim 1, wherein the server utilizes an image recognition algorithm to identify the item. [Explanation of symbols]

[1621] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for a user to scan a food item with an image capture device; means for transmitting image data of food captured by the image capture device to a server; A means for the server to analyze the transmitted image data and identify the food; means for the server to retrieve origin information and nutritional information for the identified food from a database; means for transmitting the retrieved information back to the image capture device; means for displaying the information returned by the image capture device in augmented reality; A system including:

2. The system of claim 1 , wherein the image capture device comprises a camera device.

3. The system of claim 1 , wherein the server utilizes an image recognition algorithm to identify the food item.

Images