System

Abstract

A system is provided.SOLUTION: The system includes a means for photographing a food, a means for storing the photographed image, a means including an artificial intelligence model for analyzing the stored image, a means for proposing a processing method of the food based on an analysis result, and a means for displaying the proposed processing method.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional food waste disposal systems do not properly reuse discarded food, resulting in food loss. Furthermore, it is difficult for users to easily determine the optimal disposal method based on the condition and type of discarded food. This has led to issues such as increased environmental impact and costs associated with food waste disposal. Therefore, there is a need for a system that promotes food reuse and minimizes waste. [Means for solving the problem]

[0005] The system of the present invention has a function of analyzing images of food taken by a user and proposing an optimal disposal method. Specifically, it includes a means for photographing food, a means for saving the photographed image, a means for providing an artificial intelligence model for analyzing the saved image, a means for proposing a food disposal method based on the analysis results, and a means for displaying the proposed disposal method. It also provides a function for uploading the photographed image to a server, analyzing the image on the server, generating a proposal based on the analysis results, and transmitting this to a terminal. Furthermore, by providing a means for proposing whether the food should be delivered, resold, or discarded based on the analysis results, it is possible to optimize food waste disposal and reduce food loss.

[0006] The "means for photographing food" refers to a device such as a camera or smartphone that allows a user to take an image of food to be discarded.

[0007] The "means for saving the photographed images" refers to a storage device such as a memory or storage for saving the photographed images of food.

[0008] The "artificial intelligence model that analyzes stored images" is a model based on machine learning or deep learning that analyzes the type and condition of stored food based on image data of the food.

[0009] The "means for proposing a method of food disposal based on the analysis results" refers to algorithms and logic for proposing whether the food should be reused, delivered, resold, or disposed of based on data obtained from the results of image analysis.

[0010] The "means for displaying the proposed processing method" refers to a monitor, display, or user interface for displaying to the user the proposed processing method based on the analysis results.

[0011] The "means for uploading photographed images to a server" refers to a communication module and protocol for transmitting images of food photographed by a terminal to a server via a network.

[0012] The "means for analyzing images on the server" refers to software and hardware for analyzing image data received on the server.

[0013] The "means for generating proposal content based on the analysis results" refers to an algorithm and program for determining an appropriate processing method from the results of image analysis and generating that content.

[0014] The "means for transmitting the generated proposal content to the terminal" refers to a communication module and protocol for transmitting the generated proposal content from the server to the user's terminal.

[0015] "Means for suggesting whether food should be delivered, resold, or disposed of" refers to logic and algorithms that determine whether food can be reused based on the analysis results and suggest the appropriate delivery destination, resale method, or disposal method. [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] The present invention is a system that allows a user to take a photo of food to be discarded and analyzes the image to suggest a disposal method for the food, either by recycling, delivering, reselling, or discarding it. Specific embodiments for carrying out the present invention are described below.

[0038] 1. The user takes a photo of the food

[0039] Users take photos of food items to be discarded using a device with a camera function, such as a smartphone or tablet. The device has a photo button, and users can take photos by pressing the button.

[0040] 2. The device uploads the image to the server.

[0041] Images taken by the user are temporarily stored in the device's local storage and then uploaded to a server via an internet connection.

[0042] 3. The server analyzes the image

[0043] The server receives and stores the uploaded images, then uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to analyze the images and determine the type and condition of the food in the image.

[0044] 4. The server generates an appropriate proposal

[0045] Based on the results of the image analysis, the server decides the best way to dispose of the item: for example, it suggests "delivery to a local grocery store" for fresh fruits and vegetables, "processing for resale" for bread and prepared foods, or "disposal" if the item is in poor condition.

[0046] 5. The server sends the proposal to the device

[0047] The server sends the generated proposal to the terminal in a data format such as JSON, which includes specific processing methods and procedures.

[0048] 6. The device displays the suggestions to the user.

[0049] The device displays the received suggestions to the user, either as text or images on the device screen, and the user can select the suggested processing method and proceed to the next action.

[0050] Explanation with a concrete example

[0051] Example 1: Fully ripe bananas

[0052] 1. User: Take a photo of a ripe banana with your smartphone.

[0053] 2. Device: Upload the photos you took to the server.

[0054] 3. Server: Uses an image analysis model to determine that it is a banana.

[0055] 4. Server: Generates "Delivery to local grocery store" as a suggestion.

[0056] 5. Server: Sends the proposal to the device.

[0057] 6. Terminal: Show the user "Delivery to local grocery store" and let them choose their next action.

[0058] Example 2: Stale bread

[0059] 1. User: Takes a photo of the hardened bread with a tablet.

[0060] 2. Device: Upload the photos you took to the server.

[0061] 3. Server: Performs image analysis and determines that it is bread.

[0062] 4. Server: Generates a proposal for "processing for resale."

[0063] 5. Server: Sends the proposal to the device.

[0064] 6. Terminal: Display "Process for Resale" to the user and ask them to choose their next action.

[0065] In this way, this system is designed to allow users to easily optimize food waste disposal. By smoothly performing analysis and making recommendations, it is expected to reduce food waste and alleviate the burden on the environment.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The user launches the device's camera application and presses the capture button to capture an image of the food.

[0069] Step 2:

[0070] The device temporarily saves the captured image to local storage, ensuring that the image data is available on the device and ready for the next processing step.

[0071] Step 3:

[0072] After checking the image, the user presses the upload button, which causes the device to start the process of sending the image data to the server.

[0073] Step 4:

[0074] The device uploads the captured image to the server via an internet connection, where it uses an HTTP request to send the image data to a specified endpoint on the server.

[0075] Step 5:

[0076] The server receives the uploaded image data and saves the images. The server saves the received images in temporary storage or a database, ensuring the data necessary for analysis processing.

[0077] Step 6:

[0078] The server analyzes the stored images and uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to determine the type and condition of the food in the image. The analysis results are output as information such as the type of food and its freshness.

[0079] Step 7:

[0080] Based on the analysis, the server will suggest how to dispose of the food, for example, "delivery to a local grocery store" if the food is fresh, "processing for resale" if it is slightly spoiled, or "discard" if it needs to be discarded completely.

[0081] Step 8:

[0082] The server sends the generated suggestions to the device in JSON format, and the analysis results and suggestions are returned to the user's device via network communication.

[0083] Step 9:

[0084] The terminal displays the received proposal on a user interface, where the user can review the proposed disposal method and select the next action (e.g., arranging for delivery or resale, or disposal).

[0085] Step 10:

[0086] The user selects the next action based on the suggestions, and the selection is sent via the terminal to the server, which processes the information to perform the necessary procedures based on the user's selection.

[0087] The above is the specific processing flow of the system, which allows users to easily and efficiently determine the optimal processing method for food.

[0088] Example 1

[0089] 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."

[0090] Reducing food waste is an important issue in terms of both environmental protection and economic effectiveness. However, food waste disposal at homes and stores is not currently optimized, resulting in many cases of usable food being wasted. Current systems require users to manually check the condition of food and select an appropriate disposal method, which is cumbersome and time-consuming. Therefore, there is a need for the development of a system that allows users to easily determine the condition of food and automatically suggests appropriate disposal methods.

[0091] 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.

[0092] In this invention, the server includes means for uploading images taken by a user to the server via the Internet, means for performing image analysis on the server to determine the type and condition of the food, means for determining the optimal processing method based on the determination results, and means for transmitting the determined processing method to the terminal in JSON format, thereby enabling the user to easily understand the condition of the food and quickly obtain the optimal processing method.

[0093] "Food" means any substance processed for human or animal consumption.

[0094] "Means for photographing" refers to a function for acquiring an image of an object using a device equipped with a camera function.

[0095] "Means for storing" refers to the function of temporarily or permanently storing captured images in a memory device within the device.

[0096] An "artificial intelligence model" is a system that learns from large amounts of data and has algorithms for pattern recognition and classification.

[0097] "Means for analyzing" refers to the function of using an artificial intelligence model to extract and interpret specific information from input data.

[0098] "Means of suggestion" refers to a function that shows users the optimal actions and options based on the analysis results.

[0099] "Means for displaying" refers to a function for visually conveying the proposed information to the user.

[0100] "Means for uploading via the Internet" refers to the ability to transfer image data to a remote server via a network connection.

[0101] A "server" is a computer system that manages large amounts of data and exchanges data with other devices via a network.

[0102] "Image analysis" is the process of identifying objects in a captured image and determining their type and condition.

[0103] The "JSON format" is a lightweight, easy-to-read text-based data format used for data exchange.

[0104] A "convolutional neural network (CNN) model" is an artificial intelligence model with deep learning algorithms that is primarily used for image analysis and has the ability to extract and identify features within images.

[0105] A "terminal" is a device operated by a user, and refers to a device equipped with a camera function, a storage function, a communication function, and a display function.

[0106] The present invention is a system that allows a user to take a photo of food to be discarded and analyzes the image to suggest a disposal method for the food, either by recycling, delivering, reselling, or discarding it. Specific embodiments for carrying out the present invention are described below.

[0107] This system is implemented using a device with a camera function, such as a smartphone or tablet, and a server connected via the Internet. The overall system flow is as follows:

[0108] The user uses the device to take a photo of the food to be discarded. The device has a photo button, and the user presses the button to take a photo. The captured image is temporarily saved in the device's local storage. It is then uploaded to a server via an Internet connection. When uploaded, metadata such as the user ID is also sent along with the image file.

[0109] The server then receives the uploaded images and stores them on disk storage. The server then analyzes the images using a pre-trained image analysis AI model (convolutional neural network, CNN). This analysis determines the type and condition of the food. For example, it can identify a ripe banana or stale bread. A rules engine or machine learning model runs within the server to determine the optimal processing method based on the analysis results.

[0110] Based on the results of the assessment, the server suggests the appropriate disposal method. For example, for fresh fruits and vegetables, it suggests "delivery to a local grocery store," while for bread and prepared foods, it suggests "processing for resale." If the item is in poor condition, it suggests "disposal." These suggestions are converted into a data format such as JSON and sent to the device.

[0111] The device displays the received suggestions to the user, who can then select from the suggestions displayed on the screen and proceed to the next action. The displayed information is provided in text and image format.

[0112] Examples:

[0113] Example 1: Fully ripe bananas

[0114] 1. User: Take a photo of a ripe banana with your smartphone.

[0115] 2. Device: 1. Save the captured photo to local storage and upload it to the server.

[0116] 3. Server: Receives and stores the uploaded images. It uses a trained CNN model to recognize bananas and determine whether they are overripe.

[0117] 4. Server: Generate "Delivery to local grocery store" as a suggestion.

[0118] 5. Server: Convert the proposal into JSON format and send it to the device.

[0119] 6. Terminal: "Delivery to local grocery store" is displayed as a suggestion to the user.

[0120] Example 2: Stale bread

[0121] 1. User: Takes a photo of the hardened bread with a tablet.

[0122] 2. Device: Save the captured photo to local storage and upload it to the server.

[0123] 3. Server: Receives and stores the uploaded images. It uses a trained CNN model to recognize the bread and determine whether it is cured.

[0124] 4. Server: Generates "Processing for resale" as a proposal.

[0125] 5. Server: Convert the proposal into JSON format and send it to the device.

[0126] 6. Terminal: Display "Processing for resale" as a suggestion to the user.

[0127] These steps allow users to easily understand how to process food and reduce food waste. The use of artificial intelligence models improves the accuracy of image analysis and the appropriateness of the suggested processing methods.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1: User takes a photo of food

[0130] Users use a smartphone or tablet to take a photo of the food to be discarded. The input is to launch the camera app, position the food in the center of the screen, and press the capture button. The output is that the captured image is temporarily saved in the device's local storage.

[0131] As a specific example of how it works, a user takes a photo of a ripe banana using a camera app. The photo is saved as an image file, and the app is ready to move on to the next step.

[0132] Step 2: The device uploads the image to the server

[0133] The device uploads the saved image files to a server via the Internet. The input is the image file on the local storage, and the output is the image file and associated metadata sent to the server.

[0134] As a specific example of how it works, the device sends a saved photo of a banana to the server via Wi-Fi, along with metadata such as the user ID and the date and time the photo was taken.

[0135] Step 3: The server receives and stores the image

[0136] The server receives image files and metadata uploaded from the terminal and stores them in disk storage. The input is the image file and metadata sent from the terminal, and the output is the stored image and metadata.

[0137] In a specific example, the server stores the image file of the banana and associated metadata on disk storage for subsequent analysis.

[0138] Step 4: The server performs image analysis

[0139] The server inputs the saved image files into a pre-trained convolutional neural network (CNN) model to analyze the food type and state. The input is the saved image file, and the output is the food type and its state information.

[0140] As a specific example, a Python script on the server runs a CNN model to identify the type of banana and its ripeness from the image data, and as a result, determines that the banana is fully ripe.

[0141] Step 5: Server generates proposal

[0142] The server generates recommendations based on the results of image analysis. The input is the type and condition of the analyzed food, and the output is a recommendation on the optimal disposal method (e.g., delivery, resale, disposal).

[0143] For example, if the banana is overripe, the server may decide to offer "delivery to a local grocery store." The optimal offer is generated by a rules engine or machine learning model.

[0144] Step 6: The server sends the proposal to the device

[0145] The server converts the generated proposal content into JSON format and sends it to the terminal. The input is the generated proposal content, and the output is JSON format data.

[0146] As a specific example of how it works, the server generates a JSON file containing the offer "Delivery to a local grocery store" and sends it to the device via the Internet.

[0147] Step 7: The device displays the suggestions to the user

[0148] The device parses the received JSON formatted suggestions and displays them to the user. The input is the JSON data received from the server, and the output is text or image information visually presented to the user.

[0149] As a specific example of operation, the terminal displays a "Delivery to local grocery store" offer on the screen in text and images, providing the user with options to select.

[0150] (Application example 1)

[0151] 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."

[0152] Food waste has become a major problem in modern society. A large amount of food is discarded at home and in logistics centers, which increases the burden on the environment. To solve this problem, a system is needed that can quickly and accurately suggest the optimal disposal method for food that is scheduled to be discarded, whether it be for reuse, delivery, or proper disposal. However, current systems have the problem of being unable to accurately determine the condition of food and make appropriate suggestions based on that information.

[0153] 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.

[0154] In this invention, the server includes a means for uploading the captured image to the server, a means for performing image analysis on the server and generating a proposal based on the analysis results, and a means for transmitting the generated proposal to the terminal, thereby enabling the state of the food to be accurately analyzed and the optimal processing method to be proposed based on the analysis.

[0155] "Food" means any substance consumed by humans as food or as a foodstuff.

[0156] "Means for taking pictures" refers to a method or device for capturing an image using an imaging device such as a camera.

[0157] "Means for storing images" refers to a method or device for storing captured images in local storage or cloud storage.

[0158] An "artificial intelligence model" is a collection of machine learning algorithms trained to perform a specific task, such as a convolutional neural network (CNN) for image analysis.

[0159] "Proposal means" refers to a method or device that shows the user the optimal processing method based on the analysis results.

[0160] The "display means" refers to a display device such as a display or mobile terminal that visually notifies the user of the proposed processing method.

[0161] "Means for uploading to a server via communication" refers to a method or device for transmitting data to a server via the Internet or other communication network.

[0162] The "means for generating analysis results and proposals" refers to a method or device that analyzes images on a server and generates an appropriate processing method based on the results.

[0163] The "means for transmitting to the terminal" refers to a communication means for transmitting the generated proposal content to the user's terminal.

[0164] "Recycling" refers to the process of making processed food usable again and providing it for reuse.

[0165] "Delivery" means the means by which food is transported to a specified location based on a specific processing method.

[0166] "Disposal" refers to the proper disposal of processed food when it is not suitable for reuse or distribution.

[0167] "Means for displaying to the user and allowing the user to select the next action to be taken" refers to a method or device that visually presents the proposed processing method to the user and allows the user to select the next measure based on that.

[0168] These definitions provide a clear understanding of the specific meaning of the terms set forth in the claims.

[0169] The embodiment of the present invention will now be described in detail.

[0170] The present invention is a system that allows a user to photograph food to be discarded and analyzes the image to suggest a disposal method for the food, either by reusing, delivering, reselling, or discarding it.

[0171] System Configuration

[0172] 1. User operation

[0173] Users take photos of food items to be discarded using a device with a camera function, such as a smartphone or tablet. The device has a photo button, and the user presses this button to take a photo.

[0174] 2. Save and upload images

[0175] The captured images are temporarily stored in the device's local storage, and then uploaded to a server via an Internet connection, for example via the HTTP protocol.

[0176] 3. Server Analysis

[0177] The server receives and stores the uploaded images, then analyzes them using pre-trained generative AI models, specifically convolutional neural networks (CNNs), to determine the type and condition of the food in the image.

[0178] 4. Processing method proposal

[0179] The server then uses the results of image analysis to determine the best way to dispose of the food depending on its condition. For example, it suggests "delivery to a local grocery store" for fresh fruits and vegetables, "processing for resale" for bread and prepared foods, and "disposal" if the food is in poor condition.

[0180] 5. Communication and Display of Proposal Content

[0181] The server sends the generated proposals in a data format such as JSON to the device. In response, the device displays the received proposals to the user. The proposals are displayed on the device screen as text or images, and the user can select the proposed processing method and proceed to the next action.

[0182] Hardware and software used

[0183] A smartphone or tablet with a camera, which the user uses to take photos of the food.

[0184] Internet connection: Used to upload image data to the server and receive analysis results.

[0185] Server: Receives image data and analyzes it using a generative AI model (e.g., a convolutional neural network).

[0186] Generative AI models: They use pre-trained machine learning algorithms, particularly CNNs, to analyze images.

[0187] Specific examples

[0188] Example 1: Fully ripe bananas

[0189] 1. User: Take a photo of a ripe banana with your smartphone.

[0190] 2. Device: Upload the photos you took to the server.

[0191] 3. Server: Uses a convolutional neural network (CNN) to determine that it is a banana.

[0192] 4. Server: Generates "Delivery to local grocery store" as a suggestion.

[0193] 5. Server: Sends the proposal to the device.

[0194] 6. Terminal: Show the user "Delivery to local grocery store" and let them choose their next action.

[0195] Example 2: Stale bread

[0196] 1. User: Takes a photo of the hardened bread with a tablet.

[0197] 2. Device: Upload the photos you took to the server.

[0198] 3. Server: Uses a convolutional neural network (CNN) to identify the object as bread.

[0199] 4. Server: Generates a proposal for "processing for resale."

[0200] 5. Server: Sends the proposal to the device.

[0201] 6. Terminal: Display "Process for Resale" to the user and ask them to choose their next action.

[0202] Prompt Sentence Examples

[0203] Analyze food images to determine the type and condition of the food.

[0204] Food: Bananas

[0205] Image URL: <URL of image>

[0206] Output formats:

[0207] Type: <Food type>

[0208] Condition: <Food condition>

[0209] Proposal: <Proposed solution>

[0210] In this way, the present invention is designed to enable users to easily optimize food waste disposal. By smoothly carrying out the analysis and proposal process, it is expected to reduce food waste and alleviate the burden on the environment.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] The user uses the camera function of their smartphone or tablet to take a photo of the food to be discarded. The photo is taken by pressing the camera button on the device. The input is the image of the food taken by the user. The output is an image file saved in local storage.

[0214] Step 2:

[0215] The device uploads the captured image to the server via an Internet connection. Specifically, it sends the image file to the server using an HTTP POST request. The input is the image file stored in local storage, and the output is the image data stored on the server side.

[0216] Step 3:

[0217] The server receives the uploaded images and stores them (temporarily stores them in a database, etc., as an initial process). The input is the image data sent from the terminal, and the output is the image data stored on the server.

[0218] Step 4:

[0219] The server begins image analysis using the stored image data. Specifically, it uses a pre-trained generative AI model (e.g., a convolutional neural network (CNN)) to determine the type and condition of food in the image. The input is the image data stored on the server, and the output is the analysis results regarding the type and condition of the food.

[0220] Step 5:

[0221] The server then decides the best way to dispose of the item based on the analysis results. For example, for fresh fruits and vegetables, it might suggest "delivery to a local grocery store," for bread or prepared foods, "processing for resale," or "disposal" if the item is in poor condition. The input is the image analysis results, and the output is data (e.g., JSON format) containing the suggestions.

[0222] Step 6:

[0223] The server sends the generated proposal to the device. Specifically, it formats the data in JSON format or similar and sends it to the device again via an HTTP request over an internet connection. The input is the data containing the proposal, and the output is the data sent to the device.

[0224] Step 7:

[0225] The terminal displays the received proposal to the user. The proposal is displayed on the terminal screen as text or an image, and the user confirms the proposed processing method and selects the next action. The input is data containing the proposal sent from the server, and the output is the proposal displayed to the user.

[0226] In this way, through the specific actions at each step, food to be discarded can be analyzed and appropriate disposal methods can be proposed.

[0227] 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.

[0228] The present invention combines a system that allows a user to take a photo of food to be discarded, analyzes the image, and suggests the optimal disposal method, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0229] Basic configuration

[0230] The system includes a means for photographing food, a means for saving the photographed images, a means for providing an artificial intelligence model for analyzing the saved images, a means for proposing food processing methods based on the analysis results, a means for displaying the proposed processing methods, and an emotion engine for recognizing the user's emotions, thereby enabling appropriate suggestions to be made taking into account the user's emotional state.

[0231] 1. The user takes a photo of the food

[0232] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The device has a camera application installed, and the user captures the image by pressing the capture button. The system also analyzes the user's facial expressions and voice and uses an emotion engine to recognize their emotional state.

[0233] 2. The device uploads the image to the server.

[0234] After the user confirms the image, they press the upload button, which causes the device to send the food image data and the recognized emotion data to the server. The upload is done via an internet connection, using an HTTP request to send the data.

[0235] 3. The server analyzes the image

[0236] The server receives and stores the uploaded image data and emotion data. The server then analyzes the stored images using an artificial intelligence model to determine the type and condition of the food. The analysis results are output as information such as the type and freshness of the food.

[0237] 4. The server generates an appropriate proposal

[0238] Based on the analysis, the server suggests how to dispose of the food: "Delivery to a local grocery store" if the food is fresh, "Process for resale" if it's slightly spoiled, or "Discard" if it needs to be discarded completely. It also adjusts its suggestions based on the user's perceived emotions. For example, if the user is feeling stressed, it might make more concise suggestions.

[0239] 5. The server sends the proposal to the device

[0240] The server sends the generated suggestions in JSON format to the device. The suggestions include suggestions based on the analysis results and emotion recognition results. This allows appropriate notifications and alerts to be displayed taking into account the user's emotional state.

[0241] 6. The device displays the suggestions to the user.

[0242] The device displays the received suggestions on the user interface. The suggestions are displayed as text and images on the device screen, allowing the user to confirm the suggested handling method and select the next action (e.g., arranging delivery or resale, or disposal). In addition, customized notifications and alerts are displayed according to the user's emotional state, improving the user experience.

[0243] Explanation with a concrete example

[0244] Example 1: Fully ripe bananas

[0245] 1. User: Take a photo of a ripe banana with your smartphone. At the same time, the emotion engine recognizes the user's slightly anxious facial expression.

[0246] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[0247] 3. Server: Analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[0248] 4. Server: Sends the proposal to the device.

[0249] 5. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[0250] Example 2: Stale bread

[0251] 1. User: Take a photo of the hardened bread with a tablet. At the same time, the emotion engine recognizes the user's relaxed facial expression.

[0252] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[0253] 3. Server: Analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing for resale."

[0254] 4. Server: Sends the proposal to the device.

[0255] 5. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[0256] In this way, this system, which combines an emotion engine, proposes the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The user then launches a camera application and presses the capture button to capture the image of the food. At the same time, the device uses the built-in camera and microphone to detect the user's facial expressions and voice, and the emotion engine recognizes the user's emotional state.

[0260] Step 2:

[0261] The device temporarily stores the captured image of the food in local storage, along with the user's emotion data recognized by the emotion engine.

[0262] Step 3:

[0263] After checking the image, the user presses the upload button, which causes the device to send the food image data and emotion data to the server.

[0264] Step 4:

[0265] The device uploads food image data and emotion data to the server via an internet connection, using an HTTP POST request to send the data to a specified endpoint on the server.

[0266] Step 5:

[0267] The server receives the uploaded image data and emotion data, and stores the received images in temporary storage or a database, along with the emotion data.

[0268] Step 6:

[0269] The server analyzes the stored image data and uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to identify the type and condition of the food in the image. The analysis results are output as the food type and freshness.

[0270] Step 7:

[0271] The server analyzes the stored emotional data, and the emotion engine analyzes the emotional information recognized from the user's facial expressions and voice to determine the user's emotional state.

[0272] Step 8:

[0273] The server then suggests how to dispose of the food based on the results of image and emotion analysis. If the food is fresh, it suggests "delivery to a local grocery store." If it's slightly damaged, it suggests "processing for resale." If it needs to be completely discarded, it suggests "discarding." The server also adjusts the suggestions and their explanations based on the user's emotional state. For example, if the user is stressed, it makes simple, easy-to-understand suggestions, while if they're relaxed, it provides detailed information.

[0274] Step 9:

[0275] The server generates suggestions and sends them to the device in JSON format, including analysis results, suggestions, and customization information based on emotion recognition results.

[0276] Step 10:

[0277] The device displays the received suggestions on a user interface, allowing the user to review the suggested handling method and select the next action (e.g., arrange for delivery or resale, or dispose of). It also displays customized notifications and alerts for the suggested actions based on the user's emotional state.

[0278] The above is a specific processing flow of the present invention, and by combining it with an emotion engine, it is possible to propose the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[0279] Example 2

[0280] 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."

[0281] Food waste has become a serious problem in modern society. However, it is difficult for users to determine how to dispose of food that is scheduled for disposal. The psychological stress of discarding food is also a significant issue. In particular, disposal methods proposed without considering the user's emotional state decrease user satisfaction and weaken motivation to take appropriate action. Therefore, a system is needed that allows users to easily find the optimal disposal method for food that is scheduled for disposal and that also makes appropriate suggestions taking into account the user's emotional state.

[0282] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading captured images and emotion data to the server, means for analyzing the images and emotion data on the server, and means for generating suggestions based on the analysis results. This allows the user to easily know the optimal method for disposing of food that is scheduled to be discarded, and makes it possible to make appropriate suggestions that take into account the user's emotional state.

[0283] "Means for photographing food" refers to hardware and software that allows users to use devices such as smartphones and tablets to capture images of food to be discarded.

[0284] "Means for storing captured images" refers to means for storing captured images of food in the device's internal storage or cloud storage.

[0285] "Means comprising an artificial intelligence model" refers to means comprising a machine learning model or algorithm for analyzing stored images.

[0286] "Means for suggesting food disposal methods" refers to a system for suggesting food distribution, resale, or disposal based on analyzed data.

[0287] "Means for recognizing the user's emotions" refers to emotion recognition engines and algorithms that analyze the user's facial expressions, voice, etc. and determine their emotional state.

[0288] The "means for displaying a proposed processing method" refers to a user interface for visually displaying an appropriate food processing method to the user on the screen of the terminal.

[0289] "Means for uploading captured images and emotion data to a server" refers to a communication means for transmitting this data to a remote server via the Internet.

[0290] "Means for analyzing images and emotional data on a server" refers to software and hardware for analyzing image data and emotional data stored on a server and processing the information.

[0291] "Means for generating proposals based on analysis results" refers to a mechanism for automatically generating optimal proposals using a generative AI model based on the analysis results of image and emotion data.

[0292] The "means for transmitting the generated proposal content to the terminal" refers to a communication means for transmitting the proposal content generated by the server to the user's terminal.

[0293] "Means for suggesting whether to deliver, resell, or dispose of food based on the results of image and emotion data analysis" refers to a system that uses artificial intelligence models and emotion recognition results to determine the optimal way to dispose of food.

[0294] The present invention combines a system that allows a user to take a photo of food to be discarded, analyzes the image, and suggests the optimal disposal method, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0295] Basic configuration

[0296] The system includes the following hardware and software:

[0297] 1. Devices such as smartphones and tablets (e.g., iPhone (registered trademark), iPad (registered trademark))

[0298] 2. Camera applications (e.g., iOS camera app, Google Camera)

[0299] 3. Emotion engine (e.g., Microsoft® Azure® Emotion API, IBM Watson®)

[0300] 4. Internet connection

[0301] 5. Server (e.g., Amazon EC2, Google Compute Engine)

[0302] 6. Cloud storage (e.g., Google Cloud Storage, Amazon S3)

[0303] 7. Artificial intelligence models (e.g., image recognition models using TENSORFLOW (registered trademark) and PyTorch)

[0304] 8. Generative AI models (e.g., GPT-4 (registered trademark))

[0305] Specific actions

[0306] 1. The user takes a photo of the food

[0307] A user uses the camera application on their smartphone or tablet to take a photo of food to be discarded. For example, say they use the iPhone camera app to take a photo of a ripe banana. When they press the capture button, the camera sensor captures the image and saves it in the device's storage. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[0308] 2. The device uploads the image to the server.

[0309] After the user checks the captured image and emotion data, they press the upload button, and the device sends this data to a server via an HTTP POST request. For example, the device uploads the image file and emotion data (in JSON format) to Google Cloud Storage.

[0310] 3. The server analyzes the image

[0311] The server receives the uploaded image data and emotion data and stores them in storage. The server then analyzes the stored images using an artificial intelligence model to determine the type and condition of the food. For example, TensorFlow can be used to determine whether a banana is ripe.

[0312] 4. The server generates an appropriate proposal

[0313] The server combines image recognition results with emotion data and uses a generative AI model to suggest the optimal food processing method. For example, if the user is in a hurry, it suggests "immediate delivery to a local grocery store." An example of a prompt sentence is, "A photo of a ripe banana has been uploaded. Please suggest an appropriate action, taking into account that the user is a little impatient."

[0314] 5. The server sends the proposal to the device

[0315] The server then sends the generated suggestions in JSON format to the device, which include specific advice based on the image analysis and emotion recognition results.

[0316] 6. The device displays the suggestions to the user.

[0317] The device displays the received suggestions in the user interface. For example, an iPad screen might display "Delivery to a local grocery store immediately" and provide a notification tailored to the user's emotions. The user can review the suggestions and select the next action (delivery, resale, disposal, etc.).

[0318] Explanation with a concrete example

[0319] Example 1: Fully ripe bananas

[0320] 1. The user takes a photo of a ripe banana with their smartphone. At the same time, the emotion engine recognizes the user's slightly anxious facial expression.

[0321] 2. The device uploads the captured photo and the recognized emotion data to the server.

[0322] 3. The server analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[0323] 4. The server sends the proposal to the device.

[0324] 5. The device will display "Delivery to your local grocery store soon" to the user and also add a calming message to the anxious user.

[0325] Example 2: Stale bread

[0326] 1. The user takes a photo of the hardened bread with a tablet. At the same time, the emotion engine recognizes the user's relaxed facial expression.

[0327] 2. The device uploads the captured photo and the recognized emotion data to the server.

[0328] 3. The server analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing it for resale."

[0329] 4. The server sends the proposal to the device.

[0330] 5. The device will display "processing for resale" to the user, along with detailed instructions on how to achieve a relaxed state.

[0331] In this way, this system, which combines an emotion engine, proposes the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[0332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0333] Step 1:

[0334] The user takes a photo of the food. Using the camera application on their smartphone or tablet, the user takes a photo of the food to be discarded. At this time, the camera sensor captures the image in JPEG or PNG format and saves it in the device's storage. At the same time, the emotion engine installed on the device analyzes the user's facial expressions and voice in real time, generates emotion data, and saves it on the device. The input is the user's operation and the food being photographed, and the output is image data of the food and the user's emotion data.

[0335] Step 2:

[0336] The user uploads an image and emotion data to the server. When the user presses the upload button in the application, the device sends this data to the server via an Internet connection using an HTTP POST request. Specifically, the image file (JPEG or PNG) and emotion data (JSON format) are uploaded to cloud storage. The input is the captured image and emotion data, and the output is the image file and emotion data received by the server.

[0337] Step 3:

[0338] The server receives the image and emotion data and saves it in storage. The server receives the received image data and emotion data and saves them in a database. For example, Amazon RDS or Google Cloud SQL is used to persist the data. The input is the image file and emotion data uploaded from the device, and the output is the data saved in the server's storage.

[0339] Step 4:

[0340] The server analyzes the image data using an artificial intelligence model. Using the stored image data, the server uses an artificial intelligence model (e.g., TensorFlow or PyTorch) to determine the type and condition of the food. Specifically, the image is input into a machine learning model, which outputs the analyzed food type (e.g., banana, bread) and freshness (e.g., fresh, slightly spoiled, needs to be discarded). The input is the stored image data, and the output is the analysis results of the food type and condition.

[0341] Step 5:

[0342] The server generates appropriate suggestions that also take emotional data into consideration. By combining the image recognition results and emotional data, a prompt is input into a generative AI model (e.g., GPT-4) to generate an appropriate suggestion. For example, a prompt such as "A photo of a ripe banana has been uploaded. Please suggest an appropriate action, taking into consideration that the user may be a little impatient" is used. The input is the image analysis results and emotional data, and the output is the generated suggestion.

[0343] Step 6:

[0344] The server sends the generated proposal to the terminal. The server constructs the generated proposal in JSON format and sends it to the terminal as an HTTP response. The input is the generated proposal, and the output is the proposal data sent to the terminal.

[0345] Step 7:

[0346] The proposal received by the device is displayed on the user interface. The device parses the proposal received from the server and displays it on the user interface. Specifically, the proposal is displayed using text and images, and the user selects the next action (delivery, resale, disposal, etc.). An application using Swift (for iOS) or Kotlin (for ANDROID (registered trademark)) is used for display. The input is the proposal data received from the server, and the output is the proposal displayed on the user interface.

[0347] (Application example 2)

[0348] 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."

[0349] In modern society, food waste is a serious problem, and there is a need for methods to effectively utilize food that would otherwise be discarded. Furthermore, there is a lack of systems that can improve user satisfaction by providing appropriate suggestions and notifications based on the user's emotional state. Therefore, there is a need for a system that not only suggests ways to dispose of food that would otherwise be discarded, but also makes optimal suggestions that take the user's emotions into consideration.

[0350] 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 uploading captured images to the server, means for analyzing the images on the server, means for generating proposals based on the analysis results, means for transmitting the generated proposals to the terminal, means for uploading user emotion data to the server, and means for adjusting the proposals based on the emotion data. This makes it possible to suggest methods for appropriately disposing of food to be discarded, and to provide customized notifications and alerts according to the user's emotional state.

[0351] "Means for photographing food" refers to a function for photographing food that is scheduled to be discarded using a camera installed on a device such as a smartphone or tablet.

[0352] "Means for saving captured images" refers to a function for saving captured images of food within the device or in cloud storage.

[0353] "Means for having an artificial intelligence model that analyzes stored images" refers to an artificial intelligence model that is used to analyze stored image data and identify the type and condition of food.

[0354] The "means for proposing food disposal methods based on analysis results" is a function that shows users the optimal disposal method (delivery, resale, disposal, etc.) for food that is scheduled to be discarded based on the analysis results.

[0355] The "means for displaying a proposed processing method" is a function for displaying a processing method based on the analysis results on a user interface.

[0356] The "emotion engine that recognizes user emotions" is an artificial intelligence model that analyzes the user's facial expressions and voice to recognize their emotional state.

[0357] The "means for adjusting the content of the proposal" is a function for optimizing the content of the proposal according to the emotional state of the user and providing information in a form that is more easily accepted by the user.

[0358] The "means for uploading to a server" is a function for transmitting the captured image and emotion data to a server via the Internet.

[0359] "Means for analyzing images on the server" refers to a function for analyzing received image data within the server and determining the type and condition of food.

[0360] The "means for generating proposal content" is a function for automatically creating the optimal food processing method based on the analysis results of image and emotion data.

[0361] The "means for transmitting the generated proposal content to the terminal" is a function for transmitting the generated proposal content to the terminal and displaying it to the user.

[0362] The "means for adjusting the content of suggestions based on emotional data" is a function that adjusts the details of the suggestions based on the user's emotional data recognized by the server, and provides appropriate alerts and notifications through the user interface.

[0363] The present invention combines a system that photographs food to be disposed of, analyzes the image, and proposes the optimal disposal method with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0364] Basic configuration

[0365] The system includes a means for photographing food, a means for saving the photographed images, a means for providing an artificial intelligence model for analyzing the saved images, a means for proposing food processing methods based on the analysis results, a means for displaying the proposed processing methods, and an emotion engine for recognizing the user's emotions, thereby enabling appropriate suggestions to be made taking into account the user's emotional state.

[0366] Hardware and software used

[0367] Hardware:

[0368] Smartphones, tablets (camera, microphone)

[0369] Server (Cloud-based)

[0370] software:

[0371] Mobile Applications

[0372] Emotion Engine (Python, Keras)

[0373] Image analysis model (TensorFlow)

[0374] Database (PostgreSQL)

[0375] Communication protocol (HTTPS)

[0376] Operation Flow

[0377] 1. User takes a photo of food:

[0378] Users use their smartphones or tablets to take photos of food that is to be discarded, and the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[0379] 2. The device uploads the image and emotion data to the server:

[0380] The captured images and recognized emotion data are uploaded to a server via HTTPS.

[0381] 3. The server analyzes the image and emotions:

[0382] The server uses an artificial intelligence model (TensorFlow) to analyze images and determine the type and condition of food. An emotion engine (Keras model) analyzes the emotion data and recognizes the user's emotional state.

[0383] 4. The server generates an appropriate proposal:

[0384] Based on the analysis, it suggests how to dispose of the food. For example, it suggests "delivery to a local grocery store" for fresh food, and "processing for resale" for spoiled food. If the user is stressed, it makes a brief suggestion, but if they are relaxed, it adds a detailed explanation.

[0385] 5. The server sends the proposal to the device:

[0386] The generated suggestions are sent to the user's device in JSON format.

[0387] 6. The device displays the suggestions to the user:

[0388] Suggestions are displayed in the user interface as text and images, along with customized notifications and alerts based on your emotional state.

[0389] Specific examples

[0390] Example 1: For a ripe banana:

[0391] 1. User: Take a photo of a ripe banana, and the emotion engine recognizes that the user has a slightly anxious expression.

[0392] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[0393] 3. Server: Analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[0394] 4. Server: Sends the proposal to the device.

[0395] 5. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[0396] Example 2: For hardened bread:

[0397] 1. User: Take a photo of the hardened bread with a tablet, and the emotion engine recognizes the user's relaxed facial expression.

[0398] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[0399] 3. Server: Analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing for resale."

[0400] 4. Server: Sends the proposal to the device.

[0401] 5. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[0402] Prompt Sentence Examples

[0403] 1. For ripe bananas:

[0404] Prompt: "If a user photographs a ripe banana and feels rushed, what suggestions should the system make?"

[0405] 2. For hardened bread:

[0406] Prompt: "If a user photographs a hardened loaf of bread and feels relaxed, what suggestions would the system make?"

[0407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0408] Step 1:

[0409] A user takes a photo of food to be discarded using a smartphone or tablet. At this time, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. The input is the captured image and the user's facial and voice data, and the output is the captured image data and recognized emotion data.

[0410] Step 2:

[0411] The device uploads the captured image data and the recognized emotion data to the server. The input is the image data and emotion data, and the output is the data sent to the server. Communication is via HTTPS.

[0412] Step 3:

[0413] The server stores the received image data and analyzes the images. An artificial intelligence model (TensorFlow) is used for the analysis. The input is the image data stored on the server, and the output is the image analysis results (type and condition of food). The specific operation is image classification using the TensorFlow model.

[0414] Step 4:

[0415] The server analyzes the received emotion data and recognizes the user's emotional state. An emotion engine (Keras model) is used for the analysis. The input is the emotion data stored on the server, and the output is the emotion analysis result (user's emotional state).

[0416] Step 5:

[0417] The server proposes a food disposal method based on the results of image analysis and sentiment analysis. Specifically, it proposes delivery for fresh food, resale for slightly spoiled food, and disposal for food that needs to be completely discarded. The proposal is adjusted based on the results of sentiment analysis. The input is the results of image analysis and sentiment analysis, and the output is a proposal for the optimal disposal method.

[0418] Step 6:

[0419] The server generates a proposal and sends it to the terminal in JSON format. The input is the proposal for the optimal processing method, and the output is the JSON data sent to the terminal.

[0420] Step 7:

[0421] The device displays the received proposal on the user interface. The user confirms the proposed processing method and selects the next action. The input is the JSON data sent from the server, and the output is the proposal displayed to the user. The specific operation is to display text and images on the UI.

[0422] Specific examples

[0423] Example 1: Fully ripe bananas

[0424] 1. User: Takes a photo of a ripe banana, and the emotion engine recognizes the anxious expression.

[0425] 2. Device: Uploads the captured photo and emotion data to the server.

[0426] 3. Server: Analyzes the image and determines that it is a banana.

[0427] 4. Server: Analyzes emotion data and recognizes that the user is anxious.

[0428] 5. Server: Offer "immediate delivery to your local grocery store."

[0429] 6. Server: Sends the proposal to the device.

[0430] 7. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[0431] Example 2: Stale bread

[0432] 1. User: Takes a photo of hardened bread, and the emotion engine recognizes a relaxed facial expression.

[0433] 2. Device: Uploads the captured photo and emotion data to the server.

[0434] 3. Server: Analyzes the image and determines that it is bread.

[0435] 4. Server: Analyzes the emotional data and recognizes that the user is relaxed.

[0436] 5. Server: Proposes "processing for resale."

[0437] 6. Server: Sends the proposal to the device.

[0438] 7. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] [Second embodiment]

[0443] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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).

[0449] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] In the smart glasses 214, 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.

[0454] 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."

[0455] The present invention is a system that allows a user to take a photo of food to be discarded and analyzes the image to suggest a disposal method for the food, either by recycling, delivering, reselling, or discarding it. Specific embodiments for carrying out the present invention are described below.

[0456] 1. The user takes a photo of the food

[0457] Users take photos of food items to be discarded using a device with a camera function, such as a smartphone or tablet. The device has a photo button, and users can take photos by pressing the button.

[0458] 2. The device uploads the image to the server.

[0459] Images taken by the user are temporarily stored in the device's local storage and then uploaded to a server via an internet connection.

[0460] 3. The server analyzes the image

[0461] The server receives and stores the uploaded images, then uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to analyze the images and determine the type and condition of the food in the image.

[0462] 4. The server generates an appropriate proposal

[0463] Based on the results of the image analysis, the server decides the best way to dispose of the item: for example, it suggests "delivery to a local grocery store" for fresh fruits and vegetables, "processing for resale" for bread and prepared foods, or "disposal" if the item is in poor condition.

[0464] 5. The server sends the proposal to the device

[0465] The server sends the generated proposal to the terminal in a data format such as JSON, which includes specific processing methods and procedures.

[0466] 6. The device displays the suggestions to the user.

[0467] The device displays the received suggestions to the user, either as text or images on the device screen, and the user can select the suggested processing method and proceed to the next action.

[0468] Explanation with a concrete example

[0469] Example 1: Fully ripe bananas

[0470] 1. User: Take a photo of a ripe banana with your smartphone.

[0471] 2. Device: Upload the photos you took to the server.

[0472] 3. Server: Uses an image analysis model to determine that it is a banana.

[0473] 4. Server: Generates "Delivery to local grocery store" as a suggestion.

[0474] 5. Server: Sends the proposal to the device.

[0475] 6. Terminal: Show the user "Delivery to local grocery store" and let them choose their next action.

[0476] Example 2: Stale bread

[0477] 1. User: Takes a photo of the hardened bread with a tablet.

[0478] 2. Device: Upload the photos you took to the server.

[0479] 3. Server: Performs image analysis and determines that it is bread.

[0480] 4. Server: Generates a proposal for "processing for resale."

[0481] 5. Server: Sends the proposal to the device.

[0482] 6. Terminal: Display "Process for Resale" to the user and ask them to choose their next action.

[0483] In this way, this system is designed to allow users to easily optimize food waste disposal. By smoothly performing analysis and making recommendations, it is expected to reduce food waste and alleviate the burden on the environment.

[0484] The processing flow will be explained below.

[0485] Step 1:

[0486] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The user launches the device's camera application and presses the capture button to capture an image of the food.

[0487] Step 2:

[0488] The device temporarily saves the captured image to local storage, ensuring that the image data is available on the device and ready for the next processing step.

[0489] Step 3:

[0490] After checking the image, the user presses the upload button, which causes the device to start the process of sending the image data to the server.

[0491] Step 4:

[0492] The device uploads the captured image to the server via an internet connection, where it uses an HTTP request to send the image data to a specified endpoint on the server.

[0493] Step 5:

[0494] The server receives the uploaded image data and saves the images. The server saves the received images in temporary storage or a database, ensuring the data necessary for analysis processing.

[0495] Step 6:

[0496] The server analyzes the stored images and uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to determine the type and condition of the food in the image. The analysis results are output as information such as the type of food and its freshness.

[0497] Step 7:

[0498] Based on the analysis, the server will suggest how to dispose of the food, for example, "delivery to a local grocery store" if the food is fresh, "processing for resale" if it is slightly spoiled, or "discard" if it needs to be discarded completely.

[0499] Step 8:

[0500] The server sends the generated suggestions to the device in JSON format, and the analysis results and suggestions are returned to the user's device via network communication.

[0501] Step 9:

[0502] The terminal displays the received proposal on a user interface, where the user can review the proposed disposal method and select the next action (e.g., arranging for delivery or resale, or disposal).

[0503] Step 10:

[0504] The user selects the next action based on the suggestions, and the selection is sent via the terminal to the server, which processes the information to perform the necessary procedures based on the user's selection.

[0505] The above is the specific processing flow of the system, which allows users to easily and efficiently determine the optimal processing method for food.

[0506] Example 1

[0507] 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."

[0508] Reducing food waste is an important issue in terms of both environmental protection and economic effectiveness. However, food waste disposal at homes and stores is not currently optimized, resulting in many cases of usable food being wasted. Current systems require users to manually check the condition of food and select an appropriate disposal method, which is cumbersome and time-consuming. Therefore, there is a need for the development of a system that allows users to easily determine the condition of food and automatically suggests appropriate disposal methods.

[0509] 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.

[0510] In this invention, the server includes means for uploading images taken by a user to the server via the Internet, means for performing image analysis on the server to determine the type and condition of the food, means for determining the optimal processing method based on the determination results, and means for transmitting the determined processing method to the terminal in JSON format, thereby enabling the user to easily understand the condition of the food and quickly obtain the optimal processing method.

[0511] "Food" means any substance processed for human or animal consumption.

[0512] "Means for photographing" refers to a function for acquiring an image of an object using a device equipped with a camera function.

[0513] "Means for storing" refers to the function of temporarily or permanently storing captured images in a memory device within the device.

[0514] An "artificial intelligence model" is a system that learns from large amounts of data and has algorithms for pattern recognition and classification.

[0515] "Means for analyzing" refers to the function of using an artificial intelligence model to extract and interpret specific information from input data.

[0516] "Means of suggestion" refers to a function that shows users the optimal actions and options based on the analysis results.

[0517] "Means for displaying" refers to a function for visually conveying the proposed information to the user.

[0518] "Means for uploading via the Internet" refers to the ability to transfer image data to a remote server via a network connection.

[0519] A "server" is a computer system that manages large amounts of data and exchanges data with other devices via a network.

[0520] "Image analysis" is the process of identifying objects in a captured image and determining their type and condition.

[0521] The "JSON format" is a lightweight, easy-to-read text-based data format used for data exchange.

[0522] A "convolutional neural network (CNN) model" is an artificial intelligence model with deep learning algorithms that is primarily used for image analysis and has the ability to extract and identify features within images.

[0523] A "terminal" is a device operated by a user, and refers to a device equipped with a camera function, a storage function, a communication function, and a display function.

[0524] The present invention is a system that allows a user to take a photo of food to be discarded and analyzes the image to suggest a disposal method for the food, either by recycling, delivering, reselling, or discarding it. Specific embodiments for carrying out the present invention are described below.

[0525] This system is implemented using a device with a camera function, such as a smartphone or tablet, and a server connected via the Internet. The overall system flow is as follows:

[0526] The user uses the device to take a photo of the food to be discarded. The device has a photo button, and the user presses the button to take a photo. The captured image is temporarily saved in the device's local storage. It is then uploaded to a server via an Internet connection. When uploaded, metadata such as the user ID is also sent along with the image file.

[0527] The server then receives the uploaded images and stores them on disk storage. The server then analyzes the images using a pre-trained image analysis AI model (convolutional neural network, CNN). This analysis determines the type and condition of the food. For example, it can identify a ripe banana or stale bread. A rules engine or machine learning model runs within the server to determine the optimal processing method based on the analysis results.

[0528] Based on the results of the assessment, the server suggests the appropriate disposal method. For example, for fresh fruits and vegetables, it suggests "delivery to a local grocery store," while for bread and prepared foods, it suggests "processing for resale." If the item is in poor condition, it suggests "disposal." These suggestions are converted into a data format such as JSON and sent to the device.

[0529] The device displays the received suggestions to the user, who can then select from the suggestions displayed on the screen and proceed to the next action. The displayed information is provided in text and image format.

[0530] Examples:

[0531] Example 1: Fully ripe bananas

[0532] 1. User: Take a photo of a ripe banana with your smartphone.

[0533] 2. Device: 1. Save the captured photo to local storage and upload it to the server.

[0534] 3. Server: Receives and stores the uploaded images. It uses a trained CNN model to recognize bananas and determine whether they are overripe.

[0535] 4. Server: Generate "Delivery to local grocery store" as a suggestion.

[0536] 5. Server: Convert the proposal into JSON format and send it to the device.

[0537] 6. Terminal: "Delivery to local grocery store" is displayed as a suggestion to the user.

[0538] Example 2: Stale bread

[0539] 1. User: Takes a photo of the hardened bread with a tablet.

[0540] 2. Device: Save the captured photo to local storage and upload it to the server.

[0541] 3. Server: Receives and stores the uploaded images. It uses a trained CNN model to recognize the bread and determine whether it is cured.

[0542] 4. Server: Generates "Processing for resale" as a proposal.

[0543] 5. Server: Convert the proposal into JSON format and send it to the device.

[0544] 6. Terminal: Display "Processing for resale" as a suggestion to the user.

[0545] These steps allow users to easily understand how to process food and reduce food waste. The use of artificial intelligence models improves the accuracy of image analysis and the appropriateness of the suggested processing methods.

[0546] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0547] Step 1: User takes a photo of food

[0548] Users use a smartphone or tablet to take a photo of the food to be discarded. The input is to launch the camera app, position the food in the center of the screen, and press the capture button. The output is that the captured image is temporarily saved in the device's local storage.

[0549] As a specific example of how it works, a user takes a photo of a ripe banana using a camera app. The photo is saved as an image file, and the app is ready to move on to the next step.

[0550] Step 2: The device uploads the image to the server

[0551] The device uploads the saved image files to a server via the Internet. The input is the image file on the local storage, and the output is the image file and associated metadata sent to the server.

[0552] As a specific example of how it works, the device sends a saved photo of a banana to the server via Wi-Fi, along with metadata such as the user ID and the date and time the photo was taken.

[0553] Step 3: The server receives and stores the image

[0554] The server receives image files and metadata uploaded from the terminal and stores them in disk storage. The input is the image file and metadata sent from the terminal, and the output is the stored image and metadata.

[0555] In a specific example, the server stores the image file of the banana and associated metadata on disk storage for subsequent analysis.

[0556] Step 4: The server performs image analysis

[0557] The server inputs the saved image files into a pre-trained convolutional neural network (CNN) model to analyze the food type and state. The input is the saved image file, and the output is the food type and its state information.

[0558] As a specific example, a Python script on the server runs a CNN model to identify the type of banana and its ripeness from the image data, and as a result, determines that the banana is fully ripe.

[0559] Step 5: Server generates proposal

[0560] The server generates recommendations based on the results of image analysis. The input is the type and condition of the analyzed food, and the output is a recommendation on the optimal disposal method (e.g., delivery, resale, disposal).

[0561] For example, if the banana is overripe, the server may decide to offer "delivery to a local grocery store." The optimal offer is generated by a rules engine or machine learning model.

[0562] Step 6: The server sends the proposal to the device

[0563] The server converts the generated proposal content into JSON format and sends it to the terminal. The input is the generated proposal content, and the output is JSON format data.

[0564] As a specific example of how it works, the server generates a JSON file containing the offer "Delivery to a local grocery store" and sends it to the device via the Internet.

[0565] Step 7: The device displays the suggestions to the user

[0566] The device parses the received JSON formatted suggestions and displays them to the user. The input is the JSON data received from the server, and the output is text or image information visually presented to the user.

[0567] As a specific example of operation, the terminal displays a "Delivery to local grocery store" offer on the screen in text and images, providing the user with options to select.

[0568] (Application example 1)

[0569] 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."

[0570] Food waste has become a major problem in modern society. A large amount of food is discarded at home and in logistics centers, which increases the burden on the environment. To solve this problem, a system is needed that can quickly and accurately suggest the optimal disposal method for food that is scheduled to be discarded, whether it be for reuse, delivery, or proper disposal. However, current systems have the problem of being unable to accurately determine the condition of food and make appropriate suggestions based on that information.

[0571] 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.

[0572] In this invention, the server includes a means for uploading the captured image to the server, a means for performing image analysis on the server and generating a proposal based on the analysis results, and a means for transmitting the generated proposal to the terminal, thereby enabling the state of the food to be accurately analyzed and the optimal processing method to be proposed based on the analysis.

[0573] "Food" means any substance consumed by humans as food or as a foodstuff.

[0574] "Means for taking pictures" refers to a method or device for capturing an image using an imaging device such as a camera.

[0575] "Means for storing images" refers to a method or device for storing captured images in local storage or cloud storage.

[0576] An "artificial intelligence model" is a collection of machine learning algorithms trained to perform a specific task, such as a convolutional neural network (CNN) for image analysis.

[0577] "Proposal means" refers to a method or device that shows the user the optimal processing method based on the analysis results.

[0578] The "display means" refers to a display device such as a display or mobile terminal that visually notifies the user of the proposed processing method.

[0579] "Means for uploading to a server via communication" refers to a method or device for transmitting data to a server via the Internet or other communication network.

[0580] The "means for generating analysis results and proposals" refers to a method or device that analyzes images on a server and generates an appropriate processing method based on the results.

[0581] The "means for transmitting to the terminal" refers to a communication means for transmitting the generated proposal content to the user's terminal.

[0582] "Recycling" refers to the process of making processed food usable again and providing it for reuse.

[0583] "Delivery" means the means by which food is transported to a specified location based on a specific processing method.

[0584] "Disposal" refers to the proper disposal of processed food when it is not suitable for reuse or distribution.

[0585] "Means for displaying to the user and allowing the user to select the next action to be taken" refers to a method or device that visually presents the proposed processing method to the user and allows the user to select the next measure based on that.

[0586] These definitions provide a clear understanding of the specific meaning of the terms set forth in the claims.

[0587] The embodiment of the present invention will now be described in detail.

[0588] The present invention is a system that allows a user to photograph food to be discarded and analyzes the image to suggest a disposal method for the food, either by reusing, delivering, reselling, or discarding it.

[0589] System Configuration

[0590] 1. User operation

[0591] Users take photos of food items to be discarded using a device with a camera function, such as a smartphone or tablet. The device has a photo button, and the user presses this button to take a photo.

[0592] 2. Save and upload images

[0593] The captured images are temporarily stored in the device's local storage, and then uploaded to a server via an Internet connection, for example via the HTTP protocol.

[0594] 3. Server Analysis

[0595] The server receives and stores the uploaded images, then analyzes them using pre-trained generative AI models, specifically convolutional neural networks (CNNs), to determine the type and condition of the food in the image.

[0596] 4. Processing method proposal

[0597] The server then uses the results of image analysis to determine the best way to dispose of the food depending on its condition. For example, it suggests "delivery to a local grocery store" for fresh fruits and vegetables, "processing for resale" for bread and prepared foods, and "disposal" if the food is in poor condition.

[0598] 5. Communication and Display of Proposal Content

[0599] The server sends the generated proposals in a data format such as JSON to the device. In response, the device displays the received proposals to the user. The proposals are displayed on the device screen as text or images, and the user can select the proposed processing method and proceed to the next action.

[0600] Hardware and software used

[0601] A smartphone or tablet with a camera, which the user uses to take photos of the food.

[0602] Internet connection: Used to upload image data to the server and receive analysis results.

[0603] Server: Receives image data and analyzes it using a generative AI model (e.g., a convolutional neural network).

[0604] Generative AI models: They use pre-trained machine learning algorithms, particularly CNNs, to analyze images.

[0605] Specific examples

[0606] Example 1: Fully ripe bananas

[0607] 1. User: Take a photo of a ripe banana with your smartphone.

[0608] 2. Device: Upload the photos you took to the server.

[0609] 3. Server: Uses a convolutional neural network (CNN) to determine that it is a banana.

[0610] 4. Server: Generates "Delivery to local grocery store" as a suggestion.

[0611] 5. Server: Sends the proposal to the device.

[0612] 6. Terminal: Show the user "Delivery to local grocery store" and let them choose their next action.

[0613] Example 2: Stale bread

[0614] 1. User: Takes a photo of the hardened bread with a tablet.

[0615] 2. Device: Upload the photos you took to the server.

[0616] 3. Server: Uses a convolutional neural network (CNN) to identify the object as bread.

[0617] 4. Server: Generates a proposal for "processing for resale."

[0618] 5. Server: Sends the proposal to the device.

[0619] 6. Terminal: Display "Process for Resale" to the user and ask them to choose their next action.

[0620] Prompt Sentence Examples

[0621] Analyze food images to determine the type and condition of the food.

[0622] Food: Bananas

[0623] Image URL: <URL of image>

[0624] Output formats:

[0625] Type: <Food type>

[0626] Condition: <Food condition>

[0627] Proposal: <Proposed solution>

[0628] In this way, the present invention is designed to enable users to easily optimize food waste disposal. By smoothly carrying out the analysis and proposal process, it is expected to reduce food waste and alleviate the burden on the environment.

[0629] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0630] Step 1:

[0631] The user uses the camera function of their smartphone or tablet to take a photo of the food to be discarded. The photo is taken by pressing the camera button on the device. The input is the image of the food taken by the user. The output is an image file saved in local storage.

[0632] Step 2:

[0633] The device uploads the captured image to the server via an Internet connection. Specifically, it sends the image file to the server using an HTTP POST request. The input is the image file stored in local storage, and the output is the image data stored on the server side.

[0634] Step 3:

[0635] The server receives the uploaded images and stores them (temporarily stores them in a database, etc., as an initial process). The input is the image data sent from the terminal, and the output is the image data stored on the server.

[0636] Step 4:

[0637] The server begins image analysis using the stored image data. Specifically, it uses a pre-trained generative AI model (e.g., a convolutional neural network (CNN)) to determine the type and condition of food in the image. The input is the image data stored on the server, and the output is the analysis results regarding the type and condition of the food.

[0638] Step 5:

[0639] The server then decides the best way to dispose of the item based on the analysis results. For example, for fresh fruits and vegetables, it might suggest "delivery to a local grocery store," for bread or prepared foods, "processing for resale," or "disposal" if the item is in poor condition. The input is the image analysis results, and the output is data (e.g., JSON format) containing the suggestions.

[0640] Step 6:

[0641] The server sends the generated proposal to the device. Specifically, it formats the data in JSON format or similar and sends it to the device again via an HTTP request over an internet connection. The input is the data containing the proposal, and the output is the data sent to the device.

[0642] Step 7:

[0643] The terminal displays the received proposal to the user. The proposal is displayed on the terminal screen as text or an image, and the user confirms the proposed processing method and selects the next action. The input is data containing the proposal sent from the server, and the output is the proposal displayed to the user.

[0644] In this way, through the specific actions at each step, food to be discarded can be analyzed and appropriate disposal methods can be proposed.

[0645] 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.

[0646] The present invention combines a system that allows a user to take a photo of food to be discarded, analyzes the image, and suggests the optimal disposal method, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0647] Basic configuration

[0648] The system includes a means for photographing food, a means for saving the photographed images, a means for providing an artificial intelligence model for analyzing the saved images, a means for proposing food processing methods based on the analysis results, a means for displaying the proposed processing methods, and an emotion engine for recognizing the user's emotions, thereby enabling appropriate suggestions to be made taking into account the user's emotional state.

[0649] 1. The user takes a photo of the food

[0650] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The device has a camera application installed, and the user captures the image by pressing the capture button. The system also analyzes the user's facial expressions and voice and uses an emotion engine to recognize their emotional state.

[0651] 2. The device uploads the image to the server.

[0652] After the user confirms the image, they press the upload button, which causes the device to send the food image data and the recognized emotion data to the server. The upload is done via an internet connection, using an HTTP request to send the data.

[0653] 3. The server analyzes the image

[0654] The server receives and stores the uploaded image data and emotion data. The server then analyzes the stored images using an artificial intelligence model to determine the type and condition of the food. The analysis results are output as information such as the type and freshness of the food.

[0655] 4. The server generates an appropriate proposal

[0656] Based on the analysis, the server suggests how to dispose of the food: "Delivery to a local grocery store" if the food is fresh, "Process for resale" if it's slightly spoiled, or "Discard" if it needs to be discarded completely. It also adjusts its suggestions based on the user's perceived emotions. For example, if the user is feeling stressed, it might make more concise suggestions.

[0657] 5. The server sends the proposal to the device

[0658] The server sends the generated suggestions in JSON format to the device. The suggestions include suggestions based on the analysis results and emotion recognition results. This allows appropriate notifications and alerts to be displayed taking into account the user's emotional state.

[0659] 6. The device displays the suggestions to the user.

[0660] The device displays the received suggestions on the user interface. The suggestions are displayed as text and images on the device screen, allowing the user to confirm the suggested handling method and select the next action (e.g., arranging delivery or resale, or disposal). In addition, customized notifications and alerts are displayed according to the user's emotional state, improving the user experience.

[0661] Explanation with a concrete example

[0662] Example 1: Fully ripe bananas

[0663] 1. User: Take a photo of a ripe banana with your smartphone. At the same time, the emotion engine recognizes the user's slightly anxious facial expression.

[0664] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[0665] 3. Server: Analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[0666] 4. Server: Sends the proposal to the device.

[0667] 5. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[0668] Example 2: Stale bread

[0669] 1. User: Take a photo of the hardened bread with a tablet. At the same time, the emotion engine recognizes the user's relaxed facial expression.

[0670] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[0671] 3. Server: Analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing for resale."

[0672] 4. Server: Sends the proposal to the device.

[0673] 5. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[0674] In this way, this system, which combines an emotion engine, proposes the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[0675] The processing flow will be explained below.

[0676] Step 1:

[0677] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The user then launches a camera application and presses the capture button to capture the image of the food. At the same time, the device uses the built-in camera and microphone to detect the user's facial expressions and voice, and the emotion engine recognizes the user's emotional state.

[0678] Step 2:

[0679] The device temporarily stores the captured image of the food in local storage, along with the user's emotion data recognized by the emotion engine.

[0680] Step 3:

[0681] After checking the image, the user presses the upload button, which causes the device to send the food image data and emotion data to the server.

[0682] Step 4:

[0683] The device uploads food image data and emotion data to the server via an internet connection, using an HTTP POST request to send the data to a specified endpoint on the server.

[0684] Step 5:

[0685] The server receives the uploaded image data and emotion data, and stores the received images in temporary storage or a database, along with the emotion data.

[0686] Step 6:

[0687] The server analyzes the stored image data and uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to identify the type and condition of the food in the image. The analysis results are output as the food type and freshness.

[0688] Step 7:

[0689] The server analyzes the stored emotional data, and the emotion engine analyzes the emotional information recognized from the user's facial expressions and voice to determine the user's emotional state.

[0690] Step 8:

[0691] The server then suggests how to dispose of the food based on the results of image and emotion analysis. If the food is fresh, it suggests "delivery to a local grocery store." If it's slightly damaged, it suggests "processing for resale." If it needs to be completely discarded, it suggests "discarding." The server also adjusts the suggestions and their explanations based on the user's emotional state. For example, if the user is stressed, it makes simple, easy-to-understand suggestions, while if they're relaxed, it provides detailed information.

[0692] Step 9:

[0693] The server generates suggestions and sends them to the device in JSON format, including analysis results, suggestions, and customization information based on emotion recognition results.

[0694] Step 10:

[0695] The device displays the received suggestions on a user interface, allowing the user to review the suggested handling method and select the next action (e.g., arrange for delivery or resale, or dispose of). It also displays customized notifications and alerts for the suggested actions based on the user's emotional state.

[0696] The above is a specific processing flow of the present invention, and by combining it with an emotion engine, it is possible to propose the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[0697] Example 2

[0698] 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."

[0699] Food waste has become a serious problem in modern society. However, it is difficult for users to determine how to dispose of food that is scheduled for disposal. The psychological stress of discarding food is also a significant issue. In particular, disposal methods proposed without considering the user's emotional state decrease user satisfaction and weaken motivation to take appropriate action. Therefore, a system is needed that allows users to easily find the optimal disposal method for food that is scheduled for disposal and that also makes appropriate suggestions taking into account the user's emotional state.

[0700] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading captured images and emotion data to the server, means for analyzing the images and emotion data on the server, and means for generating suggestions based on the analysis results. This allows the user to easily know the optimal method for disposing of food that is scheduled to be discarded, and makes it possible to make appropriate suggestions that take into account the user's emotional state.

[0701] "Means for photographing food" refers to hardware and software that allows users to use devices such as smartphones and tablets to capture images of food to be discarded.

[0702] "Means for storing captured images" refers to means for storing captured images of food in the device's internal storage or cloud storage.

[0703] "Means comprising an artificial intelligence model" refers to means comprising a machine learning model or algorithm for analyzing stored images.

[0704] "Means for suggesting food disposal methods" refers to a system for suggesting food distribution, resale, or disposal based on analyzed data.

[0705] "Means for recognizing the user's emotions" refers to emotion recognition engines and algorithms that analyze the user's facial expressions, voice, etc. and determine their emotional state.

[0706] The "means for displaying a proposed processing method" refers to a user interface for visually displaying an appropriate food processing method to the user on the screen of the terminal.

[0707] "Means for uploading captured images and emotion data to a server" refers to a communication means for transmitting this data to a remote server via the Internet.

[0708] "Means for analyzing images and emotional data on a server" refers to software and hardware for analyzing image data and emotional data stored on a server and processing the information.

[0709] "Means for generating proposals based on analysis results" refers to a mechanism for automatically generating optimal proposals using a generative AI model based on the analysis results of image and emotion data.

[0710] The "means for transmitting the generated proposal content to the terminal" refers to a communication means for transmitting the proposal content generated by the server to the user's terminal.

[0711] "Means for suggesting whether to deliver, resell, or dispose of food based on the results of image and emotion data analysis" refers to a system that uses artificial intelligence models and emotion recognition results to determine the optimal way to dispose of food.

[0712] The present invention combines a system that allows a user to take a photo of food to be discarded, analyzes the image, and suggests the optimal disposal method, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0713] Basic configuration

[0714] The system includes the following hardware and software:

[0715] 1. Devices such as smartphones and tablets (e.g. iPhone, iPad)

[0716] 2. Camera applications (e.g. iOS camera app, Google Camera)

[0717] 3. Emotion engines (e.g., Microsoft Azure Emotion API, IBM Watson)

[0718] 4. Internet connection

[0719] 5. Server (e.g., Amazon EC2, Google Compute Engine)

[0720] 6. Cloud storage (e.g., Google Cloud Storage, Amazon S3)

[0721] 7. Artificial intelligence models (e.g., image recognition models using TensorFlow and PyTorch)

[0722] 8. Generative AI models (e.g., GPT-4)

[0723] Specific actions

[0724] 1. The user takes a photo of the food

[0725] A user uses the camera application on their smartphone or tablet to take a photo of food to be discarded. For example, say they use the iPhone camera app to take a photo of a ripe banana. When they press the capture button, the camera sensor captures the image and saves it in the device's storage. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[0726] 2. The device uploads the image to the server.

[0727] After the user checks the captured image and emotion data, they press the upload button, and the device sends this data to a server via an HTTP POST request. For example, the device uploads the image file and emotion data (in JSON format) to Google Cloud Storage.

[0728] 3. The server analyzes the image

[0729] The server receives the uploaded image data and emotion data and stores them in storage. The server then analyzes the stored images using an artificial intelligence model to determine the type and condition of the food. For example, TensorFlow can be used to determine whether a banana is ripe.

[0730] 4. The server generates an appropriate proposal

[0731] The server combines image recognition results with emotion data and uses a generative AI model to suggest the optimal food processing method. For example, if the user is in a hurry, it suggests "immediate delivery to a local grocery store." An example of a prompt sentence is, "A photo of a ripe banana has been uploaded. Please suggest an appropriate action, taking into account that the user is a little impatient."

[0732] 5. The server sends the proposal to the device

[0733] The server then sends the generated suggestions in JSON format to the device, which include specific advice based on the image analysis and emotion recognition results.

[0734] 6. The device displays the suggestions to the user.

[0735] The device displays the received suggestions in the user interface. For example, an iPad screen might display "Delivery to a local grocery store immediately" and provide a notification tailored to the user's emotions. The user can review the suggestions and select the next action (delivery, resale, disposal, etc.).

[0736] Explanation with a concrete example

[0737] Example 1: Fully ripe bananas

[0738] 1. The user takes a photo of a ripe banana with their smartphone. At the same time, the emotion engine recognizes the user's slightly anxious facial expression.

[0739] 2. The device uploads the captured photo and the recognized emotion data to the server.

[0740] 3. The server analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[0741] 4. The server sends the proposal to the device.

[0742] 5. The device will display "Delivery to your local grocery store soon" to the user and also add a calming message to the anxious user.

[0743] Example 2: Stale bread

[0744] 1. The user takes a photo of the hardened bread with a tablet. At the same time, the emotion engine recognizes the user's relaxed facial expression.

[0745] 2. The device uploads the captured photo and the recognized emotion data to the server.

[0746] 3. The server analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing it for resale."

[0747] 4. The server sends the proposal to the device.

[0748] 5. The device will display "processing for resale" to the user, along with detailed instructions on how to achieve a relaxed state.

[0749] In this way, this system, which combines an emotion engine, proposes the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[0750] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0751] Step 1:

[0752] The user takes a photo of the food. Using the camera application on their smartphone or tablet, the user takes a photo of the food to be discarded. At this time, the camera sensor captures the image in JPEG or PNG format and saves it in the device's storage. At the same time, the emotion engine installed on the device analyzes the user's facial expressions and voice in real time, generates emotion data, and saves it on the device. The input is the user's operation and the food being photographed, and the output is image data of the food and the user's emotion data.

[0753] Step 2:

[0754] The user uploads an image and emotion data to the server. When the user presses the upload button in the application, the device sends this data to the server via an Internet connection using an HTTP POST request. Specifically, the image file (JPEG or PNG) and emotion data (JSON format) are uploaded to cloud storage. The input is the captured image and emotion data, and the output is the image file and emotion data received by the server.

[0755] Step 3:

[0756] The server receives the image and emotion data and saves it in storage. The server receives the received image data and emotion data and saves them in a database. For example, Amazon RDS or Google Cloud SQL is used to persist the data. The input is the image file and emotion data uploaded from the device, and the output is the data saved in the server's storage.

[0757] Step 4:

[0758] The server analyzes the image data using an artificial intelligence model. Using the stored image data, the server uses an artificial intelligence model (e.g., TensorFlow or PyTorch) to determine the type and condition of the food. Specifically, the image is input into a machine learning model, which outputs the analyzed food type (e.g., banana, bread) and freshness (e.g., fresh, slightly spoiled, needs to be discarded). The input is the stored image data, and the output is the analysis results of the food type and condition.

[0759] Step 5:

[0760] The server generates appropriate suggestions that also take emotional data into consideration. By combining the image recognition results and emotional data, a prompt is input into a generative AI model (e.g., GPT-4) to generate an appropriate suggestion. For example, a prompt such as "A photo of a ripe banana has been uploaded. Please suggest an appropriate action, taking into consideration that the user may be a little impatient" is used. The input is the image analysis results and emotional data, and the output is the generated suggestion.

[0761] Step 6:

[0762] The server sends the generated proposal to the terminal. The server constructs the generated proposal in JSON format and sends it to the terminal as an HTTP response. The input is the generated proposal, and the output is the proposal data sent to the terminal.

[0763] Step 7:

[0764] The proposal received by the device is displayed on the user interface. The device parses the proposal received from the server and displays it on the user interface. Specifically, the proposal is displayed using text and images, and the user selects the next action (delivery, resale, disposal, etc.). An application using Swift (for iOS) or Kotlin (for Android) is used for display. The input is the proposal data received from the server, and the output is the proposal displayed on the user interface.

[0765] (Application example 2)

[0766] 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."

[0767] In modern society, food waste is a serious problem, and there is a need for methods to effectively utilize food that would otherwise be discarded. Furthermore, there is a lack of systems that can improve user satisfaction by providing appropriate suggestions and notifications based on the user's emotional state. Therefore, there is a need for a system that not only suggests ways to dispose of food that would otherwise be discarded, but also makes optimal suggestions that take the user's emotions into consideration.

[0768] 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 uploading captured images to the server, means for analyzing the images on the server, means for generating proposals based on the analysis results, means for transmitting the generated proposals to the terminal, means for uploading user emotion data to the server, and means for adjusting the proposals based on the emotion data. This makes it possible to suggest methods for appropriately disposing of food to be discarded, and to provide customized notifications and alerts according to the user's emotional state.

[0769] "Means for photographing food" refers to a function for photographing food that is scheduled to be discarded using a camera installed on a device such as a smartphone or tablet.

[0770] "Means for saving captured images" refers to a function for saving captured images of food within the device or in cloud storage.

[0771] "Means for having an artificial intelligence model that analyzes stored images" refers to an artificial intelligence model that is used to analyze stored image data and identify the type and condition of food.

[0772] The "means for proposing food disposal methods based on analysis results" is a function that shows users the optimal disposal method (delivery, resale, disposal, etc.) for food that is scheduled to be discarded based on the analysis results.

[0773] The "means for displaying a proposed processing method" is a function for displaying a processing method based on the analysis results on a user interface.

[0774] The "emotion engine that recognizes user emotions" is an artificial intelligence model that analyzes the user's facial expressions and voice to recognize their emotional state.

[0775] The "means for adjusting the content of the proposal" is a function for optimizing the content of the proposal according to the emotional state of the user and providing information in a form that is more easily accepted by the user.

[0776] The "means for uploading to a server" is a function for transmitting the captured image and emotion data to a server via the Internet.

[0777] "Means for analyzing images on the server" refers to a function for analyzing received image data within the server and determining the type and condition of food.

[0778] The "means for generating proposal content" is a function for automatically creating the optimal food processing method based on the analysis results of image and emotion data.

[0779] The "means for transmitting the generated proposal content to the terminal" is a function for transmitting the generated proposal content to the terminal and displaying it to the user.

[0780] The "means for adjusting the content of suggestions based on emotional data" is a function that adjusts the details of the suggestions based on the user's emotional data recognized by the server, and provides appropriate alerts and notifications through the user interface.

[0781] The present invention combines a system that photographs food to be disposed of, analyzes the image, and proposes the optimal disposal method with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0782] Basic configuration

[0783] The system includes a means for photographing food, a means for saving the photographed images, a means for providing an artificial intelligence model for analyzing the saved images, a means for proposing food processing methods based on the analysis results, a means for displaying the proposed processing methods, and an emotion engine for recognizing the user's emotions, thereby enabling appropriate suggestions to be made taking into account the user's emotional state.

[0784] Hardware and software used

[0785] Hardware:

[0786] Smartphones, tablets (camera, microphone)

[0787] Server (Cloud-based)

[0788] software:

[0789] Mobile Applications

[0790] Emotion Engine (Python, Keras)

[0791] Image analysis model (TensorFlow)

[0792] Database (PostgreSQL)

[0793] Communication protocol (HTTPS)

[0794] Operation Flow

[0795] 1. User takes a photo of food:

[0796] Users use their smartphones or tablets to take photos of food that is to be discarded, and the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[0797] 2. The device uploads the image and emotion data to the server:

[0798] The captured images and recognized emotion data are uploaded to a server via HTTPS.

[0799] 3. The server analyzes the image and emotions:

[0800] The server uses an artificial intelligence model (TensorFlow) to analyze images and determine the type and condition of food. An emotion engine (Keras model) analyzes the emotion data and recognizes the user's emotional state.

[0801] 4. The server generates an appropriate proposal:

[0802] Based on the analysis, it suggests how to dispose of the food. For example, it suggests "delivery to a local grocery store" for fresh food, and "processing for resale" for spoiled food. If the user is stressed, it makes a brief suggestion, but if they are relaxed, it adds a detailed explanation.

[0803] 5. The server sends the proposal to the device:

[0804] The generated suggestions are sent to the user's device in JSON format.

[0805] 6. The device displays the suggestions to the user:

[0806] Suggestions are displayed in the user interface as text and images, along with customized notifications and alerts based on your emotional state.

[0807] Specific examples

[0808] Example 1: For a ripe banana:

[0809] 1. User: Take a photo of a ripe banana, and the emotion engine recognizes that the user has a slightly anxious expression.

[0810] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[0811] 3. Server: Analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[0812] 4. Server: Sends the proposal to the device.

[0813] 5. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[0814] Example 2: For hardened bread:

[0815] 1. User: Take a photo of the hardened bread with a tablet, and the emotion engine recognizes the user's relaxed facial expression.

[0816] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[0817] 3. Server: Analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing for resale."

[0818] 4. Server: Sends the proposal to the device.

[0819] 5. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[0820] Prompt Sentence Examples

[0821] 1. For ripe bananas:

[0822] Prompt: "If a user photographs a ripe banana and feels rushed, what suggestions should the system make?"

[0823] 2. For hardened bread:

[0824] Prompt: "If a user photographs a hardened loaf of bread and feels relaxed, what suggestions would the system make?"

[0825] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0826] Step 1:

[0827] A user takes a photo of food to be discarded using a smartphone or tablet. At this time, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. The input is the captured image and the user's facial and voice data, and the output is the captured image data and recognized emotion data.

[0828] Step 2:

[0829] The device uploads the captured image data and the recognized emotion data to the server. The input is the image data and emotion data, and the output is the data sent to the server. Communication is via HTTPS.

[0830] Step 3:

[0831] The server stores the received image data and analyzes the images. An artificial intelligence model (TensorFlow) is used for the analysis. The input is the image data stored on the server, and the output is the image analysis results (type and condition of food). The specific operation is image classification using the TensorFlow model.

[0832] Step 4:

[0833] The server analyzes the received emotion data and recognizes the user's emotional state. An emotion engine (Keras model) is used for the analysis. The input is the emotion data stored on the server, and the output is the emotion analysis result (user's emotional state).

[0834] Step 5:

[0835] The server proposes a food disposal method based on the results of image analysis and sentiment analysis. Specifically, it proposes delivery for fresh food, resale for slightly spoiled food, and disposal for food that needs to be completely discarded. The proposal is adjusted based on the results of sentiment analysis. The input is the results of image analysis and sentiment analysis, and the output is a proposal for the optimal disposal method.

[0836] Step 6:

[0837] The server generates a proposal and sends it to the terminal in JSON format. The input is the proposal for the optimal processing method, and the output is the JSON data sent to the terminal.

[0838] Step 7:

[0839] The device displays the received proposal on the user interface. The user confirms the proposed processing method and selects the next action. The input is the JSON data sent from the server, and the output is the proposal displayed to the user. The specific operation is to display text and images on the UI.

[0840] Specific examples

[0841] Example 1: Fully ripe bananas

[0842] 1. User: Takes a photo of a ripe banana, and the emotion engine recognizes the anxious expression.

[0843] 2. Device: Uploads the captured photo and emotion data to the server.

[0844] 3. Server: Analyzes the image and determines that it is a banana.

[0845] 4. Server: Analyzes emotion data and recognizes that the user is anxious.

[0846] 5. Server: Offer "immediate delivery to your local grocery store."

[0847] 6. Server: Sends the proposal to the device.

[0848] 7. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[0849] Example 2: Stale bread

[0850] 1. User: Takes a photo of hardened bread, and the emotion engine recognizes a relaxed facial expression.

[0851] 2. Device: Uploads the captured photo and emotion data to the server.

[0852] 3. Server: Analyzes the image and determines that it is bread.

[0853] 4. Server: Analyzes the emotional data and recognizes that the user is relaxed.

[0854] 5. Server: Proposes "processing for resale."

[0855] 6. Server: Sends the proposal to the device.

[0856] 7. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] [Third embodiment]

[0861] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0862] 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.

[0863] 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).

[0864] 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.

[0865] 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.

[0866] 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).

[0867] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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."

[0873] The present invention is a system that allows a user to take a photo of food to be discarded and analyzes the image to suggest a disposal method for the food, either by recycling, delivering, reselling, or discarding it. Specific embodiments for carrying out the present invention are described below.

[0874] 1. The user takes a photo of the food

[0875] Users take photos of food items to be discarded using a device with a camera function, such as a smartphone or tablet. The device has a photo button, and users can take photos by pressing the button.

[0876] 2. The device uploads the image to the server.

[0877] Images taken by the user are temporarily stored in the device's local storage and then uploaded to a server via an internet connection.

[0878] 3. The server analyzes the image

[0879] The server receives and stores the uploaded images, then uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to analyze the images and determine the type and condition of the food in the image.

[0880] 4. The server generates an appropriate proposal

[0881] Based on the results of the image analysis, the server decides the best way to dispose of the item: for example, it suggests "delivery to a local grocery store" for fresh fruits and vegetables, "processing for resale" for bread and prepared foods, or "disposal" if the item is in poor condition.

[0882] 5. The server sends the proposal to the device

[0883] The server sends the generated proposal to the terminal in a data format such as JSON, which includes specific processing methods and procedures.

[0884] 6. The device displays the suggestions to the user.

[0885] The device displays the received suggestions to the user, either as text or images on the device screen, and the user can select the suggested processing method and proceed to the next action.

[0886] Explanation with a concrete example

[0887] Example 1: Fully ripe bananas

[0888] 1. User: Take a photo of a ripe banana with your smartphone.

[0889] 2. Device: Upload the photos you took to the server.

[0890] 3. Server: Uses an image analysis model to determine that it is a banana.

[0891] 4. Server: Generates "Delivery to local grocery store" as a suggestion.

[0892] 5. Server: Sends the proposal to the device.

[0893] 6. Terminal: Show the user "Delivery to local grocery store" and let them choose their next action.

[0894] Example 2: Stale bread

[0895] 1. User: Takes a photo of the hardened bread with a tablet.

[0896] 2. Device: Upload the photos you took to the server.

[0897] 3. Server: Performs image analysis and determines that it is bread.

[0898] 4. Server: Generates a proposal for "processing for resale."

[0899] 5. Server: Sends the proposal to the device.

[0900] 6. Terminal: Display "Process for Resale" to the user and ask them to choose their next action.

[0901] In this way, this system is designed to allow users to easily optimize food waste disposal. By smoothly performing analysis and making recommendations, it is expected to reduce food waste and alleviate the burden on the environment.

[0902] The processing flow will be explained below.

[0903] Step 1:

[0904] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The user launches the device's camera application and presses the capture button to capture an image of the food.

[0905] Step 2:

[0906] The device temporarily saves the captured image to local storage, ensuring that the image data is available on the device and ready for the next processing step.

[0907] Step 3:

[0908] After checking the image, the user presses the upload button, which causes the device to start the process of sending the image data to the server.

[0909] Step 4:

[0910] The device uploads the captured image to the server via an internet connection, where it uses an HTTP request to send the image data to a specified endpoint on the server.

[0911] Step 5:

[0912] The server receives the uploaded image data and saves the images. The server saves the received images in temporary storage or a database, ensuring the data necessary for analysis processing.

[0913] Step 6:

[0914] The server analyzes the stored images and uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to determine the type and condition of the food in the image. The analysis results are output as information such as the type of food and its freshness.

[0915] Step 7:

[0916] Based on the analysis, the server will suggest how to dispose of the food, for example, "delivery to a local grocery store" if the food is fresh, "processing for resale" if it is slightly spoiled, or "discard" if it needs to be discarded completely.

[0917] Step 8:

[0918] The server sends the generated suggestions to the device in JSON format, and the analysis results and suggestions are returned to the user's device via network communication.

[0919] Step 9:

[0920] The terminal displays the received proposal on a user interface, where the user can review the proposed disposal method and select the next action (e.g., arranging for delivery or resale, or disposal).

[0921] Step 10:

[0922] The user selects the next action based on the suggestions, and the selection is sent via the terminal to the server, which processes the information to perform the necessary procedures based on the user's selection.

[0923] The above is the specific processing flow of the system, which allows users to easily and efficiently determine the optimal processing method for food.

[0924] Example 1

[0925] 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."

[0926] Reducing food waste is an important issue in terms of both environmental protection and economic effectiveness. However, food waste disposal at homes and stores is not currently optimized, resulting in many cases of usable food being wasted. Current systems require users to manually check the condition of food and select an appropriate disposal method, which is cumbersome and time-consuming. Therefore, there is a need for the development of a system that allows users to easily determine the condition of food and automatically suggests appropriate disposal methods.

[0927] 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.

[0928] In this invention, the server includes means for uploading images taken by a user to the server via the Internet, means for performing image analysis on the server to determine the type and condition of the food, means for determining the optimal processing method based on the determination results, and means for transmitting the determined processing method to the terminal in JSON format, thereby enabling the user to easily understand the condition of the food and quickly obtain the optimal processing method.

[0929] "Food" means any substance processed for human or animal consumption.

[0930] "Means for photographing" refers to a function for acquiring an image of an object using a device equipped with a camera function.

[0931] "Means for storing" refers to the function of temporarily or permanently storing captured images in a memory device within the device.

[0932] An "artificial intelligence model" is a system that learns from large amounts of data and has algorithms for pattern recognition and classification.

[0933] "Means for analyzing" refers to the function of using an artificial intelligence model to extract and interpret specific information from input data.

[0934] "Means of suggestion" refers to a function that shows users the optimal actions and options based on the analysis results.

[0935] "Means for displaying" refers to a function for visually conveying the proposed information to the user.

[0936] "Means for uploading via the Internet" refers to the ability to transfer image data to a remote server via a network connection.

[0937] A "server" is a computer system that manages large amounts of data and exchanges data with other devices via a network.

[0938] "Image analysis" is the process of identifying objects in a captured image and determining their type and condition.

[0939] The "JSON format" is a lightweight, easy-to-read text-based data format used for data exchange.

[0940] A "convolutional neural network (CNN) model" is an artificial intelligence model with deep learning algorithms that is primarily used for image analysis and has the ability to extract and identify features within images.

[0941] A "terminal" is a device operated by a user, and refers to a device equipped with a camera function, a storage function, a communication function, and a display function.

[0942] The present invention is a system that allows a user to take a photo of food to be discarded and analyzes the image to suggest a disposal method for the food, either by recycling, delivering, reselling, or discarding it. Specific embodiments for carrying out the present invention are described below.

[0943] This system is implemented using a device with a camera function, such as a smartphone or tablet, and a server connected via the Internet. The overall system flow is as follows:

[0944] The user uses the device to take a photo of the food to be discarded. The device has a photo button, and the user presses the button to take a photo. The captured image is temporarily saved in the device's local storage. It is then uploaded to a server via an Internet connection. When uploaded, metadata such as the user ID is also sent along with the image file.

[0945] The server then receives the uploaded images and stores them on disk storage. The server then analyzes the images using a pre-trained image analysis AI model (convolutional neural network, CNN). This analysis determines the type and condition of the food. For example, it can identify a ripe banana or stale bread. A rules engine or machine learning model runs within the server to determine the optimal processing method based on the analysis results.

[0946] Based on the results of the assessment, the server suggests the appropriate disposal method. For example, for fresh fruits and vegetables, it suggests "delivery to a local grocery store," while for bread and prepared foods, it suggests "processing for resale." If the item is in poor condition, it suggests "disposal." These suggestions are converted into a data format such as JSON and sent to the device.

[0947] The device displays the received suggestions to the user, who can then select from the suggestions displayed on the screen and proceed to the next action. The displayed information is provided in text and image format.

[0948] Examples:

[0949] Example 1: Fully ripe bananas

[0950] 1. User: Take a photo of a ripe banana with your smartphone.

[0951] 2. Device: 1. Save the captured photo to local storage and upload it to the server.

[0952] 3. Server: Receives and stores the uploaded images. It uses a trained CNN model to recognize bananas and determine whether they are overripe.

[0953] 4. Server: Generate "Delivery to local grocery store" as a suggestion.

[0954] 5. Server: Convert the proposal into JSON format and send it to the device.

[0955] 6. Terminal: "Delivery to local grocery store" is displayed as a suggestion to the user.

[0956] Example 2: Stale bread

[0957] 1. User: Takes a photo of the hardened bread with a tablet.

[0958] 2. Device: Save the captured photo to local storage and upload it to the server.

[0959] 3. Server: Receives and stores the uploaded images. It uses a trained CNN model to recognize the bread and determine whether it is cured.

[0960] 4. Server: Generates "Processing for resale" as a proposal.

[0961] 5. Server: Convert the proposal into JSON format and send it to the device.

[0962] 6. Terminal: Display "Processing for resale" as a suggestion to the user.

[0963] These steps allow users to easily understand how to process food and reduce food waste. The use of artificial intelligence models improves the accuracy of image analysis and the appropriateness of the suggested processing methods.

[0964] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0965] Step 1: User takes a photo of food

[0966] Users use a smartphone or tablet to take a photo of the food to be discarded. The input is to launch the camera app, position the food in the center of the screen, and press the capture button. The output is that the captured image is temporarily saved in the device's local storage.

[0967] As a specific example of how it works, a user takes a photo of a ripe banana using a camera app. The photo is saved as an image file, and the app is ready to move on to the next step.

[0968] Step 2: The device uploads the image to the server

[0969] The device uploads the saved image files to a server via the Internet. The input is the image file on the local storage, and the output is the image file and associated metadata sent to the server.

[0970] As a specific example of how it works, the device sends a saved photo of a banana to the server via Wi-Fi, along with metadata such as the user ID and the date and time the photo was taken.

[0971] Step 3: The server receives and stores the image

[0972] The server receives image files and metadata uploaded from the terminal and stores them in disk storage. The input is the image file and metadata sent from the terminal, and the output is the stored image and metadata.

[0973] In a specific example, the server stores the image file of the banana and associated metadata on disk storage for subsequent analysis.

[0974] Step 4: The server performs image analysis

[0975] The server inputs the saved image files into a pre-trained convolutional neural network (CNN) model to analyze the food type and state. The input is the saved image file, and the output is the food type and its state information.

[0976] As a specific example, a Python script on the server runs a CNN model to identify the type of banana and its ripeness from the image data, and as a result, determines that the banana is fully ripe.

[0977] Step 5: Server generates proposal

[0978] The server generates recommendations based on the results of image analysis. The input is the type and condition of the analyzed food, and the output is a recommendation on the optimal disposal method (e.g., delivery, resale, disposal).

[0979] For example, if the banana is overripe, the server may decide to offer "delivery to a local grocery store." The optimal offer is generated by a rules engine or machine learning model.

[0980] Step 6: The server sends the proposal to the device

[0981] The server converts the generated proposal content into JSON format and sends it to the terminal. The input is the generated proposal content, and the output is JSON format data.

[0982] As a specific example of how it works, the server generates a JSON file containing the offer "Delivery to a local grocery store" and sends it to the device via the Internet.

[0983] Step 7: The device displays the suggestions to the user

[0984] The device parses the received JSON formatted suggestions and displays them to the user. The input is the JSON data received from the server, and the output is text or image information visually presented to the user.

[0985] As a specific example of operation, the terminal displays a "Delivery to local grocery store" offer on the screen in text and images, providing the user with options to select.

[0986] (Application example 1)

[0987] 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."

[0988] Food waste has become a major problem in modern society. A large amount of food is discarded at home and in logistics centers, which increases the burden on the environment. To solve this problem, a system is needed that can quickly and accurately suggest the optimal disposal method for food that is scheduled to be discarded, whether it be for reuse, delivery, or proper disposal. However, current systems have the problem of being unable to accurately determine the condition of food and make appropriate suggestions based on that information.

[0989] 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.

[0990] In this invention, the server includes a means for uploading the captured image to the server, a means for performing image analysis on the server and generating a proposal based on the analysis results, and a means for transmitting the generated proposal to the terminal, thereby enabling the state of the food to be accurately analyzed and the optimal processing method to be proposed based on the analysis.

[0991] "Food" means any substance consumed by humans as food or as a foodstuff.

[0992] "Means for taking pictures" refers to a method or device for capturing an image using an imaging device such as a camera.

[0993] "Means for storing images" refers to a method or device for storing captured images in local storage or cloud storage.

[0994] An "artificial intelligence model" is a collection of machine learning algorithms trained to perform a specific task, such as a convolutional neural network (CNN) for image analysis.

[0995] "Proposal means" refers to a method or device that shows the user the optimal processing method based on the analysis results.

[0996] The "display means" refers to a display device such as a display or mobile terminal that visually notifies the user of the proposed processing method.

[0997] "Means for uploading to a server via communication" refers to a method or device for transmitting data to a server via the Internet or other communication network.

[0998] The "means for generating analysis results and proposals" refers to a method or device that analyzes images on a server and generates an appropriate processing method based on the results.

[0999] The "means for transmitting to the terminal" refers to a communication means for transmitting the generated proposal content to the user's terminal.

[1000] "Recycling" refers to the process of making processed food usable again and providing it for reuse.

[1001] "Delivery" means the means by which food is transported to a specified location based on a specific processing method.

[1002] "Disposal" refers to the proper disposal of processed food when it is not suitable for reuse or distribution.

[1003] "Means for displaying to the user and allowing the user to select the next action to be taken" refers to a method or device that visually presents the proposed processing method to the user and allows the user to select the next measure based on that.

[1004] These definitions provide a clear understanding of the specific meaning of the terms set forth in the claims.

[1005] The embodiment of the present invention will now be described in detail.

[1006] The present invention is a system that allows a user to photograph food to be discarded and analyzes the image to suggest a disposal method for the food, either by reusing, delivering, reselling, or discarding it.

[1007] System Configuration

[1008] 1. User operation

[1009] Users take photos of food items to be discarded using a device with a camera function, such as a smartphone or tablet. The device has a photo button, and the user presses this button to take a photo.

[1010] 2. Save and upload images

[1011] The captured images are temporarily stored in the device's local storage, and then uploaded to a server via an Internet connection, for example via the HTTP protocol.

[1012] 3. Server Analysis

[1013] The server receives and stores the uploaded images, then analyzes them using pre-trained generative AI models, specifically convolutional neural networks (CNNs), to determine the type and condition of the food in the image.

[1014] 4. Processing method proposal

[1015] The server then uses the results of image analysis to determine the best way to dispose of the food depending on its condition. For example, it suggests "delivery to a local grocery store" for fresh fruits and vegetables, "processing for resale" for bread and prepared foods, and "disposal" if the food is in poor condition.

[1016] 5. Communication and Display of Proposal Content

[1017] The server sends the generated proposals in a data format such as JSON to the device. In response, the device displays the received proposals to the user. The proposals are displayed on the device screen as text or images, and the user can select the proposed processing method and proceed to the next action.

[1018] Hardware and software used

[1019] A smartphone or tablet with a camera, which the user uses to take photos of the food.

[1020] Internet connection: Used to upload image data to the server and receive analysis results.

[1021] Server: Receives image data and analyzes it using a generative AI model (e.g., a convolutional neural network).

[1022] Generative AI models: They use pre-trained machine learning algorithms, particularly CNNs, to analyze images.

[1023] Specific examples

[1024] Example 1: Fully ripe bananas

[1025] 1. User: Take a photo of a ripe banana with your smartphone.

[1026] 2. Device: Upload the photos you took to the server.

[1027] 3. Server: Uses a convolutional neural network (CNN) to determine that it is a banana.

[1028] 4. Server: Generates "Delivery to local grocery store" as a suggestion.

[1029] 5. Server: Sends the proposal to the device.

[1030] 6. Terminal: Show the user "Delivery to local grocery store" and let them choose their next action.

[1031] Example 2: Stale bread

[1032] 1. User: Takes a photo of the hardened bread with a tablet.

[1033] 2. Device: Upload the photos you took to the server.

[1034] 3. Server: Uses a convolutional neural network (CNN) to identify the object as bread.

[1035] 4. Server: Generates a proposal for "processing for resale."

[1036] 5. Server: Sends the proposal to the device.

[1037] 6. Terminal: Display "Process for Resale" to the user and ask them to choose their next action.

[1038] Prompt Sentence Examples

[1039] Analyze food images to determine the type and condition of the food.

[1040] Food: Bananas

[1041] Image URL: <URL of image>

[1042] Output formats:

[1043] Type: <Food type>

[1044] Condition: <Food condition>

[1045] Proposal: <Proposed solution>

[1046] In this way, the present invention is designed to enable users to easily optimize food waste disposal. By smoothly carrying out the analysis and proposal process, it is expected to reduce food waste and alleviate the burden on the environment.

[1047] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1048] Step 1:

[1049] The user uses the camera function of their smartphone or tablet to take a photo of the food to be discarded. The photo is taken by pressing the camera button on the device. The input is the image of the food taken by the user. The output is an image file saved in local storage.

[1050] Step 2:

[1051] The device uploads the captured image to the server via an Internet connection. Specifically, it sends the image file to the server using an HTTP POST request. The input is the image file stored in local storage, and the output is the image data stored on the server side.

[1052] Step 3:

[1053] The server receives the uploaded images and stores them (temporarily stores them in a database, etc., as an initial process). The input is the image data sent from the terminal, and the output is the image data stored on the server.

[1054] Step 4:

[1055] The server begins image analysis using the stored image data. Specifically, it uses a pre-trained generative AI model (e.g., a convolutional neural network (CNN)) to determine the type and condition of food in the image. The input is the image data stored on the server, and the output is the analysis results regarding the type and condition of the food.

[1056] Step 5:

[1057] The server then decides the best way to dispose of the item based on the analysis results. For example, for fresh fruits and vegetables, it might suggest "delivery to a local grocery store," for bread or prepared foods, "processing for resale," or "disposal" if the item is in poor condition. The input is the image analysis results, and the output is data (e.g., JSON format) containing the suggestions.

[1058] Step 6:

[1059] The server sends the generated proposal to the device. Specifically, it formats the data in JSON format or similar and sends it to the device again via an HTTP request over an internet connection. The input is the data containing the proposal, and the output is the data sent to the device.

[1060] Step 7:

[1061] The terminal displays the received proposal to the user. The proposal is displayed on the terminal screen as text or an image, and the user confirms the proposed processing method and selects the next action. The input is data containing the proposal sent from the server, and the output is the proposal displayed to the user.

[1062] In this way, through the specific actions at each step, food to be discarded can be analyzed and appropriate disposal methods can be proposed.

[1063] 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.

[1064] The present invention combines a system that allows a user to take a photo of food to be discarded, analyzes the image, and suggests the optimal disposal method, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1065] Basic configuration

[1066] The system includes a means for photographing food, a means for saving the photographed images, a means for providing an artificial intelligence model for analyzing the saved images, a means for proposing food processing methods based on the analysis results, a means for displaying the proposed processing methods, and an emotion engine for recognizing the user's emotions, thereby enabling appropriate suggestions to be made taking into account the user's emotional state.

[1067] 1. The user takes a photo of the food

[1068] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The device has a camera application installed, and the user captures the image by pressing the capture button. The system also analyzes the user's facial expressions and voice and uses an emotion engine to recognize their emotional state.

[1069] 2. The device uploads the image to the server.

[1070] After the user confirms the image, they press the upload button, which causes the device to send the food image data and the recognized emotion data to the server. The upload is done via an internet connection, using an HTTP request to send the data.

[1071] 3. The server analyzes the image

[1072] The server receives and stores the uploaded image data and emotion data. The server then analyzes the stored images using an artificial intelligence model to determine the type and condition of the food. The analysis results are output as information such as the type and freshness of the food.

[1073] 4. The server generates an appropriate proposal

[1074] Based on the analysis, the server suggests how to dispose of the food: "Delivery to a local grocery store" if the food is fresh, "Process for resale" if it's slightly spoiled, or "Discard" if it needs to be discarded completely. It also adjusts its suggestions based on the user's perceived emotions. For example, if the user is feeling stressed, it might make more concise suggestions.

[1075] 5. The server sends the proposal to the device

[1076] The server sends the generated suggestions in JSON format to the device. The suggestions include suggestions based on the analysis results and emotion recognition results. This allows appropriate notifications and alerts to be displayed taking into account the user's emotional state.

[1077] 6. The device displays the suggestions to the user.

[1078] The device displays the received suggestions on the user interface. The suggestions are displayed as text and images on the device screen, allowing the user to confirm the suggested handling method and select the next action (e.g., arranging delivery or resale, or disposal). In addition, customized notifications and alerts are displayed according to the user's emotional state, improving the user experience.

[1079] Explanation with a concrete example

[1080] Example 1: Fully ripe bananas

[1081] 1. User: Take a photo of a ripe banana with your smartphone. At the same time, the emotion engine recognizes the user's slightly anxious facial expression.

[1082] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[1083] 3. Server: Analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[1084] 4. Server: Sends the proposal to the device.

[1085] 5. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[1086] Example 2: Stale bread

[1087] 1. User: Take a photo of the hardened bread with a tablet. At the same time, the emotion engine recognizes the user's relaxed facial expression.

[1088] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[1089] 3. Server: Analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing for resale."

[1090] 4. Server: Sends the proposal to the device.

[1091] 5. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[1092] In this way, this system, which combines an emotion engine, proposes the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[1093] The processing flow will be explained below.

[1094] Step 1:

[1095] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The user then launches a camera application and presses the capture button to capture the image of the food. At the same time, the device uses the built-in camera and microphone to detect the user's facial expressions and voice, and the emotion engine recognizes the user's emotional state.

[1096] Step 2:

[1097] The device temporarily stores the captured image of the food in local storage, along with the user's emotion data recognized by the emotion engine.

[1098] Step 3:

[1099] After checking the image, the user presses the upload button, which causes the device to send the food image data and emotion data to the server.

[1100] Step 4:

[1101] The device uploads food image data and emotion data to the server via an internet connection, using an HTTP POST request to send the data to a specified endpoint on the server.

[1102] Step 5:

[1103] The server receives the uploaded image data and emotion data, and stores the received images in temporary storage or a database, along with the emotion data.

[1104] Step 6:

[1105] The server analyzes the stored image data and uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to identify the type and condition of the food in the image. The analysis results are output as the food type and freshness.

[1106] Step 7:

[1107] The server analyzes the stored emotional data, and the emotion engine analyzes the emotional information recognized from the user's facial expressions and voice to determine the user's emotional state.

[1108] Step 8:

[1109] The server then suggests how to dispose of the food based on the results of image and emotion analysis. If the food is fresh, it suggests "delivery to a local grocery store." If it's slightly damaged, it suggests "processing for resale." If it needs to be completely discarded, it suggests "discarding." The server also adjusts the suggestions and their explanations based on the user's emotional state. For example, if the user is stressed, it makes simple, easy-to-understand suggestions, while if they're relaxed, it provides detailed information.

[1110] Step 9:

[1111] The server generates suggestions and sends them to the device in JSON format, including analysis results, suggestions, and customization information based on emotion recognition results.

[1112] Step 10:

[1113] The device displays the received suggestions on a user interface, allowing the user to review the suggested handling method and select the next action (e.g., arrange for delivery or resale, or dispose of). It also displays customized notifications and alerts for the suggested actions based on the user's emotional state.

[1114] The above is a specific processing flow of the present invention, and by combining it with an emotion engine, it is possible to propose the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[1115] Example 2

[1116] 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."

[1117] Food waste has become a serious problem in modern society. However, it is difficult for users to determine how to dispose of food that is scheduled for disposal. The psychological stress of discarding food is also a significant issue. In particular, disposal methods proposed without considering the user's emotional state decrease user satisfaction and weaken motivation to take appropriate action. Therefore, a system is needed that allows users to easily find the optimal disposal method for food that is scheduled for disposal and that also makes appropriate suggestions taking into account the user's emotional state.

[1118] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading captured images and emotion data to the server, means for analyzing the images and emotion data on the server, and means for generating suggestions based on the analysis results. This allows the user to easily know the optimal method for disposing of food that is scheduled to be discarded, and makes it possible to make appropriate suggestions that take into account the user's emotional state.

[1119] "Means for photographing food" refers to hardware and software that allows users to use devices such as smartphones and tablets to capture images of food to be discarded.

[1120] "Means for storing captured images" refers to means for storing captured images of food in the device's internal storage or cloud storage.

[1121] "Means comprising an artificial intelligence model" refers to means comprising a machine learning model or algorithm for analyzing stored images.

[1122] "Means for suggesting food disposal methods" refers to a system for suggesting food distribution, resale, or disposal based on analyzed data.

[1123] "Means for recognizing the user's emotions" refers to emotion recognition engines and algorithms that analyze the user's facial expressions, voice, etc. and determine their emotional state.

[1124] The "means for displaying a proposed processing method" refers to a user interface for visually displaying an appropriate food processing method to the user on the screen of the terminal.

[1125] "Means for uploading captured images and emotion data to a server" refers to a communication means for transmitting this data to a remote server via the Internet.

[1126] "Means for analyzing images and emotional data on a server" refers to software and hardware for analyzing image data and emotional data stored on a server and processing the information.

[1127] "Means for generating proposals based on analysis results" refers to a mechanism for automatically generating optimal proposals using a generative AI model based on the analysis results of image and emotion data.

[1128] The "means for transmitting the generated proposal content to the terminal" refers to a communication means for transmitting the proposal content generated by the server to the user's terminal.

[1129] "Means for suggesting whether to deliver, resell, or dispose of food based on the results of image and emotion data analysis" refers to a system that uses artificial intelligence models and emotion recognition results to determine the optimal way to dispose of food.

[1130] The present invention combines a system that allows a user to take a photo of food to be discarded, analyzes the image, and suggests the optimal disposal method, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1131] Basic configuration

[1132] The system includes the following hardware and software:

[1133] 1. Devices such as smartphones and tablets (e.g. iPhone, iPad)

[1134] 2. Camera applications (e.g. iOS camera app, Google Camera)

[1135] 3. Emotion engines (e.g., Microsoft Azure Emotion API, IBM Watson)

[1136] 4. Internet connection

[1137] 5. Server (e.g., Amazon EC2, Google Compute Engine)

[1138] 6. Cloud storage (e.g., Google Cloud Storage, Amazon S3)

[1139] 7. Artificial intelligence models (e.g., image recognition models using TensorFlow and PyTorch)

[1140] 8. Generative AI models (e.g., GPT-4)

[1141] Specific actions

[1142] 1. The user takes a photo of the food

[1143] A user uses the camera application on their smartphone or tablet to take a photo of food to be discarded. For example, say they use the iPhone camera app to take a photo of a ripe banana. When they press the capture button, the camera sensor captures the image and saves it in the device's storage. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[1144] 2. The device uploads the image to the server.

[1145] After the user checks the captured image and emotion data, they press the upload button, and the device sends this data to a server via an HTTP POST request. For example, the device uploads the image file and emotion data (in JSON format) to Google Cloud Storage.

[1146] 3. The server analyzes the image

[1147] The server receives the uploaded image data and emotion data and stores them in storage. The server then analyzes the stored images using an artificial intelligence model to determine the type and condition of the food. For example, TensorFlow can be used to determine whether a banana is ripe.

[1148] 4. The server generates an appropriate proposal

[1149] The server combines image recognition results with emotion data and uses a generative AI model to suggest the optimal food processing method. For example, if the user is in a hurry, it suggests "immediate delivery to a local grocery store." An example of a prompt sentence is, "A photo of a ripe banana has been uploaded. Please suggest an appropriate action, taking into account that the user is a little impatient."

[1150] 5. The server sends the proposal to the device

[1151] The server then sends the generated suggestions in JSON format to the device, which include specific advice based on the image analysis and emotion recognition results.

[1152] 6. The device displays the suggestions to the user.

[1153] The device displays the received suggestions in the user interface. For example, an iPad screen might display "Delivery to a local grocery store immediately" and provide a notification tailored to the user's emotions. The user can review the suggestions and select the next action (delivery, resale, disposal, etc.).

[1154] Explanation with a concrete example

[1155] Example 1: Fully ripe bananas

[1156] 1. The user takes a photo of a ripe banana with their smartphone. At the same time, the emotion engine recognizes the user's slightly anxious facial expression.

[1157] 2. The device uploads the captured photo and the recognized emotion data to the server.

[1158] 3. The server analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[1159] 4. The server sends the proposal to the device.

[1160] 5. The device will display "Delivery to your local grocery store soon" to the user and also add a calming message to the anxious user.

[1161] Example 2: Stale bread

[1162] 1. The user takes a photo of the hardened bread with a tablet. At the same time, the emotion engine recognizes the user's relaxed facial expression.

[1163] 2. The device uploads the captured photo and the recognized emotion data to the server.

[1164] 3. The server analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing it for resale."

[1165] 4. The server sends the proposal to the device.

[1166] 5. The device will display "processing for resale" to the user, along with detailed instructions on how to achieve a relaxed state.

[1167] In this way, this system, which combines an emotion engine, proposes the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[1168] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] The user takes a photo of the food. Using the camera application on their smartphone or tablet, the user takes a photo of the food to be discarded. At this time, the camera sensor captures the image in JPEG or PNG format and saves it in the device's storage. At the same time, the emotion engine installed on the device analyzes the user's facial expressions and voice in real time, generates emotion data, and saves it on the device. The input is the user's operation and the food being photographed, and the output is image data of the food and the user's emotion data.

[1171] Step 2:

[1172] The user uploads an image and emotion data to the server. When the user presses the upload button in the application, the device sends this data to the server via an Internet connection using an HTTP POST request. Specifically, the image file (JPEG or PNG) and emotion data (JSON format) are uploaded to cloud storage. The input is the captured image and emotion data, and the output is the image file and emotion data received by the server.

[1173] Step 3:

[1174] The server receives the image and emotion data and saves it in storage. The server receives the received image data and emotion data and saves them in a database. For example, Amazon RDS or Google Cloud SQL is used to persist the data. The input is the image file and emotion data uploaded from the device, and the output is the data saved in the server's storage.

[1175] Step 4:

[1176] The server analyzes the image data using an artificial intelligence model. Using the stored image data, the server uses an artificial intelligence model (e.g., TensorFlow or PyTorch) to determine the type and condition of the food. Specifically, the image is input into a machine learning model, which outputs the analyzed food type (e.g., banana, bread) and freshness (e.g., fresh, slightly spoiled, needs to be discarded). The input is the stored image data, and the output is the analysis results of the food type and condition.

[1177] Step 5:

[1178] The server generates appropriate suggestions that also take emotional data into consideration. By combining the image recognition results and emotional data, a prompt is input into a generative AI model (e.g., GPT-4) to generate an appropriate suggestion. For example, a prompt such as "A photo of a ripe banana has been uploaded. Please suggest an appropriate action, taking into consideration that the user may be a little impatient" is used. The input is the image analysis results and emotional data, and the output is the generated suggestion.

[1179] Step 6:

[1180] The server sends the generated proposal to the terminal. The server constructs the generated proposal in JSON format and sends it to the terminal as an HTTP response. The input is the generated proposal, and the output is the proposal data sent to the terminal.

[1181] Step 7:

[1182] The proposal received by the device is displayed on the user interface. The device parses the proposal received from the server and displays it on the user interface. Specifically, the proposal is displayed using text and images, and the user selects the next action (delivery, resale, disposal, etc.). An application using Swift (for iOS) or Kotlin (for Android) is used for display. The input is the proposal data received from the server, and the output is the proposal displayed on the user interface.

[1183] (Application example 2)

[1184] 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."

[1185] In modern society, food waste is a serious problem, and there is a need for methods to effectively utilize food that would otherwise be discarded. Furthermore, there is a lack of systems that can improve user satisfaction by providing appropriate suggestions and notifications based on the user's emotional state. Therefore, there is a need for a system that not only suggests ways to dispose of food that would otherwise be discarded, but also makes optimal suggestions that take the user's emotions into consideration.

[1186] 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 uploading captured images to the server, means for analyzing the images on the server, means for generating proposals based on the analysis results, means for transmitting the generated proposals to the terminal, means for uploading user emotion data to the server, and means for adjusting the proposals based on the emotion data. This makes it possible to suggest methods for appropriately disposing of food to be discarded, and to provide customized notifications and alerts according to the user's emotional state.

[1187] "Means for photographing food" refers to a function for photographing food that is scheduled to be discarded using a camera installed on a device such as a smartphone or tablet.

[1188] "Means for saving captured images" refers to a function for saving captured images of food within the device or in cloud storage.

[1189] "Means for having an artificial intelligence model that analyzes stored images" refers to an artificial intelligence model that is used to analyze stored image data and identify the type and condition of food.

[1190] The "means for proposing food disposal methods based on analysis results" is a function that shows users the optimal disposal method (delivery, resale, disposal, etc.) for food that is scheduled to be discarded based on the analysis results.

[1191] The "means for displaying a proposed processing method" is a function for displaying a processing method based on the analysis results on a user interface.

[1192] The "emotion engine that recognizes user emotions" is an artificial intelligence model that analyzes the user's facial expressions and voice to recognize their emotional state.

[1193] The "means for adjusting the content of the proposal" is a function for optimizing the content of the proposal according to the emotional state of the user and providing information in a form that is more easily accepted by the user.

[1194] The "means for uploading to a server" is a function for transmitting the captured image and emotion data to a server via the Internet.

[1195] "Means for analyzing images on the server" refers to a function for analyzing received image data within the server and determining the type and condition of food.

[1196] The "means for generating proposal content" is a function for automatically creating the optimal food processing method based on the analysis results of image and emotion data.

[1197] The "means for transmitting the generated proposal content to the terminal" is a function for transmitting the generated proposal content to the terminal and displaying it to the user.

[1198] The "means for adjusting the content of suggestions based on emotional data" is a function that adjusts the details of the suggestions based on the user's emotional data recognized by the server, and provides appropriate alerts and notifications through the user interface.

[1199] The present invention combines a system that photographs food to be disposed of, analyzes the image, and proposes the optimal disposal method with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1200] Basic configuration

[1201] The system includes a means for photographing food, a means for saving the photographed images, a means for providing an artificial intelligence model for analyzing the saved images, a means for proposing food processing methods based on the analysis results, a means for displaying the proposed processing methods, and an emotion engine for recognizing the user's emotions, thereby enabling appropriate suggestions to be made taking into account the user's emotional state.

[1202] Hardware and software used

[1203] Hardware:

[1204] Smartphones, tablets (camera, microphone)

[1205] Server (Cloud-based)

[1206] software:

[1207] Mobile Applications

[1208] Emotion Engine (Python, Keras)

[1209] Image analysis model (TensorFlow)

[1210] Database (PostgreSQL)

[1211] Communication protocol (HTTPS)

[1212] Operation Flow

[1213] 1. User takes a photo of food:

[1214] Users use their smartphones or tablets to take photos of food that is to be discarded, and the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[1215] 2. The device uploads the image and emotion data to the server:

[1216] The captured images and recognized emotion data are uploaded to a server via HTTPS.

[1217] 3. The server analyzes the image and emotions:

[1218] The server uses an artificial intelligence model (TensorFlow) to analyze images and determine the type and condition of food. An emotion engine (Keras model) analyzes the emotion data and recognizes the user's emotional state.

[1219] 4. The server generates an appropriate proposal:

[1220] Based on the analysis, it suggests how to dispose of the food. For example, it suggests "delivery to a local grocery store" for fresh food, and "processing for resale" for spoiled food. If the user is stressed, it makes a brief suggestion, but if they are relaxed, it adds a detailed explanation.

[1221] 5. The server sends the proposal to the device:

[1222] The generated suggestions are sent to the user's device in JSON format.

[1223] 6. The device displays the suggestions to the user:

[1224] Suggestions are displayed in the user interface as text and images, along with customized notifications and alerts based on your emotional state.

[1225] Specific examples

[1226] Example 1: For a ripe banana:

[1227] 1. User: Take a photo of a ripe banana, and the emotion engine recognizes that the user has a slightly anxious expression.

[1228] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[1229] 3. Server: Analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[1230] 4. Server: Sends the proposal to the device.

[1231] 5. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[1232] Example 2: For hardened bread:

[1233] 1. User: Take a photo of the hardened bread with a tablet, and the emotion engine recognizes the user's relaxed facial expression.

[1234] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[1235] 3. Server: Analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing for resale."

[1236] 4. Server: Sends the proposal to the device.

[1237] 5. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[1238] Prompt Sentence Examples

[1239] 1. For ripe bananas:

[1240] Prompt: "If a user photographs a ripe banana and feels rushed, what suggestions should the system make?"

[1241] 2. For hardened bread:

[1242] Prompt: "If a user photographs a hardened loaf of bread and feels relaxed, what suggestions would the system make?"

[1243] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1244] Step 1:

[1245] A user takes a photo of food to be discarded using a smartphone or tablet. At this time, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. The input is the captured image and the user's facial and voice data, and the output is the captured image data and recognized emotion data.

[1246] Step 2:

[1247] The device uploads the captured image data and the recognized emotion data to the server. The input is the image data and emotion data, and the output is the data sent to the server. Communication is via HTTPS.

[1248] Step 3:

[1249] The server stores the received image data and analyzes the images. An artificial intelligence model (TensorFlow) is used for the analysis. The input is the image data stored on the server, and the output is the image analysis results (type and condition of food). The specific operation is image classification using the TensorFlow model.

[1250] Step 4:

[1251] The server analyzes the received emotion data and recognizes the user's emotional state. An emotion engine (Keras model) is used for the analysis. The input is the emotion data stored on the server, and the output is the emotion analysis result (user's emotional state).

[1252] Step 5:

[1253] The server proposes a food disposal method based on the results of image analysis and sentiment analysis. Specifically, it proposes delivery for fresh food, resale for slightly spoiled food, and disposal for food that needs to be completely discarded. The proposal is adjusted based on the results of sentiment analysis. The input is the results of image analysis and sentiment analysis, and the output is a proposal for the optimal disposal method.

[1254] Step 6:

[1255] The server generates a proposal and sends it to the terminal in JSON format. The input is the proposal for the optimal processing method, and the output is the JSON data sent to the terminal.

[1256] Step 7:

[1257] The device displays the received proposal on the user interface. The user confirms the proposed processing method and selects the next action. The input is the JSON data sent from the server, and the output is the proposal displayed to the user. The specific operation is to display text and images on the UI.

[1258] Specific examples

[1259] Example 1: Fully ripe bananas

[1260] 1. User: Takes a photo of a ripe banana, and the emotion engine recognizes the anxious expression.

[1261] 2. Device: Uploads the captured photo and emotion data to the server.

[1262] 3. Server: Analyzes the image and determines that it is a banana.

[1263] 4. Server: Analyzes emotion data and recognizes that the user is anxious.

[1264] 5. Server: Offer "immediate delivery to your local grocery store."

[1265] 6. Server: Sends the proposal to the device.

[1266] 7. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[1267] Example 2: Stale bread

[1268] 1. User: Takes a photo of hardened bread, and the emotion engine recognizes a relaxed facial expression.

[1269] 2. Device: Uploads the captured photo and emotion data to the server.

[1270] 3. Server: Analyzes the image and determines that it is bread.

[1271] 4. Server: Analyzes the emotional data and recognizes that the user is relaxed.

[1272] 5. Server: Proposes "processing for resale."

[1273] 6. Server: Sends the proposal to the device.

[1274] 7. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] [Fourth embodiment]

[1279] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1280] 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.

[1281] 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).

[1282] 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.

[1283] 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.

[1284] 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).

[1285] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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."

[1292] The present invention is a system that allows a user to take a photo of food to be discarded and analyzes the image to suggest a disposal method for the food, either by recycling, delivering, reselling, or discarding it. Specific embodiments for carrying out the present invention are described below.

[1293] 1. The user takes a photo of the food

[1294] Users take photos of food items to be discarded using a device with a camera function, such as a smartphone or tablet. The device has a photo button, and users can take photos by pressing the button.

[1295] 2. The device uploads the image to the server.

[1296] Images taken by the user are temporarily stored in the device's local storage and then uploaded to a server via an internet connection.

[1297] 3. The server analyzes the image

[1298] The server receives and stores the uploaded images, then uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to analyze the images and determine the type and condition of the food in the image.

[1299] 4. The server generates an appropriate proposal

[1300] Based on the results of the image analysis, the server decides the best way to dispose of the item: for example, it suggests "delivery to a local grocery store" for fresh fruits and vegetables, "processing for resale" for bread and prepared foods, or "disposal" if the item is in poor condition.

[1301] 5. The server sends the proposal to the device

[1302] The server sends the generated proposal to the terminal in a data format such as JSON, which includes specific processing methods and procedures.

[1303] 6. The device displays the suggestions to the user.

[1304] The device displays the received suggestions to the user, either as text or images on the device screen, and the user can select the suggested processing method and proceed to the next action.

[1305] Explanation with a concrete example

[1306] Example 1: Fully ripe bananas

[1307] 1. User: Take a photo of a ripe banana with your smartphone.

[1308] 2. Device: Upload the photos you took to the server.

[1309] 3. Server: Uses an image analysis model to determine that it is a banana.

[1310] 4. Server: Generates "Delivery to local grocery store" as a suggestion.

[1311] 5. Server: Sends the proposal to the device.

[1312] 6. Terminal: Show the user "Delivery to local grocery store" and let them choose their next action.

[1313] Example 2: Stale bread

[1314] 1. User: Takes a photo of the hardened bread with a tablet.

[1315] 2. Device: Upload the photos you took to the server.

[1316] 3. Server: Performs image analysis and determines that it is bread.

[1317] 4. Server: Generates a proposal for "processing for resale."

[1318] 5. Server: Sends the proposal to the device.

[1319] 6. Terminal: Display "Process for Resale" to the user and ask them to choose their next action.

[1320] In this way, this system is designed to allow users to easily optimize food waste disposal. By smoothly performing analysis and making recommendations, it is expected to reduce food waste and alleviate the burden on the environment.

[1321] The processing flow will be explained below.

[1322] Step 1:

[1323] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The user launches the device's camera application and presses the capture button to capture an image of the food.

[1324] Step 2:

[1325] The device temporarily saves the captured image to local storage, ensuring that the image data is available on the device and ready for the next processing step.

[1326] Step 3:

[1327] After checking the image, the user presses the upload button, which causes the device to start the process of sending the image data to the server.

[1328] Step 4:

[1329] The device uploads the captured image to the server via an internet connection, where it uses an HTTP request to send the image data to a specified endpoint on the server.

[1330] Step 5:

[1331] The server receives the uploaded image data and saves the images. The server saves the received images in temporary storage or a database, ensuring the data necessary for analysis processing.

[1332] Step 6:

[1333] The server analyzes the stored images and uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to determine the type and condition of the food in the image. The analysis results are output as information such as the type of food and its freshness.

[1334] Step 7:

[1335] Based on the analysis, the server will suggest how to dispose of the food, for example, "delivery to a local grocery store" if the food is fresh, "processing for resale" if it is slightly spoiled, or "discard" if it needs to be discarded completely.

[1336] Step 8:

[1337] The server sends the generated suggestions to the device in JSON format, and the analysis results and suggestions are returned to the user's device via network communication.

[1338] Step 9:

[1339] The terminal displays the received proposal on a user interface, where the user can review the proposed disposal method and select the next action (e.g., arranging for delivery or resale, or disposal).

[1340] Step 10:

[1341] The user selects the next action based on the suggestions, and the selection is sent via the terminal to the server, which processes the information to perform the necessary procedures based on the user's selection.

[1342] The above is the specific processing flow of the system, which allows users to easily and efficiently determine the optimal processing method for food.

[1343] Example 1

[1344] 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."

[1345] Reducing food waste is an important issue in terms of both environmental protection and economic effectiveness. However, food waste disposal at homes and stores is not currently optimized, resulting in many cases of usable food being wasted. Current systems require users to manually check the condition of food and select an appropriate disposal method, which is cumbersome and time-consuming. Therefore, there is a need for the development of a system that allows users to easily determine the condition of food and automatically suggests appropriate disposal methods.

[1346] 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.

[1347] In this invention, the server includes means for uploading images taken by a user to the server via the Internet, means for performing image analysis on the server to determine the type and condition of the food, means for determining the optimal processing method based on the determination results, and means for transmitting the determined processing method to the terminal in JSON format, thereby enabling the user to easily understand the condition of the food and quickly obtain the optimal processing method.

[1348] "Food" means any substance processed for human or animal consumption.

[1349] "Means for photographing" refers to a function for acquiring an image of an object using a device equipped with a camera function.

[1350] "Means for storing" refers to the function of temporarily or permanently storing captured images in a memory device within the device.

[1351] An "artificial intelligence model" is a system that learns from large amounts of data and has algorithms for pattern recognition and classification.

[1352] "Means for analyzing" refers to the function of using an artificial intelligence model to extract and interpret specific information from input data.

[1353] "Means of suggestion" refers to a function that shows users the optimal actions and options based on the analysis results.

[1354] "Means for displaying" refers to a function for visually conveying the proposed information to the user.

[1355] "Means for uploading via the Internet" refers to the ability to transfer image data to a remote server via a network connection.

[1356] A "server" is a computer system that manages large amounts of data and exchanges data with other devices via a network.

[1357] "Image analysis" is the process of identifying objects in a captured image and determining their type and condition.

[1358] The "JSON format" is a lightweight, easy-to-read text-based data format used for data exchange.

[1359] A "convolutional neural network (CNN) model" is an artificial intelligence model with deep learning algorithms that is primarily used for image analysis and has the ability to extract and identify features within images.

[1360] A "terminal" is a device operated by a user, and refers to a device equipped with a camera function, a storage function, a communication function, and a display function.

[1361] The present invention is a system that allows a user to take a photo of food to be discarded and analyzes the image to suggest a disposal method for the food, either by recycling, delivering, reselling, or discarding it. Specific embodiments for carrying out the present invention are described below.

[1362] This system is implemented using a device with a camera function, such as a smartphone or tablet, and a server connected via the Internet. The overall system flow is as follows:

[1363] The user uses the device to take a photo of the food to be discarded. The device has a photo button, and the user presses the button to take a photo. The captured image is temporarily saved in the device's local storage. It is then uploaded to a server via an Internet connection. When uploaded, metadata such as the user ID is also sent along with the image file.

[1364] The server then receives the uploaded images and stores them on disk storage. The server then analyzes the images using a pre-trained image analysis AI model (convolutional neural network, CNN). This analysis determines the type and condition of the food. For example, it can identify a ripe banana or stale bread. A rules engine or machine learning model runs within the server to determine the optimal processing method based on the analysis results.

[1365] Based on the results of the assessment, the server suggests the appropriate disposal method. For example, for fresh fruits and vegetables, it suggests "delivery to a local grocery store," while for bread and prepared foods, it suggests "processing for resale." If the item is in poor condition, it suggests "disposal." These suggestions are converted into a data format such as JSON and sent to the device.

[1366] The device displays the received suggestions to the user, who can then select from the suggestions displayed on the screen and proceed to the next action. The displayed information is provided in text and image format.

[1367] Examples:

[1368] Example 1: Fully ripe bananas

[1369] 1. User: Take a photo of a ripe banana with your smartphone.

[1370] 2. Device: 1. Save the captured photo to local storage and upload it to the server.

[1371] 3. Server: Receives and stores the uploaded images. It uses a trained CNN model to recognize bananas and determine whether they are overripe.

[1372] 4. Server: Generate "Delivery to local grocery store" as a suggestion.

[1373] 5. Server: Convert the proposal into JSON format and send it to the device.

[1374] 6. Terminal: "Delivery to local grocery store" is displayed as a suggestion to the user.

[1375] Example 2: Stale bread

[1376] 1. User: Takes a photo of the hardened bread with a tablet.

[1377] 2. Device: Save the captured photo to local storage and upload it to the server.

[1378] 3. Server: Receives and stores the uploaded images. It uses a trained CNN model to recognize the bread and determine whether it is cured.

[1379] 4. Server: Generates "Processing for resale" as a proposal.

[1380] 5. Server: Convert the proposal into JSON format and send it to the device.

[1381] 6. Terminal: Display "Processing for resale" as a suggestion to the user.

[1382] These steps allow users to easily understand how to process food and reduce food waste. The use of artificial intelligence models improves the accuracy of image analysis and the appropriateness of the suggested processing methods.

[1383] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1384] Step 1: User takes a photo of food

[1385] Users use a smartphone or tablet to take a photo of the food to be discarded. The input is to launch the camera app, position the food in the center of the screen, and press the capture button. The output is that the captured image is temporarily saved in the device's local storage.

[1386] As a specific example of how it works, a user takes a photo of a ripe banana using a camera app. The photo is saved as an image file, and the app is ready to move on to the next step.

[1387] Step 2: The device uploads the image to the server

[1388] The device uploads the saved image files to a server via the Internet. The input is the image file on the local storage, and the output is the image file and associated metadata sent to the server.

[1389] As a specific example of how it works, the device sends a saved photo of a banana to the server via Wi-Fi, along with metadata such as the user ID and the date and time the photo was taken.

[1390] Step 3: The server receives and stores the image

[1391] The server receives image files and metadata uploaded from the terminal and stores them in disk storage. The input is the image file and metadata sent from the terminal, and the output is the stored image and metadata.

[1392] In a specific example, the server stores the image file of the banana and associated metadata on disk storage for subsequent analysis.

[1393] Step 4: The server performs image analysis

[1394] The server inputs the saved image files into a pre-trained convolutional neural network (CNN) model to analyze the food type and state. The input is the saved image file, and the output is the food type and its state information.

[1395] As a specific example, a Python script on the server runs a CNN model to identify the type of banana and its ripeness from the image data, and as a result, determines that the banana is fully ripe.

[1396] Step 5: Server generates proposal

[1397] The server generates recommendations based on the results of image analysis. The input is the type and condition of the analyzed food, and the output is a recommendation on the optimal disposal method (e.g., delivery, resale, disposal).

[1398] For example, if the banana is overripe, the server may decide to offer "delivery to a local grocery store." The optimal offer is generated by a rules engine or machine learning model.

[1399] Step 6: The server sends the proposal to the device

[1400] The server converts the generated proposal content into JSON format and sends it to the terminal. The input is the generated proposal content, and the output is JSON format data.

[1401] As a specific example of how it works, the server generates a JSON file containing the offer "Delivery to a local grocery store" and sends it to the device via the Internet.

[1402] Step 7: The device displays the suggestions to the user

[1403] The device parses the received JSON formatted suggestions and displays them to the user. The input is the JSON data received from the server, and the output is text or image information visually presented to the user.

[1404] As a specific example of operation, the terminal displays a "Delivery to local grocery store" offer on the screen in text and images, providing the user with options to select.

[1405] (Application example 1)

[1406] 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."

[1407] Food waste has become a major problem in modern society. A large amount of food is discarded at home and in logistics centers, which increases the burden on the environment. To solve this problem, a system is needed that can quickly and accurately suggest the optimal disposal method for food that is scheduled to be discarded, whether it be for reuse, delivery, or proper disposal. However, current systems have the problem of being unable to accurately determine the condition of food and make appropriate suggestions based on that information.

[1408] 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.

[1409] In this invention, the server includes a means for uploading the captured image to the server, a means for performing image analysis on the server and generating a proposal based on the analysis results, and a means for transmitting the generated proposal to the terminal, thereby enabling the state of the food to be accurately analyzed and the optimal processing method to be proposed based on the analysis.

[1410] "Food" means any substance consumed by humans as food or as a foodstuff.

[1411] "Means for taking pictures" refers to a method or device for capturing an image using an imaging device such as a camera.

[1412] "Means for storing images" refers to a method or device for storing captured images in local storage or cloud storage.

[1413] An "artificial intelligence model" is a collection of machine learning algorithms trained to perform a specific task, such as a convolutional neural network (CNN) for image analysis.

[1414] "Proposal means" refers to a method or device that shows the user the optimal processing method based on the analysis results.

[1415] The "display means" refers to a display device such as a display or mobile terminal that visually notifies the user of the proposed processing method.

[1416] "Means for uploading to a server via communication" refers to a method or device for transmitting data to a server via the Internet or other communication network.

[1417] The "means for generating analysis results and proposals" refers to a method or device that analyzes images on a server and generates an appropriate processing method based on the results.

[1418] The "means for transmitting to the terminal" refers to a communication means for transmitting the generated proposal content to the user's terminal.

[1419] "Recycling" refers to the process of making processed food usable again and providing it for reuse.

[1420] "Delivery" means the means by which food is transported to a specified location based on a specific processing method.

[1421] "Disposal" refers to the proper disposal of processed food when it is not suitable for reuse or distribution.

[1422] "Means for displaying to the user and allowing the user to select the next action to be taken" refers to a method or device that visually presents the proposed processing method to the user and allows the user to select the next measure based on that.

[1423] These definitions provide a clear understanding of the specific meaning of the terms set forth in the claims.

[1424] The embodiment of the present invention will now be described in detail.

[1425] The present invention is a system that allows a user to photograph food to be discarded and analyzes the image to suggest a disposal method for the food, either by reusing, delivering, reselling, or discarding it.

[1426] System Configuration

[1427] 1. User operation

[1428] Users take photos of food items to be discarded using a device with a camera function, such as a smartphone or tablet. The device has a photo button, and the user presses this button to take a photo.

[1429] 2. Save and upload images

[1430] The captured images are temporarily stored in the device's local storage, and then uploaded to a server via an Internet connection, for example via the HTTP protocol.

[1431] 3. Server Analysis

[1432] The server receives and stores the uploaded images, then analyzes them using pre-trained generative AI models, specifically convolutional neural networks (CNNs), to determine the type and condition of the food in the image.

[1433] 4. Processing method proposal

[1434] The server then uses the results of image analysis to determine the best way to dispose of the food depending on its condition. For example, it suggests "delivery to a local grocery store" for fresh fruits and vegetables, "processing for resale" for bread and prepared foods, and "disposal" if the food is in poor condition.

[1435] 5. Communication and Display of Proposal Content

[1436] The server sends the generated proposals in a data format such as JSON to the device. In response, the device displays the received proposals to the user. The proposals are displayed on the device screen as text or images, and the user can select the proposed processing method and proceed to the next action.

[1437] Hardware and software used

[1438] A smartphone or tablet with a camera, which the user uses to take photos of the food.

[1439] Internet connection: Used to upload image data to the server and receive analysis results.

[1440] Server: Receives image data and analyzes it using a generative AI model (e.g., a convolutional neural network).

[1441] Generative AI models: They use pre-trained machine learning algorithms, particularly CNNs, to analyze images.

[1442] Specific examples

[1443] Example 1: Fully ripe bananas

[1444] 1. User: Take a photo of a ripe banana with your smartphone.

[1445] 2. Device: Upload the photos you took to the server.

[1446] 3. Server: Uses a convolutional neural network (CNN) to determine that it is a banana.

[1447] 4. Server: Generates "Delivery to local grocery store" as a suggestion.

[1448] 5. Server: Sends the proposal to the device.

[1449] 6. Terminal: Show the user "Delivery to local grocery store" and let them choose their next action.

[1450] Example 2: Stale bread

[1451] 1. User: Takes a photo of the hardened bread with a tablet.

[1452] 2. Device: Upload the photos you took to the server.

[1453] 3. Server: Uses a convolutional neural network (CNN) to identify the object as bread.

[1454] 4. Server: Generates a proposal for "processing for resale."

[1455] 5. Server: Sends the proposal to the device.

[1456] 6. Terminal: Display "Process for Resale" to the user and ask them to choose their next action.

[1457] Prompt Sentence Examples

[1458] Analyze food images to determine the type and condition of the food.

[1459] Food: Bananas

[1460] Image URL: <URL of image>

[1461] Output formats:

[1462] Type: <Food type>

[1463] Condition: <Food condition>

[1464] Proposal: <Proposed solution>

[1465] In this way, the present invention is designed to enable users to easily optimize food waste disposal. By smoothly carrying out the analysis and proposal process, it is expected to reduce food waste and alleviate the burden on the environment.

[1466] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1467] Step 1:

[1468] The user uses the camera function of their smartphone or tablet to take a photo of the food to be discarded. The photo is taken by pressing the camera button on the device. The input is the image of the food taken by the user. The output is an image file saved in local storage.

[1469] Step 2:

[1470] The device uploads the captured image to the server via an Internet connection. Specifically, it sends the image file to the server using an HTTP POST request. The input is the image file stored in local storage, and the output is the image data stored on the server side.

[1471] Step 3:

[1472] The server receives the uploaded images and stores them (temporarily stores them in a database, etc., as an initial process). The input is the image data sent from the terminal, and the output is the image data stored on the server.

[1473] Step 4:

[1474] The server begins image analysis using the stored image data. Specifically, it uses a pre-trained generative AI model (e.g., a convolutional neural network (CNN)) to determine the type and condition of food in the image. The input is the image data stored on the server, and the output is the analysis results regarding the type and condition of the food.

[1475] Step 5:

[1476] The server then decides the best way to dispose of the item based on the analysis results. For example, for fresh fruits and vegetables, it might suggest "delivery to a local grocery store," for bread or prepared foods, "processing for resale," or "disposal" if the item is in poor condition. The input is the image analysis results, and the output is data (e.g., JSON format) containing the suggestions.

[1477] Step 6:

[1478] The server sends the generated proposal to the device. Specifically, it formats the data in JSON format or similar and sends it to the device again via an HTTP request over an internet connection. The input is the data containing the proposal, and the output is the data sent to the device.

[1479] Step 7:

[1480] The terminal displays the received proposal to the user. The proposal is displayed on the terminal screen as text or an image, and the user confirms the proposed processing method and selects the next action. The input is data containing the proposal sent from the server, and the output is the proposal displayed to the user.

[1481] In this way, through the specific actions at each step, food to be discarded can be analyzed and appropriate disposal methods can be proposed.

[1482] 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.

[1483] The present invention combines a system that allows a user to take a photo of food to be discarded, analyzes the image, and suggests the optimal disposal method, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1484] Basic configuration

[1485] The system includes a means for photographing food, a means for saving the photographed images, a means for providing an artificial intelligence model for analyzing the saved images, a means for proposing food processing methods based on the analysis results, a means for displaying the proposed processing methods, and an emotion engine for recognizing the user's emotions, thereby enabling appropriate suggestions to be made taking into account the user's emotional state.

[1486] 1. The user takes a photo of the food

[1487] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The device has a camera application installed, and the user captures the image by pressing the capture button. The system also analyzes the user's facial expressions and voice and uses an emotion engine to recognize their emotional state.

[1488] 2. The device uploads the image to the server.

[1489] After the user confirms the image, they press the upload button, which causes the device to send the food image data and the recognized emotion data to the server. The upload is done via an internet connection, using an HTTP request to send the data.

[1490] 3. The server analyzes the image

[1491] The server receives and stores the uploaded image data and emotion data. The server then analyzes the stored images using an artificial intelligence model to determine the type and condition of the food. The analysis results are output as information such as the type and freshness of the food.

[1492] 4. The server generates an appropriate proposal

[1493] Based on the analysis, the server suggests how to dispose of the food: "Delivery to a local grocery store" if the food is fresh, "Process for resale" if it's slightly spoiled, or "Discard" if it needs to be discarded completely. It also adjusts its suggestions based on the user's perceived emotions. For example, if the user is feeling stressed, it might make more concise suggestions.

[1494] 5. The server sends the proposal to the device

[1495] The server sends the generated suggestions in JSON format to the device. The suggestions include suggestions based on the analysis results and emotion recognition results. This allows appropriate notifications and alerts to be displayed taking into account the user's emotional state.

[1496] 6. The device displays the suggestions to the user.

[1497] The device displays the received suggestions on the user interface. The suggestions are displayed as text and images on the device screen, allowing the user to confirm the suggested handling method and select the next action (e.g., arranging delivery or resale, or disposal). In addition, customized notifications and alerts are displayed according to the user's emotional state, improving the user experience.

[1498] Explanation with a concrete example

[1499] Example 1: Fully ripe bananas

[1500] 1. User: Take a photo of a ripe banana with your smartphone. At the same time, the emotion engine recognizes the user's slightly anxious facial expression.

[1501] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[1502] 3. Server: Analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[1503] 4. Server: Sends the proposal to the device.

[1504] 5. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[1505] Example 2: Stale bread

[1506] 1. User: Take a photo of the hardened bread with a tablet. At the same time, the emotion engine recognizes the user's relaxed facial expression.

[1507] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[1508] 3. Server: Analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing for resale."

[1509] 4. Server: Sends the proposal to the device.

[1510] 5. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[1511] In this way, this system, which combines an emotion engine, proposes the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[1512] The processing flow will be explained below.

[1513] Step 1:

[1514] The user takes a photo of the food to be discarded using a device such as a smartphone or tablet. The user then launches a camera application and presses the capture button to capture the image of the food. At the same time, the device uses the built-in camera and microphone to detect the user's facial expressions and voice, and the emotion engine recognizes the user's emotional state.

[1515] Step 2:

[1516] The device temporarily stores the captured image of the food in local storage, along with the user's emotion data recognized by the emotion engine.

[1517] Step 3:

[1518] After checking the image, the user presses the upload button, which causes the device to send the food image data and emotion data to the server.

[1519] Step 4:

[1520] The device uploads food image data and emotion data to the server via an internet connection, using an HTTP POST request to send the data to a specified endpoint on the server.

[1521] Step 5:

[1522] The server receives the uploaded image data and emotion data, and stores the received images in temporary storage or a database, along with the emotion data.

[1523] Step 6:

[1524] The server analyzes the stored image data and uses a pre-trained artificial intelligence model (e.g., a convolutional neural network (CNN)) to identify the type and condition of the food in the image. The analysis results are output as the food type and freshness.

[1525] Step 7:

[1526] The server analyzes the stored emotional data, and the emotion engine analyzes the emotional information recognized from the user's facial expressions and voice to determine the user's emotional state.

[1527] Step 8:

[1528] The server then suggests how to dispose of the food based on the results of image and emotion analysis. If the food is fresh, it suggests "delivery to a local grocery store." If it's slightly damaged, it suggests "processing for resale." If it needs to be completely discarded, it suggests "discarding." The server also adjusts the suggestions and their explanations based on the user's emotional state. For example, if the user is stressed, it makes simple, easy-to-understand suggestions, while if they're relaxed, it provides detailed information.

[1529] Step 9:

[1530] The server generates suggestions and sends them to the device in JSON format, including analysis results, suggestions, and customization information based on emotion recognition results.

[1531] Step 10:

[1532] The device displays the received suggestions on a user interface, allowing the user to review the suggested handling method and select the next action (e.g., arrange for delivery or resale, or dispose of). It also displays customized notifications and alerts for the suggested actions based on the user's emotional state.

[1533] The above is a specific processing flow of the present invention, and by combining it with an emotion engine, it is possible to propose the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[1534] Example 2

[1535] 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."

[1536] Food waste has become a serious problem in modern society. However, it is difficult for users to determine how to dispose of food that is scheduled for disposal. The psychological stress of discarding food is also a significant issue. In particular, disposal methods proposed without considering the user's emotional state decrease user satisfaction and weaken motivation to take appropriate action. Therefore, a system is needed that allows users to easily find the optimal disposal method for food that is scheduled for disposal and that also makes appropriate suggestions taking into account the user's emotional state.

[1537] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for uploading captured images and emotion data to the server, means for analyzing the images and emotion data on the server, and means for generating suggestions based on the analysis results. This allows the user to easily know the optimal method for disposing of food that is scheduled to be discarded, and makes it possible to make appropriate suggestions that take into account the user's emotional state.

[1538] "Means for photographing food" refers to hardware and software that allows users to use devices such as smartphones and tablets to capture images of food to be discarded.

[1539] "Means for storing captured images" refers to means for storing captured images of food in the device's internal storage or cloud storage.

[1540] "Means comprising an artificial intelligence model" refers to means comprising a machine learning model or algorithm for analyzing stored images.

[1541] "Means for suggesting food disposal methods" refers to a system for suggesting food distribution, resale, or disposal based on analyzed data.

[1542] "Means for recognizing the user's emotions" refers to emotion recognition engines and algorithms that analyze the user's facial expressions, voice, etc. and determine their emotional state.

[1543] The "means for displaying a proposed processing method" refers to a user interface for visually displaying an appropriate food processing method to the user on the screen of the terminal.

[1544] "Means for uploading captured images and emotion data to a server" refers to a communication means for transmitting this data to a remote server via the Internet.

[1545] "Means for analyzing images and emotional data on a server" refers to software and hardware for analyzing image data and emotional data stored on a server and processing the information.

[1546] "Means for generating proposals based on analysis results" refers to a mechanism for automatically generating optimal proposals using a generative AI model based on the analysis results of image and emotion data.

[1547] The "means for transmitting the generated proposal content to the terminal" refers to a communication means for transmitting the proposal content generated by the server to the user's terminal.

[1548] "Means for suggesting whether to deliver, resell, or dispose of food based on the results of image and emotion data analysis" refers to a system that uses artificial intelligence models and emotion recognition results to determine the optimal way to dispose of food.

[1549] The present invention combines a system that allows a user to take a photo of food to be discarded, analyzes the image, and suggests the optimal disposal method, with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1550] Basic configuration

[1551] The system includes the following hardware and software:

[1552] 1. Devices such as smartphones and tablets (e.g. iPhone, iPad)

[1553] 2. Camera applications (e.g. iOS camera app, Google Camera)

[1554] 3. Emotion engines (e.g., Microsoft Azure Emotion API, IBM Watson)

[1555] 4. Internet connection

[1556] 5. Server (e.g., Amazon EC2, Google Compute Engine)

[1557] 6. Cloud storage (e.g., Google Cloud Storage, Amazon S3)

[1558] 7. Artificial intelligence models (e.g., image recognition models using TensorFlow and PyTorch)

[1559] 8. Generative AI models (e.g., GPT-4)

[1560] Specific actions

[1561] 1. The user takes a photo of the food

[1562] A user uses the camera application on their smartphone or tablet to take a photo of food to be discarded. For example, say they use the iPhone camera app to take a photo of a ripe banana. When they press the capture button, the camera sensor captures the image and saves it in the device's storage. At the same time, the emotion engine analyzes the user's facial expressions and voice to obtain emotional data.

[1563] 2. The device uploads the image to the server.

[1564] After the user checks the captured image and emotion data, they press the upload button, and the device sends this data to a server via an HTTP POST request. For example, the device uploads the image file and emotion data (in JSON format) to Google Cloud Storage.

[1565] 3. The server analyzes the image

[1566] The server receives the uploaded image data and emotion data and stores them in storage. The server then analyzes the stored images using an artificial intelligence model to determine the type and condition of the food. For example, TensorFlow can be used to determine whether a banana is ripe.

[1567] 4. The server generates an appropriate proposal

[1568] The server combines image recognition results with emotion data and uses a generative AI model to suggest the optimal food processing method. For example, if the user is in a hurry, it suggests "immediate delivery to a local grocery store." An example of a prompt sentence is, "A photo of a ripe banana has been uploaded. Please suggest an appropriate action, taking into account that the user is a little impatient."

[1569] 5. The server sends the proposal to the device

[1570] The server then sends the generated suggestions in JSON format to the device, which include specific advice based on the image analysis and emotion recognition results.

[1571] 6. The device displays the suggestions to the user.

[1572] The device displays the received suggestions in the user interface. For example, an iPad screen might display "Delivery to a local grocery store immediately" and provide a notification tailored to the user's emotions. The user can review the suggestions and select the next action (delivery, resale, disposal, etc.).

[1573] Explanation with a concrete example

[1574] Example 1: Fully ripe bananas

[1575] 1. The user takes a photo of a ripe banana with their smartphone. At the same time, the emotion engine recognizes the user's slightly anxious facial expression.

[1576] 2. The device uploads the captured photo and the recognized emotion data to the server.

[1577] 3. The server analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[1578] 4. The server sends the proposal to the device.

[1579] 5. The device will display "Delivery to your local grocery store soon" to the user and also add a calming message to the anxious user.

[1580] Example 2: Stale bread

[1581] 1. The user takes a photo of the hardened bread with a tablet. At the same time, the emotion engine recognizes the user's relaxed facial expression.

[1582] 2. The device uploads the captured photo and the recognized emotion data to the server.

[1583] 3. The server analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing it for resale."

[1584] 4. The server sends the proposal to the device.

[1585] 5. The device will display "processing for resale" to the user, along with detailed instructions on how to achieve a relaxed state.

[1586] In this way, this system, which combines an emotion engine, proposes the optimal food disposal method that takes into account the user's emotional state, thereby reducing food waste and improving user satisfaction.

[1587] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1588] Step 1:

[1589] The user takes a photo of the food. Using the camera application on their smartphone or tablet, the user takes a photo of the food to be discarded. At this time, the camera sensor captures the image in JPEG or PNG format and saves it in the device's storage. At the same time, the emotion engine installed on the device analyzes the user's facial expressions and voice in real time, generates emotion data, and saves it on the device. The input is the user's operation and the food being photographed, and the output is image data of the food and the user's emotion data.

[1590] Step 2:

[1591] The user uploads an image and emotion data to the server. When the user presses the upload button in the application, the device sends this data to the server via an Internet connection using an HTTP POST request. Specifically, the image file (JPEG or PNG) and emotion data (JSON format) are uploaded to cloud storage. The input is the captured image and emotion data, and the output is the image file and emotion data received by the server.

[1592] Step 3:

[1593] The server receives the image and emotion data and saves it in storage. The server receives the received image data and emotion data and saves them in a database. For example, Amazon RDS or Google Cloud SQL is used to persist the data. The input is the image file and emotion data uploaded from the device, and the output is the data saved in the server's storage.

[1594] Step 4:

[1595] The server analyzes the image data using an artificial intelligence model. Using the stored image data, the server uses an artificial intelligence model (e.g., TensorFlow or PyTorch) to determine the type and condition of the food. Specifically, the image is input into a machine learning model, which outputs the analyzed food type (e.g., banana, bread) and freshness (e.g., fresh, slightly spoiled, needs to be discarded). The input is the stored image data, and the output is the analysis results of the food type and condition.

[1596] Step 5:

[1597] The server generates appropriate suggestions that also take emotional data into consideration. By combining the image recognition results and emotional data, a prompt is input into a generative AI model (e.g., GPT-4) to generate an appropriate suggestion. For example, a prompt such as "A photo of a ripe banana has been uploaded. Please suggest an appropriate action, taking into consideration that the user may be a little impatient" is used. The input is the image analysis results and emotional data, and the output is the generated suggestion.

[1598] Step 6:

[1599] The server sends the generated proposal to the terminal. The server constructs the generated proposal in JSON format and sends it to the terminal as an HTTP response. The input is the generated proposal, and the output is the proposal data sent to the terminal.

[1600] Step 7:

[1601] The proposal received by the device is displayed on the user interface. The device parses the proposal received from the server and displays it on the user interface. Specifically, the proposal is displayed using text and images, and the user selects the next action (delivery, resale, disposal, etc.). An application using Swift (for iOS) or Kotlin (for Android) is used for display. The input is the proposal data received from the server, and the output is the proposal displayed on the user interface.

[1602] (Application example 2)

[1603] 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."

[1604] In modern society, food waste is a serious problem, and there is a need for methods to effectively utilize food that would otherwise be discarded. Furthermore, there is a lack of systems that can improve user satisfaction by providing appropriate suggestions and notifications based on the user's emotional state. Therefore, there is a need for a system that not only suggests ways to dispose of food that would otherwise be discarded, but also makes optimal suggestions that take the user's emotions into consideration.

[1605] 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 uploading captured images to the server, means for analyzing the images on the server, means for generating proposals based on the analysis results, means for transmitting the generated proposals to the terminal, means for uploading user emotion data to the server, and means for adjusting the proposals based on the emotion data. This makes it possible to suggest methods for appropriately disposing of food to be discarded, and to provide customized notifications and alerts according to the user's emotional state.

[1606] "Means for photographing food" refers to a function for photographing food that is scheduled to be discarded using a camera installed on a device such as a smartphone or tablet.

[1607] "Means for saving captured images" refers to a function for saving captured images of food within the device or in cloud storage.

[1608] "Means for having an artificial intelligence model that analyzes stored images" refers to an artificial intelligence model that is used to analyze stored image data and identify the type and condition of food.

[1609] The "means for proposing food disposal methods based on analysis results" is a function that shows users the optimal disposal method (delivery, resale, disposal, etc.) for food that is scheduled to be discarded based on the analysis results.

[1610] The "means for displaying a proposed processing method" is a function for displaying a processing method based on the analysis results on a user interface.

[1611] The "emotion engine that recognizes user emotions" is an artificial intelligence model that analyzes the user's facial expressions and voice to recognize their emotional state.

[1612] The "means for adjusting the content of the proposal" is a function for optimizing the content of the proposal according to the emotional state of the user and providing information in a form that is more easily accepted by the user.

[1613] The "means for uploading to a server" is a function for transmitting the captured image and emotion data to a server via the Internet.

[1614] "Means for analyzing images on the server" refers to a function for analyzing received image data within the server and determining the type and condition of food.

[1615] The "means for generating proposal content" is a function for automatically creating the optimal food processing method based on the analysis results of image and emotion data.

[1616] The "means for transmitting the generated proposal content to the terminal" is a function for transmitting the generated proposal content to the terminal and displaying it to the user.

[1617] The "means for adjusting the content of suggestions based on emotional data" is a function that adjusts the details of the suggestions based on the user's emotional data recognized by the server, and provides appropriate alerts and notifications through the user interface.

[1618] The present invention combines a system that photographs food to be disposed of, analyzes the image, and proposes the optimal disposal method with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1619] Basic configuration

[1620] The system includes a means for photographing food, a means for saving the photographed images, a means for providing an artificial intelligence model for analyzing the saved images, a means for proposing food processing methods based on the analysis results, a means for displaying the proposed processing methods, and an emotion engine for recognizing the user's emotions, thereby enabling appropriate suggestions to be made taking into account the user's emotional state.

[1621] Hardware and software used

[1622] Hardware:

[1623] Smartphones, tablets (camera, microphone)

[1624] Server (Cloud-based)

[1625] software:

[1626] Mobile Applications

[1627] Emotion Engine (Python, Keras)

[1628] Image analysis model (TensorFlow)

[1629] Database (PostgreSQL)

[1630] Communication protocol (HTTPS)

[1631] Operation Flow

[1632] 1. User takes a photo of food:

[1633] Users use their smartphones or tablets to take photos of food that is to be discarded, and the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state.

[1634] 2. The device uploads the image and emotion data to the server:

[1635] The captured images and recognized emotion data are uploaded to a server via HTTPS.

[1636] 3. The server analyzes the image and emotions:

[1637] The server uses an artificial intelligence model (TensorFlow) to analyze images and determine the type and condition of food. An emotion engine (Keras model) analyzes the emotion data and recognizes the user's emotional state.

[1638] 4. The server generates an appropriate proposal:

[1639] Based on the analysis, it suggests how to dispose of the food. For example, it suggests "delivery to a local grocery store" for fresh food, and "processing for resale" for spoiled food. If the user is stressed, it makes a brief suggestion, but if they are relaxed, it adds a detailed explanation.

[1640] 5. The server sends the proposal to the device:

[1641] The generated suggestions are sent to the user's device in JSON format.

[1642] 6. The device displays the suggestions to the user:

[1643] Suggestions are displayed in the user interface as text and images, along with customized notifications and alerts based on your emotional state.

[1644] Specific examples

[1645] Example 1: For a ripe banana:

[1646] 1. User: Take a photo of a ripe banana, and the emotion engine recognizes that the user has a slightly anxious expression.

[1647] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[1648] 3. Server: Analyzes the image, determines that it is a banana, and, taking into account the user's impatience, suggests "immediate delivery to a local grocery store."

[1649] 4. Server: Sends the proposal to the device.

[1650] 5. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[1651] Example 2: For hardened bread:

[1652] 1. User: Take a photo of the hardened bread with a tablet, and the emotion engine recognizes the user's relaxed facial expression.

[1653] 2. Device: Uploads the captured photo and the recognized emotion data to the server.

[1654] 3. Server: Analyzes the image, determines that it is bread, and, taking into account the user's relaxed state, suggests "processing for resale."

[1655] 4. Server: Sends the proposal to the device.

[1656] 5. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[1657] Prompt Sentence Examples

[1658] 1. For ripe bananas:

[1659] Prompt: "If a user photographs a ripe banana and feels rushed, what suggestions should the system make?"

[1660] 2. For hardened bread:

[1661] Prompt: "If a user photographs a hardened loaf of bread and feels relaxed, what suggestions would the system make?"

[1662] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1663] Step 1:

[1664] A user takes a photo of food to be discarded using a smartphone or tablet. At this time, the emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. The input is the captured image and the user's facial and voice data, and the output is the captured image data and recognized emotion data.

[1665] Step 2:

[1666] The device uploads the captured image data and the recognized emotion data to the server. The input is the image data and emotion data, and the output is the data sent to the server. Communication is via HTTPS.

[1667] Step 3:

[1668] The server stores the received image data and analyzes the images. An artificial intelligence model (TensorFlow) is used for the analysis. The input is the image data stored on the server, and the output is the image analysis results (type and condition of food). The specific operation is image classification using the TensorFlow model.

[1669] Step 4:

[1670] The server analyzes the received emotion data and recognizes the user's emotional state. An emotion engine (Keras model) is used for the analysis. The input is the emotion data stored on the server, and the output is the emotion analysis result (user's emotional state).

[1671] Step 5:

[1672] The server proposes a food disposal method based on the results of image analysis and sentiment analysis. Specifically, it proposes delivery for fresh food, resale for slightly spoiled food, and disposal for food that needs to be completely discarded. The proposal is adjusted based on the results of sentiment analysis. The input is the results of image analysis and sentiment analysis, and the output is a proposal for the optimal disposal method.

[1673] Step 6:

[1674] The server generates a proposal and sends it to the terminal in JSON format. The input is the proposal for the optimal processing method, and the output is the JSON data sent to the terminal.

[1675] Step 7:

[1676] The device displays the received proposal on the user interface. The user confirms the proposed processing method and selects the next action. The input is the JSON data sent from the server, and the output is the proposal displayed to the user. The specific operation is to display text and images on the UI.

[1677] Specific examples

[1678] Example 1: Fully ripe bananas

[1679] 1. User: Takes a photo of a ripe banana, and the emotion engine recognizes the anxious expression.

[1680] 2. Device: Uploads the captured photo and emotion data to the server.

[1681] 3. Server: Analyzes the image and determines that it is a banana.

[1682] 4. Server: Analyzes emotion data and recognizes that the user is anxious.

[1683] 5. Server: Offer "immediate delivery to your local grocery store."

[1684] 6. Server: Sends the proposal to the device.

[1685] 7. Terminal: Prompt the user for "Delivery to your local grocery store soon" and also add a calming message for impatient users.

[1686] Example 2: Stale bread

[1687] 1. User: Takes a photo of hardened bread, and the emotion engine recognizes a relaxed facial expression.

[1688] 2. Device: Uploads the captured photo and emotion data to the server.

[1689] 3. Server: Analyzes the image and determines that it is bread.

[1690] 4. Server: Analyzes the emotional data and recognizes that the user is relaxed.

[1691] 5. Server: Proposes "processing for resale."

[1692] 6. Server: Sends the proposal to the device.

[1693] 7. Terminal: Display "Processing for Resale" to the user, along with detailed instructions for adjusting to a relaxed state.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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.

[1698] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1699] 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.

[1700] 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).

[1701] 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.

[1702] 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."

[1703] 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.

[1704] 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).

[1705] 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.

[1706] 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.

[1707] 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.

[1708] 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.

[1709] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1710] 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.

[1711] 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.

[1712] 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.

[1713] 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.

[1714] 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.

[1715] The following is further disclosed regarding the above embodiment.

[1716] (Claim 1)

[1717] a means for photographing food;

[1718] a means for storing the captured image;

[1719] means for providing an artificial intelligence model for analyzing the stored images;

[1720] A means for proposing a method of processing food based on the analysis results;

[1721] means for displaying the proposed treatment;

[1722] A system including:

[1723] (Claim 2)

[1724] A means for uploading the captured image to a server;

[1725] A means for analyzing the image on the server;

[1726] means for generating proposals based on the analysis results;

[1727] means for transmitting the generated proposal to the terminal;

[1728] The system of claim 1 further comprising:

[1729] (Claim 3)

[1730] 10. The system of claim 1, further comprising means for suggesting either delivery, resale, or disposal of the food based on the analysis results.

[1731] "Example 1"

[1732] (Claim 1)

[1733] a means for photographing food;

[1734] a means for storing the captured image;

[1735] means for providing an artificial intelligence model for analyzing the stored images;

[1736] A means for proposing a method of processing food based on the analysis results;

[1737] means for displaying the proposed treatment;

[1738] A means for uploading images taken by a user to a server via the Internet;

[1739] A means of performing image analysis on the server to determine the type and condition of food;

[1740] A means for determining an optimal processing method based on the discrimination result;

[1741] A means for transmitting the determined processing method to the terminal in JSON format;

[1742] A system including:

[1743] (Claim 2)

[1744] 10. The system of claim 1, further comprising means for suggesting either delivery, resale, or disposal of the food based on the analysis results.

[1745] (Claim 3)

[1746] 10. The system of claim 1, further comprising means for using a convolutional neural network model to determine the type and state of the food product.

[1747] "Application Example 1"

[1748] (Claim 1)

[1749] a means for photographing food;

[1750] a means for storing the captured image;

[1751] means for providing an artificial intelligence model for analyzing the stored images;

[1752] A means for proposing a method of processing food based on the analysis results;

[1753] means for displaying the proposed treatment;

[1754] means for uploading the proposed content to a server via communication;

[1755] A means for the server to generate analysis results and recommendations;

[1756] means for transmitting the generated proposal to the terminal;

[1757] A system including:

[1758] (Claim 2)

[1759] A means for uploading the captured image to a server;

[1760] A means for performing image analysis on the server and generating proposal content based on the analysis results;

[1761] means for transmitting the generated proposal to the terminal;

[1762] means for displaying the proposed actions to the user and allowing the user to select a next action to take;

[1763] The system of claim 1 further comprising:

[1764] (Claim 3)

[1765] 10. The system of claim 1, further comprising means for suggesting either reuse, delivery, or disposal of the food based on the analysis results.

[1766] "Example 2: Combining Emotion Engines"

[1767] (Claim 1)

[1768] a means for photographing food;

[1769] a means for storing the captured image;

[1770] means for providing an artificial intelligence model for analyzing the stored images;

[1771] A means for proposing a method of processing food based on the analysis results;

[1772] means for recognizing a user's emotion;

[1773] means for displaying the proposed treatment;

[1774] A system including:

[1775] (Claim 2)

[1776] A means for uploading the captured image and emotion data to a server;

[1777] means for analyzing image and emotion data on a server;

[1778] means for generating proposals based on the analysis results;

[1779] means for transmitting the generated proposal to the terminal;

[1780] The system of claim 1 further comprising:

[1781] (Claim 3)

[1782] 10. The system of claim 1, further comprising: means for suggesting delivery, resale, or disposal of the food based on the analysis of the image and emotion data.

[1783] "Application example 2 when combining emotion engines"

[1784] (Claim 1)

[1785] a means for photographing food;

[1786] a means for storing the captured image;

[1787] means for providing an artificial intelligence model for analyzing the stored images;

[1788] A means for proposing a method of processing food based on the analysis results;

[1789] means for displaying the proposed treatment;

[1790] means for adjusting the suggestions based on the user's emotions, the means including an emotion engine for recognizing the user's emotions;

[1791] A system including:

[1792] (Claim 2)

[1793] A means for uploading the captured image to a server;

[1794] A means for analyzing the image on the server;

[1795] means for generating proposals based on the analysis results;

[1796] means for transmitting the generated proposal to the terminal;

[1797] means for uploading user emotion data to a server;

[1798] A way to tailor suggestions based on emotional data

[1799] The system of claim 1 further comprising:

[1800] (Claim 3)

[1801] Based on the results of the analysis, the food product may be recommended for distribution, resale, or disposal;

[1802] 10. The system of claim 1, further comprising means for displaying customized notifications and alerts according to the user's emotional state as recognized by the emotion engine. [Explanation of symbols]

[1803] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for photographing food; a means for storing the captured image; means for providing an artificial intelligence model for analyzing the stored images; A means for proposing a method of processing food based on the analysis results; means for displaying the proposed treatment; A system including:

2. A means for uploading the captured image to a server; A means for analyzing the image on the server; means for generating proposals based on the analysis results; means for transmitting the generated proposal to the terminal; The system of claim 1 further comprising:

3. The system of claim 1 further comprising means for suggesting either delivery, resale, or disposal of the food based on the analysis results.

Images