system

Abstract

We provide the system. [Solution] An analysis means that receives image data and identifies an item based on said image data, A selection means for selecting unwanted items from among the identified items, A means for setting the priority of disposal for selected unwanted items, A proposal method that suggests the optimal disposal method for each type of unwanted item, A means of arranging disposal automatically based on the proposed disposal method, A database management means that registers data on identified items and customizes disposal methods based on user data, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, an individual's living space is occupied by many items, and the need for tidying up is increasing. Conventionally, it is common to manually identify and manage items, which requires a great deal of time and effort. There is a need for a method to solve this problem, efficiently identify unnecessary items, and appropriately dispose of them. Also, technologies to assist with such tasks are limited, and there is a lack of means that individuals can easily use.

Means for Solving the Problems

[0005] This invention provides a system that receives image data transmitted by a user and automatically identifies items present in a room through image analysis. The system selects unwanted items from the identified items, sets priorities based on these selections, and proposes the optimal disposal method. It also includes a process that enables automatic disposal arrangements according to the proposed disposal method. This allows the user to organize their living space efficiently and effectively. This process consists of image analysis, selection, prioritization, proposal, and automatic arrangement.

[0006] "Image data" refers to data that represents visual information stored as electronic information.

[0007] "Image analysis means" refers to a technical method for processing received image data and identifying the items contained therein.

[0008] An "item" is a concrete, tangible object present in a room, and is an identifiable individual element.

[0009] "Selection method" refers to the process of determining which items are unnecessary from among the identified items based on specific criteria.

[0010] "Priority setting means" refers to the procedure for determining the priority order for disposal of selected unwanted items.

[0011] "Suggested methods" refer to means of showing users the optimal way to dispose of unwanted items, and play a role in supporting users' decision-making.

[0012] "Disposal arrangement means" refers to a system that automatically arranges for the execution of disposal according to the proposed disposal method. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system that efficiently identifies and selects items present in a user's space, and automatically proposes and arranges the optimal disposal method based on that identification. Specific embodiments for carrying out this invention will be described below.

[0035] The user takes a picture of the room using a device with a dedicated application installed. The captured image data is securely transmitted from the device to the server. The server performs image analysis based on the received image data and identifies items present in the room using a specific algorithm. In this process, a machine learning-based algorithm is used to identify the type, shape, and size of the items.

[0036] Identified items are registered in a database on the server, and unwanted items are selected based on past user data and other criteria. Specifically, the selection process takes into account factors such as frequency of use, the condition of the item, and its potential obsolescence. Items deemed unwanted are assigned a priority for disposal. This takes into account the user's lifestyle and the value of the items.

[0037] Next, the server suggests the best disposal method for the selected unwanted items. For example, it might suggest selling items with rarity or resale value through an e-commerce platform, while recommending collection by a suitable waste disposal company for other items. Furthermore, donation options are also presented for items that meet the criteria.

[0038] After reviewing the proposed disposal method, the user can approve it via their device and proceed. The server receives the user's approval and automatically handles tasks such as listing items on e-commerce platforms and booking disposal services. This allows users to organize and declutter their belongings with simple operations, optimizing their living space without requiring significant effort.

[0039] As a concrete example, consider a user who wants to declutter their living room by getting rid of unwanted books, old electronics, clothes, etc. Using this system, simply by taking and uploading photos, the system will suggest which items are unwanted and the most appropriate disposal method. If old electronics have market value, the system will suggest ways to maximize that value, and clothes may be donated. This allows users to dispose of items in an efficient and socially valuable way.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user takes photos of the room using their device and uploads the selected image data to the server via the application. The device compresses the captured image data and securely transmits it to the server over the network.

[0043] Step 2:

[0044] The server prepares the image data received from the terminal for analysis. Using image analysis tools, it identifies the objects depicted in the image. This process utilizes machine learning algorithms to determine the type, shape, size, and other characteristics of the objects.

[0045] Step 3:

[0046] The server stores data of identified items in a database. The stored data is then compared against past user data and other criteria (e.g., a general list of unwanted items) to determine which items are to be discarded.

[0047] Step 4:

[0048] The server sets a disposal priority for items deemed unnecessary. Factors such as the item's value, user usage frequency, and physical condition are considered in this prioritization. Based on this information, the order in which each item is disposed of is determined.

[0049] Step 5:

[0050] The server suggests the best disposal method for prioritized unwanted items. For example, it recommends selling resalable items on e-commerce platforms and using appropriate waste disposal companies for items that need to be discarded. It also offers the option of donating items that can be donated to partner NGOs.

[0051] Step 6:

[0052] Users can review the proposed disposal methods through the interface on their device and approve or modify them. The proposed methods can also be adjusted based on user feedback.

[0053] Step 7:

[0054] The server automatically arranges the appropriate disposal according to the disposal method approved by the user. This includes listing items on e-commerce platforms, requesting collection from waste disposal companies, and arranging logistics for donations.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In daily life, efficiently identifying individual items, selecting unnecessary ones, and providing the optimal disposal method requires considerable time and effort, and often relies on the user's own judgment. This challenge, especially in today's living environment where organization and tidiness are paramount, requires quick and accurate decision-making, and also requires careful consideration from the perspectives of privacy and security. A system that efficiently processes unnecessary items while automatically providing suggestions tailored to the user's lifestyle and values ​​is highly desirable.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes processing means for receiving image information and identifying objects based on the image information, determination means for selecting unnecessary objects from among the identified objects, and prioritization means for setting processing priorities for the selected unnecessary objects. This allows the user to take and transmit images and leave the necessary decisions to the system. Furthermore, it enables detailed analysis of items and the suggestion of the optimal disposal method, thereby optimizing the living space while reducing the burden on the user.

[0060] "Image information" refers to visual data acquired by optical devices, and is fundamental data for identifying and analyzing objects.

[0061] An "object" refers to a specific item or object that a system identifies or selects.

[0062] "Processing means" refers to a processing system equipped with the function of analyzing received image information to identify objects.

[0063] A "decision-making mechanism" refers to a processing system that includes a function for selecting unnecessary objects from among the identified objects.

[0064] A "prioritization mechanism" refers to a processing system that provides a function for setting processing priorities for selected unnecessary items.

[0065] "Presentation means" refers to an interface and algorithm that includes a function for presenting the optimal processing method for selected unnecessary items.

[0066] "Arrangement means" refers to a device or system that has the function of automatically managing and executing processing procedures based on the presented processing method.

[0067] "Communication means" refers to the communication protocols and mechanisms necessary for capturing and transmitting image information between a terminal and a server.

[0068] An "analysis means" is a mechanism that implements machine learning algorithms for detecting, analyzing, and processing the characteristics of an object.

[0069] This invention provides a system that allows users to efficiently identify items in their daily lives, select unwanted items, and propose and implement the optimal disposal method. First, the user installs a dedicated application on their device and takes a picture of their room. This application acquires the image via the camera function and transmits the image information to a server via the internet.

[0070] The terminal is equipped with communication means to reliably transmit image information to the server, and the security of the information is ensured through data encryption. The server analyzes the received image information using processing means, and uses known software such as OpenCV and TENSORFLOW® as image analysis libraries. In this way, the server uses machine learning algorithms to identify objects in the image and detect their characteristics.

[0071] The server has a database function to select unnecessary objects based on identified objects, using the user's past data and other relevant information for selection. Unnecessary objects are assigned a processing priority that takes into account lifestyle, market value, and even social value.

[0072] Users can review and approve disposal methods presented by the server via their device. Based on this approval, the server uses its procurement tools to automatically execute the process. Specifically, this involves quickly selling items on e-commerce platforms or scheduling pickups with waste disposal companies. This allows users to easily organize their belongings and optimize their living space without any hassle through a dedicated app.

[0073] For example, if a user wants to get rid of unwanted books or old electronic devices in their living room, this system can automatically suggest which items are unwanted and the most effective way to dispose of them, simply by taking and uploading photos. An example of a prompt might be, "Please take photos of unwanted items in your living room and suggest the most efficient disposal methods."

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user takes photos of the room using a dedicated application on their device. The input is visual information captured by the camera, which is converted into an image format within the application. The application guides the user to the optimal shooting angle and provides the functionality to capture multiple photos. The output is image data, which is prepared for analysis in the next step.

[0077] Step 2:

[0078] The terminal securely transmits the captured image data to the server. The input is the image data generated in step 1, which is transmitted over the network using encryption technology. The communication module handles data encryption and transfer. The output is the image data received by the server.

[0079] Step 3:

[0080] The server analyzes the received image data. The input used here is the image data sent to the server in step 2. The server uses image analysis software to apply a machine learning algorithm (e.g., a CNN model) for object identification. Specifically, it performs object contour extraction, feature calculation, and identification label assignment. The output is a list of identified objects.

[0081] Step 4:

[0082] The server uses the identified object list to select unnecessary objects. The input is the object list obtained from step 3, and the selection is made by referring to the user's past selection data registered in the database. In operation, the selection algorithm is applied through analysis of usage frequency and product lifecycle. The output is a list of unnecessary items.

[0083] Step 5:

[0084] The server sets disposal priorities for each object based on the list of unnecessary items and proposes the optimal disposal method using a generating AI model. The input is the list of unnecessary items from step 4. In operation, it constructs prompt statements that optimize disposal methods such as sale, recycling, and disposal, and generate proposals, while referring to the user profile and market data. The output is a list of disposal methods and their priorities.

[0085] Step 6:

[0086] The user uses a terminal to review the proposal from the server and approve the disposal method. The input is the list of disposal methods from step 5. The operation involves displaying the proposal via the user interface, and the user choosing to approve or modify it. The output is the user's approval information.

[0087] Step 7:

[0088] The server makes the actual disposal arrangements based on the user's authorization information. The input is the authorization information obtained in step 6. The system operates by automatically proceeding with the listing process on the e-commerce platform or by calling an API to request disposal services. The output is the progress status information of the disposal arrangements.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In modern urban life, efficiently managing and properly disposing of items that many people no longer use in their daily lives is a challenging task. In particular, finding the optimal disposal method depending on the type and condition of the item is time-consuming and burdensome for residents. There is a need for a system that solves these problems, efficiently optimizes living spaces, and supports the distribution and disposal of socially valuable items.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes an analysis means for receiving image data and identifying items based on the image data, a selection means for selecting unwanted items from the identified items, and a priority setting means for setting the disposal priority for the selected unwanted items. As a result, users can have the optimal disposal method automatically suggested from images of their room taken with a smartphone or smart glasses, and the process can be automated without any effort on their part.

[0094] "Image data" refers to visual information obtained using a camera or other photographic device, represented in digital format.

[0095] "Identification" is the process of identifying the type and characteristics of an object based on given data.

[0096] "Selection" is the act of choosing necessary items from a set of identified objects according to specific criteria.

[0097] "Disposal priority" refers to the criteria used to determine the order and urgency of disposal when dealing with unwanted items.

[0098] "Proposal" refers to suggesting the optimal course of action or options based on the results of analysis or interpretation.

[0099] "Arrangement" refers to the act of making the necessary preparations and procedures for a specific purpose.

[0100] "Database management" refers to systems and methods for systematically and efficiently storing, retrieving, and updating information.

[0101] "Customization" refers to the act of adjusting or modifying a system or service to meet the specific needs or requests of a user.

[0102] In this invention, users can utilize the system by installing a dedicated application on their smartphone or smart glasses and taking pictures of their room. The captured image data is securely transmitted to a cloud server. A digital camera and an HTTP communication library are used for this data transfer.

[0103] The server performs analysis based on the received image data. Machine learning algorithms utilizing TensorFlow and OpenCV are used for analysis, performing object identification. This process identifies the type, shape, and size of each object. The identified objects are registered in a database, and the registered data is efficiently utilized using database management tools. SQLite and Python scripts are used for data organization and selection.

[0104] Next, the server prioritizes the disposal of the selected unwanted items and proposes the most suitable disposal method. This process utilizes the Django server and the E-commerce API to provide disposal method suggestions and data. Once the user approves the proposed disposal method, the server automatically arranges the disposal. This includes control functions for the e-commerce platform using the REST API, such as listing resalable items and arranging for waste disposal companies.

[0105] As a concrete example, consider a scenario where a user uses their smartphone to take photos of unwanted furniture and clothing in their living room. This application would suggest listing the furniture on the most suitable sales platform if it has resale value, and recommend arranging for clothing to be sent to a recycling company.

[0106] An example of a generated AI prompt is: "Please describe a system in which you take a picture of your room with your smartphone, the app efficiently identifies unwanted items, and suggests the best disposal method."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user takes a picture of the room using a smartphone or smart glasses. This image data is sent to a cloud server via the application. The input is a digital image, and the output is image data stored in the cloud. Data transfer is performed using a digital camera and an HTTP communication library.

[0110] Step 2:

[0111] The server analyzes the received image data. Machine learning algorithms utilizing TensorFlow and OpenCV are used for the analysis process. The input is image data on the server, and the output is identification results including the type, shape, and size of the items. The server identifies the items in the image and tags each of them.

[0112] Step 3:

[0113] The server registers identified items in the database. The input is the identification result, and the output is the updated database information. Using SQLite and Python scripts, item information is stored efficiently. The current status of items is organized within the database by comparing it with past data.

[0114] Step 4:

[0115] The server selects unwanted items based on registered data and sets disposal priorities. The selection process takes into account user behavior history and usage frequency. Input is item information from the database, and output is a list of unwanted items and their disposal priorities. The importance of each item is verified using the algorithm.

[0116] Step 5:

[0117] The server suggests the best disposal method for unwanted items and notifies the user. A Django server is used to present disposal options. The input is a list of unwanted items, and the output is a suggested disposal method. The system automatically selects between recycling, resale, and donation as disposal methods.

[0118] Step 6:

[0119] The user approves the proposed disposal method via smartphone or smart glasses, and the result is sent to the server. The input is the user's approval information, and the output is instructions for the approved disposal method. Operation is easy through the interface.

[0120] Step 7:

[0121] The server automatically arranges disposal based on the approved information. A REST API is used to list items on e-commerce platforms and make requests to affiliated companies. The input is the approved disposal method, and the output is the completed disposal arrangement. This simplifies the user's process.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention provides a system that identifies and suggests the disposal of items while taking the user's emotions into consideration. By incorporating an emotion engine, it is possible to adjust the system's response according to the user's current emotional state, thereby providing a more personalized service.

[0124] The user takes a photo of the room using a dedicated application on their device and sends the image data to the server. At the same time, the user's facial expressions and voice are analyzed using the device's camera and microphone, and the emotion engine extracts the user's emotional data. This data is sent to the server along with the image data.

[0125] The server uses image analysis to identify items in a room. The identified item information is stored in a database, and unwanted items are selected based on past usage and user sentiment data. The sentiment engine evaluates the potential emotional value a user might have towards an item and incorporates this into the selection process.

[0126] Next, the server adjusts the disposal priority of the selected unwanted items based on the user's emotional state. For example, if the emotion engine determines that the user has a strong emotional attachment to a particular item, it can either postpone the disposal of that item or set it to a lower priority.

[0127] Using the suggested methods, the server presents the user with priority-based disposal options. The emotion engine adjusts the presentation method and wording to suit the user's emotions. This makes it easier for the user to accept the information without feeling stressed.

[0128] For example, if a user has an emotional attachment to a particular stuffed animal, the emotion engine can evaluate this and exclude the item when suggesting other disposal methods, or suggest special storage methods. This allows users to dispose of unwanted items in an ideal way.

[0129] Finally, after the user approves the proposal, the server automatically handles tasks such as listing the item on an e-commerce platform, contacting a waste disposal company, or making a donation to an NGO. The goal is to enhance user satisfaction and convenience by incorporating a high degree of personalization.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] When a user takes a photo of a room using their device, their facial expressions and voice are also recorded through the camera and microphone. The device then prepares to send the collected image data and emotional data to a server.

[0133] Step 2:

[0134] The device simultaneously transmits captured image data and collected emotion data to the server. During this process, the data is encrypted and sent in a privacy-protected state.

[0135] Step 3:

[0136] The server analyzes the received image data to identify the objects present in the room. It uses image analysis algorithms to determine the type, shape, and placement of the objects.

[0137] Step 4:

[0138] The server records identified item information in a database and uses an emotion engine to analyze the user's emotional data. It identifies the user's potential emotions towards the items and associates them with the database.

[0139] Step 5:

[0140] The server considers user sentiment data when selecting unwanted items from a list. For example, if sentiment data reveals a strong attachment to a particular item, it will either remove it from the selection list or lower its importance.

[0141] Step 6:

[0142] For unwanted items whose emotional significance is taken into consideration, the server sets a priority for disposal. The priority is determined by comprehensively considering factors such as importance based on emotions, the value of the item, and market supply and demand.

[0143] Step 7:

[0144] Using the suggestion mechanism, the server presents disposal methods to the user. In this process, the emotion engine adjusts the wording and suggestion methods of the guidance according to the user's current emotions, presenting them in a way that is most acceptable to the user.

[0145] Step 8:

[0146] Users review the presented disposal methods on their devices and modify or approve them based on the instructions. The interface is also emotionally responsive, allowing users to utilize options that resonate with them emotionally.

[0147] Step 9:

[0148] The server executes the disposal method approved by the user. This may include listing items on an e-commerce platform, booking a waste disposal service, or making a donation. This ensures that the disposal is carried out in the manner intended by the user.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] Traditional item disposal systems selected unwanted items based on general criteria without considering the individual emotional value of each user, which could lead to decreased user satisfaction. Furthermore, the proposed disposal methods were uniform, highlighting the need for more flexible responses tailored to each user's emotional state.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes an analysis means for receiving image data and identifying items, an emotion extraction means for extracting emotional states from the user's voice data and facial expression data, and a means for selecting unwanted items from the identified items while considering the emotional data. This enables personalized selection of unwanted items and optimal disposal suggestions that take the user's emotions into account.

[0154] "Image data" refers to electronically recorded visual information, which is used for processing such as analysis and identification.

[0155] "Analysis means" refers to technical means for analyzing information based on received data and identifying a specific object.

[0156] An "emotion extraction method" is a technical means that estimates the emotional state of a user from their voice and facial expressions and extracts that data.

[0157] "Selection methods" refer to technical means for selecting targets based on specific conditions from identified information.

[0158] A "proposed method" is a technical means that suggests the optimal method or countermeasure based on specific criteria.

[0159] "Means of arranging disposal" refers to the technical means of carrying out disposal on selected targets in accordance with established procedures.

[0160] A "learning algorithm" is a computational method used to identify patterns and features from data and utilize them for analysis and decision-making.

[0161] An "electronic trading platform" is an online marketplace where goods and services can be bought and sold electronically.

[0162] This system provides personalized item identification and disposal suggestions that take into account the user's emotions. Users can take images of their room using a dedicated application on their device. This application uses the device's camera and microphone to analyze the user's facial expressions and voice data to extract emotional data. The image data and emotional data are then transmitted from the device to a server.

[0163] The server uses image analysis software to analyze images. This software utilizes machine learning algorithms to process image data and identify items in the room. The identification information is then recorded in a database, and unnecessary items are selected based on past usage history and sentiment data. A sentiment engine evaluates the user's emotional value and incorporates this into the selection process.

[0164] The server then takes the user's emotions into account to prioritize the disposal of unwanted items. For example, if the emotion engine detects a strong attachment the user has to a particular item, it can either postpone its disposal or suggest a storage method. The suggested disposal method is then presented in a format and wording that is adjusted according to the emotion data.

[0165] For example, if a user shows emotional attachment to a particular stuffed animal, the server uses an emotion engine to evaluate this attachment and either exclude the stuffed animal from disposal suggestions or suggest alternative methods. Furthermore, once the user approves the suggestion, the server automatically arranges disposal, for instance, by listing it on an e-commerce platform or contacting a waste disposal company.

[0166] An example of a prompt might be: "Explain how the emotion engine evaluates the emotional value a user has of a particular item, and suggest ways to dispose of items that evoke positive emotions."

[0167] The overall goal of this system is to enhance satisfaction and convenience in disposing of items by providing a service that reflects the user's emotions.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user launches a dedicated application on their device and uses the camera to take pictures of the room. The image data and audio captured by the user are used as input. The device collects the user's facial expression data and audio data via the camera and microphone and sends this to the emotion engine. Specifically, it uses facial recognition technology to extract the features of the user's facial expressions and voice analysis to identify the features of their speech.

[0171] Step 2:

[0172] The device uses an emotion engine to analyze the user's emotional state and generate emotion data. The inputs used are the facial expression data and voice data extracted in step 1. The emotion engine uses a generative AI model to assign emotion labels (e.g., positive, negative, neutral). As a result, data indicating the user's emotional state is output.

[0173] Step 3:

[0174] The terminal sends image data and emotion data to the server. The output data from steps 1 and 2 is used as input. The server prepares to receive and analyze this data. Specifically, it packets the data via network communication and transfers it to the server.

[0175] Step 4:

[0176] The server uses image analysis software to analyze the received image data. Image data sent from the terminal is used as input. The server utilizes machine learning algorithms to identify objects within the image and extract information about each object. The output is the identified object information. Specifically, the server profiles the characteristics of each object and records them in a database.

[0177] Step 5:

[0178] The server selects unwanted items using identified item information and user sentiment data. The inputs used are the sentiment data generated in step 2 and the item information obtained in step 4. During the process, the sentiment engine evaluates the user's emotional value to the items and incorporates this into the selection criteria. The output is a list of unwanted items. Specifically, the server scores the importance of items based on past usage history and sentiment evaluations.

[0179] Step 6:

[0180] The server sets disposal priorities for unwanted items, taking into account the user's emotions. The input is the list of unwanted items obtained in step 5. The emotion engine analyzes the user's emotions towards the items and flexibly adjusts the priorities. The output is a disposal list with the priorities set. Specifically, items with certain emotional value are set to a low priority.

[0181] Step 7:

[0182] The server uses a suggestion mechanism to present the user with the most suitable disposal method based on a prioritized disposal list. The output data from step 6 is used as input. The emotion engine adjusts the methods and wording to match the user's emotional state. The adapted suggestions are then presented to the user as output. Specifically, this involves displaying information through the user interface and presenting emotionally sensitive options.

[0183] Step 8:

[0184] If the user approves the proposal, the server will initiate an automated process. The user's approval action is used as input. The server will then make appropriate arrangements based on the selected disposal method, such as listing the item on an e-trading platform or contacting a waste disposal company. Specifically, it will call the API of the relevant service, send the necessary information, and complete the transaction.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] Disposing of unwanted items in the home is often a burden for users, and making the right decision is often difficult, especially when dealing with items that evoke emotional attachments. Furthermore, choosing the appropriate order and method of disposal can also be a source of stress. There is a need to solve these problems and provide more personalized and appropriate disposal methods.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes emotion analysis means for analyzing the user's emotions and adjusting the response based on the analysis results; image analysis means for receiving image data and identifying items based on said image data; and priority adjustment means for adjusting the disposal priority of selected unwanted items, taking into account the user's emotional state. This makes it possible to identify unwanted items that take the user's emotions into consideration and to propose the optimal disposal method.

[0190] An "emotional analysis tool" is a function that analyzes the user's emotions and adjusts the response based on the analysis results.

[0191] "Image analysis means" refers to technology for identifying items based on received image data.

[0192] "Selection method" refers to the process of sorting out unwanted items from among identified items.

[0193] The "priority adjustment mechanism" is a function that takes into account the user's emotional state and adjusts the priority of disposal for selected unwanted items.

[0194] The "disposal suggestion mechanism" is a function that suggests the optimal disposal method for each type of unwanted item, using language that is appropriate to the user's emotions.

[0195] A "disposal arrangement system" is a system that automatically carries out disposal procedures based on the proposed disposal method.

[0196] The system for realizing this invention consists of a user terminal, a server, and a consumer robot. The user first uses a terminal with a dedicated application installed to take a photograph of a specific area in their home. The terminal sends this image data to the server and, at the same time, analyzes the user's emotions using the camera and microphone, and also sends emotional data to the server. This analysis uses an emotion analysis API (e.g., Microsoft® Azure® Emotion API) and an image analysis system (e.g., Google® Cloud Vision API).

[0197] The server identifies items in the room using an item identification algorithm based on the received image data. Then, a selection mechanism selects unwanted items and adjusts disposal priorities by referring to the user's emotional data. At this stage, a machine learning model is used, taking into account the results learned from past data. When proposing the optimal disposal method, the disposal proposal mechanism considers the user's emotional data and makes a proposal using appropriate wording, which is then notified to the user via their terminal or robot.

[0198] For example, if the collection includes a book that the user has cherished for many years, and the emotional analysis detects the user's feelings of sadness, it will suggest postponing or carefully considering the disposal of the book. This entire process enables flexible and stress-free disposal of unwanted items, improving convenience for the user. An example of an input prompt to the generative AI model could be, "Please suggest the optimal method for disposing of unwanted items, taking the user's emotions into consideration."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The user takes pictures of the room using a device with a dedicated application running. The input is image data from the camera, and the output is saved as an image file. At the same time, the device activates the camera and microphone to collect data on the user's facial expressions and voice, which are then provided to the emotion analysis engine.

[0202] Step 2:

[0203] The terminal transmits the acquired image data and user emotional data to the server. The input is a composite data package containing image data and emotional data, and the output is data packets transmitted over the internet. The data is encrypted for privacy protection.

[0204] Step 3:

[0205] The server analyzes the received image data and uses an image analysis API to identify items in the room. The input is encrypted image data, and the output is a list of identified items. The identification process involves database lookups based on the appearance and shape of the items.

[0206] Step 4:

[0207] The server uses a selection mechanism to select unwanted items from the identified items. The input is an item list and past usage history data, and the output is a list of selected unwanted items. A machine learning algorithm makes predictions based on past disposal data.

[0208] Step 5:

[0209] The server adjusts the disposal priority of selected unwanted items, taking into account the user's emotional data. The input is emotional data and a list of unwanted items, and the output is a list of the adjusted disposal priority. Items that the user has a strong attachment to are given a lower priority.

[0210] Step 6:

[0211] The server proposes the optimal disposal method to the user based on the adjusted disposal priority. The input is a priority list, and the output is a proposal message sent to the user's terminal. The disposal proposal is constructed using emotional analysis to ensure it is phrased in a way that minimizes stress for the user.

[0212] Step 7:

[0213] Once the user approves the proposal, the server automatically executes the disposal arrangements. The input is the user's approval data, and the output is the execution data for the specific disposal procedures. Items for resale are arranged to be sent to the trading platform, waste to a contractor, and donated items to the appropriate organization.

[0214] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0230] This invention provides a system that efficiently identifies and selects items present in a user's space, and automatically proposes and arranges the optimal disposal method based on that identification. Specific embodiments for carrying out this invention will be described below.

[0231] The user takes a picture of the room using a device with a dedicated application installed. The captured image data is securely transmitted from the device to the server. The server performs image analysis based on the received image data and identifies items present in the room using a specific algorithm. In this process, a machine learning-based algorithm is used to identify the type, shape, and size of the items.

[0232] Identified items are registered in a database on the server, and unwanted items are selected based on past user data and other criteria. Specifically, the selection process takes into account factors such as frequency of use, the condition of the item, and its potential obsolescence. Items deemed unwanted are assigned a priority for disposal. This takes into account the user's lifestyle and the value of the items.

[0233] Next, the server suggests the best disposal method for the selected unwanted items. For example, it might suggest selling items with rarity or resale value through an e-commerce platform, while recommending collection by a suitable waste disposal company for other items. Furthermore, donation options are also presented for items that meet the criteria.

[0234] After reviewing the proposed disposal method, the user can approve it via their device and proceed. The server receives the user's approval and automatically handles tasks such as listing items on e-commerce platforms and booking disposal services. This allows users to organize and declutter their belongings with simple operations, optimizing their living space without requiring significant effort.

[0235] As a concrete example, consider a user who wants to declutter their living room by getting rid of unwanted books, old electronics, clothes, etc. Using this system, simply by taking and uploading photos, the system will suggest which items are unwanted and the most appropriate disposal method. If old electronics have market value, the system will suggest ways to maximize that value, and clothes may be donated. This allows users to dispose of items in an efficient and socially valuable way.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user takes photos of the room using their device and uploads the selected image data to the server via the application. The device compresses the captured image data and securely transmits it to the server over the network.

[0239] Step 2:

[0240] The server prepares the image data received from the terminal for analysis. Using image analysis tools, it identifies the objects depicted in the image. This process utilizes machine learning algorithms to determine the type, shape, size, and other characteristics of the objects.

[0241] Step 3:

[0242] The server stores data of identified items in a database. The stored data is then compared against past user data and other criteria (e.g., a general list of unwanted items) to determine which items are to be discarded.

[0243] Step 4:

[0244] The server sets a disposal priority for items deemed unnecessary. Factors such as the item's value, user usage frequency, and physical condition are considered in this prioritization. Based on this information, the order in which each item is disposed of is determined.

[0245] Step 5:

[0246] The server suggests the best disposal method for prioritized unwanted items. For example, it recommends selling resalable items on e-commerce platforms and using appropriate waste disposal companies for items that need to be discarded. It also offers the option of donating items that can be donated to partner NGOs.

[0247] Step 6:

[0248] Users can review the proposed disposal methods through the interface on their device and approve or modify them. The proposed methods can also be adjusted based on user feedback.

[0249] Step 7:

[0250] The server automatically arranges the appropriate disposal according to the disposal method approved by the user. This includes listing items on e-commerce platforms, requesting collection from waste disposal companies, and arranging logistics for donations.

[0251] (Example 1)

[0252] Next, we will describe Example 1. 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".

[0253] In daily life, efficiently identifying individual items, selecting unnecessary ones, and providing the optimal disposal method requires considerable time and effort, and often relies on the user's own judgment. This challenge, especially in today's living environment where organization and tidiness are paramount, requires quick and accurate decision-making, and also requires careful consideration from the perspectives of privacy and security. A system that efficiently processes unnecessary items while automatically providing suggestions tailored to the user's lifestyle and values ​​is highly desirable.

[0254] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0255] In this invention, the server includes processing means for receiving image information and identifying objects based on the image information, determination means for selecting unnecessary objects from among the identified objects, and prioritization means for setting processing priorities for the selected unnecessary objects. This allows the user to take and transmit images and leave the necessary decisions to the system. Furthermore, it enables detailed analysis of items and the suggestion of the optimal disposal method, thereby optimizing the living space while reducing the burden on the user.

[0256] "Image information" refers to visual data acquired by optical devices, and is fundamental data for identifying and analyzing objects.

[0257] An "object" refers to a specific item or object that a system identifies or selects.

[0258] "Processing means" refers to a processing system equipped with the function of analyzing received image information to identify objects.

[0259] A "decision-making mechanism" refers to a processing system that includes a function for selecting unnecessary objects from among the identified objects.

[0260] A "prioritization mechanism" refers to a processing system that provides a function for setting processing priorities for selected unnecessary items.

[0261] "Presentation means" refers to an interface and algorithm that includes a function for presenting the optimal processing method for selected unnecessary items.

[0262] "Arrangement means" refers to a device or system that has the function of automatically managing and executing processing procedures based on the presented processing method.

[0263] "Communication means" refers to the communication protocols and mechanisms necessary for capturing and transmitting image information between a terminal and a server.

[0264] An "analysis means" is a mechanism that implements machine learning algorithms for detecting, analyzing, and processing the characteristics of an object.

[0265] This invention provides a system that allows users to efficiently identify items in their daily lives, select unwanted items, and propose and implement the optimal disposal method. First, the user installs a dedicated application on their device and takes a picture of their room. This application acquires the image via the camera function and transmits the image information to a server via the internet.

[0266] The terminal is equipped with communication means to reliably transmit image information to the server, and the security of the information is ensured through data encryption. The server analyzes the received image information using processing means, and uses known software such as OpenCV or TensorFlow as image analysis libraries. In this way, the server uses machine learning algorithms to identify objects in the image and detect their characteristics.

[0267] The server has a database function to select unnecessary objects based on identified objects, using the user's past data and other relevant information for selection. Unnecessary objects are assigned a processing priority that takes into account lifestyle, market value, and even social value.

[0268] Users can review and approve disposal methods presented by the server via their device. Based on this approval, the server uses its procurement tools to automatically execute the process. Specifically, this involves quickly selling items on e-commerce platforms or scheduling pickups with waste disposal companies. This allows users to easily organize their belongings and optimize their living space without any hassle through a dedicated app.

[0269] For example, if a user wants to get rid of unwanted books or old electronic devices in their living room, this system can automatically suggest which items are unwanted and the most effective way to dispose of them, simply by taking and uploading photos. An example of a prompt might be, "Please take photos of unwanted items in your living room and suggest the most efficient disposal methods."

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The user takes photos of the room using a dedicated application on their device. The input is visual information captured by the camera, which is converted into an image format within the application. The application guides the user to the optimal shooting angle and provides the functionality to capture multiple photos. The output is image data, which is prepared for analysis in the next step.

[0273] Step 2:

[0274] The terminal securely transmits the captured image data to the server. The input is the image data generated in step 1, which is transmitted over the network using encryption technology. The communication module handles data encryption and transfer. The output is the image data received by the server.

[0275] Step 3:

[0276] The server analyzes the received image data. The input used here is the image data sent to the server in step 2. The server uses image analysis software to apply a machine learning algorithm (e.g., a CNN model) for object identification. Specifically, it performs object contour extraction, feature calculation, and identification label assignment. The output is a list of identified objects.

[0277] Step 4:

[0278] The server uses the identified object list to select unnecessary objects. The input is the object list obtained from step 3, and the selection is made by referring to the user's past selection data registered in the database. In operation, the selection algorithm is applied through analysis of usage frequency and product lifecycle. The output is a list of unnecessary items.

[0279] Step 5:

[0280] Based on the list of unnecessary items, the server sets the disposal priority for each object and proposes an optimal disposal method using an AI model for generation. The input is the list of unnecessary items in Step 4. As an operation, while referring to the user profile and market data, a prompt sentence is composed to optimize disposal methods such as sales, recycling, and disposal, and generate a proposal. The output is a list of disposal methods and their priority levels.

[0281] Step 6:

[0282] The user uses the terminal to check the proposal from the server and approves the disposal method. The input is the list of disposal methods in Step 5. As an operation, the proposal content is displayed through the user interface, and the user makes a selection of approval or modification. The output is the user's approval information.

[0283] Step 7:

[0284] Based on the user's approval information, the server performs actual disposal arrangements. The input is the approval information obtained in Step 6. As an operation, the function of automatically proceeding with the listing procedure on the e-commerce platform or calling an API to request a waste disposal company works. The output is the progress status information of the disposal arrangement.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In modern urban life, efficiently managing and properly disposing of items that many people no longer use in their daily lives is a challenging task. In particular, finding the optimal disposal method depending on the type and condition of the item is time-consuming and burdensome for residents. There is a need for a system that solves these problems, efficiently optimizes living spaces, and supports the distribution and disposal of socially valuable items.

[0288] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes an analysis means for receiving image data and identifying items based on the image data, a selection means for selecting unwanted items from the identified items, and a priority setting means for setting the disposal priority for the selected unwanted items. As a result, users can have the optimal disposal method automatically suggested from images of their room taken with a smartphone or smart glasses, and the process can be automated without any effort on their part.

[0290] "Image data" refers to visual information obtained using a camera or other photographic device, represented in digital format.

[0291] "Identification" is the process of identifying the type and characteristics of an object based on given data.

[0292] "Selection" is the act of choosing necessary items from a set of identified objects according to specific criteria.

[0293] "Disposal priority" refers to the criteria used to determine the order and urgency of disposal when dealing with unwanted items.

[0294] "Proposal" refers to suggesting the optimal course of action or options based on the results of analysis or interpretation.

[0295] "Arrangement" refers to the act of making the necessary preparations and procedures for a specific purpose.

[0296] "Database management" refers to systems and methods for systematically and efficiently storing, retrieving, and updating information.

[0297] "Customization" refers to the act of adjusting or modifying a system or service to meet the specific needs or requests of a user.

[0298] In this invention, users can utilize the system by installing a dedicated application on their smartphone or smart glasses and taking pictures of their room. The captured image data is securely transmitted to a cloud server. A digital camera and an HTTP communication library are used for this data transfer.

[0299] The server performs analysis based on the received image data. Machine learning algorithms utilizing TensorFlow and OpenCV are used for analysis, performing object identification. This process identifies the type, shape, and size of each object. The identified objects are registered in a database, and the registered data is efficiently utilized using database management tools. SQLite and Python scripts are used for data organization and selection.

[0300] Next, the server prioritizes the disposal of the selected unwanted items and proposes the most suitable disposal method. This process utilizes the Django server and the E-commerce API to provide disposal method suggestions and data. Once the user approves the proposed disposal method, the server automatically arranges the disposal. This includes control functions for the e-commerce platform using the REST API, such as listing resalable items and arranging for waste disposal companies.

[0301] As a concrete example, consider a scenario where a user uses their smartphone to take photos of unwanted furniture and clothing in their living room. This application would suggest listing the furniture on the most suitable sales platform if it has resale value, and recommend arranging for clothing to be sent to a recycling company.

[0302] An example of generating an AI prompt sentence is "Please explain a system that takes a photo of a room with a smartphone, efficiently identifies unwanted items, and the app proposes an optimal disposal method."

[0303] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0304] Step 1:

[0305] The user takes a photo of the room using a smartphone or smart glasses. This image data is sent to the cloud server through the application. The input is a digital image, and the output is the image data stored on the cloud. Data transfer is performed using a digital camera and an HTTP communication library.

[0306] Step 2:

[0307] The server analyzes the received image data. Machine learning algorithms leveraging TensorFlow or OpenCV are used for the analysis process. The input is the image data on the server, and the output is the identification result including the type, shape, and size of the item. The server identifies the items in the image and tags each of them.

[0308] Step 3:

[0309] The server registers the identified items in the database. The input is the identification result, and the output is the updated database information. SQLite and Python scripts are utilized to efficiently store the item information. While collating with past data, the current status of the items is organized within the database.

[0310] Step 4:

[0311] The server selects unwanted items based on registered data and sets disposal priorities. The selection process takes into account user behavior history and usage frequency. Input is item information from the database, and output is a list of unwanted items and their disposal priorities. The importance of each item is verified using the algorithm.

[0312] Step 5:

[0313] The server suggests the best disposal method for unwanted items and notifies the user. A Django server is used to present disposal options. The input is a list of unwanted items, and the output is a suggested disposal method. The system automatically selects between recycling, resale, and donation as disposal methods.

[0314] Step 6:

[0315] The user approves the proposed disposal method via smartphone or smart glasses, and the result is sent to the server. The input is the user's approval information, and the output is instructions for the approved disposal method. Operation is easy through the interface.

[0316] Step 7:

[0317] The server automatically arranges disposal based on the approved information. A REST API is used to list items on e-commerce platforms and make requests to affiliated companies. The input is the approved disposal method, and the output is the completed disposal arrangement. This simplifies the user's process.

[0318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0319] This invention provides a system that identifies and suggests the disposal of items while taking the user's emotions into consideration. By incorporating an emotion engine, it is possible to adjust the system's response according to the user's current emotional state, thereby providing a more personalized service.

[0320] The user takes a photo of the room using a dedicated application on their device and sends the image data to the server. At the same time, the user's facial expressions and voice are analyzed using the device's camera and microphone, and the emotion engine extracts the user's emotional data. This data is sent to the server along with the image data.

[0321] The server uses image analysis to identify items in a room. The identified item information is stored in a database, and unwanted items are selected based on past usage and user sentiment data. The sentiment engine evaluates the potential emotional value a user might have towards an item and incorporates this into the selection process.

[0322] Next, the server adjusts the disposal priority of the selected unwanted items based on the user's emotional state. For example, if the emotion engine determines that the user has a strong emotional attachment to a particular item, it can either postpone the disposal of that item or set it to a lower priority.

[0323] Using the suggested methods, the server presents the user with priority-based disposal options. The emotion engine adjusts the presentation method and wording to suit the user's emotions. This makes it easier for the user to accept the information without feeling stressed.

[0324] For example, if a user has an emotional attachment to a particular stuffed animal, the emotion engine can evaluate this and exclude the item when suggesting other disposal methods, or suggest special storage methods. This allows users to dispose of unwanted items in an ideal way.

[0325] Finally, after the user approves the proposal, the server automatically handles tasks such as listing the item on an e-commerce platform, contacting a waste disposal company, or making a donation to an NGO. The goal is to enhance user satisfaction and convenience by incorporating a high degree of personalization.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] When a user takes a photo of a room using their device, their facial expressions and voice are also recorded through the camera and microphone. The device then prepares to send the collected image data and emotional data to a server.

[0329] Step 2:

[0330] The device simultaneously transmits captured image data and collected emotion data to the server. During this process, the data is encrypted and sent in a privacy-protected state.

[0331] Step 3:

[0332] The server analyzes the received image data to identify the objects present in the room. It uses image analysis algorithms to determine the type, shape, and placement of the objects.

[0333] Step 4:

[0334] The server records identified item information in a database and uses an emotion engine to analyze the user's emotional data. It identifies the user's potential emotions towards the items and associates them with the database.

[0335] Step 5:

[0336] The server considers user sentiment data when selecting unwanted items from a list. For example, if sentiment data reveals a strong attachment to a particular item, it will either remove it from the selection list or lower its importance.

[0337] Step 6:

[0338] For unwanted items whose emotional significance is taken into consideration, the server sets a priority for disposal. The priority is determined by comprehensively considering factors such as importance based on emotions, the value of the item, and market supply and demand.

[0339] Step 7:

[0340] Using the suggestion mechanism, the server presents disposal methods to the user. In this process, the emotion engine adjusts the wording and suggestion methods of the guidance according to the user's current emotions, presenting them in a way that is most acceptable to the user.

[0341] Step 8:

[0342] Users review the presented disposal methods on their devices and modify or approve them based on the instructions. The interface is also emotionally responsive, allowing users to utilize options that resonate with them emotionally.

[0343] Step 9:

[0344] The server executes the disposal method approved by the user. This may include listing items on an e-commerce platform, booking a waste disposal service, or making a donation. This ensures that the disposal is carried out in the manner intended by the user.

[0345] (Example 2)

[0346] Next, we will describe Example 2. 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".

[0347] Traditional item disposal systems selected unwanted items based on general criteria without considering the individual emotional value of each user, which could lead to decreased user satisfaction. Furthermore, the proposed disposal methods were uniform, highlighting the need for more flexible responses tailored to each user's emotional state.

[0348] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0349] In this invention, the server includes an analysis means for receiving image data and identifying items, an emotion extraction means for extracting emotional states from the user's voice data and facial expression data, and a means for selecting unwanted items from the identified items while considering the emotional data. This enables personalized selection of unwanted items and optimal disposal suggestions that take the user's emotions into account.

[0350] "Image data" refers to electronically recorded visual information, which is used for processing such as analysis and identification.

[0351] "Analysis means" refers to technical means for analyzing information based on received data and identifying a specific object.

[0352] An "emotion extraction method" is a technical means that estimates the emotional state of a user from their voice and facial expressions and extracts that data.

[0353] "Selection methods" refer to technical means for selecting targets based on specific conditions from identified information.

[0354] A "proposed method" is a technical means that suggests the optimal method or countermeasure based on specific criteria.

[0355] "Means of arranging disposal" refers to the technical means of carrying out disposal on selected targets in accordance with established procedures.

[0356] A "learning algorithm" is a computational method used to identify patterns and features from data and utilize them for analysis and decision-making.

[0357] An "electronic trading platform" is an online marketplace where goods and services can be bought and sold electronically.

[0358] This system provides personalized item identification and disposal suggestions that take into account the user's emotions. Users can take images of their room using a dedicated application on their device. This application uses the device's camera and microphone to analyze the user's facial expressions and voice data to extract emotional data. The image data and emotional data are then transmitted from the device to a server.

[0359] The server uses image analysis software to analyze images. This software utilizes machine learning algorithms to process image data and identify items in the room. The identification information is then recorded in a database, and unnecessary items are selected based on past usage history and sentiment data. A sentiment engine evaluates the user's emotional value and incorporates this into the selection process.

[0360] The server then takes the user's emotions into account to prioritize the disposal of unwanted items. For example, if the emotion engine detects a strong attachment the user has to a particular item, it can either postpone its disposal or suggest a storage method. The suggested disposal method is then presented in a format and wording that is adjusted according to the emotion data.

[0361] For example, if a user shows emotional attachment to a particular stuffed animal, the server uses an emotion engine to evaluate this attachment and either exclude the stuffed animal from disposal suggestions or suggest alternative methods. Furthermore, once the user approves the suggestion, the server automatically arranges disposal, for instance, by listing it on an e-commerce platform or contacting a waste disposal company.

[0362] An example of a prompt might be: "Explain how the emotion engine evaluates the emotional value a user has of a particular item, and suggest ways to dispose of items that evoke positive emotions."

[0363] The overall goal of this system is to enhance satisfaction and convenience in disposing of items by providing a service that reflects the user's emotions.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The user launches a dedicated application on their device and uses the camera to take pictures of the room. The image data and audio captured by the user are used as input. The device collects the user's facial expression data and audio data via the camera and microphone and sends this to the emotion engine. Specifically, it uses facial recognition technology to extract the features of the user's facial expressions and voice analysis to identify the features of their speech.

[0367] Step 2:

[0368] The device uses an emotion engine to analyze the user's emotional state and generate emotion data. The inputs used are the facial expression data and voice data extracted in step 1. The emotion engine uses a generative AI model to assign emotion labels (e.g., positive, negative, neutral). As a result, data indicating the user's emotional state is output.

[0369] Step 3:

[0370] The terminal sends image data and emotion data to the server. The output data from steps 1 and 2 is used as input. The server prepares to receive and analyze this data. Specifically, it packets the data via network communication and transfers it to the server.

[0371] Step 4:

[0372] The server uses image analysis software to analyze the received image data. Image data sent from the terminal is used as input. The server utilizes machine learning algorithms to identify objects within the image and extract information about each object. The output is the identified object information. Specifically, the server profiles the characteristics of each object and records them in a database.

[0373] Step 5:

[0374] The server selects unwanted items using identified item information and user sentiment data. The inputs used are the sentiment data generated in step 2 and the item information obtained in step 4. During the process, the sentiment engine evaluates the user's emotional value to the items and incorporates this into the selection criteria. The output is a list of unwanted items. Specifically, the server scores the importance of items based on past usage history and sentiment evaluations.

[0375] Step 6:

[0376] The server sets disposal priorities for unwanted items, taking into account the user's emotions. The input is the list of unwanted items obtained in step 5. The emotion engine analyzes the user's emotions towards the items and flexibly adjusts the priorities. The output is a disposal list with the priorities set. Specifically, items with certain emotional value are set to a low priority.

[0377] Step 7:

[0378] The server uses a suggestion mechanism to present the user with the most suitable disposal method based on a prioritized disposal list. The output data from step 6 is used as input. The emotion engine adjusts the methods and wording to match the user's emotional state. The adapted suggestions are then presented to the user as output. Specifically, this involves displaying information through the user interface and presenting emotionally sensitive options.

[0379] Step 8:

[0380] If the user approves the proposal, the server will initiate an automated process. The user's approval action is used as input. The server will then make appropriate arrangements based on the selected disposal method, such as listing the item on an e-trading platform or contacting a waste disposal company. Specifically, it will call the API of the relevant service, send the necessary information, and complete the transaction.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] Disposing of unwanted items in the home is often a burden for users, and making the right decision is often difficult, especially when dealing with items that evoke emotional attachments. Furthermore, choosing the appropriate order and method of disposal can also be a source of stress. There is a need to solve these problems and provide more personalized and appropriate disposal methods.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes emotion analysis means for analyzing the user's emotions and adjusting the response based on the analysis results; image analysis means for receiving image data and identifying items based on said image data; and priority adjustment means for adjusting the disposal priority of selected unwanted items, taking into account the user's emotional state. This makes it possible to identify unwanted items that take the user's emotions into consideration and to propose the optimal disposal method.

[0386] An "emotional analysis tool" is a function that analyzes the user's emotions and adjusts the response based on the analysis results.

[0387] "Image analysis means" refers to technology for identifying items based on received image data.

[0388] "Selection method" refers to the process of sorting out unwanted items from among identified items.

[0389] The "priority adjustment mechanism" is a function that takes into account the user's emotional state and adjusts the priority of disposal for selected unwanted items.

[0390] The "disposal suggestion mechanism" is a function that suggests the optimal disposal method for each type of unwanted item, using language that is appropriate to the user's emotions.

[0391] A "disposal arrangement system" is a system that automatically carries out disposal procedures based on the proposed disposal method.

[0392] The system for realizing this invention consists of a user terminal, a server, and a consumer robot. The user first uses a terminal with a dedicated application installed to take a photograph of a specific area within their home. The terminal transmits this image data to the server, and simultaneously analyzes the user's emotions using its camera and microphone, transmitting the emotional data to the server as well. This analysis utilizes an emotion analysis API (e.g., Microsoft Azure Emotion API) and an image analysis system (e.g., Google Cloud Vision API).

[0393] The server identifies items in the room using an item identification algorithm based on the received image data. Then, a selection mechanism selects unwanted items and adjusts disposal priorities by referring to the user's emotional data. At this stage, a machine learning model is used, taking into account the results learned from past data. When proposing the optimal disposal method, the disposal proposal mechanism considers the user's emotional data and makes a proposal using appropriate wording, which is then notified to the user via their terminal or robot.

[0394] For example, if the collection includes a book that the user has cherished for many years, and the emotional analysis detects the user's feelings of sadness, it will suggest postponing or carefully considering the disposal of the book. This entire process enables flexible and stress-free disposal of unwanted items, improving convenience for the user. An example of an input prompt to the generative AI model could be, "Please suggest the optimal method for disposing of unwanted items, taking the user's emotions into consideration."

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The user takes pictures of the room using a device with a dedicated application running. The input is image data from the camera, and the output is saved as an image file. At the same time, the device activates the camera and microphone to collect data on the user's facial expressions and voice, which are then provided to the emotion analysis engine.

[0398] Step 2:

[0399] The terminal transmits the acquired image data and user emotional data to the server. The input is a composite data package containing image data and emotional data, and the output is data packets transmitted over the internet. The data is encrypted for privacy protection.

[0400] Step 3:

[0401] The server analyzes the received image data and uses an image analysis API to identify items in the room. The input is encrypted image data, and the output is a list of identified items. The identification process involves database lookups based on the appearance and shape of the items.

[0402] Step 4:

[0403] The server uses a selection mechanism to select unwanted items from the identified items. The input is an item list and past usage history data, and the output is a list of selected unwanted items. A machine learning algorithm makes predictions based on past disposal data.

[0404] Step 5:

[0405] The server adjusts the disposal priority of selected unwanted items, taking into account the user's emotional data. The input is emotional data and a list of unwanted items, and the output is a list of the adjusted disposal priority. Items that the user has a strong attachment to are given a lower priority.

[0406] Step 6:

[0407] The server proposes the optimal disposal method to the user based on the adjusted disposal priority. The input is a priority list, and the output is a proposal message sent to the user's terminal. The disposal proposal is constructed using emotional analysis to ensure it is phrased in a way that minimizes stress for the user.

[0408] Step 7:

[0409] Once the user approves the proposal, the server automatically executes the disposal arrangements. The input is the user's approval data, and the output is the execution data for the specific disposal procedures. Items for resale are arranged to be sent to the trading platform, waste to a contractor, and donated items to the appropriate organization.

[0410] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0411] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0412] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0413] [Third Embodiment]

[0414] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0415] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0416] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0417] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0418] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0419] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0420] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0421] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0422] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0423] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0424] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0425] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0426] This invention provides a system that efficiently identifies and selects items present in a user's space, and automatically proposes and arranges the optimal disposal method based on that identification. Specific embodiments for carrying out this invention will be described below.

[0427] The user takes a picture of the room using a device with a dedicated application installed. The captured image data is securely transmitted from the device to the server. The server performs image analysis based on the received image data and identifies items present in the room using a specific algorithm. In this process, a machine learning-based algorithm is used to identify the type, shape, and size of the items.

[0428] Identified items are registered in a database on the server, and unwanted items are selected based on past user data and other criteria. Specifically, the selection process takes into account factors such as frequency of use, the condition of the item, and its potential obsolescence. Items deemed unwanted are assigned a priority for disposal. This takes into account the user's lifestyle and the value of the items.

[0429] Next, the server suggests the best disposal method for the selected unwanted items. For example, it might suggest selling items with rarity or resale value through an e-commerce platform, while recommending collection by a suitable waste disposal company for other items. Furthermore, donation options are also presented for items that meet the criteria.

[0430] After reviewing the proposed disposal method, the user can approve it via their device and proceed. The server receives the user's approval and automatically handles tasks such as listing items on e-commerce platforms and booking disposal services. This allows users to organize and declutter their belongings with simple operations, optimizing their living space without requiring significant effort.

[0431] As a concrete example, consider a user who wants to declutter their living room by getting rid of unwanted books, old electronics, clothes, etc. Using this system, simply by taking and uploading photos, the system will suggest which items are unwanted and the most appropriate disposal method. If old electronics have market value, the system will suggest ways to maximize that value, and clothes may be donated. This allows users to dispose of items in an efficient and socially valuable way.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user takes photos of the room using their device and uploads the selected image data to the server via the application. The device compresses the captured image data and securely transmits it to the server over the network.

[0435] Step 2:

[0436] The server prepares the image data received from the terminal for analysis. Using image analysis tools, it identifies the objects depicted in the image. This process utilizes machine learning algorithms to determine the type, shape, size, and other characteristics of the objects.

[0437] Step 3:

[0438] The server stores data of identified items in a database. The stored data is then compared against past user data and other criteria (e.g., a general list of unwanted items) to determine which items are to be discarded.

[0439] Step 4:

[0440] The server sets a disposal priority for items deemed unnecessary. Factors such as the item's value, user usage frequency, and physical condition are considered in this prioritization. Based on this information, the order in which each item is disposed of is determined.

[0441] Step 5:

[0442] The server suggests the best disposal method for prioritized unwanted items. For example, it recommends selling resalable items on e-commerce platforms and using appropriate waste disposal companies for items that need to be discarded. It also offers the option of donating items that can be donated to partner NGOs.

[0443] Step 6:

[0444] Users can review the proposed disposal methods through the interface on their device and approve or modify them. The proposed methods can also be adjusted based on user feedback.

[0445] Step 7:

[0446] The server automatically arranges the appropriate disposal according to the disposal method approved by the user. This includes listing items on e-commerce platforms, requesting collection from waste disposal companies, and arranging logistics for donations.

[0447] (Example 1)

[0448] Next, we will describe Example 1. 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."

[0449] In daily life, efficiently identifying individual items, selecting unnecessary ones, and providing the optimal disposal method requires considerable time and effort, and often relies on the user's own judgment. This challenge, especially in today's living environment where organization and tidiness are paramount, requires quick and accurate decision-making, and also requires careful consideration from the perspectives of privacy and security. A system that efficiently processes unnecessary items while automatically providing suggestions tailored to the user's lifestyle and values ​​is highly desirable.

[0450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0451] In this invention, the server includes processing means for receiving image information and identifying objects based on the image information, determination means for selecting unnecessary objects from among the identified objects, and prioritization means for setting processing priorities for the selected unnecessary objects. This allows the user to take and transmit images and leave the necessary decisions to the system. Furthermore, it enables detailed analysis of items and the suggestion of the optimal disposal method, thereby optimizing the living space while reducing the burden on the user.

[0452] "Image information" refers to visual data acquired by optical devices, and is fundamental data for identifying and analyzing objects.

[0453] An "object" refers to a specific item or object that a system identifies or selects.

[0454] "Processing means" refers to a processing system equipped with the function of analyzing received image information to identify objects.

[0455] A "decision-making mechanism" refers to a processing system that includes a function for selecting unnecessary objects from among the identified objects.

[0456] A "prioritization mechanism" refers to a processing system that provides a function for setting processing priorities for selected unnecessary items.

[0457] "Presentation means" refers to an interface and algorithm that includes a function for presenting the optimal processing method for selected unnecessary items.

[0458] "Arrangement means" refers to a device or system that has the function of automatically managing and executing processing procedures based on the presented processing method.

[0459] "Communication means" refers to the communication protocols and mechanisms necessary for capturing and transmitting image information between a terminal and a server.

[0460] An "analysis means" is a mechanism that implements machine learning algorithms for detecting, analyzing, and processing the characteristics of an object.

[0461] This invention provides a system that allows users to efficiently identify items in their daily lives, select unwanted items, and propose and implement the optimal disposal method. First, the user installs a dedicated application on their device and takes a picture of their room. This application acquires the image via the camera function and transmits the image information to a server via the internet.

[0462] The terminal is equipped with communication means to reliably transmit image information to the server, and the security of the information is ensured through data encryption. The server analyzes the received image information using processing means, and uses known software such as OpenCV or TensorFlow as image analysis libraries. In this way, the server uses machine learning algorithms to identify objects in the image and detect their characteristics.

[0463] The server has a database function to select unnecessary objects based on identified objects, using the user's past data and other relevant information for selection. Unnecessary objects are assigned a processing priority that takes into account lifestyle, market value, and even social value.

[0464] Users can review and approve disposal methods presented by the server via their device. Based on this approval, the server uses its procurement tools to automatically execute the process. Specifically, this involves quickly selling items on e-commerce platforms or scheduling pickups with waste disposal companies. This allows users to easily organize their belongings and optimize their living space without any hassle through a dedicated app.

[0465] For example, if a user wants to get rid of unwanted books or old electronic devices in their living room, this system can automatically suggest which items are unwanted and the most effective way to dispose of them, simply by taking and uploading photos. An example of a prompt might be, "Please take photos of unwanted items in your living room and suggest the most efficient disposal methods."

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] The user takes photos of the room using a dedicated application on their device. The input is visual information captured by the camera, which is converted into an image format within the application. The application guides the user to the optimal shooting angle and provides the functionality to capture multiple photos. The output is image data, which is prepared for analysis in the next step.

[0469] Step 2:

[0470] The terminal securely transmits the captured image data to the server. The input is the image data generated in step 1, which is transmitted over the network using encryption technology. The communication module handles data encryption and transfer. The output is the image data received by the server.

[0471] Step 3:

[0472] The server analyzes the received image data. The input used here is the image data sent to the server in step 2. The server uses image analysis software to apply a machine learning algorithm (e.g., a CNN model) for object identification. Specifically, it performs object contour extraction, feature calculation, and identification label assignment. The output is a list of identified objects.

[0473] Step 4:

[0474] The server uses the identified object list to select unnecessary objects. The input is the object list obtained from step 3, and the selection is made by referring to the user's past selection data registered in the database. In operation, the selection algorithm is applied through analysis of usage frequency and product lifecycle. The output is a list of unnecessary items.

[0475] Step 5:

[0476] The server sets disposal priorities for each object based on the list of unnecessary items and proposes the optimal disposal method using a generating AI model. The input is the list of unnecessary items from step 4. In operation, it constructs prompt statements that optimize disposal methods such as sale, recycling, and disposal, and generate proposals, while referring to the user profile and market data. The output is a list of disposal methods and their priorities.

[0477] Step 6:

[0478] The user uses a terminal to review the proposal from the server and approve the disposal method. The input is the list of disposal methods from step 5. The operation involves displaying the proposal via the user interface, and the user choosing to approve or modify it. The output is the user's approval information.

[0479] Step 7:

[0480] The server makes the actual disposal arrangements based on the user's authorization information. The input is the authorization information obtained in step 6. The system operates by automatically proceeding with the listing process on the e-commerce platform or by calling an API to request disposal services. The output is the progress status information of the disposal arrangements.

[0481] (Application Example 1)

[0482] Next, we will explain Application Example 1. In the following explanation, 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."

[0483] In modern urban life, efficiently managing and properly disposing of items that many people no longer use in their daily lives is a challenging task. In particular, finding the optimal disposal method depending on the type and condition of the item is time-consuming and burdensome for residents. There is a need for a system that solves these problems, efficiently optimizes living spaces, and supports the distribution and disposal of socially valuable items.

[0484] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0485] In this invention, the server includes an analysis means for receiving image data and identifying items based on the image data, a selection means for selecting unwanted items from the identified items, and a priority setting means for setting the disposal priority for the selected unwanted items. As a result, users can have the optimal disposal method automatically suggested from images of their room taken with a smartphone or smart glasses, and the process can be automated without any effort on their part.

[0486] "Image data" refers to visual information obtained using a camera or other photographic device, represented in digital format.

[0487] "Identification" is the process of identifying the type and characteristics of an object based on given data.

[0488] "Selection" is the act of choosing necessary items from a set of identified objects according to specific criteria.

[0489] "Disposal priority" refers to the criteria used to determine the order and urgency of disposal when dealing with unwanted items.

[0490] "Proposal" refers to suggesting the optimal course of action or options based on the results of analysis or interpretation.

[0491] "Arrangement" refers to the act of making the necessary preparations and procedures for a specific purpose.

[0492] "Database management" refers to systems and methods for systematically and efficiently storing, retrieving, and updating information.

[0493] "Customization" refers to the act of adjusting or modifying a system or service to meet the specific needs or requests of a user.

[0494] In this invention, users can utilize the system by installing a dedicated application on their smartphone or smart glasses and taking pictures of their room. The captured image data is securely transmitted to a cloud server. A digital camera and an HTTP communication library are used for this data transfer.

[0495] The server performs analysis based on the received image data. Machine learning algorithms utilizing TensorFlow and OpenCV are used for analysis, performing object identification. This process identifies the type, shape, and size of each object. The identified objects are registered in a database, and the registered data is efficiently utilized using database management tools. SQLite and Python scripts are used for data organization and selection.

[0496] Next, the server prioritizes the disposal of the selected unwanted items and proposes the most suitable disposal method. This process utilizes the Django server and the E-commerce API to provide disposal method suggestions and data. Once the user approves the proposed disposal method, the server automatically arranges the disposal. This includes control functions for the e-commerce platform using the REST API, such as listing resalable items and arranging for waste disposal companies.

[0497] As a concrete example, consider a scenario where a user uses their smartphone to take photos of unwanted furniture and clothing in their living room. This application would suggest listing the furniture on the most suitable sales platform if it has resale value, and recommend arranging for clothing to be sent to a recycling company.

[0498] An example of a generated AI prompt is: "Please describe a system in which you take a picture of your room with your smartphone, the app efficiently identifies unwanted items, and suggests the best disposal method."

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] The user takes a picture of the room using a smartphone or smart glasses. This image data is sent to a cloud server via the application. The input is a digital image, and the output is image data stored in the cloud. Data transfer is performed using a digital camera and an HTTP communication library.

[0502] Step 2:

[0503] The server analyzes the received image data. Machine learning algorithms utilizing TensorFlow and OpenCV are used for the analysis process. The input is image data on the server, and the output is identification results including the type, shape, and size of the items. The server identifies the items in the image and tags each of them.

[0504] Step 3:

[0505] The server registers identified items in the database. The input is the identification result, and the output is the updated database information. Using SQLite and Python scripts, item information is stored efficiently. The current status of items is organized within the database by comparing it with past data.

[0506] Step 4:

[0507] The server selects unwanted items based on registered data and sets disposal priorities. The selection process takes into account user behavior history and usage frequency. Input is item information from the database, and output is a list of unwanted items and their disposal priorities. The importance of each item is verified using the algorithm.

[0508] Step 5:

[0509] The server suggests the best disposal method for unwanted items and notifies the user. A Django server is used to present disposal options. The input is a list of unwanted items, and the output is a suggested disposal method. The system automatically selects between recycling, resale, and donation as disposal methods.

[0510] Step 6:

[0511] The user approves the proposed disposal method via smartphone or smart glasses, and the result is sent to the server. The input is the user's approval information, and the output is instructions for the approved disposal method. Operation is easy through the interface.

[0512] Step 7:

[0513] The server automatically arranges disposal based on the approved information. A REST API is used to list items on e-commerce platforms and make requests to affiliated companies. The input is the approved disposal method, and the output is the completed disposal arrangement. This simplifies the user's process.

[0514] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0515] This invention provides a system that identifies and suggests the disposal of items while taking the user's emotions into consideration. By incorporating an emotion engine, it is possible to adjust the system's response according to the user's current emotional state, thereby providing a more personalized service.

[0516] The user takes a photo of the room using a dedicated application on their device and sends the image data to the server. At the same time, the user's facial expressions and voice are analyzed using the device's camera and microphone, and the emotion engine extracts the user's emotional data. This data is sent to the server along with the image data.

[0517] The server uses image analysis to identify items in a room. The identified item information is stored in a database, and unwanted items are selected based on past usage and user sentiment data. The sentiment engine evaluates the potential emotional value a user might have towards an item and incorporates this into the selection process.

[0518] Next, the server adjusts the disposal priority of the selected unwanted items based on the user's emotional state. For example, if the emotion engine determines that the user has a strong emotional attachment to a particular item, it can either postpone the disposal of that item or set it to a lower priority.

[0519] Using the suggested methods, the server presents the user with priority-based disposal options. The emotion engine adjusts the presentation method and wording to suit the user's emotions. This makes it easier for the user to accept the information without feeling stressed.

[0520] For example, if a user has an emotional attachment to a particular stuffed animal, the emotion engine can evaluate this and exclude the item when suggesting other disposal methods, or suggest special storage methods. This allows users to dispose of unwanted items in an ideal way.

[0521] Finally, after the user approves the proposal, the server automatically handles tasks such as listing the item on an e-commerce platform, contacting a waste disposal company, or making a donation to an NGO. The goal is to enhance user satisfaction and convenience by incorporating a high degree of personalization.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] When a user takes a photo of a room using their device, their facial expressions and voice are also recorded through the camera and microphone. The device then prepares to send the collected image data and emotional data to a server.

[0525] Step 2:

[0526] The device simultaneously transmits captured image data and collected emotion data to the server. During this process, the data is encrypted and sent in a privacy-protected state.

[0527] Step 3:

[0528] The server analyzes the received image data to identify the objects present in the room. It uses image analysis algorithms to determine the type, shape, and placement of the objects.

[0529] Step 4:

[0530] The server records identified item information in a database and uses an emotion engine to analyze the user's emotional data. It identifies the user's potential emotions towards the items and associates them with the database.

[0531] Step 5:

[0532] The server considers user sentiment data when selecting unwanted items from a list. For example, if sentiment data reveals a strong attachment to a particular item, it will either remove it from the selection list or lower its importance.

[0533] Step 6:

[0534] For unwanted items whose emotional significance is taken into consideration, the server sets a priority for disposal. The priority is determined by comprehensively considering factors such as importance based on emotions, the value of the item, and market supply and demand.

[0535] Step 7:

[0536] Using the suggestion mechanism, the server presents disposal methods to the user. In this process, the emotion engine adjusts the wording and suggestion methods of the guidance according to the user's current emotions, presenting them in a way that is most acceptable to the user.

[0537] Step 8:

[0538] Users review the presented disposal methods on their devices and modify or approve them based on the instructions. The interface is also emotionally responsive, allowing users to utilize options that resonate with them emotionally.

[0539] Step 9:

[0540] The server executes the disposal method approved by the user. This may include listing items on an e-commerce platform, booking a waste disposal service, or making a donation. This ensures that the disposal is carried out in the manner intended by the user.

[0541] (Example 2)

[0542] Next, we will describe Example 2. 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."

[0543] Traditional item disposal systems selected unwanted items based on general criteria without considering the individual emotional value of each user, which could lead to decreased user satisfaction. Furthermore, the proposed disposal methods were uniform, highlighting the need for more flexible responses tailored to each user's emotional state.

[0544] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0545] In this invention, the server includes an analysis means for receiving image data and identifying items, an emotion extraction means for extracting emotional states from the user's voice data and facial expression data, and a means for selecting unwanted items from the identified items while considering the emotional data. This enables personalized selection of unwanted items and optimal disposal suggestions that take the user's emotions into account.

[0546] "Image data" refers to electronically recorded visual information, which is used for processing such as analysis and identification.

[0547] "Analysis means" refers to technical means for analyzing information based on received data and identifying a specific object.

[0548] An "emotion extraction method" is a technical means that estimates the emotional state of a user from their voice and facial expressions and extracts that data.

[0549] "Selection methods" refer to technical means for selecting targets based on specific conditions from identified information.

[0550] A "proposed method" is a technical means that suggests the optimal method or countermeasure based on specific criteria.

[0551] "Means of arranging disposal" refers to the technical means of carrying out disposal on selected targets in accordance with established procedures.

[0552] A "learning algorithm" is a computational method used to identify patterns and features from data and utilize them for analysis and decision-making.

[0553] An "electronic trading platform" is an online marketplace where goods and services can be bought and sold electronically.

[0554] This system provides personalized item identification and disposal suggestions that take into account the user's emotions. Users can take images of their room using a dedicated application on their device. This application uses the device's camera and microphone to analyze the user's facial expressions and voice data to extract emotional data. The image data and emotional data are then transmitted from the device to a server.

[0555] The server uses image analysis software to analyze images. This software utilizes machine learning algorithms to process image data and identify items in the room. The identification information is then recorded in a database, and unnecessary items are selected based on past usage history and sentiment data. A sentiment engine evaluates the user's emotional value and incorporates this into the selection process.

[0556] The server then takes the user's emotions into account to prioritize the disposal of unwanted items. For example, if the emotion engine detects a strong attachment the user has to a particular item, it can either postpone its disposal or suggest a storage method. The suggested disposal method is then presented in a format and wording that is adjusted according to the emotion data.

[0557] For example, if a user shows emotional attachment to a particular stuffed animal, the server uses an emotion engine to evaluate this attachment and either exclude the stuffed animal from disposal suggestions or suggest alternative methods. Furthermore, once the user approves the suggestion, the server automatically arranges disposal, for instance, by listing it on an e-commerce platform or contacting a waste disposal company.

[0558] An example of a prompt might be: "Explain how the emotion engine evaluates the emotional value a user has of a particular item, and suggest ways to dispose of items that evoke positive emotions."

[0559] The overall goal of this system is to enhance satisfaction and convenience in disposing of items by providing a service that reflects the user's emotions.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] The user launches a dedicated application on their device and uses the camera to take pictures of the room. The image data and audio captured by the user are used as input. The device collects the user's facial expression data and audio data via the camera and microphone and sends this to the emotion engine. Specifically, it uses facial recognition technology to extract the features of the user's facial expressions and voice analysis to identify the features of their speech.

[0563] Step 2:

[0564] The device uses an emotion engine to analyze the user's emotional state and generate emotion data. The inputs used are the facial expression data and voice data extracted in step 1. The emotion engine uses a generative AI model to assign emotion labels (e.g., positive, negative, neutral). As a result, data indicating the user's emotional state is output.

[0565] Step 3:

[0566] The terminal sends image data and emotion data to the server. The output data from steps 1 and 2 is used as input. The server prepares to receive and analyze this data. Specifically, it packets the data via network communication and transfers it to the server.

[0567] Step 4:

[0568] The server uses image analysis software to analyze the received image data. Image data sent from the terminal is used as input. The server utilizes machine learning algorithms to identify objects within the image and extract information about each object. The output is the identified object information. Specifically, the server profiles the characteristics of each object and records them in a database.

[0569] Step 5:

[0570] The server selects unwanted items using identified item information and user sentiment data. The inputs used are the sentiment data generated in step 2 and the item information obtained in step 4. During the process, the sentiment engine evaluates the user's emotional value to the items and incorporates this into the selection criteria. The output is a list of unwanted items. Specifically, the server scores the importance of items based on past usage history and sentiment evaluations.

[0571] Step 6:

[0572] The server sets disposal priorities for unwanted items, taking into account the user's emotions. The input is the list of unwanted items obtained in step 5. The emotion engine analyzes the user's emotions towards the items and flexibly adjusts the priorities. The output is a disposal list with the priorities set. Specifically, items with certain emotional value are set to a low priority.

[0573] Step 7:

[0574] The server uses a suggestion mechanism to present the user with the most suitable disposal method based on a prioritized disposal list. The output data from step 6 is used as input. The emotion engine adjusts the methods and wording to match the user's emotional state. The adapted suggestions are then presented to the user as output. Specifically, this involves displaying information through the user interface and presenting emotionally sensitive options.

[0575] Step 8:

[0576] If the user approves the proposal, the server will initiate an automated process. The user's approval action is used as input. The server will then make appropriate arrangements based on the selected disposal method, such as listing the item on an e-trading platform or contacting a waste disposal company. Specifically, it will call the API of the relevant service, send the necessary information, and complete the transaction.

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, 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."

[0579] Disposing of unwanted items in the home is often a burden for users, and making the right decision is often difficult, especially when dealing with items that evoke emotional attachments. Furthermore, choosing the appropriate order and method of disposal can also be a source of stress. There is a need to solve these problems and provide more personalized and appropriate disposal methods.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes emotion analysis means for analyzing the user's emotions and adjusting the response based on the analysis results; image analysis means for receiving image data and identifying items based on said image data; and priority adjustment means for adjusting the disposal priority of selected unwanted items, taking into account the user's emotional state. This makes it possible to identify unwanted items that take the user's emotions into consideration and to propose the optimal disposal method.

[0582] An "emotional analysis tool" is a function that analyzes the user's emotions and adjusts the response based on the analysis results.

[0583] "Image analysis means" refers to technology for identifying items based on received image data.

[0584] "Selection method" refers to the process of sorting out unwanted items from among identified items.

[0585] The "priority adjustment mechanism" is a function that takes into account the user's emotional state and adjusts the priority of disposal for selected unwanted items.

[0586] The "disposal suggestion mechanism" is a function that suggests the optimal disposal method for each type of unwanted item, using language that is appropriate to the user's emotions.

[0587] A "disposal arrangement system" is a system that automatically carries out disposal procedures based on the proposed disposal method.

[0588] The system for realizing this invention consists of a user terminal, a server, and a consumer robot. The user first uses a terminal with a dedicated application installed to take a photograph of a specific area within their home. The terminal transmits this image data to the server, and simultaneously analyzes the user's emotions using its camera and microphone, transmitting the emotional data to the server as well. This analysis utilizes an emotion analysis API (e.g., Microsoft Azure Emotion API) and an image analysis system (e.g., Google Cloud Vision API).

[0589] The server identifies items in the room using an item identification algorithm based on the received image data. Then, a selection mechanism selects unwanted items and adjusts disposal priorities by referring to the user's emotional data. At this stage, a machine learning model is used, taking into account the results learned from past data. When proposing the optimal disposal method, the disposal proposal mechanism considers the user's emotional data and makes a proposal using appropriate wording, which is then notified to the user via their terminal or robot.

[0590] For example, if the collection includes a book that the user has cherished for many years, and the emotional analysis detects the user's feelings of sadness, it will suggest postponing or carefully considering the disposal of the book. This entire process enables flexible and stress-free disposal of unwanted items, improving convenience for the user. An example of an input prompt to the generative AI model could be, "Please suggest the optimal method for disposing of unwanted items, taking the user's emotions into consideration."

[0591] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0592] Step 1:

[0593] The user takes pictures of the room using a device with a dedicated application running. The input is image data from the camera, and the output is saved as an image file. At the same time, the device activates the camera and microphone to collect data on the user's facial expressions and voice, which are then provided to the emotion analysis engine.

[0594] Step 2:

[0595] The terminal transmits the acquired image data and user emotional data to the server. The input is a composite data package containing image data and emotional data, and the output is data packets transmitted over the internet. The data is encrypted for privacy protection.

[0596] Step 3:

[0597] The server analyzes the received image data and uses an image analysis API to identify items in the room. The input is encrypted image data, and the output is a list of identified items. The identification process involves database lookups based on the appearance and shape of the items.

[0598] Step 4:

[0599] The server uses a selection mechanism to select unwanted items from the identified items. The input is an item list and past usage history data, and the output is a list of selected unwanted items. A machine learning algorithm makes predictions based on past disposal data.

[0600] Step 5:

[0601] The server adjusts the disposal priority of selected unwanted items, taking into account the user's emotional data. The input is emotional data and a list of unwanted items, and the output is a list of the adjusted disposal priority. Items that the user has a strong attachment to are given a lower priority.

[0602] Step 6:

[0603] The server proposes the optimal disposal method to the user based on the adjusted disposal priority. The input is a priority list, and the output is a proposal message sent to the user's terminal. The disposal proposal is constructed using emotional analysis to ensure it is phrased in a way that minimizes stress for the user.

[0604] Step 7:

[0605] Once the user approves the proposal, the server automatically executes the disposal arrangements. The input is the user's approval data, and the output is the execution data for the specific disposal procedures. Items for resale are arranged to be sent to the trading platform, waste to a contractor, and donated items to the appropriate organization.

[0606] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0607] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0608] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] As shown in Figure 7, the 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.

[0612] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0613] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0614] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0615] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0616] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0617] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0618] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0619] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0620] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0621] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0622] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0623] This invention provides a system that efficiently identifies and selects items present in a user's space, and automatically proposes and arranges the optimal disposal method based on that identification. Specific embodiments for carrying out this invention will be described below.

[0624] The user takes a picture of the room using a device with a dedicated application installed. The captured image data is securely transmitted from the device to the server. The server performs image analysis based on the received image data and identifies items present in the room using a specific algorithm. In this process, a machine learning-based algorithm is used to identify the type, shape, and size of the items.

[0625] Identified items are registered in a database on the server, and unwanted items are selected based on past user data and other criteria. Specifically, the selection process takes into account factors such as frequency of use, the condition of the item, and its potential obsolescence. Items deemed unwanted are assigned a priority for disposal. This takes into account the user's lifestyle and the value of the items.

[0626] Next, the server suggests the best disposal method for the selected unwanted items. For example, it might suggest selling items with rarity or resale value through an e-commerce platform, while recommending collection by a suitable waste disposal company for other items. Furthermore, donation options are also presented for items that meet the criteria.

[0627] After reviewing the proposed disposal method, the user can approve it via their device and proceed. The server receives the user's approval and automatically handles tasks such as listing items on e-commerce platforms and booking disposal services. This allows users to organize and declutter their belongings with simple operations, optimizing their living space without requiring significant effort.

[0628] As a concrete example, consider a user who wants to declutter their living room by getting rid of unwanted books, old electronics, clothes, etc. Using this system, simply by taking and uploading photos, the system will suggest which items are unwanted and the most appropriate disposal method. If old electronics have market value, the system will suggest ways to maximize that value, and clothes may be donated. This allows users to dispose of items in an efficient and socially valuable way.

[0629] The following describes the processing flow.

[0630] Step 1:

[0631] The user takes photos of the room using their device and uploads the selected image data to the server via the application. The device compresses the captured image data and securely transmits it to the server over the network.

[0632] Step 2:

[0633] The server prepares the image data received from the terminal for analysis. Using image analysis tools, it identifies the objects depicted in the image. This process utilizes machine learning algorithms to determine the type, shape, size, and other characteristics of the objects.

[0634] Step 3:

[0635] The server stores data of identified items in a database. The stored data is then compared against past user data and other criteria (e.g., a general list of unwanted items) to determine which items are to be discarded.

[0636] Step 4:

[0637] The server sets a disposal priority for items deemed unnecessary. Factors such as the item's value, user usage frequency, and physical condition are considered in this prioritization. Based on this information, the order in which each item is disposed of is determined.

[0638] Step 5:

[0639] The server suggests the best disposal method for prioritized unwanted items. For example, it recommends selling resalable items on e-commerce platforms and using appropriate waste disposal companies for items that need to be discarded. It also offers the option of donating items that can be donated to partner NGOs.

[0640] Step 6:

[0641] Users can review the proposed disposal methods through the interface on their device and approve or modify them. The proposed methods can also be adjusted based on user feedback.

[0642] Step 7:

[0643] The server automatically arranges the appropriate disposal according to the disposal method approved by the user. This includes listing items on e-commerce platforms, requesting collection from waste disposal companies, and arranging logistics for donations.

[0644] (Example 1)

[0645] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] In daily life, efficiently identifying individual items, selecting unnecessary ones, and providing the optimal disposal method requires considerable time and effort, and often relies on the user's own judgment. This challenge, especially in today's living environment where organization and tidiness are paramount, requires quick and accurate decision-making, and also requires careful consideration from the perspectives of privacy and security. A system that efficiently processes unnecessary items while automatically providing suggestions tailored to the user's lifestyle and values ​​is highly desirable.

[0647] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0648] In this invention, the server includes processing means for receiving image information and identifying objects based on the image information, determination means for selecting unnecessary objects from among the identified objects, and prioritization means for setting processing priorities for the selected unnecessary objects. This allows the user to take and transmit images and leave the necessary decisions to the system. Furthermore, it enables detailed analysis of items and the suggestion of the optimal disposal method, thereby optimizing the living space while reducing the burden on the user.

[0649] "Image information" refers to visual data acquired by optical devices, and is fundamental data for identifying and analyzing objects.

[0650] An "object" refers to a specific item or object that a system identifies or selects.

[0651] "Processing means" refers to a processing system equipped with the function of analyzing received image information to identify objects.

[0652] A "decision-making mechanism" refers to a processing system that includes a function for selecting unnecessary objects from among the identified objects.

[0653] A "prioritization mechanism" refers to a processing system that provides a function for setting processing priorities for selected unnecessary items.

[0654] "Presentation means" refers to an interface and algorithm that includes a function for presenting the optimal processing method for selected unnecessary items.

[0655] "Arrangement means" refers to a device or system that has the function of automatically managing and executing processing procedures based on the presented processing method.

[0656] "Communication means" refers to the communication protocols and mechanisms necessary for capturing and transmitting image information between a terminal and a server.

[0657] An "analysis means" is a mechanism that implements machine learning algorithms for detecting, analyzing, and processing the characteristics of an object.

[0658] This invention provides a system that allows users to efficiently identify items in their daily lives, select unwanted items, and propose and implement the optimal disposal method. First, the user installs a dedicated application on their device and takes a picture of their room. This application acquires the image via the camera function and transmits the image information to a server via the internet.

[0659] The terminal is equipped with communication means to reliably transmit image information to the server, and the security of the information is ensured through data encryption. The server analyzes the received image information using processing means, and uses known software such as OpenCV or TensorFlow as image analysis libraries. In this way, the server uses machine learning algorithms to identify objects in the image and detect their characteristics.

[0660] The server has a database function to select unnecessary objects based on identified objects, using the user's past data and other relevant information for selection. Unnecessary objects are assigned a processing priority that takes into account lifestyle, market value, and even social value.

[0661] Users can review and approve disposal methods presented by the server via their device. Based on this approval, the server uses its procurement tools to automatically execute the process. Specifically, this involves quickly selling items on e-commerce platforms or scheduling pickups with waste disposal companies. This allows users to easily organize their belongings and optimize their living space without any hassle through a dedicated app.

[0662] For example, if a user wants to get rid of unwanted books or old electronic devices in their living room, this system can automatically suggest which items are unwanted and the most effective way to dispose of them, simply by taking and uploading photos. An example of a prompt might be, "Please take photos of unwanted items in your living room and suggest the most efficient disposal methods."

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The user takes photos of the room using a dedicated application on their device. The input is visual information captured by the camera, which is converted into an image format within the application. The application guides the user to the optimal shooting angle and provides the functionality to capture multiple photos. The output is image data, which is prepared for analysis in the next step.

[0666] Step 2:

[0667] The terminal securely transmits the captured image data to the server. The input is the image data generated in step 1, which is transmitted over the network using encryption technology. The communication module handles data encryption and transfer. The output is the image data received by the server.

[0668] Step 3:

[0669] The server analyzes the received image data. The input used here is the image data sent to the server in step 2. The server uses image analysis software to apply a machine learning algorithm (e.g., a CNN model) for object identification. Specifically, it performs object contour extraction, feature calculation, and identification label assignment. The output is a list of identified objects.

[0670] Step 4:

[0671] The server uses the identified object list to select unnecessary objects. The input is the object list obtained from step 3, and the selection is made by referring to the user's past selection data registered in the database. In operation, the selection algorithm is applied through analysis of usage frequency and product lifecycle. The output is a list of unnecessary items.

[0672] Step 5:

[0673] The server sets disposal priorities for each object based on the list of unnecessary items and proposes the optimal disposal method using a generating AI model. The input is the list of unnecessary items from step 4. In operation, it constructs prompt statements that optimize disposal methods such as sale, recycling, and disposal, and generate proposals, while referring to the user profile and market data. The output is a list of disposal methods and their priorities.

[0674] Step 6:

[0675] The user uses a terminal to review the proposal from the server and approve the disposal method. The input is the list of disposal methods from step 5. The operation involves displaying the proposal via the user interface, and the user choosing to approve or modify it. The output is the user's approval information.

[0676] Step 7:

[0677] The server makes the actual disposal arrangements based on the user's authorization information. The input is the authorization information obtained in step 6. The system operates by automatically proceeding with the listing process on the e-commerce platform or by calling an API to request disposal services. The output is the progress status information of the disposal arrangements.

[0678] (Application Example 1)

[0679] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0680] In modern urban life, efficiently managing and properly disposing of items that many people no longer use in their daily lives is a challenging task. In particular, finding the optimal disposal method depending on the type and condition of the item is time-consuming and burdensome for residents. There is a need for a system that solves these problems, efficiently optimizes living spaces, and supports the distribution and disposal of socially valuable items.

[0681] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0682] In this invention, the server includes an analysis means for receiving image data and identifying items based on the image data, a selection means for selecting unwanted items from the identified items, and a priority setting means for setting the disposal priority for the selected unwanted items. As a result, users can have the optimal disposal method automatically suggested from images of their room taken with a smartphone or smart glasses, and the process can be automated without any effort on their part.

[0683] "Image data" refers to visual information obtained using a camera or other photographic device, represented in digital format.

[0684] "Identification" is the process of identifying the type and characteristics of an object based on given data.

[0685] "Selection" is the act of choosing necessary items from a set of identified objects according to specific criteria.

[0686] "Disposal priority" refers to the criteria used to determine the order and urgency of disposal when dealing with unwanted items.

[0687] "Proposal" refers to suggesting the optimal course of action or options based on the results of analysis or interpretation.

[0688] "Arrangement" refers to the act of making the necessary preparations and procedures for a specific purpose.

[0689] "Database management" refers to systems and methods for systematically and efficiently storing, retrieving, and updating information.

[0690] "Customization" refers to the act of adjusting or modifying a system or service to meet the specific needs or requests of a user.

[0691] In this invention, users can utilize the system by installing a dedicated application on their smartphone or smart glasses and taking pictures of their room. The captured image data is securely transmitted to a cloud server. A digital camera and an HTTP communication library are used for this data transfer.

[0692] The server performs analysis based on the received image data. Machine learning algorithms utilizing TensorFlow and OpenCV are used for analysis, performing object identification. This process identifies the type, shape, and size of each object. The identified objects are registered in a database, and the registered data is efficiently utilized using database management tools. SQLite and Python scripts are used for data organization and selection.

[0693] Next, the server prioritizes the disposal of the selected unwanted items and proposes the most suitable disposal method. This process utilizes the Django server and the E-commerce API to provide disposal method suggestions and data. Once the user approves the proposed disposal method, the server automatically arranges the disposal. This includes control functions for the e-commerce platform using the REST API, such as listing resalable items and arranging for waste disposal companies.

[0694] As a concrete example, consider a scenario where a user uses their smartphone to take photos of unwanted furniture and clothing in their living room. This application would suggest listing the furniture on the most suitable sales platform if it has resale value, and recommend arranging for clothing to be sent to a recycling company.

[0695] An example of a generated AI prompt is: "Please describe a system in which you take a picture of your room with your smartphone, the app efficiently identifies unwanted items, and suggests the best disposal method."

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The user takes a picture of the room using a smartphone or smart glasses. This image data is sent to a cloud server via the application. The input is a digital image, and the output is image data stored in the cloud. Data transfer is performed using a digital camera and an HTTP communication library.

[0699] Step 2:

[0700] The server analyzes the received image data. Machine learning algorithms utilizing TensorFlow and OpenCV are used for the analysis process. The input is image data on the server, and the output is identification results including the type, shape, and size of the items. The server identifies the items in the image and tags each of them.

[0701] Step 3:

[0702] The server registers identified items in the database. The input is the identification result, and the output is the updated database information. Using SQLite and Python scripts, item information is stored efficiently. The current status of items is organized within the database by comparing it with past data.

[0703] Step 4:

[0704] The server selects unwanted items based on registered data and sets disposal priorities. The selection process takes into account user behavior history and usage frequency. Input is item information from the database, and output is a list of unwanted items and their disposal priorities. The importance of each item is verified using the algorithm.

[0705] Step 5:

[0706] The server suggests the best disposal method for unwanted items and notifies the user. A Django server is used to present disposal options. The input is a list of unwanted items, and the output is a suggested disposal method. The system automatically selects between recycling, resale, and donation as disposal methods.

[0707] Step 6:

[0708] The user approves the proposed disposal method via smartphone or smart glasses, and the result is sent to the server. The input is the user's approval information, and the output is instructions for the approved disposal method. Operation is easy through the interface.

[0709] Step 7:

[0710] The server automatically arranges disposal based on the approved information. A REST API is used to list items on e-commerce platforms and make requests to affiliated companies. The input is the approved disposal method, and the output is the completed disposal arrangement. This simplifies the user's process.

[0711] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0712] This invention provides a system that identifies and suggests the disposal of items while taking the user's emotions into consideration. By incorporating an emotion engine, it is possible to adjust the system's response according to the user's current emotional state, thereby providing a more personalized service.

[0713] The user takes a photo of the room using a dedicated application on their device and sends the image data to the server. At the same time, the user's facial expressions and voice are analyzed using the device's camera and microphone, and the emotion engine extracts the user's emotional data. This data is sent to the server along with the image data.

[0714] The server uses image analysis to identify items in a room. The identified item information is stored in a database, and unwanted items are selected based on past usage and user sentiment data. The sentiment engine evaluates the potential emotional value a user might have towards an item and incorporates this into the selection process.

[0715] Next, the server adjusts the disposal priority of the selected unwanted items based on the user's emotional state. For example, if the emotion engine determines that the user has a strong emotional attachment to a particular item, it can either postpone the disposal of that item or set it to a lower priority.

[0716] Using the suggested methods, the server presents the user with priority-based disposal options. The emotion engine adjusts the presentation method and wording to suit the user's emotions. This makes it easier for the user to accept the information without feeling stressed.

[0717] For example, if a user has an emotional attachment to a particular stuffed animal, the emotion engine can evaluate this and exclude the item when suggesting other disposal methods, or suggest special storage methods. This allows users to dispose of unwanted items in an ideal way.

[0718] Finally, after the user approves the proposal, the server automatically handles tasks such as listing the item on an e-commerce platform, contacting a waste disposal company, or making a donation to an NGO. The goal is to enhance user satisfaction and convenience by incorporating a high degree of personalization.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] When a user takes a photo of a room using their device, their facial expressions and voice are also recorded through the camera and microphone. The device then prepares to send the collected image data and emotional data to a server.

[0722] Step 2:

[0723] The device simultaneously transmits captured image data and collected emotion data to the server. During this process, the data is encrypted and sent in a privacy-protected state.

[0724] Step 3:

[0725] The server analyzes the received image data to identify the objects present in the room. It uses image analysis algorithms to determine the type, shape, and placement of the objects.

[0726] Step 4:

[0727] The server records identified item information in a database and uses an emotion engine to analyze the user's emotional data. It identifies the user's potential emotions towards the items and associates them with the database.

[0728] Step 5:

[0729] The server considers user sentiment data when selecting unwanted items from a list. For example, if sentiment data reveals a strong attachment to a particular item, it will either remove it from the selection list or lower its importance.

[0730] Step 6:

[0731] For unwanted items whose emotional significance is taken into consideration, the server sets a priority for disposal. The priority is determined by comprehensively considering factors such as importance based on emotions, the value of the item, and market supply and demand.

[0732] Step 7:

[0733] Using the suggestion mechanism, the server presents disposal methods to the user. In this process, the emotion engine adjusts the wording and suggestion methods of the guidance according to the user's current emotions, presenting them in a way that is most acceptable to the user.

[0734] Step 8:

[0735] Users review the presented disposal methods on their devices and modify or approve them based on the instructions. The interface is also emotionally responsive, allowing users to utilize options that resonate with them emotionally.

[0736] Step 9:

[0737] The server executes the disposal method approved by the user. This may include listing items on an e-commerce platform, booking a waste disposal service, or making a donation. This ensures that the disposal is carried out in the manner intended by the user.

[0738] (Example 2)

[0739] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] Traditional item disposal systems selected unwanted items based on general criteria without considering the individual emotional value of each user, which could lead to decreased user satisfaction. Furthermore, the proposed disposal methods were uniform, highlighting the need for more flexible responses tailored to each user's emotional state.

[0741] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0742] In this invention, the server includes an analysis means for receiving image data and identifying items, an emotion extraction means for extracting emotional states from the user's voice data and facial expression data, and a means for selecting unwanted items from the identified items while considering the emotional data. This enables personalized selection of unwanted items and optimal disposal suggestions that take the user's emotions into account.

[0743] "Image data" refers to electronically recorded visual information, which is used for processing such as analysis and identification.

[0744] "Analysis means" refers to technical means for analyzing information based on received data and identifying a specific object.

[0745] An "emotion extraction method" is a technical means that estimates the emotional state of a user from their voice and facial expressions and extracts that data.

[0746] "Selection methods" refer to technical means for selecting targets based on specific conditions from identified information.

[0747] A "proposed method" is a technical means that suggests the optimal method or countermeasure based on specific criteria.

[0748] "Means of arranging disposal" refers to the technical means of carrying out disposal on selected targets in accordance with established procedures.

[0749] A "learning algorithm" is a computational method used to identify patterns and features from data and utilize them for analysis and decision-making.

[0750] An "electronic trading platform" is an online marketplace where goods and services can be bought and sold electronically.

[0751] This system provides personalized item identification and disposal suggestions that take into account the user's emotions. Users can take images of their room using a dedicated application on their device. This application uses the device's camera and microphone to analyze the user's facial expressions and voice data to extract emotional data. The image data and emotional data are then transmitted from the device to a server.

[0752] The server uses image analysis software to analyze images. This software utilizes machine learning algorithms to process image data and identify items in the room. The identification information is then recorded in a database, and unnecessary items are selected based on past usage history and sentiment data. A sentiment engine evaluates the user's emotional value and incorporates this into the selection process.

[0753] The server then takes the user's emotions into account to prioritize the disposal of unwanted items. For example, if the emotion engine detects a strong attachment the user has to a particular item, it can either postpone its disposal or suggest a storage method. The suggested disposal method is then presented in a format and wording that is adjusted according to the emotion data.

[0754] For example, if a user shows emotional attachment to a particular stuffed animal, the server uses an emotion engine to evaluate this attachment and either exclude the stuffed animal from disposal suggestions or suggest alternative methods. Furthermore, once the user approves the suggestion, the server automatically arranges disposal, for instance, by listing it on an e-commerce platform or contacting a waste disposal company.

[0755] An example of a prompt might be: "Explain how the emotion engine evaluates the emotional value a user has of a particular item, and suggest ways to dispose of items that evoke positive emotions."

[0756] The overall goal of this system is to enhance satisfaction and convenience in disposing of items by providing a service that reflects the user's emotions.

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] The user launches a dedicated application on their device and uses the camera to take pictures of the room. The image data and audio captured by the user are used as input. The device collects the user's facial expression data and audio data via the camera and microphone and sends this to the emotion engine. Specifically, it uses facial recognition technology to extract the features of the user's facial expressions and voice analysis to identify the features of their speech.

[0760] Step 2:

[0761] The device uses an emotion engine to analyze the user's emotional state and generate emotion data. The inputs used are the facial expression data and voice data extracted in step 1. The emotion engine uses a generative AI model to assign emotion labels (e.g., positive, negative, neutral). As a result, data indicating the user's emotional state is output.

[0762] Step 3:

[0763] The terminal sends image data and emotion data to the server. The output data from steps 1 and 2 is used as input. The server prepares to receive and analyze this data. Specifically, it packets the data via network communication and transfers it to the server.

[0764] Step 4:

[0765] The server uses image analysis software to analyze the received image data. Image data sent from the terminal is used as input. The server utilizes machine learning algorithms to identify objects within the image and extract information about each object. The output is the identified object information. Specifically, the server profiles the characteristics of each object and records them in a database.

[0766] Step 5:

[0767] The server selects unwanted items using identified item information and user sentiment data. The inputs used are the sentiment data generated in step 2 and the item information obtained in step 4. During the process, the sentiment engine evaluates the user's emotional value to the items and incorporates this into the selection criteria. The output is a list of unwanted items. Specifically, the server scores the importance of items based on past usage history and sentiment evaluations.

[0768] Step 6:

[0769] The server sets disposal priorities for unwanted items, taking into account the user's emotions. The input is the list of unwanted items obtained in step 5. The emotion engine analyzes the user's emotions towards the items and flexibly adjusts the priorities. The output is a disposal list with the priorities set. Specifically, items with certain emotional value are set to a low priority.

[0770] Step 7:

[0771] The server uses a suggestion mechanism to present the user with the most suitable disposal method based on a prioritized disposal list. The output data from step 6 is used as input. The emotion engine adjusts the methods and wording to match the user's emotional state. The adapted suggestions are then presented to the user as output. Specifically, this involves displaying information through the user interface and presenting emotionally sensitive options.

[0772] Step 8:

[0773] If the user approves the proposal, the server will initiate an automated process. The user's approval action is used as input. The server will then make appropriate arrangements based on the selected disposal method, such as listing the item on an e-trading platform or contacting a waste disposal company. Specifically, it will call the API of the relevant service, send the necessary information, and complete the transaction.

[0774] (Application Example 2)

[0775] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] Disposing of unwanted items in the home is often a burden for users, and making the right decision is often difficult, especially when dealing with items that evoke emotional attachments. Furthermore, choosing the appropriate order and method of disposal can also be a source of stress. There is a need to solve these problems and provide more personalized and appropriate disposal methods.

[0777] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0778] In this invention, the server includes emotion analysis means for analyzing the user's emotions and adjusting the response based on the analysis results; image analysis means for receiving image data and identifying items based on said image data; and priority adjustment means for adjusting the disposal priority of selected unwanted items, taking into account the user's emotional state. This makes it possible to identify unwanted items that take the user's emotions into consideration and to propose the optimal disposal method.

[0779] An "emotional analysis tool" is a function that analyzes the user's emotions and adjusts the response based on the analysis results.

[0780] "Image analysis means" refers to technology for identifying items based on received image data.

[0781] "Selection method" refers to the process of sorting out unwanted items from among identified items.

[0782] The "priority adjustment mechanism" is a function that takes into account the user's emotional state and adjusts the priority of disposal for selected unwanted items.

[0783] The "disposal suggestion mechanism" is a function that suggests the optimal disposal method for each type of unwanted item, using language that is appropriate to the user's emotions.

[0784] A "disposal arrangement system" is a system that automatically carries out disposal procedures based on the proposed disposal method.

[0785] The system for realizing this invention consists of a user terminal, a server, and a consumer robot. The user first uses a terminal with a dedicated application installed to take a photograph of a specific area within their home. The terminal transmits this image data to the server, and simultaneously analyzes the user's emotions using its camera and microphone, transmitting the emotional data to the server as well. This analysis utilizes an emotion analysis API (e.g., Microsoft Azure Emotion API) and an image analysis system (e.g., Google Cloud Vision API).

[0786] The server identifies items in the room using an item identification algorithm based on the received image data. Then, a selection mechanism selects unwanted items and adjusts disposal priorities by referring to the user's emotional data. At this stage, a machine learning model is used, taking into account the results learned from past data. When proposing the optimal disposal method, the disposal proposal mechanism considers the user's emotional data and makes a proposal using appropriate wording, which is then notified to the user via their terminal or robot.

[0787] For example, if the collection includes a book that the user has cherished for many years, and the emotional analysis detects the user's feelings of sadness, it will suggest postponing or carefully considering the disposal of the book. This entire process enables flexible and stress-free disposal of unwanted items, improving convenience for the user. An example of an input prompt to the generative AI model could be, "Please suggest the optimal method for disposing of unwanted items, taking the user's emotions into consideration."

[0788] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0789] Step 1:

[0790] The user takes pictures of the room using a device with a dedicated application running. The input is image data from the camera, and the output is saved as an image file. At the same time, the device activates the camera and microphone to collect data on the user's facial expressions and voice, which are then provided to the emotion analysis engine.

[0791] Step 2:

[0792] The terminal transmits the acquired image data and user emotional data to the server. The input is a composite data package containing image data and emotional data, and the output is data packets transmitted over the internet. The data is encrypted for privacy protection.

[0793] Step 3:

[0794] The server analyzes the received image data and uses an image analysis API to identify items in the room. The input is encrypted image data, and the output is a list of identified items. The identification process involves database lookups based on the appearance and shape of the items.

[0795] Step 4:

[0796] The server uses a selection mechanism to select unwanted items from the identified items. The input is an item list and past usage history data, and the output is a list of selected unwanted items. A machine learning algorithm makes predictions based on past disposal data.

[0797] Step 5:

[0798] The server adjusts the disposal priority of selected unwanted items, taking into account the user's emotional data. The input is emotional data and a list of unwanted items, and the output is a list of the adjusted disposal priority. Items that the user has a strong attachment to are given a lower priority.

[0799] Step 6:

[0800] The server proposes the optimal disposal method to the user based on the adjusted disposal priority. The input is a priority list, and the output is a proposal message sent to the user's terminal. The disposal proposal is constructed using emotional analysis to ensure it is phrased in a way that minimizes stress for the user.

[0801] Step 7:

[0802] Once the user approves the proposal, the server automatically executes the disposal arrangements. The input is the user's approval data, and the output is the execution data for the specific disposal procedures. Items for resale are arranged to be sent to the trading platform, waste to a contractor, and donated items to the appropriate organization.

[0803] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0804] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0805] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0806] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0807] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0808] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0809] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0810] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0811] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0812] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0813] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0814] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0815] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0816] 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.

[0817] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0818] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0819] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0820] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0821] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0822] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0823] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0824] The following is further disclosed regarding the embodiments described above.

[0825] (Claim 1)

[0826] Image analysis means that receives image data and identifies an item based on said image data,

[0827] A selection means for selecting unwanted items from among the identified items,

[0828] A means for setting the priority of disposal for selected unwanted items,

[0829] A proposal method that suggests the optimal disposal method for each type of unwanted item,

[0830] A disposal arrangement means that automatically arranges disposal based on the proposed disposal method,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, which uses a machine learning algorithm for identifying articles based on image data.

[0834] (Claim 3)

[0835] The system according to claim 1, which includes a method for listing resalable items on an e-commerce platform in the disposal arrangement.

[0836] "Example 1"

[0837] (Claim 1)

[0838] Processing means for receiving image information and identifying an object based on said image information,

[0839] A decision means for selecting unnecessary objects from among the identified objects,

[0840] A prioritization means for setting processing priorities for selected unnecessary items,

[0841] A means of presenting the optimal disposal method for each unnecessary item,

[0842] A means of arranging for the processing to be carried out automatically based on the presented processing method,

[0843] A means of communication for capturing and transmitting image information using a terminal,

[0844] An analytical means that analyzes the features of an object using a machine learning algorithm,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, which uses a machine learning algorithm to identify the type, shape, and size of an object.

[0848] (Claim 3)

[0849] The system according to claim 1, which includes a method for listing resalable objects on an online trading platform in the processing arrangement.

[0850] "Application Example 1"

[0851] (Claim 1)

[0852] An analysis means that receives image data and identifies an item based on said image data,

[0853] A selection means for selecting unwanted items from among the identified items,

[0854] A means for setting the priority of disposal for selected unwanted items,

[0855] A proposal method that suggests the optimal disposal method for each type of unwanted item,

[0856] A means of arranging disposal automatically based on the proposed disposal method,

[0857] A database management means that registers data on identified items and customizes disposal methods based on user data,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which uses a machine learning algorithm to identify items based on image data and optimizes disposal methods using a database based on the identification results.

[0861] (Claim 3)

[0862] The system according to claim 1, which includes a method for listing resalable items on an e-commerce platform in the disposal arrangement, and provides an interface for electronically approving the proposed disposal method.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] An analysis means that receives image data and identifies an item based on said image data,

[0866] An emotion extraction means for extracting emotional states from user voice data and facial expression data,

[0867] A means for selecting unnecessary items from among the identified items, taking into account the user's emotional data,

[0868] A means of setting a priority for disposal of unwanted items selected with consideration for the user's feelings,

[0869] A proposal system that suggests the optimal disposal method for each unwanted item and adjusts the presentation method according to emotional data,

[0870] A means of automatically arranging disposal based on the proposed disposal method,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, wherein the analysis means uses a learning algorithm.

[0874] (Claim 3)

[0875] The system according to claim 1, which includes a method for listing reusable items on an electronic trading platform in the disposal arrangement.

[0876] "Application example 2 when combining with an emotional engine"

[0877] (Claim 1)

[0878] An emotion analysis means that analyzes the user's emotions and adjusts the response based on the analysis results,

[0879] Image analysis means that receives image data and identifies an item based on said image data,

[0880] A selection means for selecting unwanted items from among the identified items,

[0881] A priority adjustment means for selecting unwanted items, which adjusts the priority of disposal considering the user's emotional state,

[0882] A disposal suggestion tool that proposes the optimal disposal method for each unwanted item using language appropriate to the user's emotions,

[0883] A disposal arrangement means that automatically arranges disposal based on the proposed disposal method,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, which uses a machine learning algorithm to identify items based on image data and further takes into account the user's emotional data.

[0887] (Claim 3)

[0888] The system according to claim 1, which includes a method for listing resalable items in an electronic transaction support system when arranging disposal. [Explanation of symbols]

[0889] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An analysis means that receives image data and identifies an item based on said image data, A selection means for selecting unwanted items from among the identified items, A means for setting the priority of disposal for selected unwanted items, A proposal method that suggests the optimal disposal method for each type of unwanted item, A means of arranging disposal automatically based on the proposed disposal method, A database management means that registers data on identified items and customizes disposal methods based on user data, A system that includes this.

2. The system according to claim 1, which uses a machine learning algorithm to identify items based on image data and optimizes disposal methods using a database based on the identification results.

3. The system according to claim 1, which includes a method for listing resalable items on an e-commerce platform in the disposal arrangement, and provides an interface for electronically approving the proposed disposal method.

Images