System

Abstract

A system is provided.SOLUTION: The system includes a means for acquiring the image of the disused article, a means for analyzing the acquired image and identifying the attribute of the disused article, a means for generating an optimum disposal method and the possibility of reuse on the basis of the identified attribute, and a means for notifying a user of the generated information.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] The method of sorting waste is complicated, and accurate sorting is often difficult. Incorrect sorting can lead to increased costs and illegal dumping. Furthermore, reusable items are often overlooked, which also leads to resource waste. These issues pose major challenges for individuals, local governments, and society as a whole. [Means for solving the problem]

[0005] The present invention provides a system including a means for acquiring images of unwanted items, a means for analyzing the acquired images to identify the attributes of the unwanted items, a means for generating optimal disposal methods and reuse possibilities based on the identified attributes, and a means for notifying the user of the generated information. The present invention also includes a means for reading barcode information, a means for collating the read barcode information with a product database to acquire detailed product information, a means for acquiring location information, and a means for searching for information on municipal sorting rules and collection dates based on the acquired location information, thereby enabling efficient waste sorting and reuse. This allows individuals and municipalities to easily sort waste, increasing the possibility of reuse.

[0006] "Unwanted items" refers to items or products that are destined for disposal or reuse.

[0007] "Means for obtaining images" refers to the ability to take photos of unwanted items using devices such as smartphones or tablets.

[0008] "Means for analyzing images" refers to algorithms or software that process received image data and estimate attributes such as the material and shape of unwanted items.

[0009] "Means for identifying attributes" refers to technology that determines the specific classification and characteristics of unwanted items from the results of image analysis.

[0010] "Means for generating disposal methods" refers to a function that provides optimal disposal methods or recycling procedures for unwanted items based on identified attributes.

[0011] "Means for generating reuse possibilities" refers to technology that determines whether unwanted items can be reused and suggests their market value and means for reuse.

[0012] "Means for notifying users" refers to an interface or notification function for informing users of the generated disposal method and reuse information.

[0013] "Means for reading barcode information" refers to the function of scanning the barcode attached to unwanted items and obtaining that data.

[0014] "Means for matching with a product database" refers to a technology that retrieves detailed information about a product from a database based on barcode information.

[0015] "Means for obtaining location information" refers to the function of obtaining information about the user's current location using technology such as GPS.

[0016] "Means for searching for information on sorting rules and collection days" refers to technology that searches a database for information on regional garbage sorting rules and garbage collection days based on the acquired location information. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram 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

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The system of the present invention operates by combining a plurality of means to analyze images of unwanted items and provide optimal disposal methods and reuse information. Specific embodiments of the system will be described below.

[0039] Users take pictures of unwanted items using devices such as smartphones or tablets, and the devices send the captured image data to a server, sometimes including location information and barcode information.

[0040] The server first analyzes the received image to identify the attributes of the unwanted item. For example, it uses an image analysis algorithm to estimate whether the unwanted item is made of plastic, metal, paper, etc. It also extracts information such as its shape and dimensions.

[0041] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, enabling accurate identification of the unwanted item.

[0042] The server uses generative AI to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, instructions such as "It can be recycled, but please remove the cap" are generated. For reusable items, market prices and listing methods are also provided.

[0043] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information such as "In this area, metal garbage is collected every Thursday."

[0044] The generated information is sent from the server to the device and notified to the user. The user can check this information through a chat-style interface on the device and ask the server any additional questions via chat. The server uses the generation AI to respond to the user's questions in real time.

[0045] For example, consider the case where a user takes a photo of an unwanted smartphone and sends it from the device to a server. The server performs image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server uses generative AI to inform the user via chat, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current going rate is about 10,000 yen." It also references information from local governments and provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0046] In this way, users can easily understand the appropriate disposal methods and reuse possibilities for unwanted items and take appropriate action.In addition, since disposal can be done in accordance with the sorting rules of each local government, environmental impact can be reduced and efficiency can be improved.

[0047] The processing flow will be explained below.

[0048] Step 1:

[0049] Users take photos of unwanted items they want to dispose of using a smartphone or tablet, then open the dedicated app, select the images they have taken, and upload them.

[0050] Step 2:

[0051] The device temporarily stores the captured image data and then sends it to a server, sometimes along with the user's location information and barcode information attached to the unwanted item.

[0052] Step 3:

[0053] The server analyzes the received image data. First, it applies an image analysis algorithm to extract attributes such as the material and shape of the unwanted items. For example, it identifies the type of item, such as plastic, metal, or paper.

[0054] Step 4:

[0055] If a barcode is present in the image, the device will automatically scan it and send the information to the server. If no barcode is present, this step is skipped.

[0056] Step 5:

[0057] The server decodes the received barcode information and searches a product database to obtain detailed product information, such as the product manufacturer and model number, from the barcode.

[0058] Step 6:

[0059] Based on the image analysis results and information from the product database, the server uses generative AI to generate instructions on optimal disposal methods and reuse possibilities, such as "This plastic bottle is recyclable, but please remove the cap."

[0060] Step 7:

[0061] Based on the user's location information, the server searches the local government's database for information such as local garbage sorting rules, collection days, fees, etc. For example, it obtains information such as "In this area, every Wednesday is plastic garbage collection day."

[0062] Step 8:

[0063] The server compiles the generated information and notifies the user, and the device displays the information to the user in a chat-style interface, along with prompts such as "Do you have any questions?"

[0064] Step 9:

[0065] Through a chat-style interface, users can ask additional questions or clarify things, such as "Is this product really recyclable?"

[0066] Step 10:

[0067] The server uses generative AI to answer users' questions in real time, for example, "Yes, this product is recyclable, but you must remove the battery."

[0068] Step 11:

[0069] The server refers to data from auction sites and reuse platforms for unwanted reusable items, and provides users with the current selling price and how to list them. For example, it might say, "The current market price for this smartphone is 10,000 yen. You can list it immediately by clicking the link below."

[0070] Step 12:

[0071] Based on the information provided, users decide how to dispose of unwanted items and then take action, choosing appropriate actions from options such as "separate them for recycling" or "put them on an auction site."

[0072] The above is the processing flow of this system, and this process allows users to easily understand the appropriate disposal methods and reuse possibilities for unwanted items and select appropriate actions.

[0073] Example 1

[0074] 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."

[0075] Providing information on appropriate disposal methods and recycling methods for unwanted items takes a lot of time and effort. It is also complicated to check garbage sorting rules and collection days, which vary from region to region. Furthermore, it is difficult to accurately obtain more detailed product information and local sorting rules based on the barcode information and location information of unwanted items. To solve these problems, a system is needed that can efficiently obtain information and provide it to users quickly.

[0076] 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.

[0077] In this invention, the server includes means for acquiring images of unwanted items, means for analyzing the acquired images to identify attributes of the unwanted items, means for generating optimal disposal methods and possibilities for reuse based on the identified attributes, means for notifying the generated information to the user so that the user can ask additional questions, and means for responding to the additional questions in real time, thereby making it possible to quickly and accurately provide the user with appropriate disposal methods and possibilities for reuse of unwanted items.

[0078] "Unwanted items" refers to items that have been used or are no longer needed, and items that should be considered for disposal or reuse.

[0079] "Means for acquiring images" refers to a device or system that has the function of acquiring image data of unwanted items using a camera, scanner, etc.

[0080] "Means for analyzing images to identify the attributes of unwanted items" refers to algorithms and technologies that analyze acquired images and extract and identify characteristics such as the material, shape, and dimensions of unwanted items.

[0081] "Means for generating optimal disposal methods and reuse possibilities" refers to a system or algorithm that determines and constructs information on appropriate disposal methods and reuse possibilities for unwanted items based on image analysis results and other related information.

[0082] "Means for notifying the user and allowing the user to ask further questions" refers to an interface or system that provides the generated information to the user and allows the user to ask further questions.

[0083] "Means for responding to follow-up questions in real time" refers to a system or algorithm that has the ability to instantly generate and provide appropriate answers to questions from users.

[0084] "Means for reading barcode information" refers to technology that reads barcode information attached to an item using a barcode reader, a smartphone camera, etc.

[0085] The "means for obtaining detailed product information by checking against a product database" refers to the process of checking the scanned barcode information against an existing product database to obtain detailed information about the product.

[0086] "Means for acquiring location information" refers to a system that acquires the current location of a user or item using technologies such as GPS or Wi-Fi.

[0087] "Means for searching for information on municipal sorting rules and collection days" refers to a system or algorithm that searches the municipality's official database or public information based on location information to obtain information on waste sorting rules and collection days specific to that area.

[0088] The system of the present invention is configured to provide information on appropriate disposal methods and reuse of unwanted items. Specific embodiments of the system will be described below.

[0089] Users use devices such as smartphones or tablets to take pictures of unwanted items. The device then sends the captured image data to a server, sometimes including the device's location and barcode information. A standard camera app and HTTPS are often used as the communication protocol for this process.

[0090] The server first analyzes the received image data. Image analysis algorithms such as TensorFlow and OpenCV can be used for image analysis. This allows information such as the material (e.g., plastic, metal, paper), shape, and dimensions of the unwanted items to be extracted and their attributes identified.

[0091] If the unwanted item has a barcode, the device reads it and sends the information to the server. For example, the BarcodeScanner library is used to read the barcode. The server decodes the barcode and compares it with a product database to obtain detailed product information. This allows the server to determine, for example, that "barcode 12345678" is compatible with Apple's iPhone X.

[0092] The server uses a generative AI model (e.g., GPT-4) based on the image analysis results and information obtained from the product database to generate optimal disposal methods and reuse possibilities. The generative AI model generates recommendations such as, "This smartphone can be recycled, but please remove the battery. It can be reused, so the current market price is approximately 10,000 yen."

[0093] Furthermore, the server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information specific to the area, such as "In Chiyoda Ward, Tokyo, electronic device recycling takes place on the last Friday of the month."

[0094] The generated information is sent from the server to the device and notified to the user. The user can check the provided information through a chat-style interface on the device. If the user has additional questions, the generative AI model can be used to answer them in real time. For example, if the user asks, "What are the specific steps to sell this smartphone?" the server will respond with, "List it on site X and package it like this."

[0095] As a concrete example, let's consider a case where a user wants to dispose of an unwanted smartphone (e.g., an iPhone X) and takes a picture of it with the smartphone. The device sends the image data to a server, attaching location information obtained from GPS. The server uses TensorFlow to identify the unwanted item as a smartphone and compares the barcode 12345678 with a database to obtain product information. The generative AI model generates information such as, "This smartphone is recyclable, but please remove the battery. It is reusable, so the current going rate is approximately 10,000 yen." It also references a local government database and provides information such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0096] In this way, users can understand the appropriate disposal methods and possibilities for reusing unwanted items and take action. It also enables disposal in accordance with the sorting rules of each local government, reducing the environmental burden and improving efficiency.

[0097] Example prompt sentence:

[0098] "Lost and Found: Smartphone

[0099] Features: Black color, iPhone X, barcode 12345678 displayed

[0100] Q: How do I dispose of this phone?

[0101] Location: Chiyoda-ku, Tokyo

[0102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0103] Step 1:

[0104] The user takes a picture of the unwanted item using a smartphone or tablet. If the unwanted item is a smartphone, the user takes a photo using a camera app so that the entire item and the barcode are visible. The input is the image of the unwanted item, and the output is an image file.

[0105] Step 2:

[0106] The device sends the captured image data to the server, adding location information and barcode information if necessary. Location information is obtained using the device's GPS function, and barcode information is extracted from the image captured by the camera. The input is the image file, GPS data, and barcode information, and the output is a data packet sent to the server.

[0107] Step 3:

[0108] The server analyzes the received image data. TensorFlow and OpenCV are used for the analysis to identify the material, shape, and dimensions of the unwanted items. Specifically, the server parses the image and applies an object recognition algorithm to extract attribute information. The input is image data, and the output is attribute information of the unwanted items.

[0109] Step 4:

[0110] The server decodes the barcode information and checks it against a product database to obtain detailed product information. This operation uses the BarcodeScanner library. Specifically, after decoding the barcode information, it executes a database query to obtain the corresponding product information. The input is the barcode information, and the output is detailed product information.

[0111] Step 5:

[0112] The server uses a generative AI model (e.g., GPT-4) to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. Specifically, attribute information and product information are input into the generative AI model, which then generates optimal disposal methods and reuse instructions in text format. The input is attribute information and product information, and the output is disposal methods and reuse instructions.

[0113] Step 6:

[0114] The server searches the local government database based on the user's location information to obtain information such as local waste sorting rules, collection days, and disposal fees. Specifically, it uses the location information to send a query to the local government API to obtain relevant information. The input is location information, and the output is local government information.

[0115] Step 7:

[0116] The server sends the generated disposal method, reuse information, and municipal information to the terminal.,Specifically, the server compiles the generated information and sends the data to the,terminal using the HTTP protocol.,The input is a set of generated information, and the output is,notification data sent to the terminal.

[0117] Step 8:

[0118] The user checks the information displayed on the device and asks additional questions as needed. Questions are entered through a chat-style interface to request additional instructions or information. The input is the user's question, and the output is the question data.

[0119] Step 9:

[0120] The server responds to user questions in real time using a generative AI model. Specifically, it inputs question data into the generative AI model, generates an appropriate answer, and returns it to the user. The input is the question data, and the output is the generated answer text.

[0121] (Application example 1)

[0122] 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."

[0123] Conventional systems for disposing of unwanted items and providing information on reuse lack the functionality to respond quickly and accurately to suspicious or lost items. This makes it difficult to provide appropriate countermeasures while ensuring public safety. Furthermore, the lack of real-time interaction makes it difficult to respond to user questions or emergency situations.

[0124] 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.

[0125] In this invention, the server includes means for acquiring images of unwanted items, suspicious objects, and lost items, means for analyzing the acquired images to identify their attributes, means for generating optimal disposal methods, possibilities for reuse, and countermeasures for suspicious objects based on the identified attributes, and means for notifying the user of the generated information, means for acquiring images of suspicious objects and lost items and transmitting data including location information to the server, means for analyzing the received image data and identifying the attributes of the suspicious objects or lost items, means for searching for local security rules and countermeasures based on the location information, and means for providing a chat-style interface that responds to user questions in real time, thereby enabling the provision of quick and accurate countermeasures for suspicious objects and lost items and real-time interaction with the user.

[0126] "Unwanted items" refers to items or waste that are no longer in use and have outlived their normal purpose.

[0127] "Means of acquisition" refers to the functions of the device or software used to acquire images and location information.

[0128] "Means of analysis" refers to the techniques and algorithms used to process acquired data and extract specific attributes or information.

[0129] "Means of identification" refers to the function of identifying the attributes and type of an object based on analyzed data.

[0130] "Generating means" refers to devices or programs capable of generating optimal disposal methods or countermeasures based on identified information.

[0131] "Means for notifying" refers to a device or interface for conveying the generated information to the user.

[0132] "Location Information" means data that indicates a specific geographic location using GPS or other means.

[0133] "Means for transmitting data" refers to the function of sending acquired images and location information to a central processing unit such as a server.

[0134] "Security rules" refer to regulations established in each region for crime prevention and safety.

[0135] "Means for searching for solutions" refers to systems or software functions for finding appropriate solutions based on location information, etc.

[0136] "Real-time responsive chat-style interface" refers to an interactive user interface that can provide immediate answers to users' questions and requests.

[0137] The present invention provides a system for quickly and accurately analyzing images of suspicious objects and lost items, and providing safe countermeasures. Specific embodiments for carrying out the present invention will now be described.

[0138] System Configuration

[0139] The system consists of the following major components:

[0140] 1. Device: The user uses a device such as a smartphone, smart glasses, or head-mounted display to capture images of suspicious or lost items.

[0141] 2. Server: Analyzes the acquired image data, identifies the attributes of suspicious or lost items, and generates optimal countermeasures and notifies the user.

[0142] 3. Communication network: Infrastructure for sending and receiving data between devices and servers.

[0143] Hardware and software used

[0144] Image analysis: OpenCV, Tesseract OCR

[0145] Location information acquisition: Geopy library

[0146] Barcode analysis: ZXing library

[0147] Countermeasure generation: Generative AI model

[0148] Real-time chat: Pre-trained chatbot library

[0149] Data processing and calculation

[0150] 1. Image acquisition and transmission

[0151] The user uses the device to take pictures of suspicious or lost items, and the image data is sent to the server along with location information.

[0152] 2. Image Analysis

[0153] The server analyzes the received image data using OpenCV and Tesseract OCR, extracting attribute information such as the shape, material, and dimensions of the suspicious or lost item.

[0154] 3. Barcode Analysis

[0155] If the image contains a barcode, the ZXing library is used to parse the barcode and retrieve detailed information from the product database.

[0156] 4. Generating optimal countermeasures

[0157] The server uses a generative AI model to generate optimal responses based on the analysis results, which may include notifying security guards, checking with the police, or warning the user.

[0158] 5. User Notifications and Real-Time Chat

[0159] The generated information is immediately sent to the user, who can then ask follow-up questions through a real-time chat-style interface, and the chatbot library will respond with appropriate responses to the user's questions.

[0160] Examples of concrete examples and prompts

[0161] For example, if a user takes a photo of a lost item they find in a park with smart glasses, the image is sent to the system. The server analyzes the shape, material, etc., and if it determines that the item is likely to be suspicious, it will provide a message saying, "Please notify security. Do not touch it." Local security rules, such as "Lost items in the park will be kept for three days before being transferred to the police," are also provided based on the location information. When the user asks, "What should I do with this lost item?" the generating AI responds in real time with, "Take an additional photo to enable more detailed analysis. Please notify the nearest security guard."

[0162] Prompt Sentence Examples

[0163] "Please analyze the images of the lost items you found in the park. Identify the shape and material, and tell us the best course of action."

[0164] As described above, the present invention is a system that provides quick and accurate countermeasures for suspicious objects and lost items, and enables real-time interaction with users.

[0165] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0166] Step 1:

[0167] The user uses the device to take an image of a suspicious object or lost property. The device acquires this image data and simultaneously acquires its current location information. The acquired image data and location information are sent to the server. The input data are the image and location information, and the output data is the image and location information sent to the server.

[0168] Step 2:

[0169] The server receives image data sent from the device. It then analyzes the image using OpenCV and Tesseract OCR to extract attribute information such as the shape, material, and dimensions of suspicious or lost items. The input data is the image, and the output data is the extracted attribute information.

[0170] Step 3:

[0171] If the image contains a barcode, the server analyzes the barcode using the ZXing library. Based on the analysis results, detailed information is retrieved from the product database. The input data is the barcode and image, and the output data is the barcode analysis results and product information.

[0172] Step 4:

[0173] The server uses a generative AI model based on the analysis results to generate optimal countermeasures, which may include notifying security guards, checking with the police, and warning the user. The input data is the analysis results, and the output data is the generated countermeasures.

[0174] Step 5:

[0175] The server notifies the user of the generated information. At the same time, it also searches for local security rules and countermeasures based on the location information and provides them to the user as additional information. The input data are the generated countermeasures and location information, and the output data is the notification content to the user.

[0176] Step 6:

[0177] Users can ask follow-up questions through a chat-style interface on their device, which the server responds to in real time using a pre-trained chatbot library. The input data is the user's question, and the output data is the response using generative AI.

[0178] The above processing steps enable the user to take prompt and appropriate action in response to suspicious or lost items.

[0179] 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.

[0180] The system of the present invention combines multiple means to analyze images of unwanted items and provide optimal disposal methods and reuse information. It also has an emotion engine that recognizes the user's emotions, and can adjust notification content and responses according to the user's emotions.

[0181] Users take pictures of unwanted items using devices such as smartphones or tablets, and the devices send the captured image data to a server, sometimes including location information and barcode information.

[0182] The server first analyzes the received image to identify the attributes of the unwanted item. For example, it uses an image analysis algorithm to estimate whether the unwanted item is made of plastic, metal, paper, etc. It also extracts information such as its shape and dimensions.

[0183] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, enabling accurate identification of the unwanted item.

[0184] The server uses generative AI to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, the server generates instructions such as "It can be recycled, but please remove the cap." For reusable items, the server also provides market prices and listing methods.

[0185] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information such as "In this area, metal garbage is collected every Thursday."

[0186] The generated information is sent from the server to the device and notified to the user. Here, the emotion engine recognizes the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling stressed, a reassuring message such as "Don't worry, the procedure is simple" will be added.

[0187] Users can check this information through a chat-style interface on their device, and if they have any additional questions, they can contact the server via chat. The server uses generative AI to respond to users' questions in real time. Again, the emotion engine analyzes the user's emotions and adjusts the response accordingly. For example, if the user is confused, the server might add a phrase like, "I'll explain it in an easy-to-understand way."

[0188] For example, consider the case where a user takes a photo of an unwanted smartphone and sends it from the device to a server. The server performs image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server uses generative AI to inform the user via chat, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current going rate is about 10,000 yen." It also references information from local governments and provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0189] If the emotion engine recognizes the user's emotions and determines that the user is nervous, it adds a comment saying, "Don't worry, the procedure is simple." In this way, the user can relax and receive accurate information.

[0190] In this way, users can easily understand the appropriate disposal methods and reuse possibilities for unwanted items and take appropriate action.In addition, since disposal can be done in accordance with the sorting rules of each local government, environmental impact can be reduced and efficiency can be improved.

[0191] By incorporating an emotion engine, guidance and responses to users can be more personalized, increasing user satisfaction. Furthermore, statistical analysis of emotion data can contribute to improving the system itself and the quality of services.

[0192] The processing flow will be explained below.

[0193] Step 1:

[0194] Users take photos of unwanted items they want to dispose of using a smartphone or tablet, then open the dedicated app, select the images they have taken, and upload them.

[0195] Step 2:

[0196] The device temporarily stores the captured image data and then sends it to a server, sometimes along with the user's location information and barcode information attached to the unwanted item.

[0197] Step 3:

[0198] The server analyzes the received image data. First, it applies an image analysis algorithm to extract attributes such as the material and shape of the unwanted items. For example, it identifies the type of item, such as plastic, metal, or paper.

[0199] Step 4:

[0200] If a barcode is present in the image, the device will automatically scan it and send the information to the server. If no barcode is present, this step is skipped.

[0201] Step 5:

[0202] The server decodes the received barcode information and searches a product database to obtain detailed product information, such as the product manufacturer and model number, from the barcode.

[0203] Step 6:

[0204] Based on the image analysis results and information from the product database, the server uses generative AI to generate instructions on optimal disposal methods and reuse possibilities, such as "This plastic bottle is recyclable, but please remove the cap."

[0205] Step 7:

[0206] Based on the user's location information, the server searches the local government's database for information such as local garbage sorting rules, collection days, fees, etc. For example, it obtains information such as "In this area, every Wednesday is plastic garbage collection day."

[0207] Step 8:

[0208] The server compiles the generated information and notifies the user. Here, an emotion engine recognizes the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling stressed, a reassuring message such as "Don't worry, the procedure is simple" will be added.

[0209] Step 9:

[0210] The device displays the notified information to the user in a chat-style interface and also displays prompts such as "Do you have any questions?"

[0211] Step 10:

[0212] Through a chat-style interface, users can ask additional questions or clarify things, such as "Is this product really recyclable?"

[0213] Step 11:

[0214] The server uses generative AI to answer users' questions in real time. Here too, the emotion engine analyzes the user's emotions and adjusts the response accordingly. For example, if the user is confused, the server might add a phrase like, "I'll explain it in an easy-to-understand way."

[0215] Step 12:

[0216] The server refers to data from auction sites and reuse platforms for unwanted reusable items, and provides users with the current selling price and how to list them. For example, it might say, "The current market price for this smartphone is 10,000 yen. You can list it immediately by clicking the link below."

[0217] Step 13:

[0218] Based on the information provided, users decide how to dispose of unwanted items and then take action, choosing appropriate actions from options such as "separate them for recycling" or "put them on an auction site."

[0219] This is the processing flow of this system, which allows users to easily understand the appropriate disposal methods and reuse possibilities for unwanted items and choose the appropriate action.In addition, by incorporating an emotion engine, guidance and responses to users can be more personalized, improving user satisfaction.

[0220] Example 2

[0221] 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."

[0222] In modern society, there is a demand for fast and accurate information on appropriate disposal and reuse methods for unwanted items. However, conventional systems do not adequately identify the material and shape of unwanted items, obtain regional sorting rules, or adjust notification content based on the user's emotions, which often leaves users feeling stressed. Furthermore, it is not easy to obtain detailed product information using barcodes. This has led to a lack of information on how to properly dispose of unwanted items, which increases the burden on the environment.

[0223] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0224] In this invention, the server includes a means for acquiring images of unwanted items, a means for analyzing the acquired images to identify attributes of the unwanted items, a means for using a generative artificial intelligence model to generate optimal disposal methods and reuse possibilities based on the identified attributes, a means for recognizing user emotions and adjusting notification content and responses, and a means for notifying the user of the generated information. This makes it possible to quickly and accurately provide information on appropriate disposal methods and reuse methods for unwanted items, thereby improving user satisfaction and reducing environmental impact. Furthermore, accurate product information can be provided by using a means for reading barcode information and a means for obtaining detailed product information by comparing the read barcode information with a product database.

[0225] "Means for obtaining images of unwanted items" refers to the function of taking images of unwanted items using a device such as a smartphone or tablet.

[0226] "Means for analyzing the acquired images to identify the attributes of unwanted items" refers to a technology that uses an image analysis algorithm to identify attributes such as the material, shape, and dimensions of unwanted items.

[0227] "Means using a generative artificial intelligence model that generates optimal disposal methods and reuse possibilities based on identified attributes" refers to technology that uses a generative AI model to generate disposal methods and reuse information for unwanted items.

[0228] "Means for recognizing user emotions and adjusting notification content and responses" refers to technology that uses an emotion engine to analyze user emotions and personalize notification content and responses based on that.

[0229] "Means for notifying the user of the generated information" refers to a function for sending information on how to dispose of unwanted items and reuse information to the user's terminal and notifying them.

[0230] "Means for reading barcode information" refers to technology for reading barcodes attached to unwanted items.

[0231] "Means for obtaining detailed product information by comparing the read barcode information with a product database" refers to technology for decoding the barcode and comparing the information with a product database to obtain detailed product information.

[0232] "Means for acquiring location information" refers to technology for acquiring information about a user's current location using the device's location information service.

[0233] "Means for searching for information on municipal sorting rules and collection dates based on acquired location information" refers to technology that searches a municipal database based on the user's location information to obtain information such as local garbage sorting rules and collection dates.

[0234] The system of this invention combines multiple means to analyze images of unwanted items and provide optimal disposal methods and reuse information. It also has an emotion engine that recognizes the user's emotions, and can adjust notification content and responses according to the user's emotions.

[0235] Users take pictures of unwanted items using devices such as smartphones or tablets. The devices then send the captured image data to a server, sometimes including location information and barcode information. Specifically, devices are equipped with a camera, location information service (GPS function), and barcode scanner, and these hardware components are used to acquire data.

[0236] The server first analyzes the received images and uses image analysis algorithms (e.g., TensorFlow or OpenCV) to identify the attributes of the unwanted items. Image analysis can identify the material (e.g., plastic, metal, paper), shape, dimensions, etc. of the unwanted items.

[0237] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, allowing for more accurate identification of the unwanted item.

[0238] The server uses a generative AI model (such as GPT-3 or BERT) to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, the server generates instructions such as "It can be recycled, but please remove the cap." For reusable items, the server also provides information on the current market price and specific listing methods.

[0239] The server searches the local government database based on the user's location information to obtain information such as local waste sorting rules, collection days, disposal fees, etc. Specific information could be provided such as "In this area, metal waste is collected every Thursday."

[0240] The generated information is sent from the server to the device and notified to the user. At this time, the emotion engine recognizes the user's emotions, and if the user is feeling stressed, for example, a reassuring message such as "Don't worry, the procedure is simple." This allows the user to receive the information in a relaxed state.

[0241] Users can view this information through a chat-style interface on their device, and if they have any additional questions, they can contact the server via chat. The server uses a generative AI model to respond to the user's questions in real time. Again, the emotion engine analyzes the user's emotions, and if the user is confused, for example, it will add a message such as "I'll explain it in an easy-to-understand way."

[0242] As a concrete example, let's consider a case where a user takes a photo of an unwanted smartphone and sends it to a server. The server uses image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server then uses generative AI to provide a chat message saying, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current market price is approximately 10,000 yen." Furthermore, by referencing local government information, the server also provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month." If the emotion engine recognizes the user's emotions and determines that the user is nervous, it adds a comment saying, "Don't worry, the process is simple." In this way, the user can relax and receive accurate information.

[0243] Examples of prompts include, "What is the best way to dispose of the unwanted item in this image?" or "How can I recycle or reuse this smartphone?"

[0244] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0245] Step 1:

[0246] The user takes a picture of the unwanted item

[0247] Users take pictures of unwanted items using a smartphone or tablet, and are encouraged to take pictures from multiple angles.

[0248] Input: Unwanted item to photograph

[0249] Output: Image data of unwanted items

[0250] Step 2:

[0251] The device sends image data, location information, and barcode information to the server.

[0252] The device acquires the captured image data and uses the built-in GPS to obtain location information. Furthermore, if the unwanted item has a barcode attached, the device uses the barcode scanner function to obtain the information. All of this data is then sent to the server.

[0253] Input: Image data, location information, barcode information of unwanted items

[0254] Output: Data sent to the server

[0255] Step 3:

[0256] The server analyzes the image and identifies the attributes of the unwanted items

[0257] The server analyzes the received image data using an image analysis algorithm (e.g., TensorFlow or OpenCV), which identifies the material (plastic, metal, paper, etc.), shape, dimensions, etc. of the unwanted items. Specifically, the algorithm identifies objects in the image and classifies their attributes.

[0258] Input: Image data of unwanted items

[0259] Output: Attribute data of identified unwanted items (material, shape, dimensions, etc.)

[0260] Step 4:

[0261] The server decodes the barcode information and checks it against the product database.

[0262] The server decodes the received barcode information and compares it with a product database to obtain detailed product information (manufacturer, model, product name, etc.) of the unwanted item. Specifically, the barcode decoding algorithm reads the numeric information in the barcode and queries the product database.

[0263] Input: Barcode information

[0264] Output: Product details

[0265] Step 5:

[0266] The server generates optimal disposal methods and reuse information using an AI model.

[0267] Based on the image analysis results and information obtained from the product database, the server uses a generative AI model (e.g., GPT-3, BERT) to generate optimal disposal methods and reuse possibilities for unwanted items. For example, in the case of a plastic bottle, instructions such as "It can be recycled, but please remove the cap" are generated.

[0268] Input: Attribute data of identified unwanted items, detailed product information

[0269] Output: Information on optimal disposal methods and reuse

[0270] Step 6:

[0271] The server searches the local government database and obtains the garbage sorting rules for each region.

[0272] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. Specifically, it uses the location information to query the garbage sorting rules for the relevant area.

[0273] Input:Location

[0274] Output: Information on garbage sorting rules and collection days for each region

[0275] Step 7:

[0276] The server sends the generated information to the terminal.

[0277] The server then sends the generated disposal and reuse information, as well as local waste sorting rules, to the device, allowing users to check the appropriate method for disposing of unwanted items.

[0278] Input: disposal methods, reuse information, and regional garbage sorting rules

[0279] Output: Information sent to the terminal

[0280] Step 8:

[0281] The server analyzes the user's emotions using an emotion engine and adjusts the notification content.

[0282] The server uses an emotion engine to analyze the user's emotions. If the user is stressed, it will add a message such as "Don't worry, the process is simple." For this purpose, it uses facial recognition and text analysis algorithms.

[0283] Input: User actions and input data

[0284] Output: Adjusted notification content

[0285] Step 9:

[0286] The device notifies the user

[0287] The device notifies the user of the information received from the server. Notifications are sent in chat format and displayed in a way that is easy for the user to understand.

[0288] Input: Information received from the server

[0289] Output: Information notified to the user

[0290] Step 10:

[0291] Users can enter additional questions in chat format

[0292] The user uses the device's chat interface to enter follow-up questions, such as "where can I recycle this phone?"

[0293] Input: User question

[0294] Output: Query data from the terminal to the server

[0295] Step 11:

[0296] The server responds to the question using a generative AI model and notifies the user

[0297] The server uses a generative AI model to respond to the user's questions, and here too, the emotion engine analyzes the user's emotions and can add phrases such as "I'll explain it in an easy-to-understand way."

[0298] Input: User question data

[0299] Output: Generated response data, adjusted answer content

[0300] (Application example 2)

[0301] 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."

[0302] In modern society, the disposal and reuse of unwanted items has become an important issue. However, it is not easy for users to understand the appropriate disposal method and the possibility of reuse. Furthermore, users often feel anxious and stressed because the system does not respond to their emotions. Furthermore, there is the problem that it is time-consuming to understand the sorting rules and collection date information of each local government. The present invention aims to solve these problems.

[0303] 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 acquiring images of unwanted items, means for analyzing the acquired images to identify attributes of the unwanted items, means for generating optimal disposal methods and reuse possibilities based on the identified attributes, means for notifying the user of the generated information, and means for analyzing the user's emotions and adjusting the notification content according to the emotions. This allows the user to easily understand appropriate disposal methods and reuse possibilities for unwanted items and receive appropriate guidance according to their emotions.

[0304] "Unwanted items" are items that are no longer needed by the user.

[0305] "Means for acquiring an image" refers to a method for collecting image data of an item using a camera or other image capture device.

[0306] "Means for analyzing images" refers to technology that processes collected image data and recognizes and identifies attributes and features of items.

[0307] "Means for identifying attributes" refers to a method for identifying characteristics such as the material, shape, and dimensions of an object based on information obtained from image analysis.

[0308] The "means for generating disposal methods" is a method for proposing optimal disposal or recycling methods based on the identified attributes.

[0309] "Means for generating reuse possibilities" are technologies that determine whether an item can be reused and provide the necessary information.

[0310] "Means for notifying" refers to an interface or communication means for notifying the user of the generated information.

[0311] "Means for analyzing emotions" refers to technology that recognizes and analyzes emotions from a user's facial expressions, voice, text, etc.

[0312] "Means for adjusting notification content" refers to technology that dynamically changes the content of information and messages provided in response to the user's emotions.

[0313] "Means for reading barcode information" refers to technology that scans the barcode attached to an item and acquires the data.

[0314] "Means for checking against a product database" refers to a technique for comparing acquired barcode information with an existing database to identify detailed product information.

[0315] "Means for acquiring location information" refers to methods for collecting information on the current location of users or items using technologies such as GPS.

[0316] "Means for searching municipal sorting rules" refers to technology that checks waste disposal rules and schedules in a specific area.

[0317] The system of the present invention is designed to enable users to easily obtain information on the disposal and reuse of unwanted items. This system is composed of a terminal such as a smartphone, a server, and a cloud-based service.

[0318] 1. Program Generation

[0319] First, smartphones and other devices are equipped with cameras and barcode scanners. Users take pictures of unwanted items and send the image data from their devices to a server. In some cases, location information and barcode information are also sent.

[0320] The server operates using the following primary methods:

[0321] Image analysis method: Using image analysis algorithms (e.g., OpenCV), the material, shape, and dimensions of unwanted items are identified.

[0322] Barcode analysis method: The information obtained by the barcode scanner is compared with the product database to obtain detailed product information.

[0323] Sentiment analysis method: Analyze user emotions using an emotion engine (e.g., Microsoft Azure's Face API).

[0324] Generative AI method: Use OpenAI's generative AI model (e.g., GPT-4) to generate appropriate disposal methods and reuse information.

[0325] Personalization: Dynamically adjust notification content based on sentiment analysis.

[0326] 2. Explain the program's processing in natural language

[0327] Acquisition of image data:

[0328] The user takes a picture of the unwanted item using the device's camera and sends the image data to the server. Location information and barcode information can also be included when sending the data.

[0329] Image analysis:

[0330] The server analyzes the received image data and identifies the attributes of the unwanted items (e.g., material, shape, dimensions). This is done using an image analysis algorithm (e.g., OpenCV). It also analyzes the barcode information and compares it with a product database to obtain detailed product information.

[0331] Emotion analysis:

[0332] The emotion engine analyzes the user's facial expressions, voice, and text to determine their emotional state and uses that information to tailor notifications.

[0333] Disposal Method Generation:

[0334] Generative AI models (e.g., GPT-4) are used to generate optimal disposal methods and reuse possibilities for unwanted items. For example, information such as "This smartphone is recyclable, but the battery must be removed" is generated.

[0335] Information Notice:

[0336] The generated information is personalized based on the user's emotional analysis and sent to the device. For example, if the user is nervous, a message such as "Don't worry, the procedure is simple" will be added.

[0337] 3. Examples of concrete examples and prompts

[0338] Examples:

[0339] 1. User A takes a picture of a smartphone that he no longer needs.

[0340] 2. Image data, location information, and barcode information are sent from the device to the server.

[0341] 3. The server analyzes the image and retrieves detailed information about the smartphone from the product database.

[0342] 4. The generative AI model generates the information, "Please recycle this smartphone. The current market price is 10,000 yen."

[0343] 5. The emotion engine analyzes User A's emotions and determines that he is nervous.

[0344] 6. A notification is sent to User A with the additional message, "Don't worry, it's easy."

[0345] 7. Information about garbage collection days in User A's area is also provided, informing him that "electronic waste is collected in your area on the last Friday of every month."

[0346] Example prompt sentence:

[0347] Analyze user images to obtain detailed information about unwanted items, and generate and display appropriate recycling methods and market prices. Example: Analyze images from a smartphone and provide recycling methods and current market prices.

[0348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0349] Step 1:

[0350] The user takes a picture of the unwanted item with a camera. The user then captures the image of the unwanted item using the camera on their smartphone and saves the data on their device. The input is image data, and the output is an image file.

[0351] Step 2:

[0352] The terminal sends the acquired image data to the server. The user sends the acquired image data along with location information and barcode information (if any) to the server through the application. The input is the image data, location information, and barcode information, and the output is the data sent to the server.

[0353] Step 3:

[0354] The server analyzes the image data and identifies the attributes of the unwanted items. The server uses an image analysis algorithm (e.g., OpenCV) to identify attributes such as the material, shape, and dimensions of the unwanted items from the image data. The input is the image data, and the output is the attribute data of the identified unwanted items.

[0355] Step 4:

[0356] The server analyzes the barcode information and compares it with the product database. The server analyzes the barcode information and compares it with the product database to obtain detailed product information. The input is the barcode information, and the output is the product information obtained from the product database.

[0357] Step 5:

[0358] The server analyzes the user's emotions. The server uses an emotion engine (e.g., Microsoft Azure's Face API) to analyze emotions from the user's facial expressions and text. The input is the user's facial expression data and text, and the output is the user's emotion analysis results.

[0359] Step 6:

[0360] The server generates optimal disposal methods and reuse possibilities for unwanted items. The server uses a generative AI model (e.g., GPT-4) to generate optimal disposal methods and reuse possibilities for unwanted items based on the identified attributes and product information. The input is the attribute data and product information of the unwanted items, and the output is the generated disposal methods and reuse information.

[0361] Step 7:

[0362] The server personalizes the generated information. The server personalizes the generated information based on the results of the user's emotion analysis. For example, if the user is nervous, it adds a message such as "Don't worry, the procedure is simple." The input is the emotion analysis result and the generated information, and the output is personalized notification information.

[0363] Step 8:

[0364] The server sends personalized notification information to the user's terminal. The server sends personalized notification information to the user's terminal and notifies the user. The input is the personalized notification information, and the output is a notification displayed on the user's terminal.

[0365] Step 9:

[0366] Search for municipal sorting rules and collection date information. The server searches the municipal database based on the user's location information to obtain information on regional sorting rules and collection dates. The input is location information, and the output is information on sorting rules and collection dates.

[0367] Step 10:

[0368] The server notifies the user of the sorting rules and collection date information. The server sends the acquired sorting rules and collection date information to the user's terminal and notifies the user. The input is the sorting rules and collection date information, and the output is a notification displayed on the user's terminal.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] [Second embodiment]

[0373] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0374] 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.

[0375] 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).

[0376] 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.

[0377] 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.

[0378] 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).

[0379] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0384] 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."

[0385] The system of the present invention operates by combining a plurality of means to analyze images of unwanted items and provide optimal disposal methods and reuse information. Specific embodiments of the system will be described below.

[0386] Users take pictures of unwanted items using devices such as smartphones or tablets, and the devices send the captured image data to a server, sometimes including location information and barcode information.

[0387] The server first analyzes the received image to identify the attributes of the unwanted item. For example, it uses an image analysis algorithm to estimate whether the unwanted item is made of plastic, metal, paper, etc. It also extracts information such as its shape and dimensions.

[0388] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, enabling accurate identification of the unwanted item.

[0389] The server uses generative AI to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, instructions such as "It can be recycled, but please remove the cap" are generated. For reusable items, market prices and listing methods are also provided.

[0390] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information such as "In this area, metal garbage is collected every Thursday."

[0391] The generated information is sent from the server to the device and notified to the user. The user can check this information through a chat-style interface on the device and ask the server any additional questions via chat. The server uses the generation AI to respond to the user's questions in real time.

[0392] For example, consider the case where a user takes a photo of an unwanted smartphone and sends it from the device to a server. The server performs image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server uses generative AI to inform the user via chat, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current going rate is about 10,000 yen." It also references information from local governments and provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0393] In this way, users can easily understand the appropriate disposal methods and reuse possibilities for unwanted items and take appropriate action.In addition, since disposal can be done in accordance with the sorting rules of each local government, environmental impact can be reduced and efficiency can be improved.

[0394] The processing flow will be explained below.

[0395] Step 1:

[0396] Users take photos of unwanted items they want to dispose of using a smartphone or tablet, then open the dedicated app, select the images they have taken, and upload them.

[0397] Step 2:

[0398] The device temporarily stores the captured image data and then sends it to a server, sometimes along with the user's location information and barcode information attached to the unwanted item.

[0399] Step 3:

[0400] The server analyzes the received image data. First, it applies an image analysis algorithm to extract attributes such as the material and shape of the unwanted items. For example, it identifies the type of item, such as plastic, metal, or paper.

[0401] Step 4:

[0402] If a barcode is present in the image, the device will automatically scan it and send the information to the server. If no barcode is present, this step is skipped.

[0403] Step 5:

[0404] The server decodes the received barcode information and searches a product database to obtain detailed product information, such as the product manufacturer and model number, from the barcode.

[0405] Step 6:

[0406] Based on the image analysis results and information from the product database, the server uses generative AI to generate instructions on optimal disposal methods and reuse possibilities, such as "This plastic bottle is recyclable, but please remove the cap."

[0407] Step 7:

[0408] Based on the user's location information, the server searches the local government's database for information such as local garbage sorting rules, collection days, fees, etc. For example, it obtains information such as "In this area, every Wednesday is plastic garbage collection day."

[0409] Step 8:

[0410] The server compiles the generated information and notifies the user, and the device displays the information to the user in a chat-style interface, along with prompts such as "Do you have any questions?"

[0411] Step 9:

[0412] Through a chat-style interface, users can ask additional questions or clarify things, such as "Is this product really recyclable?"

[0413] Step 10:

[0414] The server uses generative AI to answer users' questions in real time, for example, "Yes, this product is recyclable, but you must remove the battery."

[0415] Step 11:

[0416] The server refers to data from auction sites and reuse platforms for unwanted reusable items, and provides users with the current selling price and how to list them. For example, it might say, "The current market price for this smartphone is 10,000 yen. You can list it immediately by clicking the link below."

[0417] Step 12:

[0418] Based on the information provided, users decide how to dispose of unwanted items and then take action, choosing appropriate actions from options such as "separate them for recycling" or "put them on an auction site."

[0419] The above is the processing flow of this system, and this process allows users to easily understand the appropriate disposal methods and reuse possibilities for unwanted items and select appropriate actions.

[0420] Example 1

[0421] 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."

[0422] Providing information on appropriate disposal methods and recycling methods for unwanted items takes a lot of time and effort. It is also complicated to check garbage sorting rules and collection days, which vary from region to region. Furthermore, it is difficult to accurately obtain more detailed product information and local sorting rules based on the barcode information and location information of unwanted items. To solve these problems, a system is needed that can efficiently obtain information and provide it to users quickly.

[0423] 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.

[0424] In this invention, the server includes means for acquiring images of unwanted items, means for analyzing the acquired images to identify attributes of the unwanted items, means for generating optimal disposal methods and possibilities for reuse based on the identified attributes, means for notifying the generated information to the user so that the user can ask additional questions, and means for responding to the additional questions in real time, thereby making it possible to quickly and accurately provide the user with appropriate disposal methods and possibilities for reuse of unwanted items.

[0425] "Unwanted items" refers to items that have been used or are no longer needed, and items that should be considered for disposal or reuse.

[0426] "Means for acquiring images" refers to a device or system that has the function of acquiring image data of unwanted items using a camera, scanner, etc.

[0427] "Means for analyzing images to identify the attributes of unwanted items" refers to algorithms and technologies that analyze acquired images and extract and identify characteristics such as the material, shape, and dimensions of unwanted items.

[0428] "Means for generating optimal disposal methods and reuse possibilities" refers to a system or algorithm that determines and constructs information on appropriate disposal methods and reuse possibilities for unwanted items based on image analysis results and other related information.

[0429] "Means for notifying the user and allowing the user to ask further questions" refers to an interface or system that provides the generated information to the user and allows the user to ask further questions.

[0430] "Means for responding to follow-up questions in real time" refers to a system or algorithm that has the ability to instantly generate and provide appropriate answers to questions from users.

[0431] "Means for reading barcode information" refers to technology that reads barcode information attached to an item using a barcode reader, a smartphone camera, etc.

[0432] The "means for obtaining detailed product information by checking against a product database" refers to the process of checking the scanned barcode information against an existing product database to obtain detailed information about the product.

[0433] "Means for acquiring location information" refers to a system that acquires the current location of a user or item using technologies such as GPS or Wi-Fi.

[0434] "Means for searching for information on municipal sorting rules and collection days" refers to a system or algorithm that searches the municipality's official database or public information based on location information to obtain information on waste sorting rules and collection days specific to that area.

[0435] The system of the present invention is configured to provide information on appropriate disposal methods and reuse of unwanted items. Specific embodiments of the system will be described below.

[0436] Users use devices such as smartphones or tablets to take pictures of unwanted items. The device then sends the captured image data to a server, sometimes including the device's location and barcode information. A standard camera app and HTTPS are often used as the communication protocol for this process.

[0437] The server first analyzes the received image data. Image analysis algorithms such as TensorFlow and OpenCV can be used for image analysis. This allows information such as the material (e.g., plastic, metal, paper), shape, and dimensions of the unwanted items to be extracted and their attributes identified.

[0438] If the unwanted item has a barcode, the device reads it and sends the information to the server. For example, the BarcodeScanner library is used to read the barcode. The server decodes the barcode and compares it with a product database to obtain detailed product information. This allows the server to determine, for example, that "barcode 12345678" is compatible with Apple's iPhone X.

[0439] The server uses a generative AI model (e.g., GPT-4) based on the image analysis results and information obtained from the product database to generate optimal disposal methods and reuse possibilities. The generative AI model generates recommendations such as, "This smartphone can be recycled, but please remove the battery. It can be reused, so the current market price is approximately 10,000 yen."

[0440] Furthermore, the server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information specific to the area, such as "In Chiyoda Ward, Tokyo, electronic device recycling takes place on the last Friday of the month."

[0441] The generated information is sent from the server to the device and notified to the user. The user can check the provided information through a chat-style interface on the device. If the user has additional questions, the generative AI model can be used to answer them in real time. For example, if the user asks, "What are the specific steps to sell this smartphone?" the server will respond with, "List it on site X and package it like this."

[0442] As a concrete example, let's consider a case where a user wants to dispose of an unwanted smartphone (e.g., an iPhone X) and takes a picture of it with the smartphone. The device sends the image data to a server, attaching location information obtained from GPS. The server uses TensorFlow to identify the unwanted item as a smartphone and compares the barcode 12345678 with a database to obtain product information. The generative AI model generates information such as, "This smartphone is recyclable, but please remove the battery. It is reusable, so the current going rate is approximately 10,000 yen." It also references a local government database and provides information such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0443] In this way, users can understand the appropriate disposal methods and possibilities for reusing unwanted items and take action. It also enables disposal in accordance with the sorting rules of each local government, reducing the environmental burden and improving efficiency.

[0444] Example prompt sentence:

[0445] "Lost and Found: Smartphone

[0446] Features: Black color, iPhone X, barcode 12345678 displayed

[0447] Q: How do I dispose of this phone?

[0448] Location: Chiyoda-ku, Tokyo

[0449] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0450] Step 1:

[0451] The user takes a picture of the unwanted item using a smartphone or tablet. If the unwanted item is a smartphone, the user takes a photo using a camera app so that the entire item and the barcode are visible. The input is the image of the unwanted item, and the output is an image file.

[0452] Step 2:

[0453] The device sends the captured image data to the server, adding location information and barcode information if necessary. Location information is obtained using the device's GPS function, and barcode information is extracted from the image captured by the camera. The input is the image file, GPS data, and barcode information, and the output is a data packet sent to the server.

[0454] Step 3:

[0455] The server analyzes the received image data. TensorFlow and OpenCV are used for the analysis to identify the material, shape, and dimensions of the unwanted items. Specifically, the server parses the image and applies an object recognition algorithm to extract attribute information. The input is image data, and the output is attribute information of the unwanted items.

[0456] Step 4:

[0457] The server decodes the barcode information and checks it against a product database to obtain detailed product information. This operation uses the BarcodeScanner library. Specifically, after decoding the barcode information, it executes a database query to obtain the corresponding product information. The input is the barcode information, and the output is detailed product information.

[0458] Step 5:

[0459] The server uses a generative AI model (e.g., GPT-4) to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. Specifically, attribute information and product information are input into the generative AI model, which then generates optimal disposal methods and reuse instructions in text format. The input is attribute information and product information, and the output is disposal methods and reuse instructions.

[0460] Step 6:

[0461] The server searches the local government database based on the user's location information to obtain information such as local waste sorting rules, collection days, and disposal fees. Specifically, it uses the location information to send a query to the local government API to obtain relevant information. The input is location information, and the output is local government information.

[0462] Step 7:

[0463] The server sends the generated disposal method, reuse information, and municipal information to the terminal.,Specifically, the server compiles the generated information and sends the data to the,terminal using the HTTP protocol.,The input is a set of generated information, and the output is,notification data sent to the terminal.

[0464] Step 8:

[0465] The user checks the information displayed on the device and asks additional questions as needed. Questions are entered through a chat-style interface to request additional instructions or information. The input is the user's question, and the output is the question data.

[0466] Step 9:

[0467] The server responds to user questions in real time using a generative AI model. Specifically, it inputs question data into the generative AI model, generates an appropriate answer, and returns it to the user. The input is the question data, and the output is the generated answer text.

[0468] (Application example 1)

[0469] 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."

[0470] Conventional systems for disposing of unwanted items and providing information on reuse lack the functionality to respond quickly and accurately to suspicious or lost items. This makes it difficult to provide appropriate countermeasures while ensuring public safety. Furthermore, the lack of real-time interaction makes it difficult to respond to user questions or emergency situations.

[0471] 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.

[0472] In this invention, the server includes means for acquiring images of unwanted items, suspicious objects, and lost items, means for analyzing the acquired images to identify their attributes, means for generating optimal disposal methods, possibilities for reuse, and countermeasures for suspicious objects based on the identified attributes, and means for notifying the user of the generated information, means for acquiring images of suspicious objects and lost items and transmitting data including location information to the server, means for analyzing the received image data and identifying the attributes of the suspicious objects or lost items, means for searching for local security rules and countermeasures based on the location information, and means for providing a chat-style interface that responds to user questions in real time, thereby enabling the provision of quick and accurate countermeasures for suspicious objects and lost items and real-time interaction with the user.

[0473] "Unwanted items" refers to items or waste that are no longer in use and have outlived their normal purpose.

[0474] "Means of acquisition" refers to the functions of the device or software used to acquire images and location information.

[0475] "Means of analysis" refers to the techniques and algorithms used to process acquired data and extract specific attributes or information.

[0476] "Means of identification" refers to the function of identifying the attributes and type of an object based on analyzed data.

[0477] "Generating means" refers to devices or programs capable of generating optimal disposal methods or countermeasures based on identified information.

[0478] "Means for notifying" refers to a device or interface for conveying the generated information to the user.

[0479] "Location Information" means data that indicates a specific geographic location using GPS or other means.

[0480] "Means for transmitting data" refers to the function of sending acquired images and location information to a central processing unit such as a server.

[0481] "Security rules" refer to regulations established in each region for crime prevention and safety.

[0482] "Means for searching for solutions" refers to systems or software functions for finding appropriate solutions based on location information, etc.

[0483] "Real-time responsive chat-style interface" refers to an interactive user interface that can provide immediate answers to users' questions and requests.

[0484] The present invention provides a system for quickly and accurately analyzing images of suspicious objects and lost items, and providing safe countermeasures. Specific embodiments for carrying out the present invention will now be described.

[0485] System Configuration

[0486] The system consists of the following major components:

[0487] 1. Device: The user uses a device such as a smartphone, smart glasses, or head-mounted display to capture images of suspicious or lost items.

[0488] 2. Server: Analyzes the acquired image data, identifies the attributes of suspicious or lost items, and generates optimal countermeasures and notifies the user.

[0489] 3. Communication network: Infrastructure for sending and receiving data between devices and servers.

[0490] Hardware and software used

[0491] Image analysis: OpenCV, Tesseract OCR

[0492] Location information acquisition: Geopy library

[0493] Barcode analysis: ZXing library

[0494] Countermeasure generation: Generative AI model

[0495] Real-time chat: Pre-trained chatbot library

[0496] Data processing and calculation

[0497] 1. Image acquisition and transmission

[0498] The user uses the device to take pictures of suspicious or lost items, and the image data is sent to the server along with location information.

[0499] 2. Image Analysis

[0500] The server analyzes the received image data using OpenCV and Tesseract OCR, extracting attribute information such as the shape, material, and dimensions of the suspicious or lost item.

[0501] 3. Barcode Analysis

[0502] If the image contains a barcode, the ZXing library is used to parse the barcode and retrieve detailed information from the product database.

[0503] 4. Generating optimal countermeasures

[0504] The server uses a generative AI model to generate optimal responses based on the analysis results, which may include notifying security guards, checking with the police, or warning the user.

[0505] 5. User Notifications and Real-Time Chat

[0506] The generated information is immediately sent to the user, who can then ask follow-up questions through a real-time chat-style interface, and the chatbot library will respond with appropriate responses to the user's questions.

[0507] Examples of concrete examples and prompts

[0508] For example, if a user takes a photo of a lost item they find in a park with smart glasses, the image is sent to the system. The server analyzes the shape, material, etc., and if it determines that the item is likely to be suspicious, it will provide a message saying, "Please notify security. Do not touch it." Local security rules, such as "Lost items in the park will be kept for three days before being transferred to the police," are also provided based on the location information. When the user asks, "What should I do with this lost item?" the generating AI responds in real time with, "Take an additional photo to enable more detailed analysis. Please notify the nearest security guard."

[0509] Prompt Sentence Examples

[0510] "Please analyze the images of the lost items you found in the park. Identify the shape and material, and tell us the best course of action."

[0511] As described above, the present invention is a system that provides quick and accurate countermeasures for suspicious objects and lost items, and enables real-time interaction with users.

[0512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0513] Step 1:

[0514] The user uses the device to take an image of a suspicious object or lost property. The device acquires this image data and simultaneously acquires its current location information. The acquired image data and location information are sent to the server. The input data are the image and location information, and the output data is the image and location information sent to the server.

[0515] Step 2:

[0516] The server receives image data sent from the device. It then analyzes the image using OpenCV and Tesseract OCR to extract attribute information such as the shape, material, and dimensions of suspicious or lost items. The input data is the image, and the output data is the extracted attribute information.

[0517] Step 3:

[0518] If the image contains a barcode, the server analyzes the barcode using the ZXing library. Based on the analysis results, detailed information is retrieved from the product database. The input data is the barcode and image, and the output data is the barcode analysis results and product information.

[0519] Step 4:

[0520] The server uses a generative AI model based on the analysis results to generate optimal countermeasures, which may include notifying security guards, checking with the police, and warning the user. The input data is the analysis results, and the output data is the generated countermeasures.

[0521] Step 5:

[0522] The server notifies the user of the generated information. At the same time, it also searches for local security rules and countermeasures based on the location information and provides them to the user as additional information. The input data are the generated countermeasures and location information, and the output data is the notification content to the user.

[0523] Step 6:

[0524] Users can ask follow-up questions through a chat-style interface on their device, which the server responds to in real time using a pre-trained chatbot library. The input data is the user's question, and the output data is the response using generative AI.

[0525] The above processing steps enable the user to take prompt and appropriate action in response to suspicious or lost items.

[0526] 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.

[0527] The system of the present invention combines multiple means to analyze images of unwanted items and provide optimal disposal methods and reuse information. It also has an emotion engine that recognizes the user's emotions, and can adjust notification content and responses according to the user's emotions.

[0528] Users take pictures of unwanted items using devices such as smartphones or tablets, and the devices send the captured image data to a server, sometimes including location information and barcode information.

[0529] The server first analyzes the received image to identify the attributes of the unwanted item. For example, it uses an image analysis algorithm to estimate whether the unwanted item is made of plastic, metal, paper, etc. It also extracts information such as its shape and dimensions.

[0530] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, enabling accurate identification of the unwanted item.

[0531] The server uses generative AI to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, the server generates instructions such as "It can be recycled, but please remove the cap." For reusable items, the server also provides market prices and listing methods.

[0532] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information such as "In this area, metal garbage is collected every Thursday."

[0533] The generated information is sent from the server to the device and notified to the user. Here, the emotion engine recognizes the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling stressed, a reassuring message such as "Don't worry, the procedure is simple" will be added.

[0534] Users can check this information through a chat-style interface on their device, and if they have any additional questions, they can contact the server via chat. The server uses generative AI to respond to users' questions in real time. Again, the emotion engine analyzes the user's emotions and adjusts the response accordingly. For example, if the user is confused, the server might add a phrase like, "I'll explain it in an easy-to-understand way."

[0535] For example, consider the case where a user takes a photo of an unwanted smartphone and sends it from the device to a server. The server performs image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server uses generative AI to inform the user via chat, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current going rate is about 10,000 yen." It also references information from local governments and provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0536] If the emotion engine recognizes the user's emotions and determines that the user is nervous, it adds a comment saying, "Don't worry, the procedure is simple." In this way, the user can relax and receive accurate information.

[0537] In this way, users can easily understand the appropriate disposal methods and reuse possibilities for unwanted items and take appropriate action.In addition, since disposal can be done in accordance with the sorting rules of each local government, environmental impact can be reduced and efficiency can be improved.

[0538] By incorporating an emotion engine, guidance and responses to users can be more personalized, increasing user satisfaction. Furthermore, statistical analysis of emotion data can contribute to improving the system itself and the quality of services.

[0539] The processing flow will be explained below.

[0540] Step 1:

[0541] Users take photos of unwanted items they want to dispose of using a smartphone or tablet, then open the dedicated app, select the images they have taken, and upload them.

[0542] Step 2:

[0543] The device temporarily stores the captured image data and then sends it to a server, sometimes along with the user's location information and barcode information attached to the unwanted item.

[0544] Step 3:

[0545] The server analyzes the received image data. First, it applies an image analysis algorithm to extract attributes such as the material and shape of the unwanted items. For example, it identifies the type of item, such as plastic, metal, or paper.

[0546] Step 4:

[0547] If a barcode is present in the image, the device will automatically scan it and send the information to the server. If no barcode is present, this step is skipped.

[0548] Step 5:

[0549] The server decodes the received barcode information and searches a product database to obtain detailed product information, such as the product manufacturer and model number, from the barcode.

[0550] Step 6:

[0551] Based on the image analysis results and information from the product database, the server uses generative AI to generate instructions on optimal disposal methods and reuse possibilities, such as "This plastic bottle is recyclable, but please remove the cap."

[0552] Step 7:

[0553] Based on the user's location information, the server searches the local government's database for information such as local garbage sorting rules, collection days, fees, etc. For example, it obtains information such as "In this area, every Wednesday is plastic garbage collection day."

[0554] Step 8:

[0555] The server compiles the generated information and notifies the user. Here, an emotion engine recognizes the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling stressed, a reassuring message such as "Don't worry, the procedure is simple" will be added.

[0556] Step 9:

[0557] The device displays the notified information to the user in a chat-style interface and also displays prompts such as "Do you have any questions?"

[0558] Step 10:

[0559] Through a chat-style interface, users can ask additional questions or clarify things, such as "Is this product really recyclable?"

[0560] Step 11:

[0561] The server uses generative AI to answer users' questions in real time. Here too, the emotion engine analyzes the user's emotions and adjusts the response accordingly. For example, if the user is confused, the server might add a phrase like, "I'll explain it in an easy-to-understand way."

[0562] Step 12:

[0563] The server refers to data from auction sites and reuse platforms for unwanted reusable items, and provides users with the current selling price and how to list them. For example, it might say, "The current market price for this smartphone is 10,000 yen. You can list it immediately by clicking the link below."

[0564] Step 13:

[0565] Based on the information provided, users decide how to dispose of unwanted items and then take action, choosing appropriate actions from options such as "separate them for recycling" or "put them on an auction site."

[0566] This is the processing flow of this system, which allows users to easily understand the appropriate disposal methods and reuse possibilities for unwanted items and choose the appropriate action.In addition, by incorporating an emotion engine, guidance and responses to users can be more personalized, improving user satisfaction.

[0567] Example 2

[0568] 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."

[0569] In modern society, there is a demand for fast and accurate information on appropriate disposal and reuse methods for unwanted items. However, conventional systems do not adequately identify the material and shape of unwanted items, obtain regional sorting rules, or adjust notification content based on the user's emotions, which often leaves users feeling stressed. Furthermore, it is not easy to obtain detailed product information using barcodes. This has led to a lack of information on how to properly dispose of unwanted items, which increases the burden on the environment.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0571] In this invention, the server includes a means for acquiring images of unwanted items, a means for analyzing the acquired images to identify attributes of the unwanted items, a means for using a generative artificial intelligence model to generate optimal disposal methods and reuse possibilities based on the identified attributes, a means for recognizing user emotions and adjusting notification content and responses, and a means for notifying the user of the generated information. This makes it possible to quickly and accurately provide information on appropriate disposal methods and reuse methods for unwanted items, thereby improving user satisfaction and reducing environmental impact. Furthermore, accurate product information can be provided by using a means for reading barcode information and a means for obtaining detailed product information by comparing the read barcode information with a product database.

[0572] "Means for obtaining images of unwanted items" refers to the function of taking images of unwanted items using a device such as a smartphone or tablet.

[0573] "Means for analyzing the acquired images to identify the attributes of unwanted items" refers to a technology that uses an image analysis algorithm to identify attributes such as the material, shape, and dimensions of unwanted items.

[0574] "Means using a generative artificial intelligence model that generates optimal disposal methods and reuse possibilities based on identified attributes" refers to technology that uses a generative AI model to generate disposal methods and reuse information for unwanted items.

[0575] "Means for recognizing user emotions and adjusting notification content and responses" refers to technology that uses an emotion engine to analyze user emotions and personalize notification content and responses based on that.

[0576] "Means for notifying the user of the generated information" refers to a function for sending information on how to dispose of unwanted items and reuse information to the user's terminal and notifying them.

[0577] "Means for reading barcode information" refers to technology for reading barcodes attached to unwanted items.

[0578] "Means for obtaining detailed product information by comparing the read barcode information with a product database" refers to technology for decoding the barcode and comparing the information with a product database to obtain detailed product information.

[0579] "Means for acquiring location information" refers to technology for acquiring information about a user's current location using the device's location information service.

[0580] "Means for searching for information on municipal sorting rules and collection dates based on acquired location information" refers to technology that searches a municipal database based on the user's location information to obtain information such as local garbage sorting rules and collection dates.

[0581] The system of this invention combines multiple means to analyze images of unwanted items and provide optimal disposal methods and reuse information. It also has an emotion engine that recognizes the user's emotions, and can adjust notification content and responses according to the user's emotions.

[0582] Users take pictures of unwanted items using devices such as smartphones or tablets. The devices then send the captured image data to a server, sometimes including location information and barcode information. Specifically, devices are equipped with a camera, location information service (GPS function), and barcode scanner, and these hardware components are used to acquire data.

[0583] The server first analyzes the received images and uses image analysis algorithms (e.g., TensorFlow or OpenCV) to identify the attributes of the unwanted items. Image analysis can identify the material (e.g., plastic, metal, paper), shape, dimensions, etc. of the unwanted items.

[0584] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, allowing for more accurate identification of the unwanted item.

[0585] The server uses a generative AI model (such as GPT-3 or BERT) to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, the server generates instructions such as "It can be recycled, but please remove the cap." For reusable items, the server also provides information on the current market price and specific listing methods.

[0586] The server searches the local government database based on the user's location information to obtain information such as local waste sorting rules, collection days, disposal fees, etc. Specific information could be provided such as "In this area, metal waste is collected every Thursday."

[0587] The generated information is sent from the server to the device and notified to the user. At this time, the emotion engine recognizes the user's emotions, and if the user is feeling stressed, for example, a reassuring message such as "Don't worry, the procedure is simple." This allows the user to receive the information in a relaxed state.

[0588] Users can view this information through a chat-style interface on their device, and if they have any additional questions, they can contact the server via chat. The server uses a generative AI model to respond to the user's questions in real time. Again, the emotion engine analyzes the user's emotions, and if the user is confused, for example, it will add a message such as "I'll explain it in an easy-to-understand way."

[0589] As a concrete example, let's consider a case where a user takes a photo of an unwanted smartphone and sends it to a server. The server uses image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server then uses generative AI to provide a chat message saying, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current market price is approximately 10,000 yen." Furthermore, by referencing local government information, the server also provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month." If the emotion engine recognizes the user's emotions and determines that the user is nervous, it adds a comment saying, "Don't worry, the process is simple." In this way, the user can relax and receive accurate information.

[0590] Examples of prompts include, "What is the best way to dispose of the unwanted item in this image?" or "How can I recycle or reuse this smartphone?"

[0591] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0592] Step 1:

[0593] The user takes a picture of the unwanted item

[0594] Users take pictures of unwanted items using a smartphone or tablet, and are encouraged to take pictures from multiple angles.

[0595] Input: Unwanted item to photograph

[0596] Output: Image data of unwanted items

[0597] Step 2:

[0598] The device sends image data, location information, and barcode information to the server.

[0599] The device acquires the captured image data and uses the built-in GPS to obtain location information. Furthermore, if the unwanted item has a barcode attached, the device uses the barcode scanner function to obtain the information. All of this data is then sent to the server.

[0600] Input: Image data, location information, barcode information of unwanted items

[0601] Output: Data sent to the server

[0602] Step 3:

[0603] The server analyzes the image and identifies the attributes of the unwanted items

[0604] The server analyzes the received image data using an image analysis algorithm (e.g., TensorFlow or OpenCV), which identifies the material (plastic, metal, paper, etc.), shape, dimensions, etc. of the unwanted items. Specifically, the algorithm identifies objects in the image and classifies their attributes.

[0605] Input: Image data of unwanted items

[0606] Output: Attribute data of identified unwanted items (material, shape, dimensions, etc.)

[0607] Step 4:

[0608] The server decodes the barcode information and checks it against the product database.

[0609] The server decodes the received barcode information and compares it with a product database to obtain detailed product information (manufacturer, model, product name, etc.) of the unwanted item. Specifically, the barcode decoding algorithm reads the numeric information in the barcode and queries the product database.

[0610] Input: Barcode information

[0611] Output: Product details

[0612] Step 5:

[0613] The server generates optimal disposal methods and reuse information using an AI model.

[0614] Based on the image analysis results and information obtained from the product database, the server uses a generative AI model (e.g., GPT-3, BERT) to generate optimal disposal methods and reuse possibilities for unwanted items. For example, in the case of a plastic bottle, instructions such as "It can be recycled, but please remove the cap" are generated.

[0615] Input: Attribute data of identified unwanted items, detailed product information

[0616] Output: Information on optimal disposal methods and reuse

[0617] Step 6:

[0618] The server searches the local government database and obtains the garbage sorting rules for each region.

[0619] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. Specifically, it uses the location information to query the garbage sorting rules for the relevant area.

[0620] Input:Location

[0621] Output: Information on garbage sorting rules and collection days for each region

[0622] Step 7:

[0623] The server sends the generated information to the terminal.

[0624] The server then sends the generated disposal and reuse information, as well as local waste sorting rules, to the device, allowing users to check the appropriate method for disposing of unwanted items.

[0625] Input: disposal methods, reuse information, and regional garbage sorting rules

[0626] Output: Information sent to the terminal

[0627] Step 8:

[0628] The server analyzes the user's emotions using an emotion engine and adjusts the notification content.

[0629] The server uses an emotion engine to analyze the user's emotions. If the user is stressed, it will add a message such as "Don't worry, the process is simple." For this purpose, it uses facial recognition and text analysis algorithms.

[0630] Input: User actions and input data

[0631] Output: Adjusted notification content

[0632] Step 9:

[0633] The device notifies the user

[0634] The device notifies the user of the information received from the server. Notifications are sent in chat format and displayed in a way that is easy for the user to understand.

[0635] Input: Information received from the server

[0636] Output: Information notified to the user

[0637] Step 10:

[0638] Users can enter additional questions in chat format

[0639] The user uses the device's chat interface to enter follow-up questions, such as "where can I recycle this phone?"

[0640] Input: User question

[0641] Output: Query data from the terminal to the server

[0642] Step 11:

[0643] The server responds to the question using a generative AI model and notifies the user

[0644] The server uses a generative AI model to respond to the user's questions, and here too, the emotion engine analyzes the user's emotions and can add phrases such as "I'll explain it in an easy-to-understand way."

[0645] Input: User question data

[0646] Output: Generated response data, adjusted answer content

[0647] (Application example 2)

[0648] 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."

[0649] In modern society, the disposal and reuse of unwanted items has become an important issue. However, it is not easy for users to understand the appropriate disposal method and the possibility of reuse. Furthermore, users often feel anxious and stressed because the system does not respond to their emotions. Furthermore, there is the problem that it is time-consuming to understand the sorting rules and collection date information of each local government. The present invention aims to solve these problems.

[0650] 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 acquiring images of unwanted items, means for analyzing the acquired images to identify attributes of the unwanted items, means for generating optimal disposal methods and reuse possibilities based on the identified attributes, means for notifying the user of the generated information, and means for analyzing the user's emotions and adjusting the notification content according to the emotions. This allows the user to easily understand appropriate disposal methods and reuse possibilities for unwanted items and receive appropriate guidance according to their emotions.

[0651] "Unwanted items" are items that are no longer needed by the user.

[0652] "Means for acquiring an image" refers to a method for collecting image data of an item using a camera or other image capture device.

[0653] "Means for analyzing images" refers to technology that processes collected image data and recognizes and identifies attributes and features of items.

[0654] "Means for identifying attributes" refers to a method for identifying characteristics such as the material, shape, and dimensions of an object based on information obtained from image analysis.

[0655] The "means for generating disposal methods" is a method for proposing optimal disposal or recycling methods based on the identified attributes.

[0656] "Means for generating reuse possibilities" are technologies that determine whether an item can be reused and provide the necessary information.

[0657] "Means for notifying" refers to an interface or communication means for notifying the user of the generated information.

[0658] "Means for analyzing emotions" refers to technology that recognizes and analyzes emotions from a user's facial expressions, voice, text, etc.

[0659] "Means for adjusting notification content" refers to technology that dynamically changes the content of information and messages provided in response to the user's emotions.

[0660] "Means for reading barcode information" refers to technology that scans the barcode attached to an item and acquires the data.

[0661] "Means for checking against a product database" refers to a technique for comparing acquired barcode information with an existing database to identify detailed product information.

[0662] "Means for acquiring location information" refers to methods for collecting information on the current location of users or items using technologies such as GPS.

[0663] "Means for searching municipal sorting rules" refers to technology that checks waste disposal rules and schedules in a specific area.

[0664] The system of the present invention is designed to enable users to easily obtain information on the disposal and reuse of unwanted items. This system is composed of a terminal such as a smartphone, a server, and a cloud-based service.

[0665] 1. Program Generation

[0666] First, smartphones and other devices are equipped with cameras and barcode scanners. Users take pictures of unwanted items and send the image data from their devices to a server. In some cases, location information and barcode information are also sent.

[0667] The server operates using the following primary methods:

[0668] Image analysis method: Using image analysis algorithms (e.g., OpenCV), the material, shape, and dimensions of unwanted items are identified.

[0669] Barcode analysis method: The information obtained by the barcode scanner is compared with the product database to obtain detailed product information.

[0670] Sentiment analysis method: Analyze user emotions using an emotion engine (e.g., Microsoft Azure's Face API).

[0671] Generative AI method: Use OpenAI's generative AI model (e.g., GPT-4) to generate appropriate disposal methods and reuse information.

[0672] Personalization: Dynamically adjust notification content based on sentiment analysis.

[0673] 2. Explain the program's processing in natural language

[0674] Acquisition of image data:

[0675] The user takes a picture of the unwanted item using the device's camera and sends the image data to the server. Location information and barcode information can also be included when sending the data.

[0676] Image analysis:

[0677] The server analyzes the received image data and identifies the attributes of the unwanted items (e.g., material, shape, dimensions). This is done using an image analysis algorithm (e.g., OpenCV). It also analyzes the barcode information and compares it with a product database to obtain detailed product information.

[0678] Emotion analysis:

[0679] The emotion engine analyzes the user's facial expressions, voice, and text to determine their emotional state and uses that information to tailor notifications.

[0680] Disposal Method Generation:

[0681] Generative AI models (e.g., GPT-4) are used to generate optimal disposal methods and reuse possibilities for unwanted items. For example, information such as "This smartphone is recyclable, but the battery must be removed" is generated.

[0682] Information Notice:

[0683] The generated information is personalized based on the user's emotional analysis and sent to the device. For example, if the user is nervous, a message such as "Don't worry, the procedure is simple" will be added.

[0684] 3. Examples of concrete examples and prompts

[0685] Examples:

[0686] 1. User A takes a picture of a smartphone that he no longer needs.

[0687] 2. Image data, location information, and barcode information are sent from the device to the server.

[0688] 3. The server analyzes the image and retrieves detailed information about the smartphone from the product database.

[0689] 4. The generative AI model generates the information, "Please recycle this smartphone. The current market price is 10,000 yen."

[0690] 5. The emotion engine analyzes User A's emotions and determines that he is nervous.

[0691] 6. A notification is sent to User A with the additional message, "Don't worry, it's easy."

[0692] 7. Information about garbage collection days in User A's area is also provided, informing him that "electronic waste is collected in your area on the last Friday of every month."

[0693] Example prompt sentence:

[0694] Analyze user images to obtain detailed information about unwanted items, and generate and display appropriate recycling methods and market prices. Example: Analyze images from a smartphone and provide recycling methods and current market prices.

[0695] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0696] Step 1:

[0697] The user takes a picture of the unwanted item with a camera. The user then captures the image of the unwanted item using the camera on their smartphone and saves the data on their device. The input is image data, and the output is an image file.

[0698] Step 2:

[0699] The terminal sends the acquired image data to the server. The user sends the acquired image data along with location information and barcode information (if any) to the server through the application. The input is the image data, location information, and barcode information, and the output is the data sent to the server.

[0700] Step 3:

[0701] The server analyzes the image data and identifies the attributes of the unwanted items. The server uses an image analysis algorithm (e.g., OpenCV) to identify attributes such as the material, shape, and dimensions of the unwanted items from the image data. The input is the image data, and the output is the attribute data of the identified unwanted items.

[0702] Step 4:

[0703] The server analyzes the barcode information and compares it with the product database. The server analyzes the barcode information and compares it with the product database to obtain detailed product information. The input is the barcode information, and the output is the product information obtained from the product database.

[0704] Step 5:

[0705] The server analyzes the user's emotions. The server uses an emotion engine (e.g., Microsoft Azure's Face API) to analyze emotions from the user's facial expressions and text. The input is the user's facial expression data and text, and the output is the user's emotion analysis results.

[0706] Step 6:

[0707] The server generates optimal disposal methods and reuse possibilities for unwanted items. The server uses a generative AI model (e.g., GPT-4) to generate optimal disposal methods and reuse possibilities for unwanted items based on the identified attributes and product information. The input is the attribute data and product information of the unwanted items, and the output is the generated disposal methods and reuse information.

[0708] Step 7:

[0709] The server personalizes the generated information. The server personalizes the generated information based on the results of the user's emotion analysis. For example, if the user is nervous, it adds a message such as "Don't worry, the procedure is simple." The input is the emotion analysis result and the generated information, and the output is personalized notification information.

[0710] Step 8:

[0711] The server sends personalized notification information to the user's terminal. The server sends personalized notification information to the user's terminal and notifies the user. The input is the personalized notification information, and the output is a notification displayed on the user's terminal.

[0712] Step 9:

[0713] Search for municipal sorting rules and collection date information. The server searches the municipal database based on the user's location information to obtain information on regional sorting rules and collection dates. The input is location information, and the output is information on sorting rules and collection dates.

[0714] Step 10:

[0715] The server notifies the user of the sorting rules and collection date information. The server sends the acquired sorting rules and collection date information to the user's terminal and notifies the user. The input is the sorting rules and collection date information, and the output is a notification displayed on the user's terminal.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] [Third embodiment]

[0720] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0721] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0722] 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).

[0723] 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.

[0724] 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.

[0725] 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).

[0726] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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."

[0732] The system of the present invention operates by combining a plurality of means to analyze images of unwanted items and provide optimal disposal methods and reuse information. Specific embodiments of the system will be described below.

[0733] Users take pictures of unwanted items using devices such as smartphones or tablets, and the devices send the captured image data to a server, sometimes including location information and barcode information.

[0734] The server first analyzes the received image to identify the attributes of the unwanted item. For example, it uses an image analysis algorithm to estimate whether the unwanted item is made of plastic, metal, paper, etc. It also extracts information such as its shape and dimensions.

[0735] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, enabling accurate identification of the unwanted item.

[0736] The server uses generative AI to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, instructions such as "It can be recycled, but please remove the cap" are generated. For reusable items, market prices and listing methods are also provided.

[0737] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information such as "In this area, metal garbage is collected every Thursday."

[0738] The generated information is sent from the server to the device and notified to the user. The user can check this information through a chat-style interface on the device and ask the server any additional questions via chat. The server uses the generation AI to respond to the user's questions in real time.

[0739] For example, consider the case where a user takes a photo of an unwanted smartphone and sends it from the device to a server. The server performs image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server uses generative AI to inform the user via chat, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current going rate is about 10,000 yen." It also references information from local governments and provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0740] In this way, users can easily understand the appropriate disposal methods and reuse possibilities for unwanted items and take appropriate action.In addition, since disposal can be done in accordance with the sorting rules of each local government, environmental impact can be reduced and efficiency can be improved.

[0741] The processing flow will be explained below.

[0742] Step 1:

[0743] Users take photos of unwanted items they want to dispose of using a smartphone or tablet, then open the dedicated app, select the images they have taken, and upload them.

[0744] Step 2:

[0745] The device temporarily stores the captured image data and then sends it to a server, sometimes along with the user's location information and barcode information attached to the unwanted item.

[0746] Step 3:

[0747] The server analyzes the received image data. First, it applies an image analysis algorithm to extract attributes such as the material and shape of the unwanted items. For example, it identifies the type of item, such as plastic, metal, or paper.

[0748] Step 4:

[0749] If a barcode is present in the image, the device will automatically scan it and send the information to the server. If no barcode is present, this step is skipped.

[0750] Step 5:

[0751] The server decodes the received barcode information and searches a product database to obtain detailed product information, such as the product manufacturer and model number, from the barcode.

[0752] Step 6:

[0753] Based on the image analysis results and information from the product database, the server uses generative AI to generate instructions on optimal disposal methods and reuse possibilities, such as "This plastic bottle is recyclable, but please remove the cap."

[0754] Step 7:

[0755] Based on the user's location information, the server searches the local government's database for information such as local garbage sorting rules, collection days, fees, etc. For example, it obtains information such as "In this area, every Wednesday is plastic garbage collection day."

[0756] Step 8:

[0757] The server compiles the generated information and notifies the user, and the device displays the information to the user in a chat-style interface, along with prompts such as "Do you have any questions?"

[0758] Step 9:

[0759] Through a chat-style interface, users can ask additional questions or clarify things, such as "Is this product really recyclable?"

[0760] Step 10:

[0761] The server uses generative AI to answer users' questions in real time, for example, "Yes, this product is recyclable, but you must remove the battery."

[0762] Step 11:

[0763] The server refers to data from auction sites and reuse platforms for unwanted reusable items, and provides users with the current selling price and how to list them. For example, it might say, "The current market price for this smartphone is 10,000 yen. You can list it immediately by clicking the link below."

[0764] Step 12:

[0765] Based on the information provided, users decide how to dispose of unwanted items and then take action, choosing appropriate actions from options such as "separate them for recycling" or "put them on an auction site."

[0766] The above is the processing flow of this system, and this process allows users to easily understand the appropriate disposal methods and reuse possibilities for unwanted items and select appropriate actions.

[0767] Example 1

[0768] 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."

[0769] Providing information on appropriate disposal methods and recycling methods for unwanted items takes a lot of time and effort. It is also complicated to check garbage sorting rules and collection days, which vary from region to region. Furthermore, it is difficult to accurately obtain more detailed product information and local sorting rules based on the barcode information and location information of unwanted items. To solve these problems, a system is needed that can efficiently obtain information and provide it to users quickly.

[0770] 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.

[0771] In this invention, the server includes means for acquiring images of unwanted items, means for analyzing the acquired images to identify attributes of the unwanted items, means for generating optimal disposal methods and possibilities for reuse based on the identified attributes, means for notifying the generated information to the user so that the user can ask additional questions, and means for responding to the additional questions in real time, thereby making it possible to quickly and accurately provide the user with appropriate disposal methods and possibilities for reuse of unwanted items.

[0772] "Unwanted items" refers to items that have been used or are no longer needed, and items that should be considered for disposal or reuse.

[0773] "Means for acquiring images" refers to a device or system that has the function of acquiring image data of unwanted items using a camera, scanner, etc.

[0774] "Means for analyzing images to identify the attributes of unwanted items" refers to algorithms and technologies that analyze acquired images and extract and identify characteristics such as the material, shape, and dimensions of unwanted items.

[0775] "Means for generating optimal disposal methods and reuse possibilities" refers to a system or algorithm that determines and constructs information on appropriate disposal methods and reuse possibilities for unwanted items based on image analysis results and other related information.

[0776] "Means for notifying the user and allowing the user to ask further questions" refers to an interface or system that provides the generated information to the user and allows the user to ask further questions.

[0777] "Means for responding to follow-up questions in real time" refers to a system or algorithm that has the ability to instantly generate and provide appropriate answers to questions from users.

[0778] "Means for reading barcode information" refers to technology that reads barcode information attached to an item using a barcode reader, a smartphone camera, etc.

[0779] The "means for obtaining detailed product information by checking against a product database" refers to the process of checking the scanned barcode information against an existing product database to obtain detailed information about the product.

[0780] "Means for acquiring location information" refers to a system that acquires the current location of a user or item using technologies such as GPS or Wi-Fi.

[0781] "Means for searching for information on municipal sorting rules and collection days" refers to a system or algorithm that searches the municipality's official database or public information based on location information to obtain information on waste sorting rules and collection days specific to that area.

[0782] The system of the present invention is configured to provide information on appropriate disposal methods and reuse of unwanted items. Specific embodiments of the system will be described below.

[0783] Users use devices such as smartphones or tablets to take pictures of unwanted items. The device then sends the captured image data to a server, sometimes including the device's location and barcode information. A standard camera app and HTTPS are often used as the communication protocol for this process.

[0784] The server first analyzes the received image data. Image analysis algorithms such as TensorFlow and OpenCV can be used for image analysis. This allows information such as the material (e.g., plastic, metal, paper), shape, and dimensions of the unwanted items to be extracted and their attributes identified.

[0785] If the unwanted item has a barcode, the device reads it and sends the information to the server. For example, the BarcodeScanner library is used to read the barcode. The server decodes the barcode and compares it with a product database to obtain detailed product information. This allows the server to determine, for example, that "barcode 12345678" is compatible with Apple's iPhone X.

[0786] The server uses a generative AI model (e.g., GPT-4) based on the image analysis results and information obtained from the product database to generate optimal disposal methods and reuse possibilities. The generative AI model generates recommendations such as, "This smartphone can be recycled, but please remove the battery. It can be reused, so the current market price is approximately 10,000 yen."

[0787] Furthermore, the server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information specific to the area, such as "In Chiyoda Ward, Tokyo, electronic device recycling takes place on the last Friday of the month."

[0788] The generated information is sent from the server to the device and notified to the user. The user can check the provided information through a chat-style interface on the device. If the user has additional questions, the generative AI model can be used to answer them in real time. For example, if the user asks, "What are the specific steps to sell this smartphone?" the server will respond with, "List it on site X and package it like this."

[0789] As a concrete example, let's consider a case where a user wants to dispose of an unwanted smartphone (e.g., an iPhone X) and takes a picture of it with the smartphone. The device sends the image data to a server, attaching location information obtained from GPS. The server uses TensorFlow to identify the unwanted item as a smartphone and compares the barcode 12345678 with a database to obtain product information. The generative AI model generates information such as, "This smartphone is recyclable, but please remove the battery. It is reusable, so the current going rate is approximately 10,000 yen." It also references a local government database and provides information such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0790] In this way, users can understand the appropriate disposal methods and possibilities for reusing unwanted items and take action. It also enables disposal in accordance with the sorting rules of each local government, reducing the environmental burden and improving efficiency.

[0791] Example prompt sentence:

[0792] "Lost and Found: Smartphone

[0793] Features: Black color, iPhone X, barcode 12345678 displayed

[0794] Q: How do I dispose of this phone?

[0795] Location: Chiyoda-ku, Tokyo

[0796] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0797] Step 1:

[0798] The user takes a picture of the unwanted item using a smartphone or tablet. If the unwanted item is a smartphone, the user takes a photo using a camera app so that the entire item and the barcode are visible. The input is the image of the unwanted item, and the output is an image file.

[0799] Step 2:

[0800] The device sends the captured image data to the server, adding location information and barcode information if necessary. Location information is obtained using the device's GPS function, and barcode information is extracted from the image captured by the camera. The input is the image file, GPS data, and barcode information, and the output is a data packet sent to the server.

[0801] Step 3:

[0802] The server analyzes the received image data. TensorFlow and OpenCV are used for the analysis to identify the material, shape, and dimensions of the unwanted items. Specifically, the server parses the image and applies an object recognition algorithm to extract attribute information. The input is image data, and the output is attribute information of the unwanted items.

[0803] Step 4:

[0804] The server decodes the barcode information and checks it against a product database to obtain detailed product information. This operation uses the BarcodeScanner library. Specifically, after decoding the barcode information, it executes a database query to obtain the corresponding product information. The input is the barcode information, and the output is detailed product information.

[0805] Step 5:

[0806] The server uses a generative AI model (e.g., GPT-4) to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. Specifically, attribute information and product information are input into the generative AI model, which then generates optimal disposal methods and reuse instructions in text format. The input is attribute information and product information, and the output is disposal methods and reuse instructions.

[0807] Step 6:

[0808] The server searches the local government database based on the user's location information to obtain information such as local waste sorting rules, collection days, and disposal fees. Specifically, it uses the location information to send a query to the local government API to obtain relevant information. The input is location information, and the output is local government information.

[0809] Step 7:

[0810] The server sends the generated disposal method, reuse information, and municipal information to the terminal.,Specifically, the server compiles the generated information and sends the data to the,terminal using the HTTP protocol.,The input is a set of generated information, and the output is,notification data sent to the terminal.

[0811] Step 8:

[0812] The user checks the information displayed on the device and asks additional questions as needed. Questions are entered through a chat-style interface to request additional instructions or information. The input is the user's question, and the output is the question data.

[0813] Step 9:

[0814] The server responds to user questions in real time using a generative AI model. Specifically, it inputs question data into the generative AI model, generates an appropriate answer, and returns it to the user. The input is the question data, and the output is the generated answer text.

[0815] (Application example 1)

[0816] 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."

[0817] Conventional systems for disposing of unwanted items and providing information on reuse lack the functionality to respond quickly and accurately to suspicious or lost items. This makes it difficult to provide appropriate countermeasures while ensuring public safety. Furthermore, the lack of real-time interaction makes it difficult to respond to user questions or emergency situations.

[0818] 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.

[0819] In this invention, the server includes means for acquiring images of unwanted items, suspicious objects, and lost items, means for analyzing the acquired images to identify their attributes, means for generating optimal disposal methods, possibilities for reuse, and countermeasures for suspicious objects based on the identified attributes, and means for notifying the user of the generated information, means for acquiring images of suspicious objects and lost items and transmitting data including location information to the server, means for analyzing the received image data and identifying the attributes of the suspicious objects or lost items, means for searching for local security rules and countermeasures based on the location information, and means for providing a chat-style interface that responds to user questions in real time, thereby enabling the provision of quick and accurate countermeasures for suspicious objects and lost items and real-time interaction with the user.

[0820] "Unwanted items" refers to items or waste that are no longer in use and have outlived their normal purpose.

[0821] "Means of acquisition" refers to the functions of the device or software used to acquire images and location information.

[0822] "Means of analysis" refers to the techniques and algorithms used to process acquired data and extract specific attributes or information.

[0823] "Means of identification" refers to the function of identifying the attributes and type of an object based on analyzed data.

[0824] "Generating means" refers to devices or programs capable of generating optimal disposal methods or countermeasures based on identified information.

[0825] "Means for notifying" refers to a device or interface for conveying the generated information to the user.

[0826] "Location Information" means data that indicates a specific geographic location using GPS or other means.

[0827] "Means for transmitting data" refers to the function of sending acquired images and location information to a central processing unit such as a server.

[0828] "Security rules" refer to regulations established in each region for crime prevention and safety.

[0829] "Means for searching for solutions" refers to systems or software functions for finding appropriate solutions based on location information, etc.

[0830] "Real-time responsive chat-style interface" refers to an interactive user interface that can provide immediate answers to users' questions and requests.

[0831] The present invention provides a system for quickly and accurately analyzing images of suspicious objects and lost items, and providing safe countermeasures. Specific embodiments for carrying out the present invention will now be described.

[0832] System Configuration

[0833] The system consists of the following major components:

[0834] 1. Device: The user uses a device such as a smartphone, smart glasses, or head-mounted display to capture images of suspicious or lost items.

[0835] 2. Server: Analyzes the acquired image data, identifies the attributes of suspicious or lost items, and generates optimal countermeasures and notifies the user.

[0836] 3. Communication network: Infrastructure for sending and receiving data between devices and servers.

[0837] Hardware and software used

[0838] Image analysis: OpenCV, Tesseract OCR

[0839] Location information acquisition: Geopy library

[0840] Barcode analysis: ZXing library

[0841] Countermeasure generation: Generative AI model

[0842] Real-time chat: Pre-trained chatbot library

[0843] Data processing and calculation

[0844] 1. Image acquisition and transmission

[0845] The user uses the device to take pictures of suspicious or lost items, and the image data is sent to the server along with location information.

[0846] 2. Image Analysis

[0847] The server analyzes the received image data using OpenCV and Tesseract OCR, extracting attribute information such as the shape, material, and dimensions of the suspicious or lost item.

[0848] 3. Barcode Analysis

[0849] If the image contains a barcode, the ZXing library is used to parse the barcode and retrieve detailed information from the product database.

[0850] 4. Generating optimal countermeasures

[0851] The server uses a generative AI model to generate optimal responses based on the analysis results, which may include notifying security guards, checking with the police, or warning the user.

[0852] 5. User Notifications and Real-Time Chat

[0853] The generated information is immediately sent to the user, who can then ask follow-up questions through a real-time chat-style interface, and the chatbot library will respond with appropriate responses to the user's questions.

[0854] Examples of concrete examples and prompts

[0855] For example, if a user takes a photo of a lost item they find in a park with smart glasses, the image is sent to the system. The server analyzes the shape, material, etc., and if it determines that the item is likely to be suspicious, it will provide a message saying, "Please notify security. Do not touch it." Local security rules, such as "Lost items in the park will be kept for three days before being transferred to the police," are also provided based on the location information. When the user asks, "What should I do with this lost item?" the generating AI responds in real time with, "Take an additional photo to enable more detailed analysis. Please notify the nearest security guard."

[0856] Prompt Sentence Examples

[0857] "Please analyze the images of the lost items you found in the park. Identify the shape and material, and tell us the best course of action."

[0858] As described above, the present invention is a system that provides quick and accurate countermeasures for suspicious objects and lost items, and enables real-time interaction with users.

[0859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0860] Step 1:

[0861] The user uses the device to take an image of a suspicious object or lost property. The device acquires this image data and simultaneously acquires its current location information. The acquired image data and location information are sent to the server. The input data are the image and location information, and the output data is the image and location information sent to the server.

[0862] Step 2:

[0863] The server receives image data sent from the device. It then analyzes the image using OpenCV and Tesseract OCR to extract attribute information such as the shape, material, and dimensions of suspicious or lost items. The input data is the image, and the output data is the extracted attribute information.

[0864] Step 3:

[0865] If the image contains a barcode, the server analyzes the barcode using the ZXing library. Based on the analysis results, detailed information is retrieved from the product database. The input data is the barcode and image, and the output data is the barcode analysis results and product information.

[0866] Step 4:

[0867] The server uses a generative AI model based on the analysis results to generate optimal countermeasures, which may include notifying security guards, checking with the police, and warning the user. The input data is the analysis results, and the output data is the generated countermeasures.

[0868] Step 5:

[0869] The server notifies the user of the generated information. At the same time, it also searches for local security rules and countermeasures based on the location information and provides them to the user as additional information. The input data are the generated countermeasures and location information, and the output data is the notification content to the user.

[0870] Step 6:

[0871] Users can ask follow-up questions through a chat-style interface on their device, which the server responds to in real time using a pre-trained chatbot library. The input data is the user's question, and the output data is the response using generative AI.

[0872] The above processing steps enable the user to take prompt and appropriate action in response to suspicious or lost items.

[0873] 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.

[0874] The system of the present invention combines multiple means to analyze images of unwanted items and provide optimal disposal methods and reuse information. It also has an emotion engine that recognizes the user's emotions, and can adjust notification content and responses according to the user's emotions.

[0875] Users take pictures of unwanted items using devices such as smartphones or tablets, and the devices send the captured image data to a server, sometimes including location information and barcode information.

[0876] The server first analyzes the received image to identify the attributes of the unwanted item. For example, it uses an image analysis algorithm to estimate whether the unwanted item is made of plastic, metal, paper, etc. It also extracts information such as its shape and dimensions.

[0877] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, enabling accurate identification of the unwanted item.

[0878] The server uses generative AI to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, the server generates instructions such as "It can be recycled, but please remove the cap." For reusable items, the server also provides market prices and listing methods.

[0879] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information such as "In this area, metal garbage is collected every Thursday."

[0880] The generated information is sent from the server to the device and notified to the user. Here, the emotion engine recognizes the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling stressed, a reassuring message such as "Don't worry, the procedure is simple" will be added.

[0881] Users can check this information through a chat-style interface on their device, and if they have any additional questions, they can contact the server via chat. The server uses generative AI to respond to users' questions in real time. Again, the emotion engine analyzes the user's emotions and adjusts the response accordingly. For example, if the user is confused, the server might add a phrase like, "I'll explain it in an easy-to-understand way."

[0882] For example, consider the case where a user takes a photo of an unwanted smartphone and sends it from the device to a server. The server performs image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server uses generative AI to inform the user via chat, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current going rate is about 10,000 yen." It also references information from local governments and provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[0883] If the emotion engine recognizes the user's emotions and determines that the user is nervous, it adds a comment saying, "Don't worry, the procedure is simple." In this way, the user can relax and receive accurate information.

[0884] In this way, users can easily understand the appropriate disposal methods and reuse possibilities for unwanted items and take appropriate action.In addition, since disposal can be done in accordance with the sorting rules of each local government, environmental impact can be reduced and efficiency can be improved.

[0885] By incorporating an emotion engine, guidance and responses to users can be more personalized, increasing user satisfaction. Furthermore, statistical analysis of emotion data can contribute to improving the system itself and the quality of services.

[0886] The processing flow will be explained below.

[0887] Step 1:

[0888] Users take photos of unwanted items they want to dispose of using a smartphone or tablet, then open the dedicated app, select the images they have taken, and upload them.

[0889] Step 2:

[0890] The device temporarily stores the captured image data and then sends it to a server, sometimes along with the user's location information and barcode information attached to the unwanted item.

[0891] Step 3:

[0892] The server analyzes the received image data. First, it applies an image analysis algorithm to extract attributes such as the material and shape of the unwanted items. For example, it identifies the type of item, such as plastic, metal, or paper.

[0893] Step 4:

[0894] If a barcode is present in the image, the device will automatically scan it and send the information to the server. If no barcode is present, this step is skipped.

[0895] Step 5:

[0896] The server decodes the received barcode information and searches a product database to obtain detailed product information, such as the product manufacturer and model number, from the barcode.

[0897] Step 6:

[0898] Based on the image analysis results and information from the product database, the server uses generative AI to generate instructions on optimal disposal methods and reuse possibilities, such as "This plastic bottle is recyclable, but please remove the cap."

[0899] Step 7:

[0900] Based on the user's location information, the server searches the local government's database for information such as local garbage sorting rules, collection days, fees, etc. For example, it obtains information such as "In this area, every Wednesday is plastic garbage collection day."

[0901] Step 8:

[0902] The server compiles the generated information and notifies the user. Here, an emotion engine recognizes the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling stressed, a reassuring message such as "Don't worry, the procedure is simple" will be added.

[0903] Step 9:

[0904] The device displays the notified information to the user in a chat-style interface and also displays prompts such as "Do you have any questions?"

[0905] Step 10:

[0906] Through a chat-style interface, users can ask additional questions or clarify things, such as "Is this product really recyclable?"

[0907] Step 11:

[0908] The server uses generative AI to answer users' questions in real time. Here too, the emotion engine analyzes the user's emotions and adjusts the response accordingly. For example, if the user is confused, the server might add a phrase like, "I'll explain it in an easy-to-understand way."

[0909] Step 12:

[0910] The server refers to data from auction sites and reuse platforms for unwanted reusable items, and provides users with the current selling price and how to list them. For example, it might say, "The current market price for this smartphone is 10,000 yen. You can list it immediately by clicking the link below."

[0911] Step 13:

[0912] Based on the information provided, users decide how to dispose of unwanted items and then take action, choosing appropriate actions from options such as "separate them for recycling" or "put them on an auction site."

[0913] This is the processing flow of this system, which allows users to easily understand the appropriate disposal methods and reuse possibilities for unwanted items and choose the appropriate action.In addition, by incorporating an emotion engine, guidance and responses to users can be more personalized, improving user satisfaction.

[0914] Example 2

[0915] 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."

[0916] In modern society, there is a demand for fast and accurate information on appropriate disposal and reuse methods for unwanted items. However, conventional systems do not adequately identify the material and shape of unwanted items, obtain regional sorting rules, or adjust notification content based on the user's emotions, which often leaves users feeling stressed. Furthermore, it is not easy to obtain detailed product information using barcodes. This has led to a lack of information on how to properly dispose of unwanted items, which increases the burden on the environment.

[0917] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0918] In this invention, the server includes a means for acquiring images of unwanted items, a means for analyzing the acquired images to identify attributes of the unwanted items, a means for using a generative artificial intelligence model to generate optimal disposal methods and reuse possibilities based on the identified attributes, a means for recognizing user emotions and adjusting notification content and responses, and a means for notifying the user of the generated information. This makes it possible to quickly and accurately provide information on appropriate disposal methods and reuse methods for unwanted items, thereby improving user satisfaction and reducing environmental impact. Furthermore, accurate product information can be provided by using a means for reading barcode information and a means for obtaining detailed product information by comparing the read barcode information with a product database.

[0919] "Means for obtaining images of unwanted items" refers to the function of taking images of unwanted items using a device such as a smartphone or tablet.

[0920] "Means for analyzing the acquired images to identify the attributes of unwanted items" refers to a technology that uses an image analysis algorithm to identify attributes such as the material, shape, and dimensions of unwanted items.

[0921] "Means using a generative artificial intelligence model that generates optimal disposal methods and reuse possibilities based on identified attributes" refers to technology that uses a generative AI model to generate disposal methods and reuse information for unwanted items.

[0922] "Means for recognizing user emotions and adjusting notification content and responses" refers to technology that uses an emotion engine to analyze user emotions and personalize notification content and responses based on that.

[0923] "Means for notifying the user of the generated information" refers to a function for sending information on how to dispose of unwanted items and reuse information to the user's terminal and notifying them.

[0924] "Means for reading barcode information" refers to technology for reading barcodes attached to unwanted items.

[0925] "Means for obtaining detailed product information by comparing the read barcode information with a product database" refers to technology for decoding the barcode and comparing the information with a product database to obtain detailed product information.

[0926] "Means for acquiring location information" refers to technology for acquiring information about a user's current location using the device's location information service.

[0927] "Means for searching for information on municipal sorting rules and collection dates based on acquired location information" refers to technology that searches a municipal database based on the user's location information to obtain information such as local garbage sorting rules and collection dates.

[0928] The system of this invention combines multiple means to analyze images of unwanted items and provide optimal disposal methods and reuse information. It also has an emotion engine that recognizes the user's emotions, and can adjust notification content and responses according to the user's emotions.

[0929] Users take pictures of unwanted items using devices such as smartphones or tablets. The devices then send the captured image data to a server, sometimes including location information and barcode information. Specifically, devices are equipped with a camera, location information service (GPS function), and barcode scanner, and these hardware components are used to acquire data.

[0930] The server first analyzes the received images and uses image analysis algorithms (e.g., TensorFlow or OpenCV) to identify the attributes of the unwanted items. Image analysis can identify the material (e.g., plastic, metal, paper), shape, dimensions, etc. of the unwanted items.

[0931] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, allowing for more accurate identification of the unwanted item.

[0932] The server uses a generative AI model (such as GPT-3 or BERT) to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, the server generates instructions such as "It can be recycled, but please remove the cap." For reusable items, the server also provides information on the current market price and specific listing methods.

[0933] The server searches the local government database based on the user's location information to obtain information such as local waste sorting rules, collection days, disposal fees, etc. Specific information could be provided such as "In this area, metal waste is collected every Thursday."

[0934] The generated information is sent from the server to the device and notified to the user. At this time, the emotion engine recognizes the user's emotions, and if the user is feeling stressed, for example, a reassuring message such as "Don't worry, the procedure is simple." This allows the user to receive the information in a relaxed state.

[0935] Users can view this information through a chat-style interface on their device, and if they have any additional questions, they can contact the server via chat. The server uses a generative AI model to respond to the user's questions in real time. Again, the emotion engine analyzes the user's emotions, and if the user is confused, for example, it will add a message such as "I'll explain it in an easy-to-understand way."

[0936] As a concrete example, let's consider a case where a user takes a photo of an unwanted smartphone and sends it to a server. The server uses image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server then uses generative AI to provide a chat message saying, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current market price is approximately 10,000 yen." Furthermore, by referencing local government information, the server also provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month." If the emotion engine recognizes the user's emotions and determines that the user is nervous, it adds a comment saying, "Don't worry, the process is simple." In this way, the user can relax and receive accurate information.

[0937] Examples of prompts include, "What is the best way to dispose of the unwanted item in this image?" or "How can I recycle or reuse this smartphone?"

[0938] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0939] Step 1:

[0940] The user takes a picture of the unwanted item

[0941] Users take pictures of unwanted items using a smartphone or tablet, and are encouraged to take pictures from multiple angles.

[0942] Input: Unwanted item to photograph

[0943] Output: Image data of unwanted items

[0944] Step 2:

[0945] The device sends image data, location information, and barcode information to the server.

[0946] The device acquires the captured image data and uses the built-in GPS to obtain location information. Furthermore, if the unwanted item has a barcode attached, the device uses the barcode scanner function to obtain the information. All of this data is then sent to the server.

[0947] Input: Image data, location information, barcode information of unwanted items

[0948] Output: Data sent to the server

[0949] Step 3:

[0950] The server analyzes the image and identifies the attributes of the unwanted items

[0951] The server analyzes the received image data using an image analysis algorithm (e.g., TensorFlow or OpenCV), which identifies the material (plastic, metal, paper, etc.), shape, dimensions, etc. of the unwanted items. Specifically, the algorithm identifies objects in the image and classifies their attributes.

[0952] Input: Image data of unwanted items

[0953] Output: Attribute data of identified unwanted items (material, shape, dimensions, etc.)

[0954] Step 4:

[0955] The server decodes the barcode information and checks it against the product database.

[0956] The server decodes the received barcode information and compares it with a product database to obtain detailed product information (manufacturer, model, product name, etc.) of the unwanted item. Specifically, the barcode decoding algorithm reads the numeric information in the barcode and queries the product database.

[0957] Input: Barcode information

[0958] Output: Product details

[0959] Step 5:

[0960] The server generates optimal disposal methods and reuse information using an AI model.

[0961] Based on the image analysis results and information obtained from the product database, the server uses a generative AI model (e.g., GPT-3, BERT) to generate optimal disposal methods and reuse possibilities for unwanted items. For example, in the case of a plastic bottle, instructions such as "It can be recycled, but please remove the cap" are generated.

[0962] Input: Attribute data of identified unwanted items, detailed product information

[0963] Output: Information on optimal disposal methods and reuse

[0964] Step 6:

[0965] The server searches the local government database and obtains the garbage sorting rules for each region.

[0966] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. Specifically, it uses the location information to query the garbage sorting rules for the relevant area.

[0967] Input:Location

[0968] Output: Information on garbage sorting rules and collection days for each region

[0969] Step 7:

[0970] The server sends the generated information to the terminal.

[0971] The server then sends the generated disposal and reuse information, as well as local waste sorting rules, to the device, allowing users to check the appropriate method for disposing of unwanted items.

[0972] Input: disposal methods, reuse information, and regional garbage sorting rules

[0973] Output: Information sent to the terminal

[0974] Step 8:

[0975] The server analyzes the user's emotions using an emotion engine and adjusts the notification content.

[0976] The server uses an emotion engine to analyze the user's emotions. If the user is stressed, it will add a message such as "Don't worry, the process is simple." For this purpose, it uses facial recognition and text analysis algorithms.

[0977] Input: User actions and input data

[0978] Output: Adjusted notification content

[0979] Step 9:

[0980] The device notifies the user

[0981] The device notifies the user of the information received from the server. Notifications are sent in chat format and displayed in a way that is easy for the user to understand.

[0982] Input: Information received from the server

[0983] Output: Information notified to the user

[0984] Step 10:

[0985] Users can enter additional questions in chat format

[0986] The user uses the device's chat interface to enter follow-up questions, such as "where can I recycle this phone?"

[0987] Input: User question

[0988] Output: Query data from the terminal to the server

[0989] Step 11:

[0990] The server responds to the question using a generative AI model and notifies the user

[0991] The server uses a generative AI model to respond to the user's questions, and here too, the emotion engine analyzes the user's emotions and can add phrases such as "I'll explain it in an easy-to-understand way."

[0992] Input: User question data

[0993] Output: Generated response data, adjusted answer content

[0994] (Application example 2)

[0995] 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."

[0996] In modern society, the disposal and reuse of unwanted items has become an important issue. However, it is not easy for users to understand the appropriate disposal method and the possibility of reuse. Furthermore, users often feel anxious and stressed because the system does not respond to their emotions. Furthermore, there is the problem that it is time-consuming to understand the sorting rules and collection date information of each local government. The present invention aims to solve these problems.

[0997] 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 acquiring images of unwanted items, means for analyzing the acquired images to identify attributes of the unwanted items, means for generating optimal disposal methods and reuse possibilities based on the identified attributes, means for notifying the user of the generated information, and means for analyzing the user's emotions and adjusting the notification content according to the emotions. This allows the user to easily understand appropriate disposal methods and reuse possibilities for unwanted items and receive appropriate guidance according to their emotions.

[0998] "Unwanted items" are items that are no longer needed by the user.

[0999] "Means for acquiring an image" refers to a method for collecting image data of an item using a camera or other image capture device.

[1000] "Means for analyzing images" refers to technology that processes collected image data and recognizes and identifies attributes and features of items.

[1001] "Means for identifying attributes" refers to a method for identifying characteristics such as the material, shape, and dimensions of an object based on information obtained from image analysis.

[1002] The "means for generating disposal methods" is a method for proposing optimal disposal or recycling methods based on the identified attributes.

[1003] "Means for generating reuse possibilities" are technologies that determine whether an item can be reused and provide the necessary information.

[1004] "Means for notifying" refers to an interface or communication means for notifying the user of the generated information.

[1005] "Means for analyzing emotions" refers to technology that recognizes and analyzes emotions from a user's facial expressions, voice, text, etc.

[1006] "Means for adjusting notification content" refers to technology that dynamically changes the content of information and messages provided in response to the user's emotions.

[1007] "Means for reading barcode information" refers to technology that scans the barcode attached to an item and acquires the data.

[1008] "Means for checking against a product database" refers to a technique for comparing acquired barcode information with an existing database to identify detailed product information.

[1009] "Means for acquiring location information" refers to methods for collecting information on the current location of users or items using technologies such as GPS.

[1010] "Means for searching municipal sorting rules" refers to technology that checks waste disposal rules and schedules in a specific area.

[1011] The system of the present invention is designed to enable users to easily obtain information on the disposal and reuse of unwanted items. This system is composed of a terminal such as a smartphone, a server, and a cloud-based service.

[1012] 1. Program Generation

[1013] First, smartphones and other devices are equipped with cameras and barcode scanners. Users take pictures of unwanted items and send the image data from their devices to a server. In some cases, location information and barcode information are also sent.

[1014] The server operates using the following primary methods:

[1015] Image analysis method: Using image analysis algorithms (e.g., OpenCV), the material, shape, and dimensions of unwanted items are identified.

[1016] Barcode analysis method: The information obtained by the barcode scanner is compared with the product database to obtain detailed product information.

[1017] Sentiment analysis method: Analyze user emotions using an emotion engine (e.g., Microsoft Azure's Face API).

[1018] Generative AI method: Use OpenAI's generative AI model (e.g., GPT-4) to generate appropriate disposal methods and reuse information.

[1019] Personalization: Dynamically adjust notification content based on sentiment analysis.

[1020] 2. Explain the program's processing in natural language

[1021] Acquisition of image data:

[1022] The user takes a picture of the unwanted item using the device's camera and sends the image data to the server. Location information and barcode information can also be included when sending the data.

[1023] Image analysis:

[1024] The server analyzes the received image data and identifies the attributes of the unwanted items (e.g., material, shape, dimensions). This is done using an image analysis algorithm (e.g., OpenCV). It also analyzes the barcode information and compares it with a product database to obtain detailed product information.

[1025] Emotion analysis:

[1026] The emotion engine analyzes the user's facial expressions, voice, and text to determine their emotional state and uses that information to tailor notifications.

[1027] Disposal Method Generation:

[1028] Generative AI models (e.g., GPT-4) are used to generate optimal disposal methods and reuse possibilities for unwanted items. For example, information such as "This smartphone is recyclable, but the battery must be removed" is generated.

[1029] Information Notice:

[1030] The generated information is personalized based on the user's emotional analysis and sent to the device. For example, if the user is nervous, a message such as "Don't worry, the procedure is simple" will be added.

[1031] 3. Examples of concrete examples and prompts

[1032] Examples:

[1033] 1. User A takes a picture of a smartphone that he no longer needs.

[1034] 2. Image data, location information, and barcode information are sent from the device to the server.

[1035] 3. The server analyzes the image and retrieves detailed information about the smartphone from the product database.

[1036] 4. The generative AI model generates the information, "Please recycle this smartphone. The current market price is 10,000 yen."

[1037] 5. The emotion engine analyzes User A's emotions and determines that he is nervous.

[1038] 6. A notification is sent to User A with the additional message, "Don't worry, it's easy."

[1039] 7. Information about garbage collection days in User A's area is also provided, informing him that "electronic waste is collected in your area on the last Friday of every month."

[1040] Example prompt sentence:

[1041] Analyze user images to obtain detailed information about unwanted items, and generate and display appropriate recycling methods and market prices. Example: Analyze images from a smartphone and provide recycling methods and current market prices.

[1042] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1043] Step 1:

[1044] The user takes a picture of the unwanted item with a camera. The user then captures the image of the unwanted item using the camera on their smartphone and saves the data on their device. The input is image data, and the output is an image file.

[1045] Step 2:

[1046] The terminal sends the acquired image data to the server. The user sends the acquired image data along with location information and barcode information (if any) to the server through the application. The input is the image data, location information, and barcode information, and the output is the data sent to the server.

[1047] Step 3:

[1048] The server analyzes the image data and identifies the attributes of the unwanted items. The server uses an image analysis algorithm (e.g., OpenCV) to identify attributes such as the material, shape, and dimensions of the unwanted items from the image data. The input is the image data, and the output is the attribute data of the identified unwanted items.

[1049] Step 4:

[1050] The server analyzes the barcode information and compares it with the product database. The server analyzes the barcode information and compares it with the product database to obtain detailed product information. The input is the barcode information, and the output is the product information obtained from the product database.

[1051] Step 5:

[1052] The server analyzes the user's emotions. The server uses an emotion engine (e.g., Microsoft Azure's Face API) to analyze emotions from the user's facial expressions and text. The input is the user's facial expression data and text, and the output is the user's emotion analysis results.

[1053] Step 6:

[1054] The server generates optimal disposal methods and reuse possibilities for unwanted items. The server uses a generative AI model (e.g., GPT-4) to generate optimal disposal methods and reuse possibilities for unwanted items based on the identified attributes and product information. The input is the attribute data and product information of the unwanted items, and the output is the generated disposal methods and reuse information.

[1055] Step 7:

[1056] The server personalizes the generated information. The server personalizes the generated information based on the results of the user's emotion analysis. For example, if the user is nervous, it adds a message such as "Don't worry, the procedure is simple." The input is the emotion analysis result and the generated information, and the output is personalized notification information.

[1057] Step 8:

[1058] The server sends personalized notification information to the user's terminal. The server sends personalized notification information to the user's terminal and notifies the user. The input is the personalized notification information, and the output is a notification displayed on the user's terminal.

[1059] Step 9:

[1060] Search for municipal sorting rules and collection date information. The server searches the municipal database based on the user's location information to obtain information on regional sorting rules and collection dates. The input is location information, and the output is information on sorting rules and collection dates.

[1061] Step 10:

[1062] The server notifies the user of the sorting rules and collection date information. The server sends the acquired sorting rules and collection date information to the user's terminal and notifies the user. The input is the sorting rules and collection date information, and the output is a notification displayed on the user's terminal.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] [Fourth embodiment]

[1067] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1068] 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.

[1069] 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).

[1070] 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.

[1071] 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.

[1072] 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).

[1073] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1074] 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.

[1075] 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.

[1076] 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.

[1077] 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.

[1078] 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.

[1079] 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."

[1080] The system of the present invention operates by combining a plurality of means to analyze images of unwanted items and provide optimal disposal methods and reuse information. Specific embodiments of the system will be described below.

[1081] Users take pictures of unwanted items using devices such as smartphones or tablets, and the devices send the captured image data to a server, sometimes including location information and barcode information.

[1082] The server first analyzes the received image to identify the attributes of the unwanted item. For example, it uses an image analysis algorithm to estimate whether the unwanted item is made of plastic, metal, paper, etc. It also extracts information such as its shape and dimensions.

[1083] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, enabling accurate identification of the unwanted item.

[1084] The server uses generative AI to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, instructions such as "It can be recycled, but please remove the cap" are generated. For reusable items, market prices and listing methods are also provided.

[1085] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information such as "In this area, metal garbage is collected every Thursday."

[1086] The generated information is sent from the server to the device and notified to the user. The user can check this information through a chat-style interface on the device and ask the server any additional questions via chat. The server uses the generation AI to respond to the user's questions in real time.

[1087] For example, consider the case where a user takes a photo of an unwanted smartphone and sends it from the device to a server. The server performs image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server uses generative AI to inform the user via chat, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current going rate is about 10,000 yen." It also references information from local governments and provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[1088] In this way, users can easily understand the appropriate disposal methods and reuse possibilities for unwanted items and take appropriate action.In addition, since disposal can be done in accordance with the sorting rules of each local government, environmental impact can be reduced and efficiency can be improved.

[1089] The processing flow will be explained below.

[1090] Step 1:

[1091] Users take photos of unwanted items they want to dispose of using a smartphone or tablet, then open the dedicated app, select the images they have taken, and upload them.

[1092] Step 2:

[1093] The device temporarily stores the captured image data and then sends it to a server, sometimes along with the user's location information and barcode information attached to the unwanted item.

[1094] Step 3:

[1095] The server analyzes the received image data. First, it applies an image analysis algorithm to extract attributes such as the material and shape of the unwanted items. For example, it identifies the type of item, such as plastic, metal, or paper.

[1096] Step 4:

[1097] If a barcode is present in the image, the device will automatically scan it and send the information to the server. If no barcode is present, this step is skipped.

[1098] Step 5:

[1099] The server decodes the received barcode information and searches a product database to obtain detailed product information, such as the product manufacturer and model number, from the barcode.

[1100] Step 6:

[1101] Based on the image analysis results and information from the product database, the server uses generative AI to generate instructions on optimal disposal methods and reuse possibilities, such as "This plastic bottle is recyclable, but please remove the cap."

[1102] Step 7:

[1103] Based on the user's location information, the server searches the local government's database for information such as local garbage sorting rules, collection days, fees, etc. For example, it obtains information such as "In this area, every Wednesday is plastic garbage collection day."

[1104] Step 8:

[1105] The server compiles the generated information and notifies the user, and the device displays the information to the user in a chat-style interface, along with prompts such as "Do you have any questions?"

[1106] Step 9:

[1107] Through a chat-style interface, users can ask additional questions or clarify things, such as "Is this product really recyclable?"

[1108] Step 10:

[1109] The server uses generative AI to answer users' questions in real time, for example, "Yes, this product is recyclable, but you must remove the battery."

[1110] Step 11:

[1111] The server refers to data from auction sites and reuse platforms for unwanted reusable items, and provides users with the current selling price and how to list them. For example, it might say, "The current market price for this smartphone is 10,000 yen. You can list it immediately by clicking the link below."

[1112] Step 12:

[1113] Based on the information provided, users decide how to dispose of unwanted items and then take action, choosing appropriate actions from options such as "separate them for recycling" or "put them on an auction site."

[1114] The above is the processing flow of this system, and this process allows users to easily understand the appropriate disposal methods and reuse possibilities for unwanted items and select appropriate actions.

[1115] Example 1

[1116] 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."

[1117] Providing information on appropriate disposal methods and recycling methods for unwanted items takes a lot of time and effort. It is also complicated to check garbage sorting rules and collection days, which vary from region to region. Furthermore, it is difficult to accurately obtain more detailed product information and local sorting rules based on the barcode information and location information of unwanted items. To solve these problems, a system is needed that can efficiently obtain information and provide it to users quickly.

[1118] 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.

[1119] In this invention, the server includes means for acquiring images of unwanted items, means for analyzing the acquired images to identify attributes of the unwanted items, means for generating optimal disposal methods and possibilities for reuse based on the identified attributes, means for notifying the generated information to the user so that the user can ask additional questions, and means for responding to the additional questions in real time, thereby making it possible to quickly and accurately provide the user with appropriate disposal methods and possibilities for reuse of unwanted items.

[1120] "Unwanted items" refers to items that have been used or are no longer needed, and items that should be considered for disposal or reuse.

[1121] "Means for acquiring images" refers to a device or system that has the function of acquiring image data of unwanted items using a camera, scanner, etc.

[1122] "Means for analyzing images to identify the attributes of unwanted items" refers to algorithms and technologies that analyze acquired images and extract and identify characteristics such as the material, shape, and dimensions of unwanted items.

[1123] "Means for generating optimal disposal methods and reuse possibilities" refers to a system or algorithm that determines and constructs information on appropriate disposal methods and reuse possibilities for unwanted items based on image analysis results and other related information.

[1124] "Means for notifying the user and allowing the user to ask further questions" refers to an interface or system that provides the generated information to the user and allows the user to ask further questions.

[1125] "Means for responding to follow-up questions in real time" refers to a system or algorithm that has the ability to instantly generate and provide appropriate answers to questions from users.

[1126] "Means for reading barcode information" refers to technology that reads barcode information attached to an item using a barcode reader, a smartphone camera, etc.

[1127] The "means for obtaining detailed product information by checking against a product database" refers to the process of checking the scanned barcode information against an existing product database to obtain detailed information about the product.

[1128] "Means for acquiring location information" refers to a system that acquires the current location of a user or item using technologies such as GPS or Wi-Fi.

[1129] "Means for searching for information on municipal sorting rules and collection days" refers to a system or algorithm that searches the municipality's official database or public information based on location information to obtain information on waste sorting rules and collection days specific to that area.

[1130] The system of the present invention is configured to provide information on appropriate disposal methods and reuse of unwanted items. Specific embodiments of the system will be described below.

[1131] Users use devices such as smartphones or tablets to take pictures of unwanted items. The device then sends the captured image data to a server, sometimes including the device's location and barcode information. A standard camera app and HTTPS are often used as the communication protocol for this process.

[1132] The server first analyzes the received image data. Image analysis algorithms such as TensorFlow and OpenCV can be used for image analysis. This allows information such as the material (e.g., plastic, metal, paper), shape, and dimensions of the unwanted items to be extracted and their attributes identified.

[1133] If the unwanted item has a barcode, the device reads it and sends the information to the server. For example, the BarcodeScanner library is used to read the barcode. The server decodes the barcode and compares it with a product database to obtain detailed product information. This allows the server to determine, for example, that "barcode 12345678" is compatible with Apple's iPhone X.

[1134] The server uses a generative AI model (e.g., GPT-4) based on the image analysis results and information obtained from the product database to generate optimal disposal methods and reuse possibilities. The generative AI model generates recommendations such as, "This smartphone can be recycled, but please remove the battery. It can be reused, so the current market price is approximately 10,000 yen."

[1135] Furthermore, the server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information specific to the area, such as "In Chiyoda Ward, Tokyo, electronic device recycling takes place on the last Friday of the month."

[1136] The generated information is sent from the server to the device and notified to the user. The user can check the provided information through a chat-style interface on the device. If the user has additional questions, the generative AI model can be used to answer them in real time. For example, if the user asks, "What are the specific steps to sell this smartphone?" the server will respond with, "List it on site X and package it like this."

[1137] As a concrete example, let's consider a case where a user wants to dispose of an unwanted smartphone (e.g., an iPhone X) and takes a picture of it with the smartphone. The device sends the image data to a server, attaching location information obtained from GPS. The server uses TensorFlow to identify the unwanted item as a smartphone and compares the barcode 12345678 with a database to obtain product information. The generative AI model generates information such as, "This smartphone is recyclable, but please remove the battery. It is reusable, so the current going rate is approximately 10,000 yen." It also references a local government database and provides information such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[1138] In this way, users can understand the appropriate disposal methods and possibilities for reusing unwanted items and take action. It also enables disposal in accordance with the sorting rules of each local government, reducing the environmental burden and improving efficiency.

[1139] Example prompt sentence:

[1140] "Lost and Found: Smartphone

[1141] Features: Black color, iPhone X, barcode 12345678 displayed

[1142] Q: How do I dispose of this phone?

[1143] Location: Chiyoda-ku, Tokyo

[1144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1145] Step 1:

[1146] The user takes a picture of the unwanted item using a smartphone or tablet. If the unwanted item is a smartphone, the user takes a photo using a camera app so that the entire item and the barcode are visible. The input is the image of the unwanted item, and the output is an image file.

[1147] Step 2:

[1148] The device sends the captured image data to the server, adding location information and barcode information if necessary. Location information is obtained using the device's GPS function, and barcode information is extracted from the image captured by the camera. The input is the image file, GPS data, and barcode information, and the output is a data packet sent to the server.

[1149] Step 3:

[1150] The server analyzes the received image data. TensorFlow and OpenCV are used for the analysis to identify the material, shape, and dimensions of the unwanted items. Specifically, the server parses the image and applies an object recognition algorithm to extract attribute information. The input is image data, and the output is attribute information of the unwanted items.

[1151] Step 4:

[1152] The server decodes the barcode information and checks it against a product database to obtain detailed product information. This operation uses the BarcodeScanner library. Specifically, after decoding the barcode information, it executes a database query to obtain the corresponding product information. The input is the barcode information, and the output is detailed product information.

[1153] Step 5:

[1154] The server uses a generative AI model (e.g., GPT-4) to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. Specifically, attribute information and product information are input into the generative AI model, which then generates optimal disposal methods and reuse instructions in text format. The input is attribute information and product information, and the output is disposal methods and reuse instructions.

[1155] Step 6:

[1156] The server searches the local government database based on the user's location information to obtain information such as local waste sorting rules, collection days, and disposal fees. Specifically, it uses the location information to send a query to the local government API to obtain relevant information. The input is location information, and the output is local government information.

[1157] Step 7:

[1158] The server sends the generated disposal method, reuse information, and municipal information to the terminal.,Specifically, the server compiles the generated information and sends the data to the,terminal using the HTTP protocol.,The input is a set of generated information, and the output is,notification data sent to the terminal.

[1159] Step 8:

[1160] The user checks the information displayed on the device and asks additional questions as needed. Questions are entered through a chat-style interface to request additional instructions or information. The input is the user's question, and the output is the question data.

[1161] Step 9:

[1162] The server responds to user questions in real time using a generative AI model. Specifically, it inputs question data into the generative AI model, generates an appropriate answer, and returns it to the user. The input is the question data, and the output is the generated answer text.

[1163] (Application example 1)

[1164] 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."

[1165] Conventional systems for disposing of unwanted items and providing information on reuse lack the functionality to respond quickly and accurately to suspicious or lost items. This makes it difficult to provide appropriate countermeasures while ensuring public safety. Furthermore, the lack of real-time interaction makes it difficult to respond to user questions or emergency situations.

[1166] 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.

[1167] In this invention, the server includes means for acquiring images of unwanted items, suspicious objects, and lost items, means for analyzing the acquired images to identify their attributes, means for generating optimal disposal methods, possibilities for reuse, and countermeasures for suspicious objects based on the identified attributes, and means for notifying the user of the generated information, means for acquiring images of suspicious objects and lost items and transmitting data including location information to the server, means for analyzing the received image data and identifying the attributes of the suspicious objects or lost items, means for searching for local security rules and countermeasures based on the location information, and means for providing a chat-style interface that responds to user questions in real time, thereby enabling the provision of quick and accurate countermeasures for suspicious objects and lost items and real-time interaction with the user.

[1168] "Unwanted items" refers to items or waste that are no longer in use and have outlived their normal purpose.

[1169] "Means of acquisition" refers to the functions of the device or software used to acquire images and location information.

[1170] "Means of analysis" refers to the techniques and algorithms used to process acquired data and extract specific attributes or information.

[1171] "Means of identification" refers to the function of identifying the attributes and type of an object based on analyzed data.

[1172] "Generating means" refers to devices or programs capable of generating optimal disposal methods or countermeasures based on identified information.

[1173] "Means for notifying" refers to a device or interface for conveying the generated information to the user.

[1174] "Location Information" means data that indicates a specific geographic location using GPS or other means.

[1175] "Means for transmitting data" refers to the function of sending acquired images and location information to a central processing unit such as a server.

[1176] "Security rules" refer to regulations established in each region for crime prevention and safety.

[1177] "Means for searching for solutions" refers to systems or software functions for finding appropriate solutions based on location information, etc.

[1178] "Real-time responsive chat-style interface" refers to an interactive user interface that can provide immediate answers to users' questions and requests.

[1179] The present invention provides a system for quickly and accurately analyzing images of suspicious objects and lost items, and providing safe countermeasures. Specific embodiments for carrying out the present invention will now be described.

[1180] System Configuration

[1181] The system consists of the following major components:

[1182] 1. Device: The user uses a device such as a smartphone, smart glasses, or head-mounted display to capture images of suspicious or lost items.

[1183] 2. Server: Analyzes the acquired image data, identifies the attributes of suspicious or lost items, and generates optimal countermeasures and notifies the user.

[1184] 3. Communication network: Infrastructure for sending and receiving data between devices and servers.

[1185] Hardware and software used

[1186] Image analysis: OpenCV, Tesseract OCR

[1187] Location information acquisition: Geopy library

[1188] Barcode analysis: ZXing library

[1189] Countermeasure generation: Generative AI model

[1190] Real-time chat: Pre-trained chatbot library

[1191] Data processing and calculation

[1192] 1. Image acquisition and transmission

[1193] The user uses the device to take pictures of suspicious or lost items, and the image data is sent to the server along with location information.

[1194] 2. Image Analysis

[1195] The server analyzes the received image data using OpenCV and Tesseract OCR, extracting attribute information such as the shape, material, and dimensions of the suspicious or lost item.

[1196] 3. Barcode Analysis

[1197] If the image contains a barcode, the ZXing library is used to parse the barcode and retrieve detailed information from the product database.

[1198] 4. Generating optimal countermeasures

[1199] The server uses a generative AI model to generate optimal responses based on the analysis results, which may include notifying security guards, checking with the police, or warning the user.

[1200] 5. User Notifications and Real-Time Chat

[1201] The generated information is immediately sent to the user, who can then ask follow-up questions through a real-time chat-style interface, and the chatbot library will respond with appropriate responses to the user's questions.

[1202] Examples of concrete examples and prompts

[1203] For example, if a user takes a photo of a lost item they find in a park with smart glasses, the image is sent to the system. The server analyzes the shape, material, etc., and if it determines that the item is likely to be suspicious, it will provide a message saying, "Please notify security. Do not touch it." Local security rules, such as "Lost items in the park will be kept for three days before being transferred to the police," are also provided based on the location information. When the user asks, "What should I do with this lost item?" the generating AI responds in real time with, "Take an additional photo to enable more detailed analysis. Please notify the nearest security guard."

[1204] Prompt Sentence Examples

[1205] "Please analyze the images of the lost items you found in the park. Identify the shape and material, and tell us the best course of action."

[1206] As described above, the present invention is a system that provides quick and accurate countermeasures for suspicious objects and lost items, and enables real-time interaction with users.

[1207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1208] Step 1:

[1209] The user uses the device to take an image of a suspicious object or lost property. The device acquires this image data and simultaneously acquires its current location information. The acquired image data and location information are sent to the server. The input data are the image and location information, and the output data is the image and location information sent to the server.

[1210] Step 2:

[1211] The server receives image data sent from the device. It then analyzes the image using OpenCV and Tesseract OCR to extract attribute information such as the shape, material, and dimensions of suspicious or lost items. The input data is the image, and the output data is the extracted attribute information.

[1212] Step 3:

[1213] If the image contains a barcode, the server analyzes the barcode using the ZXing library. Based on the analysis results, detailed information is retrieved from the product database. The input data is the barcode and image, and the output data is the barcode analysis results and product information.

[1214] Step 4:

[1215] The server uses a generative AI model based on the analysis results to generate optimal countermeasures, which may include notifying security guards, checking with the police, and warning the user. The input data is the analysis results, and the output data is the generated countermeasures.

[1216] Step 5:

[1217] The server notifies the user of the generated information. At the same time, it also searches for local security rules and countermeasures based on the location information and provides them to the user as additional information. The input data are the generated countermeasures and location information, and the output data is the notification content to the user.

[1218] Step 6:

[1219] Users can ask follow-up questions through a chat-style interface on their device, which the server responds to in real time using a pre-trained chatbot library. The input data is the user's question, and the output data is the response using generative AI.

[1220] The above processing steps enable the user to take prompt and appropriate action in response to suspicious or lost items.

[1221] 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.

[1222] The system of the present invention combines multiple means to analyze images of unwanted items and provide optimal disposal methods and reuse information. It also has an emotion engine that recognizes the user's emotions, and can adjust notification content and responses according to the user's emotions.

[1223] Users take pictures of unwanted items using devices such as smartphones or tablets, and the devices send the captured image data to a server, sometimes including location information and barcode information.

[1224] The server first analyzes the received image to identify the attributes of the unwanted item. For example, it uses an image analysis algorithm to estimate whether the unwanted item is made of plastic, metal, paper, etc. It also extracts information such as its shape and dimensions.

[1225] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, enabling accurate identification of the unwanted item.

[1226] The server uses generative AI to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, the server generates instructions such as "It can be recycled, but please remove the cap." For reusable items, the server also provides market prices and listing methods.

[1227] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. For example, it can provide information such as "In this area, metal garbage is collected every Thursday."

[1228] The generated information is sent from the server to the device and notified to the user. Here, the emotion engine recognizes the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling stressed, a reassuring message such as "Don't worry, the procedure is simple" will be added.

[1229] Users can check this information through a chat-style interface on their device, and if they have any additional questions, they can contact the server via chat. The server uses generative AI to respond to users' questions in real time. Again, the emotion engine analyzes the user's emotions and adjusts the response accordingly. For example, if the user is confused, the server might add a phrase like, "I'll explain it in an easy-to-understand way."

[1230] For example, consider the case where a user takes a photo of an unwanted smartphone and sends it from the device to a server. The server performs image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server uses generative AI to inform the user via chat, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current going rate is about 10,000 yen." It also references information from local governments and provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month."

[1231] If the emotion engine recognizes the user's emotions and determines that the user is nervous, it adds a comment saying, "Don't worry, the procedure is simple." In this way, the user can relax and receive accurate information.

[1232] In this way, users can easily understand the appropriate disposal methods and reuse possibilities for unwanted items and take appropriate action.In addition, since disposal can be done in accordance with the sorting rules of each local government, environmental impact can be reduced and efficiency can be improved.

[1233] By incorporating an emotion engine, guidance and responses to users can be more personalized, increasing user satisfaction. Furthermore, statistical analysis of emotion data can contribute to improving the system itself and the quality of services.

[1234] The processing flow will be explained below.

[1235] Step 1:

[1236] Users take photos of unwanted items they want to dispose of using a smartphone or tablet, then open the dedicated app, select the images they have taken, and upload them.

[1237] Step 2:

[1238] The device temporarily stores the captured image data and then sends it to a server, sometimes along with the user's location information and barcode information attached to the unwanted item.

[1239] Step 3:

[1240] The server analyzes the received image data. First, it applies an image analysis algorithm to extract attributes such as the material and shape of the unwanted items. For example, it identifies the type of item, such as plastic, metal, or paper.

[1241] Step 4:

[1242] If a barcode is present in the image, the device will automatically scan it and send the information to the server. If no barcode is present, this step is skipped.

[1243] Step 5:

[1244] The server decodes the received barcode information and searches a product database to obtain detailed product information, such as the product manufacturer and model number, from the barcode.

[1245] Step 6:

[1246] Based on the image analysis results and information from the product database, the server uses generative AI to generate instructions on optimal disposal methods and reuse possibilities, such as "This plastic bottle is recyclable, but please remove the cap."

[1247] Step 7:

[1248] Based on the user's location information, the server searches the local government's database for information such as local garbage sorting rules, collection days, fees, etc. For example, it obtains information such as "In this area, every Wednesday is plastic garbage collection day."

[1249] Step 8:

[1250] The server compiles the generated information and notifies the user. Here, an emotion engine recognizes the user's emotions and adjusts the notification content based on those emotions. For example, if the user is feeling stressed, a reassuring message such as "Don't worry, the procedure is simple" will be added.

[1251] Step 9:

[1252] The device displays the notified information to the user in a chat-style interface and also displays prompts such as "Do you have any questions?"

[1253] Step 10:

[1254] Through a chat-style interface, users can ask additional questions or clarify things, such as "Is this product really recyclable?"

[1255] Step 11:

[1256] The server uses generative AI to answer users' questions in real time. Here too, the emotion engine analyzes the user's emotions and adjusts the response accordingly. For example, if the user is confused, the server might add a phrase like, "I'll explain it in an easy-to-understand way."

[1257] Step 12:

[1258] The server refers to data from auction sites and reuse platforms for unwanted reusable items, and provides users with the current selling price and how to list them. For example, it might say, "The current market price for this smartphone is 10,000 yen. You can list it immediately by clicking the link below."

[1259] Step 13:

[1260] Based on the information provided, users decide how to dispose of unwanted items and then take action, choosing appropriate actions from options such as "separate them for recycling" or "put them on an auction site."

[1261] This is the processing flow of this system, which allows users to easily understand the appropriate disposal methods and reuse possibilities for unwanted items and choose the appropriate action.In addition, by incorporating an emotion engine, guidance and responses to users can be more personalized, improving user satisfaction.

[1262] Example 2

[1263] 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."

[1264] In modern society, there is a demand for fast and accurate information on appropriate disposal and reuse methods for unwanted items. However, conventional systems do not adequately identify the material and shape of unwanted items, obtain regional sorting rules, or adjust notification content based on the user's emotions, which often leaves users feeling stressed. Furthermore, it is not easy to obtain detailed product information using barcodes. This has led to a lack of information on how to properly dispose of unwanted items, which increases the burden on the environment.

[1265] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1266] In this invention, the server includes a means for acquiring images of unwanted items, a means for analyzing the acquired images to identify attributes of the unwanted items, a means for using a generative artificial intelligence model to generate optimal disposal methods and reuse possibilities based on the identified attributes, a means for recognizing user emotions and adjusting notification content and responses, and a means for notifying the user of the generated information. This makes it possible to quickly and accurately provide information on appropriate disposal methods and reuse methods for unwanted items, thereby improving user satisfaction and reducing environmental impact. Furthermore, accurate product information can be provided by using a means for reading barcode information and a means for obtaining detailed product information by comparing the read barcode information with a product database.

[1267] "Means for obtaining images of unwanted items" refers to the function of taking images of unwanted items using a device such as a smartphone or tablet.

[1268] "Means for analyzing the acquired images to identify the attributes of unwanted items" refers to a technology that uses an image analysis algorithm to identify attributes such as the material, shape, and dimensions of unwanted items.

[1269] "Means using a generative artificial intelligence model that generates optimal disposal methods and reuse possibilities based on identified attributes" refers to technology that uses a generative AI model to generate disposal methods and reuse information for unwanted items.

[1270] "Means for recognizing user emotions and adjusting notification content and responses" refers to technology that uses an emotion engine to analyze user emotions and personalize notification content and responses based on that.

[1271] "Means for notifying the user of the generated information" refers to a function for sending information on how to dispose of unwanted items and reuse information to the user's terminal and notifying them.

[1272] "Means for reading barcode information" refers to technology for reading barcodes attached to unwanted items.

[1273] "Means for obtaining detailed product information by comparing the read barcode information with a product database" refers to technology for decoding the barcode and comparing the information with a product database to obtain detailed product information.

[1274] "Means for acquiring location information" refers to technology for acquiring information about a user's current location using the device's location information service.

[1275] "Means for searching for information on municipal sorting rules and collection dates based on acquired location information" refers to technology that searches a municipal database based on the user's location information to obtain information such as local garbage sorting rules and collection dates.

[1276] The system of this invention combines multiple means to analyze images of unwanted items and provide optimal disposal methods and reuse information. It also has an emotion engine that recognizes the user's emotions, and can adjust notification content and responses according to the user's emotions.

[1277] Users take pictures of unwanted items using devices such as smartphones or tablets. The devices then send the captured image data to a server, sometimes including location information and barcode information. Specifically, devices are equipped with a camera, location information service (GPS function), and barcode scanner, and these hardware components are used to acquire data.

[1278] The server first analyzes the received images and uses image analysis algorithms (e.g., TensorFlow or OpenCV) to identify the attributes of the unwanted items. Image analysis can identify the material (e.g., plastic, metal, paper), shape, dimensions, etc. of the unwanted items.

[1279] Furthermore, if the unwanted item has a barcode, the terminal reads the barcode and transmits the information to the server, which decodes the barcode and compares it with a product database to obtain detailed product information, allowing for more accurate identification of the unwanted item.

[1280] The server uses a generative AI model (such as GPT-3 or BERT) to generate optimal disposal methods and reuse possibilities based on the image analysis results and information obtained from the product database. For example, for a plastic bottle, the server generates instructions such as "It can be recycled, but please remove the cap." For reusable items, the server also provides information on the current market price and specific listing methods.

[1281] The server searches the local government database based on the user's location information to obtain information such as local waste sorting rules, collection days, disposal fees, etc. Specific information could be provided such as "In this area, metal waste is collected every Thursday."

[1282] The generated information is sent from the server to the device and notified to the user. At this time, the emotion engine recognizes the user's emotions, and if the user is feeling stressed, for example, a reassuring message such as "Don't worry, the procedure is simple." This allows the user to receive the information in a relaxed state.

[1283] Users can view this information through a chat-style interface on their device, and if they have any additional questions, they can contact the server via chat. The server uses a generative AI model to respond to the user's questions in real time. Again, the emotion engine analyzes the user's emotions, and if the user is confused, for example, it will add a message such as "I'll explain it in an easy-to-understand way."

[1284] As a concrete example, let's consider a case where a user takes a photo of an unwanted smartphone and sends it to a server. The server uses image analysis to determine that the smartphone is a specific model from a specific manufacturer. It also scans the barcode to obtain product information. The server then uses generative AI to provide a chat message saying, "This smartphone is recyclable, but please remove the battery. It can be reused, so the current market price is approximately 10,000 yen." Furthermore, by referencing local government information, the server also provides additional information, such as, "In your area, electronic device recycling takes place on the last Friday of the month." If the emotion engine recognizes the user's emotions and determines that the user is nervous, it adds a comment saying, "Don't worry, the process is simple." In this way, the user can relax and receive accurate information.

[1285] Examples of prompts include, "What is the best way to dispose of the unwanted item in this image?" or "How can I recycle or reuse this smartphone?"

[1286] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1287] Step 1:

[1288] The user takes a picture of the unwanted item

[1289] Users take pictures of unwanted items using a smartphone or tablet, and are encouraged to take pictures from multiple angles.

[1290] Input: Unwanted item to photograph

[1291] Output: Image data of unwanted items

[1292] Step 2:

[1293] The device sends image data, location information, and barcode information to the server.

[1294] The device acquires the captured image data and uses the built-in GPS to obtain location information. Furthermore, if the unwanted item has a barcode attached, the device uses the barcode scanner function to obtain the information. All of this data is then sent to the server.

[1295] Input: Image data, location information, barcode information of unwanted items

[1296] Output: Data sent to the server

[1297] Step 3:

[1298] The server analyzes the image and identifies the attributes of the unwanted items

[1299] The server analyzes the received image data using an image analysis algorithm (e.g., TensorFlow or OpenCV), which identifies the material (plastic, metal, paper, etc.), shape, dimensions, etc. of the unwanted items. Specifically, the algorithm identifies objects in the image and classifies their attributes.

[1300] Input: Image data of unwanted items

[1301] Output: Attribute data of identified unwanted items (material, shape, dimensions, etc.)

[1302] Step 4:

[1303] The server decodes the barcode information and checks it against the product database.

[1304] The server decodes the received barcode information and compares it with a product database to obtain detailed product information (manufacturer, model, product name, etc.) of the unwanted item. Specifically, the barcode decoding algorithm reads the numeric information in the barcode and queries the product database.

[1305] Input: Barcode information

[1306] Output: Product details

[1307] Step 5:

[1308] The server generates optimal disposal methods and reuse information using an AI model.

[1309] Based on the image analysis results and information obtained from the product database, the server uses a generative AI model (e.g., GPT-3, BERT) to generate optimal disposal methods and reuse possibilities for unwanted items. For example, in the case of a plastic bottle, instructions such as "It can be recycled, but please remove the cap" are generated.

[1310] Input: Attribute data of identified unwanted items, detailed product information

[1311] Output: Information on optimal disposal methods and reuse

[1312] Step 6:

[1313] The server searches the local government database and obtains the garbage sorting rules for each region.

[1314] The server searches the local government database based on the user's location information to obtain information such as local garbage sorting rules, collection days, disposal fees, etc. Specifically, it uses the location information to query the garbage sorting rules for the relevant area.

[1315] Input:Location

[1316] Output: Information on garbage sorting rules and collection days for each region

[1317] Step 7:

[1318] The server sends the generated information to the terminal.

[1319] The server then sends the generated disposal and reuse information, as well as local waste sorting rules, to the device, allowing users to check the appropriate method for disposing of unwanted items.

[1320] Input: disposal methods, reuse information, and regional garbage sorting rules

[1321] Output: Information sent to the terminal

[1322] Step 8:

[1323] The server analyzes the user's emotions using an emotion engine and adjusts the notification content.

[1324] The server uses an emotion engine to analyze the user's emotions. If the user is stressed, it will add a message such as "Don't worry, the process is simple." For this purpose, it uses facial recognition and text analysis algorithms.

[1325] Input: User actions and input data

[1326] Output: Adjusted notification content

[1327] Step 9:

[1328] The device notifies the user

[1329] The device notifies the user of the information received from the server. Notifications are sent in chat format and displayed in a way that is easy for the user to understand.

[1330] Input: Information received from the server

[1331] Output: Information notified to the user

[1332] Step 10:

[1333] Users can enter additional questions in chat format

[1334] The user uses the device's chat interface to enter follow-up questions, such as "where can I recycle this phone?"

[1335] Input: User question

[1336] Output: Query data from the terminal to the server

[1337] Step 11:

[1338] The server responds to the question using a generative AI model and notifies the user

[1339] The server uses a generative AI model to respond to the user's questions, and here too, the emotion engine analyzes the user's emotions and can add phrases such as "I'll explain it in an easy-to-understand way."

[1340] Input: User question data

[1341] Output: Generated response data, adjusted answer content

[1342] (Application example 2)

[1343] 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."

[1344] In modern society, the disposal and reuse of unwanted items has become an important issue. However, it is not easy for users to understand the appropriate disposal method and the possibility of reuse. Furthermore, users often feel anxious and stressed because the system does not respond to their emotions. Furthermore, there is the problem that it is time-consuming to understand the sorting rules and collection date information of each local government. The present invention aims to solve these problems.

[1345] 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 acquiring images of unwanted items, means for analyzing the acquired images to identify attributes of the unwanted items, means for generating optimal disposal methods and reuse possibilities based on the identified attributes, means for notifying the user of the generated information, and means for analyzing the user's emotions and adjusting the notification content according to the emotions. This allows the user to easily understand appropriate disposal methods and reuse possibilities for unwanted items and receive appropriate guidance according to their emotions.

[1346] "Unwanted items" are items that are no longer needed by the user.

[1347] "Means for acquiring an image" refers to a method for collecting image data of an item using a camera or other image capture device.

[1348] "Means for analyzing images" refers to technology that processes collected image data and recognizes and identifies attributes and features of items.

[1349] "Means for identifying attributes" refers to a method for identifying characteristics such as the material, shape, and dimensions of an object based on information obtained from image analysis.

[1350] The "means for generating disposal methods" is a method for proposing optimal disposal or recycling methods based on the identified attributes.

[1351] "Means for generating reuse possibilities" are technologies that determine whether an item can be reused and provide the necessary information.

[1352] "Means for notifying" refers to an interface or communication means for notifying the user of the generated information.

[1353] "Means for analyzing emotions" refers to technology that recognizes and analyzes emotions from a user's facial expressions, voice, text, etc.

[1354] "Means for adjusting notification content" refers to technology that dynamically changes the content of information and messages provided in response to the user's emotions.

[1355] "Means for reading barcode information" refers to technology that scans the barcode attached to an item and acquires the data.

[1356] "Means for checking against a product database" refers to a technique for comparing acquired barcode information with an existing database to identify detailed product information.

[1357] "Means for acquiring location information" refers to methods for collecting information on the current location of users or items using technologies such as GPS.

[1358] "Means for searching municipal sorting rules" refers to technology that checks waste disposal rules and schedules in a specific area.

[1359] The system of the present invention is designed to enable users to easily obtain information on the disposal and reuse of unwanted items. This system is composed of a terminal such as a smartphone, a server, and a cloud-based service.

[1360] 1. Program Generation

[1361] First, smartphones and other devices are equipped with cameras and barcode scanners. Users take pictures of unwanted items and send the image data from their devices to a server. In some cases, location information and barcode information are also sent.

[1362] The server operates using the following primary methods:

[1363] Image analysis method: Using image analysis algorithms (e.g., OpenCV), the material, shape, and dimensions of unwanted items are identified.

[1364] Barcode analysis method: The information obtained by the barcode scanner is compared with the product database to obtain detailed product information.

[1365] Sentiment analysis method: Analyze user emotions using an emotion engine (e.g., Microsoft Azure's Face API).

[1366] Generative AI method: Use OpenAI's generative AI model (e.g., GPT-4) to generate appropriate disposal methods and reuse information.

[1367] Personalization: Dynamically adjust notification content based on sentiment analysis.

[1368] 2. Explain the program's processing in natural language

[1369] Acquisition of image data:

[1370] The user takes a picture of the unwanted item using the device's camera and sends the image data to the server. Location information and barcode information can also be included when sending the data.

[1371] Image analysis:

[1372] The server analyzes the received image data and identifies the attributes of the unwanted items (e.g., material, shape, dimensions). This is done using an image analysis algorithm (e.g., OpenCV). It also analyzes the barcode information and compares it with a product database to obtain detailed product information.

[1373] Emotion analysis:

[1374] The emotion engine analyzes the user's facial expressions, voice, and text to determine their emotional state and uses that information to tailor notifications.

[1375] Disposal Method Generation:

[1376] Generative AI models (e.g., GPT-4) are used to generate optimal disposal methods and reuse possibilities for unwanted items. For example, information such as "This smartphone is recyclable, but the battery must be removed" is generated.

[1377] Information Notice:

[1378] The generated information is personalized based on the user's emotional analysis and sent to the device. For example, if the user is nervous, a message such as "Don't worry, the procedure is simple" will be added.

[1379] 3. Examples of concrete examples and prompts

[1380] Examples:

[1381] 1. User A takes a picture of a smartphone that he no longer needs.

[1382] 2. Image data, location information, and barcode information are sent from the device to the server.

[1383] 3. The server analyzes the image and retrieves detailed information about the smartphone from the product database.

[1384] 4. The generative AI model generates the information, "Please recycle this smartphone. The current market price is 10,000 yen."

[1385] 5. The emotion engine analyzes User A's emotions and determines that he is nervous.

[1386] 6. A notification is sent to User A with the additional message, "Don't worry, it's easy."

[1387] 7. Information about garbage collection days in User A's area is also provided, informing him that "electronic waste is collected in your area on the last Friday of every month."

[1388] Example prompt sentence:

[1389] Analyze user images to obtain detailed information about unwanted items, and generate and display appropriate recycling methods and market prices. Example: Analyze images from a smartphone and provide recycling methods and current market prices.

[1390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1391] Step 1:

[1392] The user takes a picture of the unwanted item with a camera. The user then captures the image of the unwanted item using the camera on their smartphone and saves the data on their device. The input is image data, and the output is an image file.

[1393] Step 2:

[1394] The terminal sends the acquired image data to the server. The user sends the acquired image data along with location information and barcode information (if any) to the server through the application. The input is the image data, location information, and barcode information, and the output is the data sent to the server.

[1395] Step 3:

[1396] The server analyzes the image data and identifies the attributes of the unwanted items. The server uses an image analysis algorithm (e.g., OpenCV) to identify attributes such as the material, shape, and dimensions of the unwanted items from the image data. The input is the image data, and the output is the attribute data of the identified unwanted items.

[1397] Step 4:

[1398] The server analyzes the barcode information and compares it with the product database. The server analyzes the barcode information and compares it with the product database to obtain detailed product information. The input is the barcode information, and the output is the product information obtained from the product database.

[1399] Step 5:

[1400] The server analyzes the user's emotions. The server uses an emotion engine (e.g., Microsoft Azure's Face API) to analyze emotions from the user's facial expressions and text. The input is the user's facial expression data and text, and the output is the user's emotion analysis results.

[1401] Step 6:

[1402] The server generates optimal disposal methods and reuse possibilities for unwanted items. The server uses a generative AI model (e.g., GPT-4) to generate optimal disposal methods and reuse possibilities for unwanted items based on the identified attributes and product information. The input is the attribute data and product information of the unwanted items, and the output is the generated disposal methods and reuse information.

[1403] Step 7:

[1404] The server personalizes the generated information. The server personalizes the generated information based on the results of the user's emotion analysis. For example, if the user is nervous, it adds a message such as "Don't worry, the procedure is simple." The input is the emotion analysis result and the generated information, and the output is personalized notification information.

[1405] Step 8:

[1406] The server sends personalized notification information to the user's terminal. The server sends personalized notification information to the user's terminal and notifies the user. The input is the personalized notification information, and the output is a notification displayed on the user's terminal.

[1407] Step 9:

[1408] Search for municipal sorting rules and collection date information. The server searches the municipal database based on the user's location information to obtain information on regional sorting rules and collection dates. The input is location information, and the output is information on sorting rules and collection dates.

[1409] Step 10:

[1410] The server notifies the user of the sorting rules and collection date information. The server sends the acquired sorting rules and collection date information to the user's terminal and notifies the user. The input is the sorting rules and collection date information, and the output is a notification displayed on the user's terminal.

[1411] 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.

[1412] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1413] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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).

[1418] 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.

[1419] 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."

[1420] 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.

[1421] 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).

[1422] 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.

[1423] 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.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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.

[1432] The following is further disclosed regarding the above embodiment.

[1433] (Claim 1)

[1434] a means for acquiring an image of the unwanted item;

[1435] A means for analyzing the acquired image to identify attributes of the unwanted items;

[1436] a means of generating optimal disposal methods and reuse possibilities based on the identified attributes;

[1437] means for notifying a user of the generated information;

[1438] A system including:

[1439] (Claim 2)

[1440] means for reading barcode information;

[1441] A means for collating the read barcode information with a product database to obtain detailed product information;

[1442] The system of claim 1 further comprising:

[1443] (Claim 3)

[1444] A means for acquiring location information;

[1445] A means for searching for information on municipal sorting rules and collection dates based on the acquired location information;

[1446] The system of claim 1 further comprising:

[1447] "Example 1"

[1448] (Claim 1)

[1449] a means for acquiring an image of the unwanted item;

[1450] A means for analyzing the acquired image to identify attributes of the unwanted items;

[1451] a means for generating optimal disposal methods or reuse possibilities based on identified attributes;

[1452] means for informing the user of the generated information and allowing the user to ask follow-up questions;

[1453] a means of responding to follow-up questions in real time;

[1454] A system including:

[1455] (Claim 2)

[1456] means for reading barcode information;

[1457] A means for collating the read barcode information with a product database to obtain detailed product information;

[1458] The system of claim 1 further comprising:

[1459] (Claim 3)

[1460] A means for acquiring location information;

[1461] A means for searching for information on municipal sorting rules and collection dates based on the acquired location information;

[1462] The system of claim 1 further comprising:

[1463] "Application Example 1"

[1464] (Claim 1)

[1465] a means for acquiring an image of the unwanted item;

[1466] A means for analyzing the acquired image to identify attributes of the unwanted items;

[1467] a means of generating optimal disposal methods and reuse possibilities based on the identified attributes;

[1468] means for notifying a user of the generated information;

[1469] A means for acquiring an image of a suspicious object or a lost item and transmitting data including location information to a server;

[1470] means for analyzing the received image data and identifying attributes of the suspicious or lost item;

[1471] A way to search for local security rules and countermeasures based on location information, and

[1472] means for providing a chat-style interface that responds to user questions in real time;

[1473] A system including:

[1474] (Claim 2)

[1475] means for reading barcode information;

[1476] A means for collating the read barcode information with a product database to obtain detailed product information;

[1477] The system of claim 1 further comprising:

[1478] (Claim 3)

[1479] A means for acquiring location information;

[1480] A means for searching for information on municipal sorting rules and collection dates based on the acquired location information;

[1481] The system of claim 1 further comprising:

[1482] "Example 2: Combining Emotion Engines"

[1483] (Claim 1)

[1484] a means for acquiring an image of the unwanted item;

[1485] A means for analyzing the acquired image to identify attributes of the unwanted items;

[1486] using a generative artificial intelligence model to generate optimal disposal methods or reuse possibilities based on the identified attributes;

[1487] A means for recognizing a user's emotions and adjusting notification content and responses accordingly;

[1488] means for notifying a user of the generated information;

[1489] A system including:

[1490] (Claim 2)

[1491] means for reading barcode information;

[1492] A means for collating the read barcode information with a product database to obtain detailed product information;

[1493] The system of claim 1 further comprising:

[1494] (Claim 3)

[1495] A means for acquiring location information;

[1496] A means for searching for information on municipal sorting rules and collection dates based on the acquired location information;

[1497] The system of claim 1 further comprising:

[1498] "Application example 2 when combining emotion engines"

[1499] (Claim 1)

[1500] a means for acquiring an image of the unwanted item;

[1501] A means for analyzing the acquired image to identify attributes of the unwanted items;

[1502] a means of generating optimal disposal methods and reuse possibilities based on the identified attributes;

[1503] means for notifying a user of the generated information;

[1504] A means for analyzing a user's emotions and adjusting the notification content according to the emotions;

[1505] A system including:

[1506] (Claim 2)

[1507] means for reading barcode information;

[1508] A means for collating the read barcode information with a product database to obtain detailed product information;

[1509] a means to personalize the content;

[1510] The system of claim 1 further comprising:

[1511] (Claim 3)

[1512] A means for acquiring location information;

[1513] A means for searching for information on municipal sorting rules and collection dates based on the acquired location information;

[1514] The system of claim 1 further comprising: [Explanation of symbols]

[1515] 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 acquiring an image of the unwanted item; A means for analyzing the acquired image to identify attributes of the unwanted items; a means of generating optimal disposal methods and reuse possibilities based on the identified attributes; means for notifying a user of the generated information; A system including:

2. means for reading barcode information; A means for collating the read barcode information with a product database to obtain detailed product information; The system of claim 1 further comprising:

3. A means for acquiring location information; A means for searching for information on municipal sorting rules and collection dates based on the acquired location information; The system of claim 1 further comprising:

Images