System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting an image of an object desired to be cleaned by a user; means for analyzing the image by image analysis means to recognize the type of the object and dirt on the object; means for specifying an optimal cleaning method and necessary tools and detergents according to the type of the object and dirt based on a result of the recognition; and means for providing information on the cleaning method, tools and detergents to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Cleaning is a daily task that must be performed, but many users have many problems, such as not knowing the proper cleaning method, the hassle of preparing and purchasing tools and detergents, and the hassle of scrubbing away dirt. Finding an effective method is particularly difficult when it comes to stubborn stains, and this often consumes extra time and effort. Furthermore, cleaning at the end of the year or when moving is large-scale and even more burdensome. Therefore, there is a need for a system that allows users to easily learn the proper cleaning method and necessary preparations, and to efficiently carry out cleaning procedures. [Means for solving the problem]

[0005] The present invention begins when a user takes an image of an object they wish to clean using an input device and uploads it to the system. The image is analyzed by an image analysis device, and the object and the type of dirt on it are recognized. Based on the recognition results, the optimal cleaning method, necessary tools, and detergents for the object and type of dirt are identified. Finally, information on the cleaning method, tools, and detergents is provided to the user. This allows the user to clean efficiently without tedious preparation or trial and error.

[0006] "User" refers to a person who uses the cleaning system to upload an image of the object they want to clean and receive instructions on how to clean it properly.

[0007] "Image" refers to digital data such as a photograph or picture that the user inputs into the terminal in a visually recognizable form of the object they wish to clean and sends to the system.

[0008] "Input means" refers to a device or method for the user to import an image of the object they want to clean into the system, including a camera or smartphone application.

[0009] "Image analysis means" refers to the software algorithms and hardware configuration that allow the system to analyze input images of objects and identify the objects and the type of contamination on them.

[0010] An "object" refers to a specific object or location that the user wants to clean, such as a kitchen gas range or bathroom tiles.

[0011] "Dirt" refers to unnecessary and harmful substances or conditions that adhere to an object, including oil stains, water stains, dust, etc.

[0012] The "recognition means" refers to a process for identifying an object and the type of dirt on it based on the information analyzed by the image analysis means.

[0013] A "cleaning method" refers to a set of steps or processes for effectively removing dirt based on the identified object and type of dirt.

[0014] "Tools" refers to the physical implements or devices used in cleaning, including, for example, sponges, brushes, scrapers, etc.

[0015] "Detergent" refers to chemical cleaning agents used in cleaning, including detergents for oil stains and multi-purpose cleaners.

[0016] "Information" refers to the content provided to the user, including details of the cleaning method identified by the recognition means and the necessary tools and detergents.

[0017] "Providing means" refers to a method or device by which the system communicates the recognition results and information about cleaning methods to the user, and includes screen displays, voice assistants, etc. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method, as well as the necessary tools and detergents. Detailed embodiments of this system will be described below with specific examples.

[0040] System Overview

[0041] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of this analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user.

[0042] Program processing

[0043] Step 1: Upload an image

[0044] The user takes a picture

[0045] The user takes a picture of the object they want to clean (for example, a gas range in the kitchen or tiles in the bathroom) using their smartphone camera.

[0046] The device uploads the image

[0047] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0048] Step 2: Image analysis

[0049] The server receives the image

[0050] The server receives the image data sent from the terminal.

[0051] The server analyzes the image

[0052] The server uses image analysis means (e.g., an object detection model using deep learning) to identify the objects and types of dirt appearing in the image.

[0053] Step 3: Cleaning instructions

[0054] The server retrieves the information from the database

[0055] Based on the analysis results, the server retrieves information from the database regarding the optimal cleaning method, necessary tools and detergents depending on the object and type of dirt.

[0056] The server generates the cleaning instructions.

[0057] The server uses the acquired information to generate effective cleaning instructions, including how to use detergents, tools, and precautions.

[0058] Step 4: Provide information

[0059] The server sends the results to the device

[0060] The server sends the generated cleaning instructions and a list of necessary tools and detergents to the terminal.

[0061] The terminal displays the results

[0062] The device displays the information received from the server to the user. For example, the device screen may display instructions such as "Spray oil-stain detergent on the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[0063] Specific examples

[0064] Consider a case where a user wants to clean stubborn grease stains from a gas range in the kitchen.

[0065] 1. Take and upload an image

[0066] Users take a photo of a dirty gas range with their smartphone and upload it to the system.

[0067] 2. Image Analysis

[0068] The server identifies the gas range and oil stains using image analysis means.

[0069] 3. Cleaning instructions

[0070] The server retrieves the cleaning method appropriate for the oil stain from the database and recognizes that detergent and a scraper are required.

[0071] 4. Provision of Information

[0072] The server generates specific instructions such as "spray oil stain detergent, leave for 10 minutes, then remove the stain with a scraper and wipe with a sponge" and sends them to the terminal.

[0073] 5. Display results

[0074] The device displays instructions to the user, who then follows the instructions to clean. This allows users to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[0075] In this way, the system allows users to easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] The user takes a picture.

[0079] The user takes a picture of the object they want to clean (e.g., the gas range in the kitchen) using their smartphone camera.

[0080] Step 2:

[0081] The device uploads the image.

[0082] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0083] Step 3:

[0084] The server receives the image.

[0085] The server receives the image data sent from the terminal and prepares for analysis.

[0086] Step 4:

[0087] The server analyzes the image.

[0088] The server uses image analysis algorithms (e.g., deep learning-based object detection models) to identify objects in the image and the type of dirt on them, for example, recognizing grease stains on a gas range.

[0089] Step 5:

[0090] The server retrieves the information from the database.

[0091] Based on the analysis results, the server retrieves information from its internal database regarding cleaning methods, necessary tools, and detergents according to the type of object and dirt.

[0092] Step 6:

[0093] The server generates the cleaning procedure.

[0094] The server generates effective cleaning instructions based on the acquired information. These instructions include how to use detergents, tools, and precautions. For example, it generates specific instructions such as "spray oil-stain detergent on the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[0095] Step 7:

[0096] The server sends the results to the terminal.

[0097] The server sends the generated cleaning procedure and a list of necessary tools and detergents to the terminal.

[0098] Step 8:

[0099] The terminal displays the results.

[0100] The terminal displays the information received from the server to the user, for example, by displaying a list of cleaning procedures and the necessary tools and detergents on the terminal screen.

[0101] Step 9:

[0102] The user follows the instructions to clean.

[0103] The user prepares the necessary tools and detergents based on the instructions displayed on the terminal and performs effective cleaning.

[0104] In this way, the present system helps the user to easily acquire an appropriate cleaning method and perform effective cleaning through a series of steps.

[0105] Example 1

[0106] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0107] Conventional cleaning methods rely heavily on experience and intuition, making it difficult to select the appropriate procedures, tools, and detergents. Furthermore, not knowing effective cleaning methods can lead to wasted time and effort. Furthermore, there are concerns about the handling of image data due to privacy concerns. Therefore, there is a need for a system that allows users to easily obtain appropriate cleaning methods and clean effectively while protecting their privacy.

[0108] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0109] In this invention, the server includes means for inputting an image of an object that a user wants to clean, means for analyzing the image using image analysis means to recognize the object and the type of dirt on it, means for identifying the optimal cleaning method and necessary tools and detergent for the object and the type of dirt based on the recognition results, means for providing the user with information about the cleaning method, tools, and detergent, means for encrypting image data uploaded from the terminal to protect the user's privacy, and means for retrieving the optimal cleaning method based on the analysis from a database and generating an effective cleaning procedure. This allows the user to easily obtain an appropriate and effective cleaning method, significantly reducing the effort and time required for cleaning while protecting their privacy.

[0110] "User" refers to an individual who uses this system to receive cleaning method suggestions.

[0111] "Device" refers to an electronic device that a user uses to take images and upload them to the system, such as a smartphone or tablet.

[0112] "Server" refers to a computer system that analyzes images sent by users and performs the data processing necessary to provide appropriate cleaning methods.

[0113] "Image analysis means" refers to an algorithm executed on the server that utilizes deep learning to identify objects and stains within an image.

[0114] The "recognition result" refers to information about the object and the type of dirt on it obtained by the image analysis means.

[0115] "Cleaning method" refers to the optimal cleaning procedure identified based on the recognition results, including specific methods for using detergents and tools.

[0116] "Tools" refer to items needed to carry out the cleaning method, such as scrapers and sponges.

[0117] "Detergent" refers to a chemical product used to remove specific stains.

[0118] "Database" refers to a digital storage device that stores information about cleaning methods, tools, and detergents.

[0119] "Means for providing information" refers to the mechanism for transmitting and displaying the cleaning instructions and list of required tools and detergents generated by the server to the user's device.

[0120] "Encryption" is a technology for protecting communication data, and refers to the process of making the contents incomprehensible to anyone other than the recipient.

[0121] "Protecting privacy" refers to a series of measures to prevent users' personal information from being leaked to third parties.

[0122] An "effective cleaning procedure" is one that is designed to achieve maximum effectiveness with minimum effort and time.

[0123] This invention is a comprehensive cleaning support system that allows a user to input an image of an object they wish to clean, and a server analyzes the image and suggests an appropriate cleaning method, as well as the necessary tools and detergents. This system starts when a user takes an image of the object using a device such as a smartphone or tablet and uploads the image data to the server.

[0124] Hardware and software used

[0125] The main hardware used in this invention includes a user's device such as a smartphone or tablet, and a server consisting of a high-performance computer. The software used is an object detection model based on deep learning for image analysis. Specifically, machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch are used.

[0126] Specific data processing and calculation

[0127] When a user takes a picture of an object they wish to clean with their device, the device uploads the image data to a server. The uploaded image data is encrypted to protect the user's privacy. When the server receives the image data, it uses image analysis means to identify the object in the image and the type of dirt. Based on the analysis results, the server then retrieves information from a database about the optimal cleaning method, as well as the necessary tools and detergents.

[0128] Based on the acquired information, the server generates effective cleaning instructions. For example, if the analysis results indicate "grease stains on the gas range," the server will create specific instructions such as "spray oil-removing detergent, leave for 10 minutes, then remove the stains with a scraper and wipe with a sponge." The generated cleaning instructions are sent from the server to the device and displayed to the user.

[0129] Specific examples

[0130] Take the example of a user wanting to clean stubborn grease stains from a gas range in the kitchen.

[0131] 1. Take and upload an image

[0132] Users take a photo of a dirty gas range with their smartphone and upload it to the system, where the image is encrypted and sent from the device to a server.

[0133] 2. Image Analysis

[0134] The server uses a deep learning model (e.g., using TensorFlow or PyTorch) to identify the gas range and its oil stains.

[0135] 3. Cleaning instructions

[0136] The server retrieves the cleaning method appropriate for the oil stain from the database and recognizes that detergent and a scraper are required.

[0137] 4. Provision of Information

[0138] The server generates specific instructions such as "spray oil stain detergent, leave for 10 minutes, then remove the stain with a scraper and wipe with a sponge" and sends them to the terminal.

[0139] 5. Display results

[0140] The device displays instructions to the user, who then follows the instructions to clean. This allows users to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[0141] Prompt Sentence Examples

[0142] The following are examples of specific prompt sentences:

[0143] "Please analyze the image of the dirty gas range and tell me how to clean it effectively. Also, what tools and cleaning agents are needed?"

[0144] In this way, the system allows users to easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1:

[0147] The user takes a picture of the object

[0148] The user uses the camera on their smartphone or tablet to take a picture of the object they want to clean (e.g., kitchen stove, bathroom tiles, etc.). The input is an image of the area that needs cleaning. This image is the data that will be used for further processing. The output is the captured image file.

[0149] Step 2:

[0150] The device uploads the image to the system

[0151] The user uploads the image they have taken to a dedicated application or website. The image taken by the user is given as input. The device uses an Internet connection to send the image data to a server. At this time, the image data is encrypted to protect the user's privacy. The output is the encrypted image data sent to the server.

[0152] Step 3:

[0153] The server receives the image

[0154] The server receives the encrypted image data sent from the terminal. As input, it has the encrypted image data sent from the terminal. The server decrypts this data and verifies the integrity of the image data. The output is the decrypted image data.

[0155] Step 4:

[0156] The server analyzes the image

[0157] The server uses a deep learning-based object detection model (e.g., using TensorFlow or PyTorch) to identify objects and their stain types in the image. As input, it receives the decoded image data. Advanced image recognition algorithms are used to analyze the data. The output is a recognition result of the object and stain type.

[0158] Step 5:

[0159] The server retrieves the information from the database

[0160] Based on the results of image analysis, the server retrieves information from the database about the optimal cleaning method for the object and type of dirt, as well as the necessary tools and detergents. The input is the recognition results of image analysis. The server sends a query to the database to retrieve the necessary information. The output is information about the optimal cleaning method and the necessary tools and detergents.

[0161] Step 6:

[0162] The server generates the cleaning instructions.

[0163] The server generates specific and effective cleaning procedures based on the acquired information. For example, if the analysis results indicate "grease stains on the gas range," it generates the procedure "spray oil stain detergent, leave for 10 minutes, then remove the stains with a scraper and wipe with a sponge." The input is the cleaning method and information on tools and detergents acquired from the database. The output is the generated specific cleaning procedure.

[0164] Step 7:

[0165] The server sends the results to the device

[0166] The server sends the generated cleaning instructions and a list of the necessary tools and detergents to the terminal. The input is the specific cleaning instructions and information on the tools and detergents. The server encrypts this information and sends it to the terminal. The output is the encrypted result data sent to the terminal.

[0167] Step 8:

[0168] The terminal displays the results

[0169] The terminal decrypts the information received from the server and displays it to the user. The input is the encrypted result data sent from the server. The terminal decrypts the data and displays it in a format that is easy for the user to understand. The output is specific cleaning steps and information on the necessary tools and detergents that the user can see. This allows the user to clean according to the provided steps.

[0170] (Application example 1)

[0171] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0172] Today's cleaning tasks are diverse, and users often spend a lot of time and effort selecting the appropriate cleaning method, tools, and detergents. Furthermore, in commercial facilities and brick-and-mortar stores, efficient and effective cleaning is essential, but the specific methods and procedures for achieving this are often unclear. To address these challenges, there is a need for a comprehensive cleaning support system that allows users to easily input an image of the object to be cleaned, and the system will suggest the appropriate cleaning method and the necessary tools and detergents.

[0173] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0174] In this invention, the server includes means for inputting an image of an object that a user wants to clean, means for analyzing the image using image analysis means to recognize the object and the type of dirt on it, means for identifying the optimal cleaning method and necessary tools and detergents according to the object and the type of dirt based on the recognition results, means for providing information about the cleaning method, tools, and detergents to the user, and means for using an electronic device to display the results. This allows the user to quickly and accurately obtain information about the optimal cleaning method and necessary tools and detergents simply by inputting an image.

[0175] A "user" is a person who inputs an image of an object to be cleaned or a user of the system.

[0176] An "object" is a specific object or location that a user wishes to clean.

[0177] An "image" is visual data that is captured or uploaded by a user using an input means.

[0178] "Image analysis means" refers to technology or algorithms for analyzing input images and recognizing the type of object and dirt.

[0179] The "recognition result" is information about the object and the type of dirt obtained by the image analysis means.

[0180] The "cleaning method" refers to the cleaning procedure or technique that is determined based on the recognition results and is appropriate for the type of object and dirt.

[0181] "Tools" are the tools and equipment needed to carry out the cleaning method.

[0182] "Detergents" are cleaning agents or chemicals used in carrying out cleaning methods.

[0183] "Means for providing information" refers to a method or device for informing or displaying information to the user about appropriate cleaning methods, tools, and detergents.

[0184] "Electronic devices" are terminals or devices used to display cleaning method, tool and detergent information to users.

[0185] The present invention is a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method and the necessary tools and detergents. Detailed embodiments for carrying out the present invention will be described below with specific examples.

[0186] System Overview

[0187] This system allows users to upload images of the object they wish to clean from a device (such as a smartphone or tablet), which is then analyzed on a server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. The main hardware used is a smartphone camera, server, and electronic device (device), while the software uses deep learning models, image recognition algorithms, and libraries for processing HTTP requests (such as Python requests).

[0188] Program processing

[0189] The program performs the following data processing and calculations:

[0190] 1. User takes a picture:

[0191] The user uses the smartphone camera to take a picture of the object they want to clean, such as a glass window or shelf in a store.

[0192] 2. The device uploads the image:

[0193] The user uploads the image they took to a system application or website. The device sends the image data to the server. The image data is uploaded to the API server using an HTTP POST request.

[0194] 3. The server analyzes the image:

[0195] The server analyzes the received image data using an image recognition algorithm powered by deep learning, which identifies the object and type of dirt. The deep learning model uses a generative AI model trained for object detection and classification.

[0196] 4. Identify cleaning methods based on the type of soiling:

[0197] Based on the recognition results, the server retrieves information from the database about the optimal cleaning method, necessary tools, and detergents for each type of object and dirt. The cleaning procedure includes how to use the detergent and tools, as well as precautions.

[0198] 5. Provision of Information:

[0199] The server then provides the user with the information about cleaning methods, tools, and detergents. The user then cleans by referring to the instructions displayed on the device screen. For example, the specific instructions displayed might be, "Spray oil-repellent detergent onto the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[0200] Specific examples

[0201] Consider a case where a user wants to clean stubborn stains on a glass window in a brick-and-mortar store. The user takes a photo of the dirty glass window with their smartphone and uploads the image to the system. The server uses image analysis to identify the glass window and the stain, and generates information on the appropriate cleaning method, tools, and detergents, and provides it to the user.

[0202] Example prompt sentence:

[0203] "I would like the glass windows of my store cleaned. Please analyze this image and suggest the best cleaning method, tools, and detergents needed."

[0204] This allows users to perform cleaning tasks efficiently and effectively. Through this system, users can easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] The user takes a picture

[0208] The user uses the smartphone camera to take an image of the object they want to clean (e.g., a glass window or shelf in a store). The input is the image data captured by the camera, and the output is an image file stored on the smartphone. This image file is used in subsequent processing steps.

[0209] Step 2:

[0210] The device uploads the image

[0211] The user takes a photo and uploads it to a system application or website. The input is the image file taken earlier, and the application sends the image data to the server using an HTTP POST request. The output is the status of the completion of sending the image data to the server.

[0212] Step 3:

[0213] The server receives the image

[0214] The server receives image data sent from the terminal. The input is an HTTP request from the terminal, and the server processes it to obtain the image data. The output is image data stored in a temporary file system on the server.

[0215] Step 4:

[0216] The server analyzes the image

[0217] The server analyzes the received image data. This step uses an image recognition algorithm that utilizes deep learning. The input is the image data stored on the server, and a generative AI model is used to recognize the object and type of dirt. The output is data on the object and type of dirt as the recognition result.

[0218] Step 5:

[0219] Identifying cleaning methods according to the type of dirt

[0220] The server retrieves information about the optimal cleaning method and the necessary tools and detergents from a database based on the recognition results. The input is the data about the recognition results, and the server uses this to execute a database query. The output is the retrieved information about the cleaning method and the necessary tools and detergents.

[0221] Step 6:

[0222] Providing information

[0223] The server organizes the acquired information about cleaning methods, tools, and detergents, and generates data to provide to the user. The input is information about cleaning methods, tools, and detergents, and the output is specific instruction data to display to the user.

[0224] Step 7:

[0225] The terminal displays the results

[0226] The terminal receives the result data sent from the server and displays it to the user. The input is the result data from the server, which the application visualizes and presents to the user. The output is the specific cleaning procedure and information on the necessary tools and detergents displayed on the terminal screen.

[0227] This allows the user to obtain the information necessary to perform cleaning work efficiently and effectively through a series of processing steps.

[0228] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0229] This invention combines a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method and the necessary tools and detergents, with an emotion engine that recognizes the user's emotions. The emotion engine adjusts the cleaning method according to the user's emotional state and provides encouragement as needed, making cleaning work more comfortable and effective.

[0230] System Overview

[0231] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. It also combines an emotion engine that recognizes emotions from the user's facial images, and adjusts the cleaning method suggestions according to the user's emotional state.

[0232] Program processing

[0233] Step 1: Upload an image

[0234] The user takes a picture

[0235] The user takes an image of the object they want to clean (for example, a gas range in the kitchen) using the smartphone camera.

[0236] The device uploads the image

[0237] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0238] Step 2: Image analysis

[0239] The server receives the image

[0240] The server receives the image data sent from the terminal.

[0241] The server analyzes the image

[0242] The server uses image analysis techniques (e.g., object detection models using deep learning) to identify objects in the image and the type of dirt on them, for example, recognizing oil stains on a gas range.

[0243] Step 3: Emotion recognition with the emotion engine

[0244] Get a user's face image

[0245] The device takes a picture of the user's face with a camera and sends it to the server.

[0246] The server recognizes emotions using an emotion engine

[0247] The server uses an emotion engine to analyze the user's facial image and determine the user's current emotional state (e.g., stress, joy, fatigue).

[0248] Step 4: Cleaning instructions

[0249] The server retrieves the information from the database

[0250] Based on the analysis results, the server retrieves information from its internal database regarding the optimal cleaning method, necessary tools and detergents depending on the object and type of dirt.

[0251] The server generates cleaning procedures taking into account emotional information.

[0252] The server takes the user's emotional state into account when generating effective cleaning instructions based on the information it has acquired. For example, if the user is feeling stressed, it will suggest simple and effective cleaning methods. Another feature is that it can also display encouraging messages.

[0253] Step 5: Provide information

[0254] The server sends the results to the device

[0255] The server transmits the generated cleaning procedure and a list of necessary tools and detergents to the terminal.

[0256] The terminal displays the results

[0257] The terminal displays the information received from the server to the user.

[0258] Specific examples

[0259] If a user wants to clean stubborn grease stains from their kitchen gas range:

[0260] 1. Take and upload an image

[0261] Users take a photo of a dirty gas range with their smartphone and upload it to the system.

[0262] 2. Image Analysis

[0263] The server identifies the gas range and oil stains using image analysis means.

[0264] 3. Emotion recognition

[0265] The server analyzes the user's facial image and recognizes that the user is feeling stressed.

[0266] 4. Cleaning instructions

[0267] The server retrieves cleaning methods suitable for oil stains from the database and generates simple and effective cleaning instructions taking into account the user's emotional state, including encouraging messages.

[0268] 5. Provision of Information

[0269] The server generates specific instructions such as "Spray the oil stain detergent, leave it for 10 minutes, then remove the dirt with a scraper and wipe it off with a sponge," along with an encouraging message such as "Keep up the good work, you're almost there!" and sends it to the device.

[0270] 6. Results display

[0271] The device provides instructions and encouraging messages to the user, who then follows the instructions to clean, allowing them to complete the cleaning efficiently without the need for tedious preparations or trying methods whose effectiveness is unknown.

[0272] In this way, the system takes into account the user's emotional state, helping them to clean more comfortably and effectively.

[0273] The processing flow will be explained below.

[0274] Step 1:

[0275] The user takes an image of the object to be cleaned.

[0276] The user takes a picture of the object they want to clean (e.g., the gas range in the kitchen) using their smartphone camera.

[0277] Step 2:

[0278] The device uploads the image.

[0279] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0280] Step 3:

[0281] The server receives the image.

[0282] The server receives the image data sent from the terminal and prepares for analysis.

[0283] Step 4:

[0284] The server analyzes the image.

[0285] The server uses image analysis algorithms (e.g., deep learning-based object detection models) to identify objects in the image and the type of dirt on them, for example, recognizing grease stains on a gas range.

[0286] Step 5:

[0287] The terminal takes a picture of the user's face.

[0288] To analyze the user's facial expression, the device's camera is used to capture an image of the user's face.

[0289] Step 6:

[0290] The device sends the facial image to the server.

[0291] The terminal transmits the captured image data of the user's face to the server.

[0292] Step 7:

[0293] The server analyzes the facial image.

[0294] The server uses an emotion engine to analyze the user's facial image and recognize the user's emotional state (for example, whether they are feeling stressed or happy).

[0295] Step 8:

[0296] The server retrieves the information from the database.

[0297] Based on the results of image analysis and emotion recognition, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents depending on the object and type of dirt.

[0298] Step 9:

[0299] The server generates the cleaning procedure.

[0300] The server generates effective cleaning instructions based on information retrieved from the database, including instructions on how to use cleaning products, tools, and precautions, and may also include emotional adjustments and encouraging messages based on the user's emotional state.

[0301] Step 10:

[0302] The server sends the results to the terminal.

[0303] The server sends the generated cleaning instructions, a list of necessary tools and detergents, and a message based on the user's emotion to the device.

[0304] Step 11:

[0305] The terminal displays the results.

[0306] The device displays the information received from the server to the user, such as "Spray the detergent for grease stains, leave it for 10 minutes, then remove the stains with a scraper and wipe it off with a sponge," along with an encouraging message such as "Keep up the good work, you're almost there!"

[0307] Step 12:

[0308] The user follows the instructions to clean.

[0309] Users can prepare the necessary tools and detergents and carry out effective cleaning according to the instructions displayed on the device, allowing them to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[0310] In this way, this system allows users to easily acquire appropriate cleaning methods through a series of steps, and by taking their emotional state into consideration, it reduces the burden of cleaning and supports comfortable work.

[0311] Example 2

[0312] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0313] Conventional cleaning support systems only analyze images of the object the user wants to clean and provide the optimal cleaning method and necessary tools and detergents, but they are unable to take the user's emotional state into consideration, which can make cleaning tasks stressful. Furthermore, because the system does not respond individually to the user's emotions, it is difficult to maintain motivation to clean.

[0314] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting an image of an object that the user wants to clean, a means for analyzing the image using an image analysis means and recognizing the object and the type of dirt on it, a means for acquiring an image of the user's face and recognizing their emotions, a means for identifying an optimal cleaning method and necessary tools and detergents according to the object and the type of dirt based on the recognition results, a means for adjusting the cleaning method according to the user's emotional state and generating an encouraging message as necessary, and a means for providing the user with information about the cleaning method, tools, and detergents. This allows the system to suggest an optimal cleaning method taking the user's emotional state into consideration, enabling the user to perform cleaning work comfortably and effectively.

[0315] "User" refers to an individual who uses the system to receive cleaning support.

[0316] "Object desired to be cleaned" refers to an object or surface that the user wishes to clean.

[0317] "Image" refers to a photograph, picture, or the like that contains visual information about the object that the user wants to clean.

[0318] "Image analysis means" refers to algorithms and techniques that process image data and identify the object and the type of dirt on it.

[0319] The term "target object" refers to an object that is recognized by the image analysis means and that is desired to be cleaned.

[0320] "Soil type" refers to the type of foreign matter or contamination present on the surface of the object.

[0321] "Optimal cleaning method" refers to the most effective and efficient cleaning procedure for the object and type of soil.

[0322] "Tools" refers to the equipment and implements required to carry out the cleaning.

[0323] "Detergent" refers to chemicals or liquid cleaning agents used in cleaning.

[0324] "User's facial image" refers to visual data that captures the user's facial features.

[0325] "Means for recognizing emotions" refers to technologies and algorithms that analyze a user's facial image and estimate their emotional state.

[0326] An "emotional state" refers to the psychological state (e.g., stress, joy, fatigue, etc.) that a user is feeling at a particular point in time.

[0327] "Encouraging messages" refer to text messages that are suggested based on the user's emotional state to improve motivation and support.

[0328] "Means for providing information" refers to the means or method by which the server communicates the analysis results and optimal cleaning methods to the user.

[0329] This invention combines a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, and the system analyzes the image to suggest an appropriate cleaning method and the necessary tools and detergents, with an emotion engine that recognizes the user's emotions. The emotion engine adjusts the cleaning method according to the user's emotional state and provides encouragement as needed, making cleaning work more comfortable and effective.

[0330] System Overview

[0331] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. It also combines an emotion engine that recognizes emotions from the user's facial images, and adjusts the cleaning method suggestions according to the user's emotional state.

[0332] Hardware and software used

[0333] Device: A device such as a smartphone or tablet that has a camera function. Use a dedicated application or web browser.

[0334] Server: A high-performance computing resource operated in a cloud environment or on-premise, containing software to run databases, deep learning models, emotion engines, etc.

[0335] Image analysis method: An object detection model using deep learning (e.g., YOLO, ResNet) is installed on the server.

[0336] Emotion engine: An emotion recognition model that utilizes deep learning (e.g., FaceNet, VGGFace) is installed on the server.

[0337] Specific actions

[0338] 1. The user takes a picture of the object they want to clean (e.g., a kitchen gas range) using their smartphone camera, and then uploads the image to a server via a dedicated application or website.

[0339] 2. The server receives the uploaded image and analyzes it using a deep learning-based object detection model to identify the object (e.g., gas range) and the type of dirt (e.g., grease stains).

[0340] 3. Next, the device takes a picture of the user's face and sends it back to the server. The server uses an emotion engine to analyze the face image and recognize the user's emotional state (e.g., stress, joy, fatigue).

[0341] 4. Based on the results of image analysis and emotion recognition, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents, and generates specific and effective cleaning instructions while taking into account the user's emotional state.

[0342] 5. If the user feels stressed, the robot will generate an encouraging message to help the user clean the room comfortably.

[0343] 6. Finally, the server sends the generated cleaning instructions, a list of necessary tools and detergents, and an encouraging message to the user's device, which then displays the information on its screen.

[0344] Specific examples

[0345] If a user wants to clean stubborn grease stains from a gas range in the kitchen, the process works as follows.

[0346] 1. A user takes a photo of a dirty gas range with their smartphone and uploads it to the system.

[0347] 2. The server uses deep learning to identify the gas range and oil stains in the image.

[0348] 3. The device sends the facial image to the server, which then performs emotion recognition and determines that the user is feeling stressed.

[0349] 4. The server retrieves cleaning methods suitable for the oil stain from the database and generates simple and effective cleaning instructions taking into account the user's emotional state, as well as encouraging messages such as "Keep up the good work, you're almost there!"

[0350] 5. The server sends specific instructions and an encouraging message to the device, such as "Spray the detergent for grease stains, leave it for 10 minutes, then remove the stains with a scraper and wipe it off with a sponge."

[0351] 6. The device presents this information to the user, who then follows the instructions to clean efficiently.

[0352] Example prompt sentence:

[0353] "To clean stubborn grease stains, first spray on the surface with a grease cleaner. Let it sit for 10 minutes, then use a scraper to remove the stain and wipe with a sponge. Keep going, you're almost there!"

[0354] In this way, the system takes the user's emotional state into consideration to help make cleaning more comfortable and effective.

[0355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0356] Step 1:

[0357] The user takes a picture.

[0358] The user takes a picture of the object they want to clean (for example, a kitchen gas range) with their smartphone camera. The input is the image of the object to be cleaned, and the output is the image data captured by the camera. Specifically, the user captures the image of the object by pressing the "take a picture" button.

[0359] Step 2:

[0360] The device uploads the image.

[0361] The user uploads the image they have taken to a dedicated application or website. The device then sends the image data to the server. The input is the image data taken by the camera, and the output is the image data sent to the server. Specifically, the user presses the "Upload" button, and the progress is displayed until the image data is sent to the server.

[0362] Step 3:

[0363] The server receives the image.

[0364] The server receives image data sent from the terminal. The input is the image data sent from the terminal, and the output is the image data temporarily stored on the server. Specifically, the server receives image data through an HTTP request and temporarily stores it.

[0365] Step 4:

[0366] The server analyzes the image.

[0367] The server uses a deep learning-based object detection model (e.g., YOLO or ResNet) to identify the object and the type of dirt in the image. The input is the received image data, and the output is the analysis result indicating the object and the type of dirt. Specifically, the image data is input into the image analysis model, and information on the type of object and dirt is extracted.

[0368] Step 5:

[0369] The terminal takes a picture of the user's face and sends it to the server.

[0370] The device takes a picture of the user's face with a camera and sends it to the server. The input is the user's face image, and the output is the face image data sent to the server. Specifically, when the user presses the "face recognition" button, the camera automatically takes a face image and sends the data to the server.

[0371] Step 6:

[0372] The server recognizes emotions using an emotion engine.

[0373] The server uses an emotion engine to analyze the user's facial image and identify the user's emotional state (e.g., stress, joy, fatigue). The input is the transmitted facial image data, and the output is an analysis result indicating the user's emotional state. Specifically, the facial image is input into an emotion recognition model, and the emotional state is estimated.

[0374] Step 7:

[0375] The server retrieves the information from the database.

[0376] Based on the results of image analysis, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents for the object and type of dirt. The input is the image analysis results, and the output is information about the optimal cleaning method and necessary tools and detergents. Specifically, it uses an SQL query to search for records of cleaning methods that correspond to the type of dirt.

[0377] Step 8:

[0378] The server generates cleaning procedures taking into account emotional information.

[0379] The server takes the user's emotional state into account when generating cleaning instructions based on the acquired information. The input is the acquired cleaning method and emotion recognition results, and the output is an adjusted cleaning procedure and an encouraging message. Specifically, it selects steps tagged as "easy" and adds a text message.

[0380] Step 9:

[0381] The server sends the results to the terminal.

[0382] The server sends the generated cleaning instructions and a list of the required tools and detergents to the terminal. The input is the adjusted cleaning instructions and information on the required tools and detergents, and the output is the data to be sent to the terminal. Specifically, the results are packaged in JSON format and sent to the terminal as an HTTP response.

[0383] Step 10:

[0384] The terminal displays the results.

[0385] The terminal displays the information received from the server to the user. The input is the data sent from the server, and the output is the displayed cleaning procedure, tool list, detergent information, and encouraging messages. Specifically, this information is displayed on the UI of the dedicated application.

[0386] (Application example 2)

[0387] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0388] In conventional self-driving vehicles, cleaning the interior of the vehicle is a time-consuming task, and it is difficult to determine the optimal cleaning method. Furthermore, cleaning methods are suggested without taking into account the emotional state of the driver or passengers, which can place a heavy burden on users and cause stress. Especially after a long drive or on the way home from work, users often do not have the energy to clean. This makes it difficult to maintain the interior environment, resulting in a decrease in user comfort.

[0389] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0390] In this invention, the server includes: means for inputting an image of an object that a user wants to clean; means for analyzing the image using image analysis means and recognizing the object and the type of dirt on it; means for identifying an optimal cleaning method and necessary tools and detergents according to the recognition results based on the object and the type of dirt; means for providing the user with information about the cleaning method, tools, and detergents; means for periodically taking and transmitting images of the interior of the autonomous vehicle using a camera installed in the autonomous vehicle; means for acquiring facial images of the driver and passengers and performing emotion analysis on them; means for generating a cleaning procedure based on the analysis results including the acquired emotion information and providing an encouraging message to the user; and means for displaying the information on an in-vehicle display. This enables the interior of the autonomous vehicle to be cleaned effectively and without burdening the user.

[0391] The "means for inputting an image of an object that the user wishes to clean" is an interface that allows the user to take an image of the object to be cleaned and upload it to the system.

[0392] "Image analysis means" refers to the technology or algorithms used to analyze the transmitted image and identify the type of object or dirt in the image.

[0393] "Means for identifying the optimal cleaning method and the necessary tools and detergents" refers to a process or mechanism for determining the optimal cleaning method and the necessary tools and detergents based on the results of image analysis.

[0394] The "means for providing the user with information on cleaning methods, tools, and detergents" is a function that displays or notifies the user of the optimal cleaning method, necessary tools, and detergents based on the analysis results.

[0395] "Autonomous vehicle-mounted camera" means a camera mounted inside or outside an autonomous vehicle that monitors and analyzes the environment as part of the autonomous driving system.

[0396] "Means for acquiring facial images and performing emotion analysis" refers to technologies and systems for capturing a user's facial image and analyzing and identifying the user's emotional state from the captured image.

[0397] The "means for providing an encouraging message" is a function for generating and displaying a message to cheer up the user in accordance with the analyzed emotional state of the user.

[0398] An "in-vehicle display" is a display device installed inside an autonomous vehicle that serves as a user interface and indicates the vehicle's status.

[0399] This invention is a system for effectively cleaning the environment inside an autonomous vehicle. The user inputs an image of the object they wish to clean, and the system analyzes the image and suggests the optimal cleaning method. Furthermore, by recognizing emotions from facial images, the system adjusts cleaning procedures and provides encouraging messages according to the user's emotional state. The following describes a specific embodiment of this system.

[0400] Hardware and software used

[0401] 1. Hardware:

[0402] Cameras installed in self-driving vehicles: devices that periodically capture images of the interior of the vehicle.

[0403] In-car display: A device for displaying suggested cleaning methods and messages.

[0404] Facial image recognition camera: A device placed inside an autonomous vehicle to capture facial images of the user.

[0405] 2. Software:

[0406] Image analysis method: Using object detection algorithms such as YOLO (You Only Look Once), images of the interior of the vehicle are analyzed to identify the location and type of dirt.

[0407] Emotion engine: Analyzes the user's emotional state from facial images using the emotion analysis functions of Microsoft® Azure® Face API and Amazon Rekognition.

[0408] Processing Description

[0409] 1. Image input and analysis:

[0410] The server receives images of the interior of the autonomous vehicle sent from the camera. Image analysis is used to identify objects in the image and the type of dirt present. Deep learning-based object detection algorithms such as YOLO are used.

[0411] 2. Emotion recognition:

[0412] A facial image recognition camera in an autonomous vehicle captures a user's facial image and sends it to a server, which then uses an emotion engine to analyze and identify the user's emotional state (stress, fatigue, joy, etc.). This uses Microsoft Azure Face API and Amazon Rekognition.

[0413] 3. Generate cleaning procedures:

[0414] Based on the analysis results, the server retrieves information from a database about the optimal cleaning method, necessary tools, and detergents for each type of object and dirt. It also generates cleaning instructions taking into account the user's emotional state and adds encouraging messages.

[0415] 4. Provision of Information:

[0416] The server sends the generated cleaning instructions and encouraging messages to a display in the car, where the user can check the information and follow the instructions to clean.

[0417] Specific examples

[0418] For example, if a user spills a drink, the in-car camera takes a picture of the spill and sends it to the server. The server analyzes the image and identifies the location of the spilled drink. At the same time, it recognizes from the facial image that the user is feeling stressed. As a result, it displays cleaning instructions such as "Quickly wipe up the spilled drink with a paper towel, then use a cleaner" along with an encouraging message such as "You're almost there, keep trying."

[0419] Prompt Sentence Examples

[0420] "Please upload a picture of the interior of the car."

[0421] "Reading face image"

[0422] "For spilled drinks, wipe them up with paper towels and use cleaner."

[0423] "You're feeling stressed right now. I'll suggest some easy cleaning methods."

[0424] "It's almost there, just do your best."

[0425] In this way, the present invention can effectively clean the interior of an autonomous vehicle, reducing the burden on the user and providing a comfortable in-vehicle environment.

[0426] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0427] Step 1:

[0428] The user takes a picture of the object they want to clean. The image is saved on a device such as a smartphone or tablet. The device then uploads the image to the system's application or website, and sends the image data to a server.

[0429] Input: Image of object to be cleaned

[0430] Output: Image data sent to the server

[0431] Step 2:

[0432] The server receives the image data sent from the device. It then uses image analysis techniques (for example, an object detection model using deep learning) to identify the object in the image and the type of dirt. If the object is a kitchen gas range, it will recognize oil stains, etc.

[0433] Input: Received image data

[0434] Output: Analysis results including location and type of contamination

[0435] Step 3:

[0436] A facial image recognition camera in the autonomous vehicle captures a user's facial image, which is then sent to a server, which uses an emotion engine to identify the user's emotional state (e.g., stress, fatigue, joy).

[0437] Input: Face image data

[0438] Output: Analysis results showing the user's emotional state

[0439] Step 4:

[0440] Based on the image analysis results from step 2 and the emotion analysis results from step 3, the server retrieves information from a database about optimal cleaning methods, necessary tools, and detergents. It also generates cleaning instructions taking into account the user's emotional state and adds encouraging messages. For example, if the user is feeling stressed, it generates simple and effective cleaning instructions.

[0441] Input: Image analysis results, emotion analysis results

[0442] Output: Optimal cleaning instructions and encouraging messages

[0443] Step 5:

[0444] The server sends the generated cleaning instructions and encouraging messages to a display in the car, which visually displays this information to the user. The user then follows the suggested cleaning instructions while checking the display.

[0445] Input: Cleaning instructions and encouraging messages

[0446] Output: Information shown on the display

[0447] This allows users to receive optimal cleaning procedures and encouraging messages based on their emotional state, allowing them to clean efficiently.

[0448] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0449] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0450] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0451] [Second embodiment]

[0452] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0453] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0454] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0455] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0456] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0458] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0459] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0460] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0461] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0462] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0463] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0464] The present invention is a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method, as well as the necessary tools and detergents. Detailed embodiments of this system will be described below with specific examples.

[0465] System Overview

[0466] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of this analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user.

[0467] Program processing

[0468] Step 1: Upload an image

[0469] The user takes a picture

[0470] The user takes a picture of the object they want to clean (for example, a gas range in the kitchen or tiles in the bathroom) using their smartphone camera.

[0471] The device uploads the image

[0472] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0473] Step 2: Image analysis

[0474] The server receives the image

[0475] The server receives the image data sent from the terminal.

[0476] The server analyzes the image

[0477] The server uses image analysis means (e.g., an object detection model using deep learning) to identify the objects and types of dirt appearing in the image.

[0478] Step 3: Cleaning instructions

[0479] The server retrieves the information from the database

[0480] Based on the analysis results, the server retrieves information from the database regarding the optimal cleaning method, necessary tools and detergents depending on the object and type of dirt.

[0481] The server generates the cleaning instructions.

[0482] The server uses the acquired information to generate effective cleaning instructions, including how to use detergents, tools, and precautions.

[0483] Step 4: Provide information

[0484] The server sends the results to the device

[0485] The server sends the generated cleaning instructions and a list of necessary tools and detergents to the terminal.

[0486] The terminal displays the results

[0487] The device displays the information received from the server to the user. For example, the device screen may display instructions such as "Spray oil-stain detergent on the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[0488] Specific examples

[0489] Consider a case where a user wants to clean stubborn grease stains from a gas range in the kitchen.

[0490] 1. Take and upload an image

[0491] Users take a photo of a dirty gas range with their smartphone and upload it to the system.

[0492] 2. Image Analysis

[0493] The server identifies the gas range and oil stains using image analysis means.

[0494] 3. Cleaning instructions

[0495] The server retrieves the cleaning method appropriate for the oil stain from the database and recognizes that detergent and a scraper are required.

[0496] 4. Provision of Information

[0497] The server generates specific instructions such as "spray oil stain detergent, leave for 10 minutes, then remove the stain with a scraper and wipe with a sponge" and sends them to the terminal.

[0498] 5. Display results

[0499] The device displays instructions to the user, who then follows the instructions to clean. This allows users to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[0500] In this way, the system allows users to easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[0501] The processing flow will be explained below.

[0502] Step 1:

[0503] The user takes a picture.

[0504] The user takes a picture of the object they want to clean (e.g., the gas range in the kitchen) using their smartphone camera.

[0505] Step 2:

[0506] The device uploads the image.

[0507] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0508] Step 3:

[0509] The server receives the image.

[0510] The server receives the image data sent from the terminal and prepares for analysis.

[0511] Step 4:

[0512] The server analyzes the image.

[0513] The server uses image analysis algorithms (e.g., deep learning-based object detection models) to identify objects in the image and the type of dirt on them, for example, recognizing grease stains on a gas range.

[0514] Step 5:

[0515] The server retrieves the information from the database.

[0516] Based on the analysis results, the server retrieves information from its internal database regarding cleaning methods, necessary tools, and detergents according to the type of object and dirt.

[0517] Step 6:

[0518] The server generates the cleaning procedure.

[0519] The server generates effective cleaning instructions based on the acquired information. These instructions include how to use detergents, tools, and precautions. For example, it generates specific instructions such as "spray oil-stain detergent on the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[0520] Step 7:

[0521] The server sends the results to the terminal.

[0522] The server sends the generated cleaning procedure and a list of necessary tools and detergents to the terminal.

[0523] Step 8:

[0524] The terminal displays the results.

[0525] The terminal displays the information received from the server to the user, for example, by displaying a list of cleaning procedures and the necessary tools and detergents on the terminal screen.

[0526] Step 9:

[0527] The user follows the instructions to clean.

[0528] The user prepares the necessary tools and detergents based on the instructions displayed on the terminal and performs effective cleaning.

[0529] In this way, the present system helps the user to easily acquire an appropriate cleaning method and perform effective cleaning through a series of steps.

[0530] Example 1

[0531] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0532] Conventional cleaning methods rely heavily on experience and intuition, making it difficult to select the appropriate procedures, tools, and detergents. Furthermore, not knowing effective cleaning methods can lead to wasted time and effort. Furthermore, there are concerns about the handling of image data due to privacy concerns. Therefore, there is a need for a system that allows users to easily obtain appropriate cleaning methods and clean effectively while protecting their privacy.

[0533] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0534] In this invention, the server includes means for inputting an image of an object that a user wants to clean, means for analyzing the image using image analysis means to recognize the object and the type of dirt on it, means for identifying the optimal cleaning method and necessary tools and detergent for the object and the type of dirt based on the recognition results, means for providing the user with information about the cleaning method, tools, and detergent, means for encrypting image data uploaded from the terminal to protect the user's privacy, and means for retrieving the optimal cleaning method based on the analysis from a database and generating an effective cleaning procedure. This allows the user to easily obtain an appropriate and effective cleaning method, significantly reducing the effort and time required for cleaning while protecting their privacy.

[0535] "User" refers to an individual who uses this system to receive cleaning method suggestions.

[0536] "Device" refers to an electronic device that a user uses to take images and upload them to the system, such as a smartphone or tablet.

[0537] "Server" refers to a computer system that analyzes images sent by users and performs the data processing necessary to provide appropriate cleaning methods.

[0538] "Image analysis means" refers to an algorithm executed on the server that utilizes deep learning to identify objects and stains within an image.

[0539] The "recognition result" refers to information about the object and the type of dirt on it obtained by the image analysis means.

[0540] "Cleaning method" refers to the optimal cleaning procedure identified based on the recognition results, including specific methods for using detergents and tools.

[0541] "Tools" refer to items needed to carry out the cleaning method, such as scrapers and sponges.

[0542] "Detergent" refers to a chemical product used to remove specific stains.

[0543] "Database" refers to a digital storage device that stores information about cleaning methods, tools, and detergents.

[0544] "Means for providing information" refers to the mechanism for transmitting and displaying the cleaning instructions and list of required tools and detergents generated by the server to the user's device.

[0545] "Encryption" is a technology for protecting communication data, and refers to the process of making the contents incomprehensible to anyone other than the recipient.

[0546] "Protecting privacy" refers to a series of measures to prevent users' personal information from being leaked to third parties.

[0547] An "effective cleaning procedure" is one that is designed to achieve maximum effectiveness with minimum effort and time.

[0548] This invention is a comprehensive cleaning support system that allows a user to input an image of an object they wish to clean, and a server analyzes the image and suggests an appropriate cleaning method, as well as the necessary tools and detergents. This system starts when a user takes an image of the object using a device such as a smartphone or tablet and uploads the image data to the server.

[0549] Hardware and software used

[0550] The main hardware used in this invention includes the user's device, such as a smartphone or tablet, and a server consisting of a high-performance computer. The software used is a deep learning-based object detection model for image analysis. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used.

[0551] Specific data processing and calculation

[0552] When a user takes a picture of an object they wish to clean with their device, the device uploads the image data to a server. The uploaded image data is encrypted to protect the user's privacy. When the server receives the image data, it uses image analysis means to identify the object in the image and the type of dirt. Based on the analysis results, the server then retrieves information from a database about the optimal cleaning method, as well as the necessary tools and detergents.

[0553] Based on the acquired information, the server generates effective cleaning instructions. For example, if the analysis results indicate "grease stains on the gas range," the server will create specific instructions such as "spray oil-removing detergent, leave for 10 minutes, then remove the stains with a scraper and wipe with a sponge." The generated cleaning instructions are sent from the server to the device and displayed to the user.

[0554] Specific examples

[0555] Take the example of a user wanting to clean stubborn grease stains from a gas range in the kitchen.

[0556] 1. Take and upload an image

[0557] Users take a photo of a dirty gas range with their smartphone and upload it to the system, where the image is encrypted and sent from the device to a server.

[0558] 2. Image Analysis

[0559] The server uses a deep learning model (e.g., using TensorFlow or PyTorch) to identify the gas range and its oil stains.

[0560] 3. Cleaning instructions

[0561] The server retrieves the cleaning method appropriate for the oil stain from the database and recognizes that detergent and a scraper are required.

[0562] 4. Provision of Information

[0563] The server generates specific instructions such as "spray oil stain detergent, leave for 10 minutes, then remove the stain with a scraper and wipe with a sponge" and sends them to the terminal.

[0564] 5. Display results

[0565] The device displays instructions to the user, who then follows the instructions to clean. This allows users to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[0566] Prompt Sentence Examples

[0567] The following are examples of specific prompt sentences:

[0568] "Please analyze the image of the dirty gas range and tell me how to clean it effectively. Also, what tools and cleaning agents are needed?"

[0569] In this way, the system allows users to easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[0570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0571] Step 1:

[0572] The user takes a picture of the object

[0573] The user uses the camera on their smartphone or tablet to take a picture of the object they want to clean (e.g., kitchen stove, bathroom tiles, etc.). The input is an image of the area that needs cleaning. This image is the data that will be used for further processing. The output is the captured image file.

[0574] Step 2:

[0575] The device uploads the image to the system

[0576] The user uploads the image they have taken to a dedicated application or website. The image taken by the user is given as input. The device uses an Internet connection to send the image data to a server. At this time, the image data is encrypted to protect the user's privacy. The output is the encrypted image data sent to the server.

[0577] Step 3:

[0578] The server receives the image

[0579] The server receives the encrypted image data sent from the terminal. As input, it has the encrypted image data sent from the terminal. The server decrypts this data and verifies the integrity of the image data. The output is the decrypted image data.

[0580] Step 4:

[0581] The server analyzes the image

[0582] The server uses a deep learning-based object detection model (e.g., using TensorFlow or PyTorch) to identify objects and their stain types in the image. As input, it receives the decoded image data. Advanced image recognition algorithms are used to analyze the data. The output is a recognition result of the object and stain type.

[0583] Step 5:

[0584] The server retrieves the information from the database

[0585] Based on the results of image analysis, the server retrieves information from the database about the optimal cleaning method for the object and type of dirt, as well as the necessary tools and detergents. The input is the recognition results of image analysis. The server sends a query to the database to retrieve the necessary information. The output is information about the optimal cleaning method and the necessary tools and detergents.

[0586] Step 6:

[0587] The server generates the cleaning instructions.

[0588] The server generates specific and effective cleaning procedures based on the acquired information. For example, if the analysis results indicate "grease stains on the gas range," it generates the procedure "spray oil stain detergent, leave for 10 minutes, then remove the stains with a scraper and wipe with a sponge." The input is the cleaning method and information on tools and detergents acquired from the database. The output is the generated specific cleaning procedure.

[0589] Step 7:

[0590] The server sends the results to the device

[0591] The server sends the generated cleaning instructions and a list of the necessary tools and detergents to the terminal. The input is the specific cleaning instructions and information on the tools and detergents. The server encrypts this information and sends it to the terminal. The output is the encrypted result data sent to the terminal.

[0592] Step 8:

[0593] The terminal displays the results

[0594] The terminal decrypts the information received from the server and displays it to the user. The input is the encrypted result data sent from the server. The terminal decrypts the data and displays it in a format that is easy for the user to understand. The output is specific cleaning steps and information on the necessary tools and detergents that the user can see. This allows the user to clean according to the provided steps.

[0595] (Application example 1)

[0596] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0597] Today's cleaning tasks are diverse, and users often spend a lot of time and effort selecting the appropriate cleaning method, tools, and detergents. Furthermore, in commercial facilities and brick-and-mortar stores, efficient and effective cleaning is essential, but the specific methods and procedures for achieving this are often unclear. To address these challenges, there is a need for a comprehensive cleaning support system that allows users to easily input an image of the object to be cleaned, and the system will suggest the appropriate cleaning method and the necessary tools and detergents.

[0598] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0599] In this invention, the server includes means for inputting an image of an object that a user wants to clean, means for analyzing the image using image analysis means to recognize the object and the type of dirt on it, means for identifying the optimal cleaning method and necessary tools and detergents according to the object and the type of dirt based on the recognition results, means for providing information about the cleaning method, tools, and detergents to the user, and means for using an electronic device to display the results. This allows the user to quickly and accurately obtain information about the optimal cleaning method and necessary tools and detergents simply by inputting an image.

[0600] A "user" is a person who inputs an image of an object to be cleaned or a user of the system.

[0601] An "object" is a specific object or location that a user wishes to clean.

[0602] An "image" is visual data that is captured or uploaded by a user using an input means.

[0603] "Image analysis means" refers to technology or algorithms for analyzing input images and recognizing the type of object and dirt.

[0604] The "recognition result" is information about the object and the type of dirt obtained by the image analysis means.

[0605] The "cleaning method" refers to the cleaning procedure or technique that is determined based on the recognition results and is appropriate for the type of object and dirt.

[0606] "Tools" are the tools and equipment needed to carry out the cleaning method.

[0607] "Detergents" are cleaning agents or chemicals used in carrying out cleaning methods.

[0608] "Means for providing information" refers to a method or device for informing or displaying information to the user about appropriate cleaning methods, tools, and detergents.

[0609] "Electronic devices" are terminals or devices used to display cleaning method, tool and detergent information to users.

[0610] The present invention is a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method and the necessary tools and detergents. Detailed embodiments for carrying out the present invention will be described below with specific examples.

[0611] System Overview

[0612] This system allows users to upload images of the object they wish to clean from a device (such as a smartphone or tablet), which is then analyzed on a server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. The main hardware used is a smartphone camera, server, and electronic device (device), while the software uses deep learning models, image recognition algorithms, and libraries for processing HTTP requests (such as Python requests).

[0613] Program processing

[0614] The program performs the following data processing and calculations:

[0615] 1. User takes a picture:

[0616] The user uses the smartphone camera to take a picture of the object they want to clean, such as a glass window or shelf in a store.

[0617] 2. The device uploads the image:

[0618] The user uploads the image they took to a system application or website. The device sends the image data to the server. The image data is uploaded to the API server using an HTTP POST request.

[0619] 3. The server analyzes the image:

[0620] The server analyzes the received image data using an image recognition algorithm powered by deep learning, which identifies the object and type of dirt. The deep learning model uses a generative AI model trained for object detection and classification.

[0621] 4. Identify cleaning methods based on the type of soiling:

[0622] Based on the recognition results, the server retrieves information from the database about the optimal cleaning method, necessary tools, and detergents for each type of object and dirt. The cleaning procedure includes how to use the detergent and tools, as well as precautions.

[0623] 5. Provision of Information:

[0624] The server then provides the user with the information about cleaning methods, tools, and detergents. The user then cleans by referring to the instructions displayed on the device screen. For example, the specific instructions displayed might be, "Spray oil-repellent detergent onto the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[0625] Specific examples

[0626] Consider a case where a user wants to clean stubborn stains on a glass window in a brick-and-mortar store. The user takes a photo of the dirty glass window with their smartphone and uploads the image to the system. The server uses image analysis to identify the glass window and the stain, and generates information on the appropriate cleaning method, tools, and detergents, and provides it to the user.

[0627] Example prompt sentence:

[0628] "I would like the glass windows of my store cleaned. Please analyze this image and suggest the best cleaning method, tools, and detergents needed."

[0629] This allows users to perform cleaning tasks efficiently and effectively. Through this system, users can easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[0630] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0631] Step 1:

[0632] The user takes a picture

[0633] The user uses the smartphone camera to take an image of the object they want to clean (e.g., a glass window or shelf in a store). The input is the image data captured by the camera, and the output is an image file stored on the smartphone. This image file is used in subsequent processing steps.

[0634] Step 2:

[0635] The device uploads the image

[0636] The user takes a photo and uploads it to a system application or website. The input is the image file taken earlier, and the application sends the image data to the server using an HTTP POST request. The output is the status of the completion of sending the image data to the server.

[0637] Step 3:

[0638] The server receives the image

[0639] The server receives image data sent from the terminal. The input is an HTTP request from the terminal, and the server processes it to obtain the image data. The output is image data stored in a temporary file system on the server.

[0640] Step 4:

[0641] The server analyzes the image

[0642] The server analyzes the received image data. This step uses an image recognition algorithm that utilizes deep learning. The input is the image data stored on the server, and a generative AI model is used to recognize the object and type of dirt. The output is data on the object and type of dirt as the recognition result.

[0643] Step 5:

[0644] Identifying cleaning methods according to the type of dirt

[0645] The server retrieves information about the optimal cleaning method and the necessary tools and detergents from a database based on the recognition results. The input is the data about the recognition results, and the server uses this to execute a database query. The output is the retrieved information about the cleaning method and the necessary tools and detergents.

[0646] Step 6:

[0647] Providing information

[0648] The server organizes the acquired information about cleaning methods, tools, and detergents, and generates data to provide to the user. The input is information about cleaning methods, tools, and detergents, and the output is specific instruction data to display to the user.

[0649] Step 7:

[0650] The terminal displays the results

[0651] The terminal receives the result data sent from the server and displays it to the user. The input is the result data from the server, which the application visualizes and presents to the user. The output is the specific cleaning procedure and information on the necessary tools and detergents displayed on the terminal screen.

[0652] This allows the user to obtain the information necessary to perform cleaning work efficiently and effectively through a series of processing steps.

[0653] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0654] This invention combines a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method and the necessary tools and detergents, with an emotion engine that recognizes the user's emotions. The emotion engine adjusts the cleaning method according to the user's emotional state and provides encouragement as needed, making cleaning work more comfortable and effective.

[0655] System Overview

[0656] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. It also combines an emotion engine that recognizes emotions from the user's facial images, and adjusts the cleaning method suggestions according to the user's emotional state.

[0657] Program processing

[0658] Step 1: Upload an image

[0659] The user takes a picture

[0660] The user takes an image of the object they want to clean (for example, a gas range in the kitchen) using the smartphone camera.

[0661] The device uploads the image

[0662] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0663] Step 2: Image analysis

[0664] The server receives the image

[0665] The server receives the image data sent from the terminal.

[0666] The server analyzes the image

[0667] The server uses image analysis techniques (e.g., object detection models using deep learning) to identify objects in the image and the type of dirt on them, for example, recognizing oil stains on a gas range.

[0668] Step 3: Emotion recognition with the emotion engine

[0669] Get a user's face image

[0670] The device takes a picture of the user's face with a camera and sends it to the server.

[0671] The server recognizes emotions using an emotion engine

[0672] The server uses an emotion engine to analyze the user's facial image and determine the user's current emotional state (e.g., stress, joy, fatigue).

[0673] Step 4: Cleaning instructions

[0674] The server retrieves the information from the database

[0675] Based on the analysis results, the server retrieves information from its internal database regarding the optimal cleaning method, necessary tools and detergents depending on the object and type of dirt.

[0676] The server generates cleaning procedures taking into account emotional information.

[0677] The server takes the user's emotional state into account when generating effective cleaning instructions based on the information it has acquired. For example, if the user is feeling stressed, it will suggest simple and effective cleaning methods. Another feature is that it can also display encouraging messages.

[0678] Step 5: Provide information

[0679] The server sends the results to the device

[0680] The server transmits the generated cleaning procedure and a list of necessary tools and detergents to the terminal.

[0681] The terminal displays the results

[0682] The terminal displays the information received from the server to the user.

[0683] Specific examples

[0684] If a user wants to clean stubborn grease stains from their kitchen gas range:

[0685] 1. Take and upload an image

[0686] Users take a photo of a dirty gas range with their smartphone and upload it to the system.

[0687] 2. Image Analysis

[0688] The server identifies the gas range and oil stains using image analysis means.

[0689] 3. Emotion recognition

[0690] The server analyzes the user's facial image and recognizes that the user is feeling stressed.

[0691] 4. Cleaning instructions

[0692] The server retrieves cleaning methods suitable for oil stains from the database and generates simple and effective cleaning instructions taking into account the user's emotional state, including encouraging messages.

[0693] 5. Provision of Information

[0694] The server generates specific instructions such as "Spray the oil stain detergent, leave it for 10 minutes, then remove the dirt with a scraper and wipe it off with a sponge," along with an encouraging message such as "Keep up the good work, you're almost there!" and sends it to the device.

[0695] 6. Results display

[0696] The device provides instructions and encouraging messages to the user, who then follows the instructions to clean, allowing them to complete the cleaning efficiently without the need for tedious preparations or trying methods whose effectiveness is unknown.

[0697] In this way, the system takes into account the user's emotional state, helping them to clean more comfortably and effectively.

[0698] The processing flow will be explained below.

[0699] Step 1:

[0700] The user takes an image of the object to be cleaned.

[0701] The user takes a picture of the object they want to clean (e.g., the gas range in the kitchen) using their smartphone camera.

[0702] Step 2:

[0703] The device uploads the image.

[0704] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0705] Step 3:

[0706] The server receives the image.

[0707] The server receives the image data sent from the terminal and prepares for analysis.

[0708] Step 4:

[0709] The server analyzes the image.

[0710] The server uses image analysis algorithms (e.g., deep learning-based object detection models) to identify objects in the image and the type of dirt on them, for example, recognizing grease stains on a gas range.

[0711] Step 5:

[0712] The terminal takes a picture of the user's face.

[0713] To analyze the user's facial expression, the device's camera is used to capture an image of the user's face.

[0714] Step 6:

[0715] The device sends the facial image to the server.

[0716] The terminal transmits the captured image data of the user's face to the server.

[0717] Step 7:

[0718] The server analyzes the facial image.

[0719] The server uses an emotion engine to analyze the user's facial image and recognize the user's emotional state (for example, whether they are feeling stressed or happy).

[0720] Step 8:

[0721] The server retrieves the information from the database.

[0722] Based on the results of image analysis and emotion recognition, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents depending on the object and type of dirt.

[0723] Step 9:

[0724] The server generates the cleaning procedure.

[0725] The server generates effective cleaning instructions based on information retrieved from the database, including instructions on how to use cleaning products, tools, and precautions, and may also include emotional adjustments and encouraging messages based on the user's emotional state.

[0726] Step 10:

[0727] The server sends the results to the terminal.

[0728] The server sends the generated cleaning instructions, a list of necessary tools and detergents, and a message based on the user's emotion to the device.

[0729] Step 11:

[0730] The terminal displays the results.

[0731] The device displays the information received from the server to the user, such as "Spray the detergent for grease stains, leave it for 10 minutes, then remove the stains with a scraper and wipe it off with a sponge," along with an encouraging message such as "Keep up the good work, you're almost there!"

[0732] Step 12:

[0733] The user follows the instructions to clean.

[0734] Users can prepare the necessary tools and detergents and carry out effective cleaning according to the instructions displayed on the device, allowing them to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[0735] In this way, this system allows users to easily acquire appropriate cleaning methods through a series of steps, and by taking their emotional state into consideration, it reduces the burden of cleaning and supports comfortable work.

[0736] Example 2

[0737] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0738] Conventional cleaning support systems only analyze images of the object the user wants to clean and provide the optimal cleaning method and necessary tools and detergents, but they are unable to take the user's emotional state into consideration, which can make cleaning tasks stressful. Furthermore, because the system does not respond individually to the user's emotions, it is difficult to maintain motivation to clean.

[0739] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting an image of an object that the user wants to clean, a means for analyzing the image using an image analysis means and recognizing the object and the type of dirt on it, a means for acquiring an image of the user's face and recognizing their emotions, a means for identifying an optimal cleaning method and necessary tools and detergents according to the object and the type of dirt based on the recognition results, a means for adjusting the cleaning method according to the user's emotional state and generating an encouraging message as necessary, and a means for providing the user with information about the cleaning method, tools, and detergents. This allows the system to suggest an optimal cleaning method taking the user's emotional state into consideration, enabling the user to perform cleaning work comfortably and effectively.

[0740] "User" refers to an individual who uses the system to receive cleaning support.

[0741] "Object desired to be cleaned" refers to an object or surface that the user wishes to clean.

[0742] "Image" refers to a photograph, picture, or the like that contains visual information about the object that the user wants to clean.

[0743] "Image analysis means" refers to algorithms and techniques that process image data and identify the object and the type of dirt on it.

[0744] The term "target object" refers to an object that is recognized by the image analysis means and that is desired to be cleaned.

[0745] "Soil type" refers to the type of foreign matter or contamination present on the surface of the object.

[0746] "Optimal cleaning method" refers to the most effective and efficient cleaning procedure for the object and type of soil.

[0747] "Tools" refers to the equipment and implements required to carry out the cleaning.

[0748] "Detergent" refers to chemicals or liquid cleaning agents used in cleaning.

[0749] "User's facial image" refers to visual data that captures the user's facial features.

[0750] "Means for recognizing emotions" refers to technologies and algorithms that analyze a user's facial image and estimate their emotional state.

[0751] An "emotional state" refers to the psychological state (e.g., stress, joy, fatigue, etc.) that a user is feeling at a particular point in time.

[0752] "Encouraging messages" refer to text messages that are suggested based on the user's emotional state to improve motivation and support.

[0753] "Means for providing information" refers to the means or method by which the server communicates the analysis results and optimal cleaning methods to the user.

[0754] This invention combines a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, and the system analyzes the image to suggest an appropriate cleaning method and the necessary tools and detergents, with an emotion engine that recognizes the user's emotions. The emotion engine adjusts the cleaning method according to the user's emotional state and provides encouragement as needed, making cleaning work more comfortable and effective.

[0755] System Overview

[0756] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. It also combines an emotion engine that recognizes emotions from the user's facial images, and adjusts the cleaning method suggestions according to the user's emotional state.

[0757] Hardware and software used

[0758] Device: A device such as a smartphone or tablet that has a camera function. Use a dedicated application or web browser.

[0759] Server: A high-performance computing resource operated in a cloud environment or on-premise, containing software to run databases, deep learning models, emotion engines, etc.

[0760] Image analysis method: An object detection model using deep learning (e.g., YOLO, ResNet) is installed on the server.

[0761] Emotion engine: An emotion recognition model that utilizes deep learning (e.g., FaceNet, VGGFace) is installed on the server.

[0762] Specific actions

[0763] 1. The user takes a picture of the object they want to clean (e.g., a kitchen gas range) using their smartphone camera, and then uploads the image to a server via a dedicated application or website.

[0764] 2. The server receives the uploaded image and analyzes it using a deep learning-based object detection model to identify the object (e.g., gas range) and the type of dirt (e.g., grease stains).

[0765] 3. Next, the device takes a picture of the user's face and sends it back to the server. The server uses an emotion engine to analyze the face image and recognize the user's emotional state (e.g., stress, joy, fatigue).

[0766] 4. Based on the results of image analysis and emotion recognition, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents, and generates specific and effective cleaning instructions while taking into account the user's emotional state.

[0767] 5. If the user feels stressed, the robot will generate an encouraging message to help the user clean the room comfortably.

[0768] 6. Finally, the server sends the generated cleaning instructions, a list of necessary tools and detergents, and an encouraging message to the user's device, which then displays the information on its screen.

[0769] Specific examples

[0770] If a user wants to clean stubborn grease stains from a gas range in the kitchen, the process works as follows.

[0771] 1. A user takes a photo of a dirty gas range with their smartphone and uploads it to the system.

[0772] 2. The server uses deep learning to identify the gas range and oil stains in the image.

[0773] 3. The device sends the facial image to the server, which then performs emotion recognition and determines that the user is feeling stressed.

[0774] 4. The server retrieves cleaning methods suitable for the oil stain from the database and generates simple and effective cleaning instructions taking into account the user's emotional state, as well as encouraging messages such as "Keep up the good work, you're almost there!"

[0775] 5. The server sends specific instructions and an encouraging message to the device, such as "Spray the detergent for grease stains, leave it for 10 minutes, then remove the stains with a scraper and wipe it off with a sponge."

[0776] 6. The device presents this information to the user, who then follows the instructions to clean efficiently.

[0777] Example prompt sentence:

[0778] "To clean stubborn grease stains, first spray on the surface with a grease cleaner. Let it sit for 10 minutes, then use a scraper to remove the stain and wipe with a sponge. Keep going, you're almost there!"

[0779] In this way, the system takes the user's emotional state into consideration to help make cleaning more comfortable and effective.

[0780] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0781] Step 1:

[0782] The user takes a picture.

[0783] The user takes a picture of the object they want to clean (for example, a kitchen gas range) with their smartphone camera. The input is the image of the object to be cleaned, and the output is the image data captured by the camera. Specifically, the user captures the image of the object by pressing the "take a picture" button.

[0784] Step 2:

[0785] The device uploads the image.

[0786] The user uploads the image they have taken to a dedicated application or website. The device then sends the image data to the server. The input is the image data taken by the camera, and the output is the image data sent to the server. Specifically, the user presses the "Upload" button, and the progress is displayed until the image data is sent to the server.

[0787] Step 3:

[0788] The server receives the image.

[0789] The server receives image data sent from the terminal. The input is the image data sent from the terminal, and the output is the image data temporarily stored on the server. Specifically, the server receives image data through an HTTP request and temporarily stores it.

[0790] Step 4:

[0791] The server analyzes the image.

[0792] The server uses a deep learning-based object detection model (e.g., YOLO or ResNet) to identify the object and the type of dirt in the image. The input is the received image data, and the output is the analysis result indicating the object and the type of dirt. Specifically, the image data is input into the image analysis model, and information on the type of object and dirt is extracted.

[0793] Step 5:

[0794] The terminal takes a picture of the user's face and sends it to the server.

[0795] The device takes a picture of the user's face with a camera and sends it to the server. The input is the user's face image, and the output is the face image data sent to the server. Specifically, when the user presses the "face recognition" button, the camera automatically takes a face image and sends the data to the server.

[0796] Step 6:

[0797] The server recognizes emotions using an emotion engine.

[0798] The server uses an emotion engine to analyze the user's facial image and identify the user's emotional state (e.g., stress, joy, fatigue). The input is the transmitted facial image data, and the output is an analysis result indicating the user's emotional state. Specifically, the facial image is input into an emotion recognition model, and the emotional state is estimated.

[0799] Step 7:

[0800] The server retrieves the information from the database.

[0801] Based on the results of image analysis, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents for the object and type of dirt. The input is the image analysis results, and the output is information about the optimal cleaning method and necessary tools and detergents. Specifically, it uses an SQL query to search for records of cleaning methods that correspond to the type of dirt.

[0802] Step 8:

[0803] The server generates cleaning procedures taking into account emotional information.

[0804] The server takes the user's emotional state into account when generating cleaning instructions based on the acquired information. The input is the acquired cleaning method and emotion recognition results, and the output is an adjusted cleaning procedure and an encouraging message. Specifically, it selects steps tagged as "easy" and adds a text message.

[0805] Step 9:

[0806] The server sends the results to the terminal.

[0807] The server sends the generated cleaning instructions and a list of the required tools and detergents to the terminal. The input is the adjusted cleaning instructions and information on the required tools and detergents, and the output is the data to be sent to the terminal. Specifically, the results are packaged in JSON format and sent to the terminal as an HTTP response.

[0808] Step 10:

[0809] The terminal displays the results.

[0810] The terminal displays the information received from the server to the user. The input is the data sent from the server, and the output is the displayed cleaning procedure, tool list, detergent information, and encouraging messages. Specifically, this information is displayed on the UI of the dedicated application.

[0811] (Application example 2)

[0812] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0813] In conventional self-driving vehicles, cleaning the interior of the vehicle is a time-consuming task, and it is difficult to determine the optimal cleaning method. Furthermore, cleaning methods are suggested without taking into account the emotional state of the driver or passengers, which can place a heavy burden on users and cause stress. Especially after a long drive or on the way home from work, users often do not have the energy to clean. This makes it difficult to maintain the interior environment, resulting in a decrease in user comfort.

[0814] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0815] In this invention, the server includes: means for inputting an image of an object that a user wants to clean; means for analyzing the image using image analysis means and recognizing the object and the type of dirt on it; means for identifying an optimal cleaning method and necessary tools and detergents according to the recognition results based on the object and the type of dirt; means for providing the user with information about the cleaning method, tools, and detergents; means for periodically taking and transmitting images of the interior of the autonomous vehicle using a camera installed in the autonomous vehicle; means for acquiring facial images of the driver and passengers and performing emotion analysis on them; means for generating a cleaning procedure based on the analysis results including the acquired emotion information and providing an encouraging message to the user; and means for displaying the information on an in-vehicle display. This enables the interior of the autonomous vehicle to be cleaned effectively and without burdening the user.

[0816] The "means for inputting an image of an object that the user wishes to clean" is an interface that allows the user to take an image of the object to be cleaned and upload it to the system.

[0817] "Image analysis means" refers to the technology or algorithms used to analyze the transmitted image and identify the type of object or dirt in the image.

[0818] "Means for identifying the optimal cleaning method and the necessary tools and detergents" refers to a process or mechanism for determining the optimal cleaning method and the necessary tools and detergents based on the results of image analysis.

[0819] The "means for providing the user with information on cleaning methods, tools, and detergents" is a function that displays or notifies the user of the optimal cleaning method, necessary tools, and detergents based on the analysis results.

[0820] "Autonomous vehicle-mounted camera" means a camera mounted inside or outside an autonomous vehicle that monitors and analyzes the environment as part of the autonomous driving system.

[0821] "Means for acquiring facial images and performing emotion analysis" refers to technologies and systems for capturing a user's facial image and analyzing and identifying the user's emotional state from the captured image.

[0822] The "means for providing an encouraging message" is a function for generating and displaying a message to cheer up the user in accordance with the analyzed emotional state of the user.

[0823] An "in-vehicle display" is a display device installed inside an autonomous vehicle that serves as a user interface and indicates the vehicle's status.

[0824] This invention is a system for effectively cleaning the environment inside an autonomous vehicle. The user inputs an image of the object they wish to clean, and the system analyzes the image and suggests the optimal cleaning method. Furthermore, by recognizing emotions from facial images, the system adjusts cleaning procedures and provides encouraging messages according to the user's emotional state. The following describes a specific embodiment of this system.

[0825] Hardware and software used

[0826] 1. Hardware:

[0827] Cameras installed in self-driving vehicles: devices that periodically capture images of the interior of the vehicle.

[0828] In-car display: A device for displaying suggested cleaning methods and messages.

[0829] Facial image recognition camera: A device placed inside an autonomous vehicle to capture facial images of the user.

[0830] 2. Software:

[0831] Image analysis method: Using object detection algorithms such as YOLO (You Only Look Once), images of the interior of the vehicle are analyzed to identify the location and type of dirt.

[0832] Emotion engine: Analyzes the user's emotional state from facial images using the emotion analysis functions of Microsoft Azure Face API and Amazon Rekognition.

[0833] Processing Description

[0834] 1. Image input and analysis:

[0835] The server receives images of the interior of the autonomous vehicle sent from the camera. Image analysis is used to identify objects in the image and the type of dirt present. Deep learning-based object detection algorithms such as YOLO are used.

[0836] 2. Emotion recognition:

[0837] A facial image recognition camera in an autonomous vehicle captures a user's facial image and sends it to a server, which then uses an emotion engine to analyze and identify the user's emotional state (stress, fatigue, joy, etc.). This uses Microsoft Azure Face API and Amazon Rekognition.

[0838] 3. Generate cleaning procedures:

[0839] Based on the analysis results, the server retrieves information from a database about the optimal cleaning method, necessary tools, and detergents for each type of object and dirt. It also generates cleaning instructions taking into account the user's emotional state and adds encouraging messages.

[0840] 4. Provision of Information:

[0841] The server sends the generated cleaning instructions and encouraging messages to a display in the car, where the user can check the information and follow the instructions to clean.

[0842] Specific examples

[0843] For example, if a user spills a drink, the in-car camera takes a picture of the spill and sends it to the server. The server analyzes the image and identifies the location of the spilled drink. At the same time, it recognizes from the facial image that the user is feeling stressed. As a result, it displays cleaning instructions such as "Quickly wipe up the spilled drink with a paper towel, then use a cleaner" along with an encouraging message such as "You're almost there, keep trying."

[0844] Prompt Sentence Examples

[0845] "Please upload a picture of the interior of the car."

[0846] "Reading face image"

[0847] "For spilled drinks, wipe them up with paper towels and use cleaner."

[0848] "You're feeling stressed right now. I'll suggest some easy cleaning methods."

[0849] "It's almost there, just do your best."

[0850] In this way, the present invention can effectively clean the interior of an autonomous vehicle, reducing the burden on the user and providing a comfortable in-vehicle environment.

[0851] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0852] Step 1:

[0853] The user takes a picture of the object they want to clean. The image is saved on a device such as a smartphone or tablet. The device then uploads the image to the system's application or website, and sends the image data to a server.

[0854] Input: Image of object to be cleaned

[0855] Output: Image data sent to the server

[0856] Step 2:

[0857] The server receives the image data sent from the device. It then uses image analysis techniques (for example, an object detection model using deep learning) to identify the object in the image and the type of dirt. If the object is a kitchen gas range, it will recognize oil stains, etc.

[0858] Input: Received image data

[0859] Output: Analysis results including location and type of contamination

[0860] Step 3:

[0861] A facial image recognition camera in the autonomous vehicle captures a user's facial image, which is then sent to a server, which uses an emotion engine to identify the user's emotional state (e.g., stress, fatigue, joy).

[0862] Input: Face image data

[0863] Output: Analysis results showing the user's emotional state

[0864] Step 4:

[0865] Based on the image analysis results from step 2 and the emotion analysis results from step 3, the server retrieves information from a database about optimal cleaning methods, necessary tools, and detergents. It also generates cleaning instructions taking into account the user's emotional state and adds encouraging messages. For example, if the user is feeling stressed, it generates simple and effective cleaning instructions.

[0866] Input: Image analysis results, emotion analysis results

[0867] Output: Optimal cleaning instructions and encouraging messages

[0868] Step 5:

[0869] The server sends the generated cleaning instructions and encouraging messages to a display in the car, which visually displays this information to the user. The user then follows the suggested cleaning instructions while checking the display.

[0870] Input: Cleaning instructions and encouraging messages

[0871] Output: Information shown on the display

[0872] This allows users to receive optimal cleaning procedures and encouraging messages based on their emotional state, allowing them to clean efficiently.

[0873] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0874] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0875] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0876] [Third embodiment]

[0877] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0878] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0879] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0880] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0881] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0882] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0883] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0884] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0885] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0886] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0887] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0888] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0889] The present invention is a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method, as well as the necessary tools and detergents. Detailed embodiments of this system will be described below with specific examples.

[0890] System Overview

[0891] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of this analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user.

[0892] Program processing

[0893] Step 1: Upload an image

[0894] The user takes a picture

[0895] The user takes a picture of the object they want to clean (for example, a gas range in the kitchen or tiles in the bathroom) using their smartphone camera.

[0896] The device uploads the image

[0897] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0898] Step 2: Image analysis

[0899] The server receives the image

[0900] The server receives the image data sent from the terminal.

[0901] The server analyzes the image

[0902] The server uses image analysis means (e.g., an object detection model using deep learning) to identify the objects and types of dirt appearing in the image.

[0903] Step 3: Cleaning instructions

[0904] The server retrieves the information from the database

[0905] Based on the analysis results, the server retrieves information from the database regarding the optimal cleaning method, necessary tools and detergents depending on the object and type of dirt.

[0906] The server generates the cleaning instructions.

[0907] The server uses the acquired information to generate effective cleaning instructions, including how to use detergents, tools, and precautions.

[0908] Step 4: Provide information

[0909] The server sends the results to the device

[0910] The server sends the generated cleaning instructions and a list of necessary tools and detergents to the terminal.

[0911] The terminal displays the results

[0912] The device displays the information received from the server to the user. For example, the device screen may display instructions such as "Spray oil-stain detergent on the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[0913] Specific examples

[0914] Consider a case where a user wants to clean stubborn grease stains from a gas range in the kitchen.

[0915] 1. Take and upload an image

[0916] Users take a photo of a dirty gas range with their smartphone and upload it to the system.

[0917] 2. Image Analysis

[0918] The server identifies the gas range and oil stains using image analysis means.

[0919] 3. Cleaning instructions

[0920] The server retrieves the cleaning method appropriate for the oil stain from the database and recognizes that detergent and a scraper are required.

[0921] 4. Provision of Information

[0922] The server generates specific instructions such as "spray oil stain detergent, leave for 10 minutes, then remove the stain with a scraper and wipe with a sponge" and sends them to the terminal.

[0923] 5. Display results

[0924] The device displays instructions to the user, who then follows the instructions to clean. This allows users to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[0925] In this way, the system allows users to easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[0926] The processing flow will be explained below.

[0927] Step 1:

[0928] The user takes a picture.

[0929] The user takes a picture of the object they want to clean (e.g., the gas range in the kitchen) using their smartphone camera.

[0930] Step 2:

[0931] The device uploads the image.

[0932] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[0933] Step 3:

[0934] The server receives the image.

[0935] The server receives the image data sent from the terminal and prepares for analysis.

[0936] Step 4:

[0937] The server analyzes the image.

[0938] The server uses image analysis algorithms (e.g., deep learning-based object detection models) to identify objects in the image and the type of dirt on them, for example, recognizing grease stains on a gas range.

[0939] Step 5:

[0940] The server retrieves the information from the database.

[0941] Based on the analysis results, the server retrieves information from its internal database regarding cleaning methods, necessary tools, and detergents according to the type of object and dirt.

[0942] Step 6:

[0943] The server generates the cleaning procedure.

[0944] The server generates effective cleaning instructions based on the acquired information. These instructions include how to use detergents, tools, and precautions. For example, it generates specific instructions such as "spray oil-stain detergent on the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[0945] Step 7:

[0946] The server sends the results to the terminal.

[0947] The server sends the generated cleaning procedure and a list of necessary tools and detergents to the terminal.

[0948] Step 8:

[0949] The terminal displays the results.

[0950] The terminal displays the information received from the server to the user, for example, by displaying a list of cleaning procedures and the necessary tools and detergents on the terminal screen.

[0951] Step 9:

[0952] The user follows the instructions to clean.

[0953] The user prepares the necessary tools and detergents based on the instructions displayed on the terminal and performs effective cleaning.

[0954] In this way, the present system helps the user to easily acquire an appropriate cleaning method and perform effective cleaning through a series of steps.

[0955] Example 1

[0956] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0957] Conventional cleaning methods rely heavily on experience and intuition, making it difficult to select the appropriate procedures, tools, and detergents. Furthermore, not knowing effective cleaning methods can lead to wasted time and effort. Furthermore, there are concerns about the handling of image data due to privacy concerns. Therefore, there is a need for a system that allows users to easily obtain appropriate cleaning methods and clean effectively while protecting their privacy.

[0958] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0959] In this invention, the server includes means for inputting an image of an object that a user wants to clean, means for analyzing the image using image analysis means to recognize the object and the type of dirt on it, means for identifying the optimal cleaning method and necessary tools and detergent for the object and the type of dirt based on the recognition results, means for providing the user with information about the cleaning method, tools, and detergent, means for encrypting image data uploaded from the terminal to protect the user's privacy, and means for retrieving the optimal cleaning method based on the analysis from a database and generating an effective cleaning procedure. This allows the user to easily obtain an appropriate and effective cleaning method, significantly reducing the effort and time required for cleaning while protecting their privacy.

[0960] "User" refers to an individual who uses this system to receive cleaning method suggestions.

[0961] "Device" refers to an electronic device that a user uses to take images and upload them to the system, such as a smartphone or tablet.

[0962] "Server" refers to a computer system that analyzes images sent by users and performs the data processing necessary to provide appropriate cleaning methods.

[0963] "Image analysis means" refers to an algorithm executed on the server that utilizes deep learning to identify objects and stains within an image.

[0964] The "recognition result" refers to information about the object and the type of dirt on it obtained by the image analysis means.

[0965] "Cleaning method" refers to the optimal cleaning procedure identified based on the recognition results, including specific methods for using detergents and tools.

[0966] "Tools" refer to items needed to carry out the cleaning method, such as scrapers and sponges.

[0967] "Detergent" refers to a chemical product used to remove specific stains.

[0968] "Database" refers to a digital storage device that stores information about cleaning methods, tools, and detergents.

[0969] "Means for providing information" refers to the mechanism for transmitting and displaying the cleaning instructions and list of required tools and detergents generated by the server to the user's device.

[0970] "Encryption" is a technology for protecting communication data, and refers to the process of making the contents incomprehensible to anyone other than the recipient.

[0971] "Protecting privacy" refers to a series of measures to prevent users' personal information from being leaked to third parties.

[0972] An "effective cleaning procedure" is one that is designed to achieve maximum effectiveness with minimum effort and time.

[0973] This invention is a comprehensive cleaning support system that allows a user to input an image of an object they wish to clean, and a server analyzes the image and suggests an appropriate cleaning method, as well as the necessary tools and detergents. This system starts when a user takes an image of the object using a device such as a smartphone or tablet and uploads the image data to the server.

[0974] Hardware and software used

[0975] The main hardware used in this invention includes the user's device, such as a smartphone or tablet, and a server consisting of a high-performance computer. The software used is a deep learning-based object detection model for image analysis. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used.

[0976] Specific data processing and calculation

[0977] When a user takes a picture of an object they wish to clean with their device, the device uploads the image data to a server. The uploaded image data is encrypted to protect the user's privacy. When the server receives the image data, it uses image analysis means to identify the object in the image and the type of dirt. Based on the analysis results, the server then retrieves information from a database about the optimal cleaning method, as well as the necessary tools and detergents.

[0978] Based on the acquired information, the server generates effective cleaning instructions. For example, if the analysis results indicate "grease stains on the gas range," the server will create specific instructions such as "spray oil-removing detergent, leave for 10 minutes, then remove the stains with a scraper and wipe with a sponge." The generated cleaning instructions are sent from the server to the device and displayed to the user.

[0979] Specific examples

[0980] Take the example of a user wanting to clean stubborn grease stains from a gas range in the kitchen.

[0981] 1. Take and upload an image

[0982] Users take a photo of a dirty gas range with their smartphone and upload it to the system, where the image is encrypted and sent from the device to a server.

[0983] 2. Image Analysis

[0984] The server uses a deep learning model (e.g., using TensorFlow or PyTorch) to identify the gas range and its oil stains.

[0985] 3. Cleaning instructions

[0986] The server retrieves the cleaning method appropriate for the oil stain from the database and recognizes that detergent and a scraper are required.

[0987] 4. Provision of Information

[0988] The server generates specific instructions such as "spray oil stain detergent, leave for 10 minutes, then remove the stain with a scraper and wipe with a sponge" and sends them to the terminal.

[0989] 5. Display results

[0990] The device displays instructions to the user, who then follows the instructions to clean. This allows users to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[0991] Prompt Sentence Examples

[0992] The following are examples of specific prompt sentences:

[0993] "Please analyze the image of the dirty gas range and tell me how to clean it effectively. Also, what tools and cleaning agents are needed?"

[0994] In this way, the system allows users to easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[0995] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0996] Step 1:

[0997] The user takes a picture of the object

[0998] The user uses the camera on their smartphone or tablet to take a picture of the object they want to clean (e.g., kitchen stove, bathroom tiles, etc.). The input is an image of the area that needs cleaning. This image is the data that will be used for further processing. The output is the captured image file.

[0999] Step 2:

[1000] The device uploads the image to the system

[1001] The user uploads the image they have taken to a dedicated application or website. The image taken by the user is given as input. The device uses an Internet connection to send the image data to a server. At this time, the image data is encrypted to protect the user's privacy. The output is the encrypted image data sent to the server.

[1002] Step 3:

[1003] The server receives the image

[1004] The server receives the encrypted image data sent from the terminal. As input, it has the encrypted image data sent from the terminal. The server decrypts this data and verifies the integrity of the image data. The output is the decrypted image data.

[1005] Step 4:

[1006] The server analyzes the image

[1007] The server uses a deep learning-based object detection model (e.g., using TensorFlow or PyTorch) to identify objects and their stain types in the image. As input, it receives the decoded image data. Advanced image recognition algorithms are used to analyze the data. The output is a recognition result of the object and stain type.

[1008] Step 5:

[1009] The server retrieves the information from the database

[1010] Based on the results of image analysis, the server retrieves information from the database about the optimal cleaning method for the object and type of dirt, as well as the necessary tools and detergents. The input is the recognition results of image analysis. The server sends a query to the database to retrieve the necessary information. The output is information about the optimal cleaning method and the necessary tools and detergents.

[1011] Step 6:

[1012] The server generates the cleaning instructions.

[1013] The server generates specific and effective cleaning procedures based on the acquired information. For example, if the analysis results indicate "grease stains on the gas range," it generates the procedure "spray oil stain detergent, leave for 10 minutes, then remove the stains with a scraper and wipe with a sponge." The input is the cleaning method and information on tools and detergents acquired from the database. The output is the generated specific cleaning procedure.

[1014] Step 7:

[1015] The server sends the results to the device

[1016] The server sends the generated cleaning instructions and a list of the necessary tools and detergents to the terminal. The input is the specific cleaning instructions and information on the tools and detergents. The server encrypts this information and sends it to the terminal. The output is the encrypted result data sent to the terminal.

[1017] Step 8:

[1018] The terminal displays the results

[1019] The terminal decrypts the information received from the server and displays it to the user. The input is the encrypted result data sent from the server. The terminal decrypts the data and displays it in a format that is easy for the user to understand. The output is specific cleaning steps and information on the necessary tools and detergents that the user can see. This allows the user to clean according to the provided steps.

[1020] (Application example 1)

[1021] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1022] Today's cleaning tasks are diverse, and users often spend a lot of time and effort selecting the appropriate cleaning method, tools, and detergents. Furthermore, in commercial facilities and brick-and-mortar stores, efficient and effective cleaning is essential, but the specific methods and procedures for achieving this are often unclear. To address these challenges, there is a need for a comprehensive cleaning support system that allows users to easily input an image of the object to be cleaned, and the system will suggest the appropriate cleaning method and the necessary tools and detergents.

[1023] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1024] In this invention, the server includes means for inputting an image of an object that a user wants to clean, means for analyzing the image using image analysis means to recognize the object and the type of dirt on it, means for identifying the optimal cleaning method and necessary tools and detergents according to the object and the type of dirt based on the recognition results, means for providing information about the cleaning method, tools, and detergents to the user, and means for using an electronic device to display the results. This allows the user to quickly and accurately obtain information about the optimal cleaning method and necessary tools and detergents simply by inputting an image.

[1025] A "user" is a person who inputs an image of an object to be cleaned or a user of the system.

[1026] An "object" is a specific object or location that a user wishes to clean.

[1027] An "image" is visual data that is captured or uploaded by a user using an input means.

[1028] "Image analysis means" refers to technology or algorithms for analyzing input images and recognizing the type of object and dirt.

[1029] The "recognition result" is information about the object and the type of dirt obtained by the image analysis means.

[1030] The "cleaning method" refers to the cleaning procedure or technique that is determined based on the recognition results and is appropriate for the type of object and dirt.

[1031] "Tools" are the tools and equipment needed to carry out the cleaning method.

[1032] "Detergents" are cleaning agents or chemicals used in carrying out cleaning methods.

[1033] "Means for providing information" refers to a method or device for informing or displaying information to the user about appropriate cleaning methods, tools, and detergents.

[1034] "Electronic devices" are terminals or devices used to display cleaning method, tool and detergent information to users.

[1035] The present invention is a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method and the necessary tools and detergents. Detailed embodiments for carrying out the present invention will be described below with specific examples.

[1036] System Overview

[1037] This system allows users to upload images of the object they wish to clean from a device (such as a smartphone or tablet), which is then analyzed on a server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. The main hardware used is a smartphone camera, server, and electronic device (device), while the software uses deep learning models, image recognition algorithms, and libraries for processing HTTP requests (such as Python requests).

[1038] Program processing

[1039] The program performs the following data processing and calculations:

[1040] 1. User takes a picture:

[1041] The user uses the smartphone camera to take a picture of the object they want to clean, such as a glass window or shelf in a store.

[1042] 2. The device uploads the image:

[1043] The user uploads the image they took to a system application or website. The device sends the image data to the server. The image data is uploaded to the API server using an HTTP POST request.

[1044] 3. The server analyzes the image:

[1045] The server analyzes the received image data using an image recognition algorithm powered by deep learning, which identifies the object and type of dirt. The deep learning model uses a generative AI model trained for object detection and classification.

[1046] 4. Identify cleaning methods based on the type of soiling:

[1047] Based on the recognition results, the server retrieves information from the database about the optimal cleaning method, necessary tools, and detergents for each type of object and dirt. The cleaning procedure includes how to use the detergent and tools, as well as precautions.

[1048] 5. Provision of Information:

[1049] The server then provides the user with the information about cleaning methods, tools, and detergents. The user then cleans by referring to the instructions displayed on the device screen. For example, the specific instructions displayed might be, "Spray oil-repellent detergent onto the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[1050] Specific examples

[1051] Consider a case where a user wants to clean stubborn stains on a glass window in a brick-and-mortar store. The user takes a photo of the dirty glass window with their smartphone and uploads the image to the system. The server uses image analysis to identify the glass window and the stain, and generates information on the appropriate cleaning method, tools, and detergents, and provides it to the user.

[1052] Example prompt sentence:

[1053] "I would like the glass windows of my store cleaned. Please analyze this image and suggest the best cleaning method, tools, and detergents needed."

[1054] This allows users to perform cleaning tasks efficiently and effectively. Through this system, users can easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[1055] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1056] Step 1:

[1057] The user takes a picture

[1058] The user uses the smartphone camera to take an image of the object they want to clean (e.g., a glass window or shelf in a store). The input is the image data captured by the camera, and the output is an image file stored on the smartphone. This image file is used in subsequent processing steps.

[1059] Step 2:

[1060] The device uploads the image

[1061] The user takes a photo and uploads it to a system application or website. The input is the image file taken earlier, and the application sends the image data to the server using an HTTP POST request. The output is the status of the completion of sending the image data to the server.

[1062] Step 3:

[1063] The server receives the image

[1064] The server receives image data sent from the terminal. The input is an HTTP request from the terminal, and the server processes it to obtain the image data. The output is image data stored in a temporary file system on the server.

[1065] Step 4:

[1066] The server analyzes the image

[1067] The server analyzes the received image data. This step uses an image recognition algorithm that utilizes deep learning. The input is the image data stored on the server, and a generative AI model is used to recognize the object and type of dirt. The output is data on the object and type of dirt as the recognition result.

[1068] Step 5:

[1069] Identifying cleaning methods according to the type of dirt

[1070] The server retrieves information about the optimal cleaning method and the necessary tools and detergents from a database based on the recognition results. The input is the data about the recognition results, and the server uses this to execute a database query. The output is the retrieved information about the cleaning method and the necessary tools and detergents.

[1071] Step 6:

[1072] Providing information

[1073] The server organizes the acquired information about cleaning methods, tools, and detergents, and generates data to provide to the user. The input is information about cleaning methods, tools, and detergents, and the output is specific instruction data to display to the user.

[1074] Step 7:

[1075] The terminal displays the results

[1076] The terminal receives the result data sent from the server and displays it to the user. The input is the result data from the server, which the application visualizes and presents to the user. The output is the specific cleaning procedure and information on the necessary tools and detergents displayed on the terminal screen.

[1077] This allows the user to obtain the information necessary to perform cleaning work efficiently and effectively through a series of processing steps.

[1078] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1079] This invention combines a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method and the necessary tools and detergents, with an emotion engine that recognizes the user's emotions. The emotion engine adjusts the cleaning method according to the user's emotional state and provides encouragement as needed, making cleaning work more comfortable and effective.

[1080] System Overview

[1081] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. It also combines an emotion engine that recognizes emotions from the user's facial images, and adjusts the cleaning method suggestions according to the user's emotional state.

[1082] Program processing

[1083] Step 1: Upload an image

[1084] The user takes a picture

[1085] The user takes an image of the object they want to clean (for example, a gas range in the kitchen) using the smartphone camera.

[1086] The device uploads the image

[1087] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[1088] Step 2: Image analysis

[1089] The server receives the image

[1090] The server receives the image data sent from the terminal.

[1091] The server analyzes the image

[1092] The server uses image analysis techniques (e.g., object detection models using deep learning) to identify objects in the image and the type of dirt on them, for example, recognizing oil stains on a gas range.

[1093] Step 3: Emotion recognition with the emotion engine

[1094] Get a user's face image

[1095] The device takes a picture of the user's face with a camera and sends it to the server.

[1096] The server recognizes emotions using an emotion engine

[1097] The server uses an emotion engine to analyze the user's facial image and determine the user's current emotional state (e.g., stress, joy, fatigue).

[1098] Step 4: Cleaning instructions

[1099] The server retrieves the information from the database

[1100] Based on the analysis results, the server retrieves information from its internal database regarding the optimal cleaning method, necessary tools and detergents depending on the object and type of dirt.

[1101] The server generates cleaning procedures taking into account emotional information.

[1102] The server takes the user's emotional state into account when generating effective cleaning instructions based on the information it has acquired. For example, if the user is feeling stressed, it will suggest simple and effective cleaning methods. Another feature is that it can also display encouraging messages.

[1103] Step 5: Provide information

[1104] The server sends the results to the device

[1105] The server transmits the generated cleaning procedure and a list of necessary tools and detergents to the terminal.

[1106] The terminal displays the results

[1107] The terminal displays the information received from the server to the user.

[1108] Specific examples

[1109] If a user wants to clean stubborn grease stains from their kitchen gas range:

[1110] 1. Take and upload an image

[1111] Users take a photo of a dirty gas range with their smartphone and upload it to the system.

[1112] 2. Image Analysis

[1113] The server identifies the gas range and oil stains using image analysis means.

[1114] 3. Emotion recognition

[1115] The server analyzes the user's facial image and recognizes that the user is feeling stressed.

[1116] 4. Cleaning instructions

[1117] The server retrieves cleaning methods suitable for oil stains from the database and generates simple and effective cleaning instructions taking into account the user's emotional state, including encouraging messages.

[1118] 5. Provision of Information

[1119] The server generates specific instructions such as "Spray the oil stain detergent, leave it for 10 minutes, then remove the dirt with a scraper and wipe it off with a sponge," along with an encouraging message such as "Keep up the good work, you're almost there!" and sends it to the device.

[1120] 6. Results display

[1121] The device provides instructions and encouraging messages to the user, who then follows the instructions to clean, allowing them to complete the cleaning efficiently without the need for tedious preparations or trying methods whose effectiveness is unknown.

[1122] In this way, the system takes into account the user's emotional state, helping them to clean more comfortably and effectively.

[1123] The processing flow will be explained below.

[1124] Step 1:

[1125] The user takes an image of the object to be cleaned.

[1126] The user takes a picture of the object they want to clean (e.g., the gas range in the kitchen) using their smartphone camera.

[1127] Step 2:

[1128] The device uploads the image.

[1129] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[1130] Step 3:

[1131] The server receives the image.

[1132] The server receives the image data sent from the terminal and prepares for analysis.

[1133] Step 4:

[1134] The server analyzes the image.

[1135] The server uses image analysis algorithms (e.g., deep learning-based object detection models) to identify objects in the image and the type of dirt on them, for example, recognizing grease stains on a gas range.

[1136] Step 5:

[1137] The terminal takes a picture of the user's face.

[1138] To analyze the user's facial expression, the device's camera is used to capture an image of the user's face.

[1139] Step 6:

[1140] The device sends the facial image to the server.

[1141] The terminal transmits the captured image data of the user's face to the server.

[1142] Step 7:

[1143] The server analyzes the facial image.

[1144] The server uses an emotion engine to analyze the user's facial image and recognize the user's emotional state (for example, whether they are feeling stressed or happy).

[1145] Step 8:

[1146] The server retrieves the information from the database.

[1147] Based on the results of image analysis and emotion recognition, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents depending on the object and type of dirt.

[1148] Step 9:

[1149] The server generates the cleaning procedure.

[1150] The server generates effective cleaning instructions based on information retrieved from the database, including instructions on how to use cleaning products, tools, and precautions, and may also include emotional adjustments and encouraging messages based on the user's emotional state.

[1151] Step 10:

[1152] The server sends the results to the terminal.

[1153] The server sends the generated cleaning instructions, a list of necessary tools and detergents, and a message based on the user's emotion to the device.

[1154] Step 11:

[1155] The terminal displays the results.

[1156] The device displays the information received from the server to the user, such as "Spray the detergent for grease stains, leave it for 10 minutes, then remove the stains with a scraper and wipe it off with a sponge," along with an encouraging message such as "Keep up the good work, you're almost there!"

[1157] Step 12:

[1158] The user follows the instructions to clean.

[1159] Users can prepare the necessary tools and detergents and carry out effective cleaning according to the instructions displayed on the device, allowing them to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[1160] In this way, this system allows users to easily acquire appropriate cleaning methods through a series of steps, and by taking their emotional state into consideration, it reduces the burden of cleaning and supports comfortable work.

[1161] Example 2

[1162] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1163] Conventional cleaning support systems only analyze images of the object the user wants to clean and provide the optimal cleaning method and necessary tools and detergents, but they are unable to take the user's emotional state into consideration, which can make cleaning tasks stressful. Furthermore, because the system does not respond individually to the user's emotions, it is difficult to maintain motivation to clean.

[1164] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting an image of an object that the user wants to clean, a means for analyzing the image using an image analysis means and recognizing the object and the type of dirt on it, a means for acquiring an image of the user's face and recognizing their emotions, a means for identifying an optimal cleaning method and necessary tools and detergents according to the object and the type of dirt based on the recognition results, a means for adjusting the cleaning method according to the user's emotional state and generating an encouraging message as necessary, and a means for providing the user with information about the cleaning method, tools, and detergents. This allows the system to suggest an optimal cleaning method taking the user's emotional state into consideration, enabling the user to perform cleaning work comfortably and effectively.

[1165] "User" refers to an individual who uses the system to receive cleaning support.

[1166] "Object desired to be cleaned" refers to an object or surface that the user wishes to clean.

[1167] "Image" refers to a photograph, picture, or the like that contains visual information about the object that the user wants to clean.

[1168] "Image analysis means" refers to algorithms and techniques that process image data and identify the object and the type of dirt on it.

[1169] The term "target object" refers to an object that is recognized by the image analysis means and that is desired to be cleaned.

[1170] "Soil type" refers to the type of foreign matter or contamination present on the surface of the object.

[1171] "Optimal cleaning method" refers to the most effective and efficient cleaning procedure for the object and type of soil.

[1172] "Tools" refers to the equipment and implements required to carry out the cleaning.

[1173] "Detergent" refers to chemicals or liquid cleaning agents used in cleaning.

[1174] "User's facial image" refers to visual data that captures the user's facial features.

[1175] "Means for recognizing emotions" refers to technologies and algorithms that analyze a user's facial image and estimate their emotional state.

[1176] An "emotional state" refers to the psychological state (e.g., stress, joy, fatigue, etc.) that a user is feeling at a particular point in time.

[1177] "Encouraging messages" refer to text messages that are suggested based on the user's emotional state to improve motivation and support.

[1178] "Means for providing information" refers to the means or method by which the server communicates the analysis results and optimal cleaning methods to the user.

[1179] This invention combines a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, and the system analyzes the image to suggest an appropriate cleaning method and the necessary tools and detergents, with an emotion engine that recognizes the user's emotions. The emotion engine adjusts the cleaning method according to the user's emotional state and provides encouragement as needed, making cleaning work more comfortable and effective.

[1180] System Overview

[1181] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. It also combines an emotion engine that recognizes emotions from the user's facial images, and adjusts the cleaning method suggestions according to the user's emotional state.

[1182] Hardware and software used

[1183] Device: A device such as a smartphone or tablet that has a camera function. Use a dedicated application or web browser.

[1184] Server: A high-performance computing resource operated in a cloud environment or on-premise, containing software to run databases, deep learning models, emotion engines, etc.

[1185] Image analysis method: An object detection model using deep learning (e.g., YOLO, ResNet) is installed on the server.

[1186] Emotion engine: An emotion recognition model that utilizes deep learning (e.g., FaceNet, VGGFace) is installed on the server.

[1187] Specific actions

[1188] 1. The user takes a picture of the object they want to clean (e.g., a kitchen gas range) using their smartphone camera, and then uploads the image to a server via a dedicated application or website.

[1189] 2. The server receives the uploaded image and analyzes it using a deep learning-based object detection model to identify the object (e.g., gas range) and the type of dirt (e.g., grease stains).

[1190] 3. Next, the device takes a picture of the user's face and sends it back to the server. The server uses an emotion engine to analyze the face image and recognize the user's emotional state (e.g., stress, joy, fatigue).

[1191] 4. Based on the results of image analysis and emotion recognition, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents, and generates specific and effective cleaning instructions while taking into account the user's emotional state.

[1192] 5. If the user feels stressed, the robot will generate an encouraging message to help the user clean the room comfortably.

[1193] 6. Finally, the server sends the generated cleaning instructions, a list of necessary tools and detergents, and an encouraging message to the user's device, which then displays the information on its screen.

[1194] Specific examples

[1195] If a user wants to clean stubborn grease stains from a gas range in the kitchen, the process works as follows.

[1196] 1. A user takes a photo of a dirty gas range with their smartphone and uploads it to the system.

[1197] 2. The server uses deep learning to identify the gas range and oil stains in the image.

[1198] 3. The device sends the facial image to the server, which then performs emotion recognition and determines that the user is feeling stressed.

[1199] 4. The server retrieves cleaning methods suitable for the oil stain from the database and generates simple and effective cleaning instructions taking into account the user's emotional state, as well as encouraging messages such as "Keep up the good work, you're almost there!"

[1200] 5. The server sends specific instructions and an encouraging message to the device, such as "Spray the detergent for grease stains, leave it for 10 minutes, then remove the stains with a scraper and wipe it off with a sponge."

[1201] 6. The device presents this information to the user, who then follows the instructions to clean efficiently.

[1202] Example prompt sentence:

[1203] "To clean stubborn grease stains, first spray on the surface with a grease cleaner. Let it sit for 10 minutes, then use a scraper to remove the stain and wipe with a sponge. Keep going, you're almost there!"

[1204] In this way, the system takes the user's emotional state into consideration to help make cleaning more comfortable and effective.

[1205] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1206] Step 1:

[1207] The user takes a picture.

[1208] The user takes a picture of the object they want to clean (for example, a kitchen gas range) with their smartphone camera. The input is the image of the object to be cleaned, and the output is the image data captured by the camera. Specifically, the user captures the image of the object by pressing the "take a picture" button.

[1209] Step 2:

[1210] The device uploads the image.

[1211] The user uploads the image they have taken to a dedicated application or website. The device then sends the image data to the server. The input is the image data taken by the camera, and the output is the image data sent to the server. Specifically, the user presses the "Upload" button, and the progress is displayed until the image data is sent to the server.

[1212] Step 3:

[1213] The server receives the image.

[1214] The server receives image data sent from the terminal. The input is the image data sent from the terminal, and the output is the image data temporarily stored on the server. Specifically, the server receives image data through an HTTP request and temporarily stores it.

[1215] Step 4:

[1216] The server analyzes the image.

[1217] The server uses a deep learning-based object detection model (e.g., YOLO or ResNet) to identify the object and the type of dirt in the image. The input is the received image data, and the output is the analysis result indicating the object and the type of dirt. Specifically, the image data is input into the image analysis model, and information on the type of object and dirt is extracted.

[1218] Step 5:

[1219] The terminal takes a picture of the user's face and sends it to the server.

[1220] The device takes a picture of the user's face with a camera and sends it to the server. The input is the user's face image, and the output is the face image data sent to the server. Specifically, when the user presses the "face recognition" button, the camera automatically takes a face image and sends the data to the server.

[1221] Step 6:

[1222] The server recognizes emotions using an emotion engine.

[1223] The server uses an emotion engine to analyze the user's facial image and identify the user's emotional state (e.g., stress, joy, fatigue). The input is the transmitted facial image data, and the output is an analysis result indicating the user's emotional state. Specifically, the facial image is input into an emotion recognition model, and the emotional state is estimated.

[1224] Step 7:

[1225] The server retrieves the information from the database.

[1226] Based on the results of image analysis, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents for the object and type of dirt. The input is the image analysis results, and the output is information about the optimal cleaning method and necessary tools and detergents. Specifically, it uses an SQL query to search for records of cleaning methods that correspond to the type of dirt.

[1227] Step 8:

[1228] The server generates cleaning procedures taking into account emotional information.

[1229] The server takes the user's emotional state into account when generating cleaning instructions based on the acquired information. The input is the acquired cleaning method and emotion recognition results, and the output is an adjusted cleaning procedure and an encouraging message. Specifically, it selects steps tagged as "easy" and adds a text message.

[1230] Step 9:

[1231] The server sends the results to the terminal.

[1232] The server sends the generated cleaning instructions and a list of the required tools and detergents to the terminal. The input is the adjusted cleaning instructions and information on the required tools and detergents, and the output is the data to be sent to the terminal. Specifically, the results are packaged in JSON format and sent to the terminal as an HTTP response.

[1233] Step 10:

[1234] The terminal displays the results.

[1235] The terminal displays the information received from the server to the user. The input is the data sent from the server, and the output is the displayed cleaning procedure, tool list, detergent information, and encouraging messages. Specifically, this information is displayed on the UI of the dedicated application.

[1236] (Application example 2)

[1237] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1238] In conventional self-driving vehicles, cleaning the interior of the vehicle is a time-consuming task, and it is difficult to determine the optimal cleaning method. Furthermore, cleaning methods are suggested without taking into account the emotional state of the driver or passengers, which can place a heavy burden on users and cause stress. Especially after a long drive or on the way home from work, users often do not have the energy to clean. This makes it difficult to maintain the interior environment, resulting in a decrease in user comfort.

[1239] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1240] In this invention, the server includes: means for inputting an image of an object that a user wants to clean; means for analyzing the image using image analysis means and recognizing the object and the type of dirt on it; means for identifying an optimal cleaning method and necessary tools and detergents according to the recognition results based on the object and the type of dirt; means for providing the user with information about the cleaning method, tools, and detergents; means for periodically taking and transmitting images of the interior of the autonomous vehicle using a camera installed in the autonomous vehicle; means for acquiring facial images of the driver and passengers and performing emotion analysis on them; means for generating a cleaning procedure based on the analysis results including the acquired emotion information and providing an encouraging message to the user; and means for displaying the information on an in-vehicle display. This enables the interior of the autonomous vehicle to be cleaned effectively and without burdening the user.

[1241] The "means for inputting an image of an object that the user wishes to clean" is an interface that allows the user to take an image of the object to be cleaned and upload it to the system.

[1242] "Image analysis means" refers to the technology or algorithms used to analyze the transmitted image and identify the type of object or dirt in the image.

[1243] "Means for identifying the optimal cleaning method and the necessary tools and detergents" refers to a process or mechanism for determining the optimal cleaning method and the necessary tools and detergents based on the results of image analysis.

[1244] The "means for providing the user with information on cleaning methods, tools, and detergents" is a function that displays or notifies the user of the optimal cleaning method, necessary tools, and detergents based on the analysis results.

[1245] "Autonomous vehicle-mounted camera" means a camera mounted inside or outside an autonomous vehicle that monitors and analyzes the environment as part of the autonomous driving system.

[1246] "Means for acquiring facial images and performing emotion analysis" refers to technologies and systems for capturing a user's facial image and analyzing and identifying the user's emotional state from the captured image.

[1247] The "means for providing an encouraging message" is a function for generating and displaying a message to cheer up the user in accordance with the analyzed emotional state of the user.

[1248] An "in-vehicle display" is a display device installed inside an autonomous vehicle that serves as a user interface and indicates the vehicle's status.

[1249] This invention is a system for effectively cleaning the environment inside an autonomous vehicle. The user inputs an image of the object they wish to clean, and the system analyzes the image and suggests the optimal cleaning method. Furthermore, by recognizing emotions from facial images, the system adjusts cleaning procedures and provides encouraging messages according to the user's emotional state. The following describes a specific embodiment of this system.

[1250] Hardware and software used

[1251] 1. Hardware:

[1252] Cameras installed in self-driving vehicles: devices that periodically capture images of the interior of the vehicle.

[1253] In-car display: A device for displaying suggested cleaning methods and messages.

[1254] Facial image recognition camera: A device placed inside an autonomous vehicle to capture facial images of the user.

[1255] 2. Software:

[1256] Image analysis method: Using object detection algorithms such as YOLO (You Only Look Once), images of the interior of the vehicle are analyzed to identify the location and type of dirt.

[1257] Emotion engine: Analyzes the user's emotional state from facial images using the emotion analysis functions of Microsoft Azure Face API and Amazon Rekognition.

[1258] Processing Description

[1259] 1. Image input and analysis:

[1260] The server receives images of the interior of the autonomous vehicle sent from the camera. Image analysis is used to identify objects in the image and the type of dirt present. Deep learning-based object detection algorithms such as YOLO are used.

[1261] 2. Emotion recognition:

[1262] A facial image recognition camera in an autonomous vehicle captures a user's facial image and sends it to a server, which then uses an emotion engine to analyze and identify the user's emotional state (stress, fatigue, joy, etc.). This uses Microsoft Azure Face API and Amazon Rekognition.

[1263] 3. Generate cleaning procedures:

[1264] Based on the analysis results, the server retrieves information from a database about the optimal cleaning method, necessary tools, and detergents for each type of object and dirt. It also generates cleaning instructions taking into account the user's emotional state and adds encouraging messages.

[1265] 4. Provision of Information:

[1266] The server sends the generated cleaning instructions and encouraging messages to a display in the car, where the user can check the information and follow the instructions to clean.

[1267] Specific examples

[1268] For example, if a user spills a drink, the in-car camera takes a picture of the spill and sends it to the server. The server analyzes the image and identifies the location of the spilled drink. At the same time, it recognizes from the facial image that the user is feeling stressed. As a result, it displays cleaning instructions such as "Quickly wipe up the spilled drink with a paper towel, then use a cleaner" along with an encouraging message such as "You're almost there, keep trying."

[1269] Prompt Sentence Examples

[1270] "Please upload a picture of the interior of the car."

[1271] "Reading face image"

[1272] "For spilled drinks, wipe them up with paper towels and use cleaner."

[1273] "You're feeling stressed right now. I'll suggest some easy cleaning methods."

[1274] "It's almost there, just do your best."

[1275] In this way, the present invention can effectively clean the interior of an autonomous vehicle, reducing the burden on the user and providing a comfortable in-vehicle environment.

[1276] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1277] Step 1:

[1278] The user takes a picture of the object they want to clean. The image is saved on a device such as a smartphone or tablet. The device then uploads the image to the system's application or website, and sends the image data to a server.

[1279] Input: Image of object to be cleaned

[1280] Output: Image data sent to the server

[1281] Step 2:

[1282] The server receives the image data sent from the device. It then uses image analysis techniques (for example, an object detection model using deep learning) to identify the object in the image and the type of dirt. If the object is a kitchen gas range, it will recognize oil stains, etc.

[1283] Input: Received image data

[1284] Output: Analysis results including location and type of contamination

[1285] Step 3:

[1286] A facial image recognition camera in the autonomous vehicle captures a user's facial image, which is then sent to a server, which uses an emotion engine to identify the user's emotional state (e.g., stress, fatigue, joy).

[1287] Input: Face image data

[1288] Output: Analysis results showing the user's emotional state

[1289] Step 4:

[1290] Based on the image analysis results from step 2 and the emotion analysis results from step 3, the server retrieves information from a database about optimal cleaning methods, necessary tools, and detergents. It also generates cleaning instructions taking into account the user's emotional state and adds encouraging messages. For example, if the user is feeling stressed, it generates simple and effective cleaning instructions.

[1291] Input: Image analysis results, emotion analysis results

[1292] Output: Optimal cleaning instructions and encouraging messages

[1293] Step 5:

[1294] The server sends the generated cleaning instructions and encouraging messages to a display in the car, which visually displays this information to the user. The user then follows the suggested cleaning instructions while checking the display.

[1295] Input: Cleaning instructions and encouraging messages

[1296] Output: Information shown on the display

[1297] This allows users to receive optimal cleaning procedures and encouraging messages based on their emotional state, allowing them to clean efficiently.

[1298] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1299] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1300] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1301] [Fourth embodiment]

[1302] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1303] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1304] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1305] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1306] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1307] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1308] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1309] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1310] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1311] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1312] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1313] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1314] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1315] The present invention is a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method, as well as the necessary tools and detergents. Detailed embodiments of this system will be described below with specific examples.

[1316] System Overview

[1317] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of this analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user.

[1318] Program processing

[1319] Step 1: Upload an image

[1320] The user takes a picture

[1321] The user takes a picture of the object they want to clean (for example, a gas range in the kitchen or tiles in the bathroom) using their smartphone camera.

[1322] The device uploads the image

[1323] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[1324] Step 2: Image analysis

[1325] The server receives the image

[1326] The server receives the image data sent from the terminal.

[1327] The server analyzes the image

[1328] The server uses image analysis means (e.g., an object detection model using deep learning) to identify the objects and types of dirt appearing in the image.

[1329] Step 3: Cleaning instructions

[1330] The server retrieves the information from the database

[1331] Based on the analysis results, the server retrieves information from the database regarding the optimal cleaning method, necessary tools and detergents depending on the object and type of dirt.

[1332] The server generates the cleaning instructions.

[1333] The server uses the acquired information to generate effective cleaning instructions, including how to use detergents, tools, and precautions.

[1334] Step 4: Provide information

[1335] The server sends the results to the device

[1336] The server sends the generated cleaning instructions and a list of necessary tools and detergents to the terminal.

[1337] The terminal displays the results

[1338] The device displays the information received from the server to the user. For example, the device screen may display instructions such as "Spray oil-stain detergent on the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[1339] Specific examples

[1340] Consider a case where a user wants to clean stubborn grease stains from a gas range in the kitchen.

[1341] 1. Take and upload an image

[1342] Users take a photo of a dirty gas range with their smartphone and upload it to the system.

[1343] 2. Image Analysis

[1344] The server identifies the gas range and oil stains using image analysis means.

[1345] 3. Cleaning instructions

[1346] The server retrieves the cleaning method appropriate for the oil stain from the database and recognizes that detergent and a scraper are required.

[1347] 4. Provision of Information

[1348] The server generates specific instructions such as "spray oil stain detergent, leave for 10 minutes, then remove the stain with a scraper and wipe with a sponge" and sends them to the terminal.

[1349] 5. Display results

[1350] The device displays instructions to the user, who then follows the instructions to clean. This allows users to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[1351] In this way, the system allows users to easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[1352] The processing flow will be explained below.

[1353] Step 1:

[1354] The user takes a picture.

[1355] The user takes a picture of the object they want to clean (e.g., the gas range in the kitchen) using their smartphone camera.

[1356] Step 2:

[1357] The device uploads the image.

[1358] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[1359] Step 3:

[1360] The server receives the image.

[1361] The server receives the image data sent from the terminal and prepares for analysis.

[1362] Step 4:

[1363] The server analyzes the image.

[1364] The server uses image analysis algorithms (e.g., deep learning-based object detection models) to identify objects in the image and the type of dirt on them, for example, recognizing grease stains on a gas range.

[1365] Step 5:

[1366] The server retrieves the information from the database.

[1367] Based on the analysis results, the server retrieves information from its internal database regarding cleaning methods, necessary tools, and detergents according to the type of object and dirt.

[1368] Step 6:

[1369] The server generates the cleaning procedure.

[1370] The server generates effective cleaning instructions based on the acquired information. These instructions include how to use detergents, tools, and precautions. For example, it generates specific instructions such as "spray oil-stain detergent on the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[1371] Step 7:

[1372] The server sends the results to the terminal.

[1373] The server sends the generated cleaning procedure and a list of necessary tools and detergents to the terminal.

[1374] Step 8:

[1375] The terminal displays the results.

[1376] The terminal displays the information received from the server to the user, for example, by displaying a list of cleaning procedures and the necessary tools and detergents on the terminal screen.

[1377] Step 9:

[1378] The user follows the instructions to clean.

[1379] The user prepares the necessary tools and detergents based on the instructions displayed on the terminal and performs effective cleaning.

[1380] In this way, the present system helps the user to easily acquire an appropriate cleaning method and perform effective cleaning through a series of steps.

[1381] Example 1

[1382] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1383] Conventional cleaning methods rely heavily on experience and intuition, making it difficult to select the appropriate procedures, tools, and detergents. Furthermore, not knowing effective cleaning methods can lead to wasted time and effort. Furthermore, there are concerns about the handling of image data due to privacy concerns. Therefore, there is a need for a system that allows users to easily obtain appropriate cleaning methods and clean effectively while protecting their privacy.

[1384] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1385] In this invention, the server includes means for inputting an image of an object that a user wants to clean, means for analyzing the image using image analysis means to recognize the object and the type of dirt on it, means for identifying the optimal cleaning method and necessary tools and detergent for the object and the type of dirt based on the recognition results, means for providing the user with information about the cleaning method, tools, and detergent, means for encrypting image data uploaded from the terminal to protect the user's privacy, and means for retrieving the optimal cleaning method based on the analysis from a database and generating an effective cleaning procedure. This allows the user to easily obtain an appropriate and effective cleaning method, significantly reducing the effort and time required for cleaning while protecting their privacy.

[1386] "User" refers to an individual who uses this system to receive cleaning method suggestions.

[1387] "Device" refers to an electronic device that a user uses to take images and upload them to the system, such as a smartphone or tablet.

[1388] "Server" refers to a computer system that analyzes images sent by users and performs the data processing necessary to provide appropriate cleaning methods.

[1389] "Image analysis means" refers to an algorithm executed on the server that utilizes deep learning to identify objects and stains within an image.

[1390] The "recognition result" refers to information about the object and the type of dirt on it obtained by the image analysis means.

[1391] "Cleaning method" refers to the optimal cleaning procedure identified based on the recognition results, including specific methods for using detergents and tools.

[1392] "Tools" refer to items needed to carry out the cleaning method, such as scrapers and sponges.

[1393] "Detergent" refers to a chemical product used to remove specific stains.

[1394] "Database" refers to a digital storage device that stores information about cleaning methods, tools, and detergents.

[1395] "Means for providing information" refers to the mechanism for transmitting and displaying the cleaning instructions and list of required tools and detergents generated by the server to the user's device.

[1396] "Encryption" is a technology for protecting communication data, and refers to the process of making the contents incomprehensible to anyone other than the recipient.

[1397] "Protecting privacy" refers to a series of measures to prevent users' personal information from being leaked to third parties.

[1398] An "effective cleaning procedure" is one that is designed to achieve maximum effectiveness with minimum effort and time.

[1399] This invention is a comprehensive cleaning support system that allows a user to input an image of an object they wish to clean, and a server analyzes the image and suggests an appropriate cleaning method, as well as the necessary tools and detergents. This system starts when a user takes an image of the object using a device such as a smartphone or tablet and uploads the image data to the server.

[1400] Hardware and software used

[1401] The main hardware used in this invention includes the user's device, such as a smartphone or tablet, and a server consisting of a high-performance computer. The software used is a deep learning-based object detection model for image analysis. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used.

[1402] Specific data processing and calculation

[1403] When a user takes a picture of an object they wish to clean with their device, the device uploads the image data to a server. The uploaded image data is encrypted to protect the user's privacy. When the server receives the image data, it uses image analysis means to identify the object in the image and the type of dirt. Based on the analysis results, the server then retrieves information from a database about the optimal cleaning method, as well as the necessary tools and detergents.

[1404] Based on the acquired information, the server generates effective cleaning instructions. For example, if the analysis results indicate "grease stains on the gas range," the server will create specific instructions such as "spray oil-removing detergent, leave for 10 minutes, then remove the stains with a scraper and wipe with a sponge." The generated cleaning instructions are sent from the server to the device and displayed to the user.

[1405] Specific examples

[1406] Take the example of a user wanting to clean stubborn grease stains from a gas range in the kitchen.

[1407] 1. Take and upload an image

[1408] Users take a photo of a dirty gas range with their smartphone and upload it to the system, where the image is encrypted and sent from the device to a server.

[1409] 2. Image Analysis

[1410] The server uses a deep learning model (e.g., using TensorFlow or PyTorch) to identify the gas range and its oil stains.

[1411] 3. Cleaning instructions

[1412] The server retrieves the cleaning method appropriate for the oil stain from the database and recognizes that detergent and a scraper are required.

[1413] 4. Provision of Information

[1414] The server generates specific instructions such as "spray oil stain detergent, leave for 10 minutes, then remove the stain with a scraper and wipe with a sponge" and sends them to the terminal.

[1415] 5. Display results

[1416] The device displays instructions to the user, who then follows the instructions to clean. This allows users to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[1417] Prompt Sentence Examples

[1418] The following are examples of specific prompt sentences:

[1419] "Please analyze the image of the dirty gas range and tell me how to clean it effectively. Also, what tools and cleaning agents are needed?"

[1420] In this way, the system allows users to easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[1421] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1422] Step 1:

[1423] The user takes a picture of the object

[1424] The user uses the camera on their smartphone or tablet to take a picture of the object they want to clean (e.g., kitchen stove, bathroom tiles, etc.). The input is an image of the area that needs cleaning. This image is the data that will be used for further processing. The output is the captured image file.

[1425] Step 2:

[1426] The device uploads the image to the system

[1427] The user uploads the image they have taken to a dedicated application or website. The image taken by the user is given as input. The device uses an Internet connection to send the image data to a server. At this time, the image data is encrypted to protect the user's privacy. The output is the encrypted image data sent to the server.

[1428] Step 3:

[1429] The server receives the image

[1430] The server receives the encrypted image data sent from the terminal. As input, it has the encrypted image data sent from the terminal. The server decrypts this data and verifies the integrity of the image data. The output is the decrypted image data.

[1431] Step 4:

[1432] The server analyzes the image

[1433] The server uses a deep learning-based object detection model (e.g., using TensorFlow or PyTorch) to identify objects and their stain types in the image. As input, it receives the decoded image data. Advanced image recognition algorithms are used to analyze the data. The output is a recognition result of the object and stain type.

[1434] Step 5:

[1435] The server retrieves the information from the database

[1436] Based on the results of image analysis, the server retrieves information from the database about the optimal cleaning method for the object and type of dirt, as well as the necessary tools and detergents. The input is the recognition results of image analysis. The server sends a query to the database to retrieve the necessary information. The output is information about the optimal cleaning method and the necessary tools and detergents.

[1437] Step 6:

[1438] The server generates the cleaning instructions.

[1439] The server generates specific and effective cleaning procedures based on the acquired information. For example, if the analysis results indicate "grease stains on the gas range," it generates the procedure "spray oil stain detergent, leave for 10 minutes, then remove the stains with a scraper and wipe with a sponge." The input is the cleaning method and information on tools and detergents acquired from the database. The output is the generated specific cleaning procedure.

[1440] Step 7:

[1441] The server sends the results to the device

[1442] The server sends the generated cleaning instructions and a list of the necessary tools and detergents to the terminal. The input is the specific cleaning instructions and information on the tools and detergents. The server encrypts this information and sends it to the terminal. The output is the encrypted result data sent to the terminal.

[1443] Step 8:

[1444] The terminal displays the results

[1445] The terminal decrypts the information received from the server and displays it to the user. The input is the encrypted result data sent from the server. The terminal decrypts the data and displays it in a format that is easy for the user to understand. The output is specific cleaning steps and information on the necessary tools and detergents that the user can see. This allows the user to clean according to the provided steps.

[1446] (Application example 1)

[1447] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1448] Today's cleaning tasks are diverse, and users often spend a lot of time and effort selecting the appropriate cleaning method, tools, and detergents. Furthermore, in commercial facilities and brick-and-mortar stores, efficient and effective cleaning is essential, but the specific methods and procedures for achieving this are often unclear. To address these challenges, there is a need for a comprehensive cleaning support system that allows users to easily input an image of the object to be cleaned, and the system will suggest the appropriate cleaning method and the necessary tools and detergents.

[1449] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1450] In this invention, the server includes means for inputting an image of an object that a user wants to clean, means for analyzing the image using image analysis means to recognize the object and the type of dirt on it, means for identifying the optimal cleaning method and necessary tools and detergents according to the object and the type of dirt based on the recognition results, means for providing information about the cleaning method, tools, and detergents to the user, and means for using an electronic device to display the results. This allows the user to quickly and accurately obtain information about the optimal cleaning method and necessary tools and detergents simply by inputting an image.

[1451] A "user" is a person who inputs an image of an object to be cleaned or a user of the system.

[1452] An "object" is a specific object or location that a user wishes to clean.

[1453] An "image" is visual data that is captured or uploaded by a user using an input means.

[1454] "Image analysis means" refers to technology or algorithms for analyzing input images and recognizing the type of object and dirt.

[1455] The "recognition result" is information about the object and the type of dirt obtained by the image analysis means.

[1456] The "cleaning method" refers to the cleaning procedure or technique that is determined based on the recognition results and is appropriate for the type of object and dirt.

[1457] "Tools" are the tools and equipment needed to carry out the cleaning method.

[1458] "Detergents" are cleaning agents or chemicals used in carrying out cleaning methods.

[1459] "Means for providing information" refers to a method or device for informing or displaying information to the user about appropriate cleaning methods, tools, and detergents.

[1460] "Electronic devices" are terminals or devices used to display cleaning method, tool and detergent information to users.

[1461] The present invention is a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method and the necessary tools and detergents. Detailed embodiments for carrying out the present invention will be described below with specific examples.

[1462] System Overview

[1463] This system allows users to upload images of the object they wish to clean from a device (such as a smartphone or tablet), which is then analyzed on a server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. The main hardware used is a smartphone camera, server, and electronic device (device), while the software uses deep learning models, image recognition algorithms, and libraries for processing HTTP requests (such as Python requests).

[1464] Program processing

[1465] The program performs the following data processing and calculations:

[1466] 1. User takes a picture:

[1467] The user uses the smartphone camera to take a picture of the object they want to clean, such as a glass window or shelf in a store.

[1468] 2. The device uploads the image:

[1469] The user uploads the image they took to a system application or website. The device sends the image data to the server. The image data is uploaded to the API server using an HTTP POST request.

[1470] 3. The server analyzes the image:

[1471] The server analyzes the received image data using an image recognition algorithm powered by deep learning, which identifies the object and type of dirt. The deep learning model uses a generative AI model trained for object detection and classification.

[1472] 4. Identify cleaning methods based on the type of soiling:

[1473] Based on the recognition results, the server retrieves information from the database about the optimal cleaning method, necessary tools, and detergents for each type of object and dirt. The cleaning procedure includes how to use the detergent and tools, as well as precautions.

[1474] 5. Provision of Information:

[1475] The server then provides the user with the information about cleaning methods, tools, and detergents. The user then cleans by referring to the instructions displayed on the device screen. For example, the specific instructions displayed might be, "Spray oil-repellent detergent onto the stain, leave it for 10 minutes, then remove the stain with a scraper and wipe it off with a sponge."

[1476] Specific examples

[1477] Consider a case where a user wants to clean stubborn stains on a glass window in a brick-and-mortar store. The user takes a photo of the dirty glass window with their smartphone and uploads the image to the system. The server uses image analysis to identify the glass window and the stain, and generates information on the appropriate cleaning method, tools, and detergents, and provides it to the user.

[1478] Example prompt sentence:

[1479] "I would like the glass windows of my store cleaned. Please analyze this image and suggest the best cleaning method, tools, and detergents needed."

[1480] This allows users to perform cleaning tasks efficiently and effectively. Through this system, users can easily obtain appropriate and effective cleaning methods, significantly reducing the effort and time required for cleaning.

[1481] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1482] Step 1:

[1483] The user takes a picture

[1484] The user uses the smartphone camera to take an image of the object they want to clean (e.g., a glass window or shelf in a store). The input is the image data captured by the camera, and the output is an image file stored on the smartphone. This image file is used in subsequent processing steps.

[1485] Step 2:

[1486] The device uploads the image

[1487] The user takes a photo and uploads it to a system application or website. The input is the image file taken earlier, and the application sends the image data to the server using an HTTP POST request. The output is the status of the completion of sending the image data to the server.

[1488] Step 3:

[1489] The server receives the image

[1490] The server receives image data sent from the terminal. The input is an HTTP request from the terminal, and the server processes it to obtain the image data. The output is image data stored in a temporary file system on the server.

[1491] Step 4:

[1492] The server analyzes the image

[1493] The server analyzes the received image data. This step uses an image recognition algorithm that utilizes deep learning. The input is the image data stored on the server, and a generative AI model is used to recognize the object and type of dirt. The output is data on the object and type of dirt as the recognition result.

[1494] Step 5:

[1495] Identifying cleaning methods according to the type of dirt

[1496] The server retrieves information about the optimal cleaning method and the necessary tools and detergents from a database based on the recognition results. The input is the data about the recognition results, and the server uses this to execute a database query. The output is the retrieved information about the cleaning method and the necessary tools and detergents.

[1497] Step 6:

[1498] Providing information

[1499] The server organizes the acquired information about cleaning methods, tools, and detergents, and generates data to provide to the user. The input is information about cleaning methods, tools, and detergents, and the output is specific instruction data to display to the user.

[1500] Step 7:

[1501] The terminal displays the results

[1502] The terminal receives the result data sent from the server and displays it to the user. The input is the result data from the server, which the application visualizes and presents to the user. The output is the specific cleaning procedure and information on the necessary tools and detergents displayed on the terminal screen.

[1503] This allows the user to obtain the information necessary to perform cleaning work efficiently and effectively through a series of processing steps.

[1504] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1505] This invention combines a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, the system analyzes the image, and suggests an appropriate cleaning method and the necessary tools and detergents, with an emotion engine that recognizes the user's emotions. The emotion engine adjusts the cleaning method according to the user's emotional state and provides encouragement as needed, making cleaning work more comfortable and effective.

[1506] System Overview

[1507] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. It also combines an emotion engine that recognizes emotions from the user's facial images, and adjusts the cleaning method suggestions according to the user's emotional state.

[1508] Program processing

[1509] Step 1: Upload an image

[1510] The user takes a picture

[1511] The user takes an image of the object they want to clean (for example, a gas range in the kitchen) using the smartphone camera.

[1512] The device uploads the image

[1513] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[1514] Step 2: Image analysis

[1515] The server receives the image

[1516] The server receives the image data sent from the terminal.

[1517] The server analyzes the image

[1518] The server uses image analysis techniques (e.g., object detection models using deep learning) to identify objects in the image and the type of dirt on them, for example, recognizing oil stains on a gas range.

[1519] Step 3: Emotion recognition with the emotion engine

[1520] Get a user's face image

[1521] The device takes a picture of the user's face with a camera and sends it to the server.

[1522] The server recognizes emotions using an emotion engine

[1523] The server uses an emotion engine to analyze the user's facial image and determine the user's current emotional state (e.g., stress, joy, fatigue).

[1524] Step 4: Cleaning instructions

[1525] The server retrieves the information from the database

[1526] Based on the analysis results, the server retrieves information from its internal database regarding the optimal cleaning method, necessary tools and detergents depending on the object and type of dirt.

[1527] The server generates cleaning procedures taking into account emotional information.

[1528] The server takes the user's emotional state into account when generating effective cleaning instructions based on the information it has acquired. For example, if the user is feeling stressed, it will suggest simple and effective cleaning methods. Another feature is that it can also display encouraging messages.

[1529] Step 5: Provide information

[1530] The server sends the results to the device

[1531] The server transmits the generated cleaning procedure and a list of necessary tools and detergents to the terminal.

[1532] The terminal displays the results

[1533] The terminal displays the information received from the server to the user.

[1534] Specific examples

[1535] If a user wants to clean stubborn grease stains from their kitchen gas range:

[1536] 1. Take and upload an image

[1537] Users take a photo of a dirty gas range with their smartphone and upload it to the system.

[1538] 2. Image Analysis

[1539] The server identifies the gas range and oil stains using image analysis means.

[1540] 3. Emotion recognition

[1541] The server analyzes the user's facial image and recognizes that the user is feeling stressed.

[1542] 4. Cleaning instructions

[1543] The server retrieves cleaning methods suitable for oil stains from the database and generates simple and effective cleaning instructions taking into account the user's emotional state, including encouraging messages.

[1544] 5. Provision of Information

[1545] The server generates specific instructions such as "Spray the oil stain detergent, leave it for 10 minutes, then remove the dirt with a scraper and wipe it off with a sponge," along with an encouraging message such as "Keep up the good work, you're almost there!" and sends it to the device.

[1546] 6. Results display

[1547] The device provides instructions and encouraging messages to the user, who then follows the instructions to clean, allowing them to complete the cleaning efficiently without the need for tedious preparations or trying methods whose effectiveness is unknown.

[1548] In this way, the system takes into account the user's emotional state, helping them to clean more comfortably and effectively.

[1549] The processing flow will be explained below.

[1550] Step 1:

[1551] The user takes an image of the object to be cleaned.

[1552] The user takes a picture of the object they want to clean (e.g., the gas range in the kitchen) using their smartphone camera.

[1553] Step 2:

[1554] The device uploads the image.

[1555] The user uploads the captured image to the system's application or website, and the device sends the image data to the server.

[1556] Step 3:

[1557] The server receives the image.

[1558] The server receives the image data sent from the terminal and prepares for analysis.

[1559] Step 4:

[1560] The server analyzes the image.

[1561] The server uses image analysis algorithms (e.g., deep learning-based object detection models) to identify objects in the image and the type of dirt on them, for example, recognizing grease stains on a gas range.

[1562] Step 5:

[1563] The terminal takes a picture of the user's face.

[1564] To analyze the user's facial expression, the device's camera is used to capture an image of the user's face.

[1565] Step 6:

[1566] The device sends the facial image to the server.

[1567] The terminal transmits the captured image data of the user's face to the server.

[1568] Step 7:

[1569] The server analyzes the facial image.

[1570] The server uses an emotion engine to analyze the user's facial image and recognize the user's emotional state (for example, whether they are feeling stressed or happy).

[1571] Step 8:

[1572] The server retrieves the information from the database.

[1573] Based on the results of image analysis and emotion recognition, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents depending on the object and type of dirt.

[1574] Step 9:

[1575] The server generates the cleaning procedure.

[1576] The server generates effective cleaning instructions based on information retrieved from the database, including instructions on how to use cleaning products, tools, and precautions, and may also include emotional adjustments and encouraging messages based on the user's emotional state.

[1577] Step 10:

[1578] The server sends the results to the terminal.

[1579] The server sends the generated cleaning instructions, a list of necessary tools and detergents, and a message based on the user's emotion to the device.

[1580] Step 11:

[1581] The terminal displays the results.

[1582] The device displays the information received from the server to the user, such as "Spray the detergent for grease stains, leave it for 10 minutes, then remove the stains with a scraper and wipe it off with a sponge," along with an encouraging message such as "Keep up the good work, you're almost there!"

[1583] Step 12:

[1584] The user follows the instructions to clean.

[1585] Users can prepare the necessary tools and detergents and carry out effective cleaning according to the instructions displayed on the device, allowing them to complete cleaning efficiently without having to go through tedious preparations or trying methods whose effectiveness is unknown.

[1586] In this way, this system allows users to easily acquire appropriate cleaning methods through a series of steps, and by taking their emotional state into consideration, it reduces the burden of cleaning and supports comfortable work.

[1587] Example 2

[1588] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1589] Conventional cleaning support systems only analyze images of the object the user wants to clean and provide the optimal cleaning method and necessary tools and detergents, but they are unable to take the user's emotional state into consideration, which can make cleaning tasks stressful. Furthermore, because the system does not respond individually to the user's emotions, it is difficult to maintain motivation to clean.

[1590] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting an image of an object that the user wants to clean, a means for analyzing the image using an image analysis means and recognizing the object and the type of dirt on it, a means for acquiring an image of the user's face and recognizing their emotions, a means for identifying an optimal cleaning method and necessary tools and detergents according to the object and the type of dirt based on the recognition results, a means for adjusting the cleaning method according to the user's emotional state and generating an encouraging message as necessary, and a means for providing the user with information about the cleaning method, tools, and detergents. This allows the system to suggest an optimal cleaning method taking the user's emotional state into consideration, enabling the user to perform cleaning work comfortably and effectively.

[1591] "User" refers to an individual who uses the system to receive cleaning support.

[1592] "Object desired to be cleaned" refers to an object or surface that the user wishes to clean.

[1593] "Image" refers to a photograph, picture, or the like that contains visual information about the object that the user wants to clean.

[1594] "Image analysis means" refers to algorithms and techniques that process image data and identify the object and the type of dirt on it.

[1595] The term "target object" refers to an object that is recognized by the image analysis means and that is desired to be cleaned.

[1596] "Soil type" refers to the type of foreign matter or contamination present on the surface of the object.

[1597] "Optimal cleaning method" refers to the most effective and efficient cleaning procedure for the object and type of soil.

[1598] "Tools" refers to the equipment and implements required to carry out the cleaning.

[1599] "Detergent" refers to chemicals or liquid cleaning agents used in cleaning.

[1600] "User's facial image" refers to visual data that captures the user's facial features.

[1601] "Means for recognizing emotions" refers to technologies and algorithms that analyze a user's facial image and estimate their emotional state.

[1602] An "emotional state" refers to the psychological state (e.g., stress, joy, fatigue, etc.) that a user is feeling at a particular point in time.

[1603] "Encouraging messages" refer to text messages that are suggested based on the user's emotional state to improve motivation and support.

[1604] "Means for providing information" refers to the means or method by which the server communicates the analysis results and optimal cleaning methods to the user.

[1605] This invention combines a comprehensive cleaning support system in which a user inputs an image of an object they wish to clean, and the system analyzes the image to suggest an appropriate cleaning method and the necessary tools and detergents, with an emotion engine that recognizes the user's emotions. The emotion engine adjusts the cleaning method according to the user's emotional state and provides encouragement as needed, making cleaning work more comfortable and effective.

[1606] System Overview

[1607] This system allows users to upload images of the object they wish to clean from their device (such as a smartphone or tablet), which is then analyzed on the server. Based on the results of the analysis, the system identifies the optimal cleaning method, necessary tools, and detergents, and provides this information to the user. It also combines an emotion engine that recognizes emotions from the user's facial images, and adjusts the cleaning method suggestions according to the user's emotional state.

[1608] Hardware and software used

[1609] Device: A device such as a smartphone or tablet that has a camera function. Use a dedicated application or web browser.

[1610] Server: A high-performance computing resource operated in a cloud environment or on-premise, containing software to run databases, deep learning models, emotion engines, etc.

[1611] Image analysis method: An object detection model using deep learning (e.g., YOLO, ResNet) is installed on the server.

[1612] Emotion engine: An emotion recognition model that utilizes deep learning (e.g., FaceNet, VGGFace) is installed on the server.

[1613] Specific actions

[1614] 1. The user takes a picture of the object they want to clean (e.g., a kitchen gas range) using their smartphone camera, and then uploads the image to a server via a dedicated application or website.

[1615] 2. The server receives the uploaded image and analyzes it using a deep learning-based object detection model to identify the object (e.g., gas range) and the type of dirt (e.g., grease stains).

[1616] 3. Next, the device takes a picture of the user's face and sends it back to the server. The server uses an emotion engine to analyze the face image and recognize the user's emotional state (e.g., stress, joy, fatigue).

[1617] 4. Based on the results of image analysis and emotion recognition, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents, and generates specific and effective cleaning instructions while taking into account the user's emotional state.

[1618] 5. If the user feels stressed, the robot will generate an encouraging message to help the user clean the room comfortably.

[1619] 6. Finally, the server sends the generated cleaning instructions, a list of necessary tools and detergents, and an encouraging message to the user's device, which then displays the information on its screen.

[1620] Specific examples

[1621] If a user wants to clean stubborn grease stains from a gas range in the kitchen, the process works as follows.

[1622] 1. A user takes a photo of a dirty gas range with their smartphone and uploads it to the system.

[1623] 2. The server uses deep learning to identify the gas range and oil stains in the image.

[1624] 3. The device sends the facial image to the server, which then performs emotion recognition and determines that the user is feeling stressed.

[1625] 4. The server retrieves cleaning methods suitable for the oil stain from the database and generates simple and effective cleaning instructions taking into account the user's emotional state, as well as encouraging messages such as "Keep up the good work, you're almost there!"

[1626] 5. The server sends specific instructions and an encouraging message to the device, such as "Spray the detergent for grease stains, leave it for 10 minutes, then remove the stains with a scraper and wipe it off with a sponge."

[1627] 6. The device presents this information to the user, who then follows the instructions to clean efficiently.

[1628] Example prompt sentence:

[1629] "To clean stubborn grease stains, first spray on the surface with a grease cleaner. Let it sit for 10 minutes, then use a scraper to remove the stain and wipe with a sponge. Keep going, you're almost there!"

[1630] In this way, the system takes the user's emotional state into consideration to help make cleaning more comfortable and effective.

[1631] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1632] Step 1:

[1633] The user takes a picture.

[1634] The user takes a picture of the object they want to clean (for example, a kitchen gas range) with their smartphone camera. The input is the image of the object to be cleaned, and the output is the image data captured by the camera. Specifically, the user captures the image of the object by pressing the "take a picture" button.

[1635] Step 2:

[1636] The device uploads the image.

[1637] The user uploads the image they have taken to a dedicated application or website. The device then sends the image data to the server. The input is the image data taken by the camera, and the output is the image data sent to the server. Specifically, the user presses the "Upload" button, and the progress is displayed until the image data is sent to the server.

[1638] Step 3:

[1639] The server receives the image.

[1640] The server receives image data sent from the terminal. The input is the image data sent from the terminal, and the output is the image data temporarily stored on the server. Specifically, the server receives image data through an HTTP request and temporarily stores it.

[1641] Step 4:

[1642] The server analyzes the image.

[1643] The server uses a deep learning-based object detection model (e.g., YOLO or ResNet) to identify the object and the type of dirt in the image. The input is the received image data, and the output is the analysis result indicating the object and the type of dirt. Specifically, the image data is input into the image analysis model, and information on the type of object and dirt is extracted.

[1644] Step 5:

[1645] The terminal takes a picture of the user's face and sends it to the server.

[1646] The device takes a picture of the user's face with a camera and sends it to the server. The input is the user's face image, and the output is the face image data sent to the server. Specifically, when the user presses the "face recognition" button, the camera automatically takes a face image and sends the data to the server.

[1647] Step 6:

[1648] The server recognizes emotions using an emotion engine.

[1649] The server uses an emotion engine to analyze the user's facial image and identify the user's emotional state (e.g., stress, joy, fatigue). The input is the transmitted facial image data, and the output is an analysis result indicating the user's emotional state. Specifically, the facial image is input into an emotion recognition model, and the emotional state is estimated.

[1650] Step 7:

[1651] The server retrieves the information from the database.

[1652] Based on the results of image analysis, the server retrieves information from its internal database about the optimal cleaning method, necessary tools, and detergents for the object and type of dirt. The input is the image analysis results, and the output is information about the optimal cleaning method and necessary tools and detergents. Specifically, it uses an SQL query to search for records of cleaning methods that correspond to the type of dirt.

[1653] Step 8:

[1654] The server generates cleaning procedures taking into account emotional information.

[1655] The server takes the user's emotional state into account when generating cleaning instructions based on the acquired information. The input is the acquired cleaning method and emotion recognition results, and the output is an adjusted cleaning procedure and an encouraging message. Specifically, it selects steps tagged as "easy" and adds a text message.

[1656] Step 9:

[1657] The server sends the results to the terminal.

[1658] The server sends the generated cleaning instructions and a list of the required tools and detergents to the terminal. The input is the adjusted cleaning instructions and information on the required tools and detergents, and the output is the data to be sent to the terminal. Specifically, the results are packaged in JSON format and sent to the terminal as an HTTP response.

[1659] Step 10:

[1660] The terminal displays the results.

[1661] The terminal displays the information received from the server to the user. The input is the data sent from the server, and the output is the displayed cleaning procedure, tool list, detergent information, and encouraging messages. Specifically, this information is displayed on the UI of the dedicated application.

[1662] (Application example 2)

[1663] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1664] In conventional self-driving vehicles, cleaning the interior of the vehicle is a time-consuming task, and it is difficult to determine the optimal cleaning method. Furthermore, cleaning methods are suggested without taking into account the emotional state of the driver or passengers, which can place a heavy burden on users and cause stress. Especially after a long drive or on the way home from work, users often do not have the energy to clean. This makes it difficult to maintain the interior environment, resulting in a decrease in user comfort.

[1665] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1666] In this invention, the server includes: means for inputting an image of an object that a user wants to clean; means for analyzing the image using image analysis means and recognizing the object and the type of dirt on it; means for identifying an optimal cleaning method and necessary tools and detergents according to the recognition results based on the object and the type of dirt; means for providing the user with information about the cleaning method, tools, and detergents; means for periodically taking and transmitting images of the interior of the autonomous vehicle using a camera installed in the autonomous vehicle; means for acquiring facial images of the driver and passengers and performing emotion analysis on them; means for generating a cleaning procedure based on the analysis results including the acquired emotion information and providing an encouraging message to the user; and means for displaying the information on an in-vehicle display. This enables the interior of the autonomous vehicle to be cleaned effectively and without burdening the user.

[1667] The "means for inputting an image of an object that the user wishes to clean" is an interface that allows the user to take an image of the object to be cleaned and upload it to the system.

[1668] "Image analysis means" refers to the technology or algorithms used to analyze the transmitted image and identify the type of object or dirt in the image.

[1669] "Means for identifying the optimal cleaning method and the necessary tools and detergents" refers to a process or mechanism for determining the optimal cleaning method and the necessary tools and detergents based on the results of image analysis.

[1670] The "means for providing the user with information on cleaning methods, tools, and detergents" is a function that displays or notifies the user of the optimal cleaning method, necessary tools, and detergents based on the analysis results.

[1671] "Autonomous vehicle-mounted camera" means a camera mounted inside or outside an autonomous vehicle that monitors and analyzes the environment as part of the autonomous driving system.

[1672] "Means for acquiring facial images and performing emotion analysis" refers to technologies and systems for capturing a user's facial image and analyzing and identifying the user's emotional state from the captured image.

[1673] The "means for providing an encouraging message" is a function for generating and displaying a message to cheer up the user in accordance with the analyzed emotional state of the user.

[1674] An "in-vehicle display" is a display device installed inside an autonomous vehicle that serves as a user interface and indicates the vehicle's status.

[1675] This invention is a system for effectively cleaning the environment inside an autonomous vehicle. The user inputs an image of the object they wish to clean, and the system analyzes the image and suggests the optimal cleaning method. Furthermore, by recognizing emotions from facial images, the system adjusts cleaning procedures and provides encouraging messages according to the user's emotional state. The following describes a specific embodiment of this system.

[1676] Hardware and software used

[1677] 1. Hardware:

[1678] Cameras installed in self-driving vehicles: devices that periodically capture images of the interior of the vehicle.

[1679] In-car display: A device for displaying suggested cleaning methods and messages.

[1680] Facial image recognition camera: A device placed inside an autonomous vehicle to capture facial images of the user.

[1681] 2. Software:

[1682] Image analysis method: Using object detection algorithms such as YOLO (You Only Look Once), images of the interior of the vehicle are analyzed to identify the location and type of dirt.

[1683] Emotion engine: Analyzes the user's emotional state from facial images using the emotion analysis functions of Microsoft Azure Face API and Amazon Rekognition.

[1684] Processing Description

[1685] 1. Image input and analysis:

[1686] The server receives images of the interior of the autonomous vehicle sent from the camera. Image analysis is used to identify objects in the image and the type of dirt present. Deep learning-based object detection algorithms such as YOLO are used.

[1687] 2. Emotion recognition:

[1688] A facial image recognition camera in an autonomous vehicle captures a user's facial image and sends it to a server, which then uses an emotion engine to analyze and identify the user's emotional state (stress, fatigue, joy, etc.). This uses Microsoft Azure Face API and Amazon Rekognition.

[1689] 3. Generate cleaning procedures:

[1690] Based on the analysis results, the server retrieves information from a database about the optimal cleaning method, necessary tools, and detergents for each type of object and dirt. It also generates cleaning instructions taking into account the user's emotional state and adds encouraging messages.

[1691] 4. Provision of Information:

[1692] The server sends the generated cleaning instructions and encouraging messages to a display in the car, where the user can check the information and follow the instructions to clean.

[1693] Specific examples

[1694] For example, if a user spills a drink, the in-car camera takes a picture of the spill and sends it to the server. The server analyzes the image and identifies the location of the spilled drink. At the same time, it recognizes from the facial image that the user is feeling stressed. As a result, it displays cleaning instructions such as "Quickly wipe up the spilled drink with a paper towel, then use a cleaner" along with an encouraging message such as "You're almost there, keep trying."

[1695] Prompt Sentence Examples

[1696] "Please upload a picture of the interior of the car."

[1697] "Reading face image"

[1698] "For spilled drinks, wipe them up with paper towels and use cleaner."

[1699] "You're feeling stressed right now. I'll suggest some easy cleaning methods."

[1700] "It's almost there, just do your best."

[1701] In this way, the present invention can effectively clean the interior of an autonomous vehicle, reducing the burden on the user and providing a comfortable in-vehicle environment.

[1702] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1703] Step 1:

[1704] The user takes a picture of the object they want to clean. The image is saved on a device such as a smartphone or tablet. The device then uploads the image to the system's application or website, and sends the image data to a server.

[1705] Input: Image of object to be cleaned

[1706] Output: Image data sent to the server

[1707] Step 2:

[1708] The server receives the image data sent from the device. It then uses image analysis techniques (for example, an object detection model using deep learning) to identify the object in the image and the type of dirt. If the object is a kitchen gas range, it will recognize oil stains, etc.

[1709] Input: Received image data

[1710] Output: Analysis results including location and type of contamination

[1711] Step 3:

[1712] A facial image recognition camera in the autonomous vehicle captures a user's facial image, which is then sent to a server, which uses an emotion engine to identify the user's emotional state (e.g., stress, fatigue, joy).

[1713] Input: Face image data

[1714] Output: Analysis results showing the user's emotional state

[1715] Step 4:

[1716] Based on the image analysis results from step 2 and the emotion analysis results from step 3, the server retrieves information from a database about optimal cleaning methods, necessary tools, and detergents. It also generates cleaning instructions taking into account the user's emotional state and adds encouraging messages. For example, if the user is feeling stressed, it generates simple and effective cleaning instructions.

[1717] Input: Image analysis results, emotion analysis results

[1718] Output: Optimal cleaning instructions and encouraging messages

[1719] Step 5:

[1720] The server sends the generated cleaning instructions and encouraging messages to a display in the car, which visually displays this information to the user. The user then follows the suggested cleaning instructions while checking the display.

[1721] Input: Cleaning instructions and encouraging messages

[1722] Output: Information shown on the display

[1723] This allows users to receive optimal cleaning procedures and encouraging messages based on their emotional state, allowing them to clean efficiently.

[1724] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1725] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1726] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1727] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1728] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1729] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1730] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1731] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1732] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1733] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1734] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1735] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1736] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1737] 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.

[1738] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1739] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1740] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1741] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1742] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1743] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1744] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1745] The following is further disclosed regarding the above embodiment.

[1746] (Claim 1)

[1747] A means for inputting an image of an object that a user wishes to clean;

[1748] A means for analyzing the image by an image analysis means and recognizing the object and the type of dirt thereon;

[1749] A means for identifying an optimum cleaning method according to the type of object and dirt, as well as necessary tools and detergents, based on the recognition results;

[1750] means for providing a user with information about the cleaning method, tools, and detergent;

[1751] A system including:

[1752] (Claim 2)

[1753] The system of claim 1, wherein the image analysis means uses an image recognition algorithm that utilizes deep learning.

[1754] (Claim 3)

[1755] The system according to claim 1, wherein the cleaning method generated based on the recognition result includes specific instructions on how to use detergents and tools.

[1756] "Example 1"

[1757] (Claim 1)

[1758] A means for inputting an image of an object that a user wishes to clean;

[1759] A means for analyzing the image by an image analysis means and recognizing the object and the type of dirt thereon;

[1760] A means for identifying an optimum cleaning method according to the type of object and dirt, as well as necessary tools and detergents, based on the recognition results;

[1761] means for providing a user with information about the cleaning method, tools, and detergent;

[1762] A means for encrypting image data uploaded from the terminal to protect user privacy;

[1763] A means for obtaining an optimal cleaning method based on the analysis from a database and generating an effective cleaning procedure;

[1764] A system including:

[1765] (Claim 2)

[1766] The system of claim 1, wherein the image analysis means uses an image recognition algorithm that utilizes deep learning.

[1767] (Claim 3)

[1768] The system of claim 1, wherein the cleaning method generated based on the recognition result includes specific steps on how to use detergents and tools, and the steps are notified to the user's terminal.

[1769] "Application Example 1"

[1770] (Claim 1)

[1771] A means for inputting an image of an object that a user wishes to clean;

[1772] A means for analyzing the image by an image analysis means and recognizing the object and the type of dirt thereon;

[1773] A means for identifying an optimum cleaning method according to the type of object and dirt, as well as necessary tools and detergents, based on the recognition results;

[1774] means for providing a user with information about the cleaning method, tools, and detergent;

[1775] an electronic means for displaying the results;

[1776] A system including:

[1777] (Claim 2)

[1778] The system of claim 1, wherein the image analysis means uses an image recognition algorithm that utilizes deep learning.

[1779] (Claim 3)

[1780] The system according to claim 1, wherein the cleaning method generated based on the recognition result includes specific instructions on how to use detergents and tools.

[1781] "Example 2: Combining Emotion Engines"

[1782] (Claim 1)

[1783] A means for inputting an image of an object that a user wishes to clean;

[1784] A means for analyzing the image by an image analysis means and recognizing the object and the type of dirt thereon;

[1785] means for acquiring a facial image of a user and recognizing emotions;

[1786] A means for identifying an optimum cleaning method according to the type of object and dirt, as well as necessary tools and detergents, based on the recognition results;

[1787] means for adjusting the cleaning method in response to the user's emotional state and generating encouraging messages as needed;

[1788] means for providing a user with information about the cleaning method, tools, and detergent;

[1789] A system including:

[1790] (Claim 2)

[1791] The system of claim 1, wherein the image analysis means uses an image recognition algorithm that utilizes deep learning.

[1792] (Claim 3)

[1793] The system according to claim 1, wherein the cleaning method generated based on the recognition result includes specific instructions on how to use detergents and tools.

[1794] (Claim 4)

[1795] 10. The system of claim 1, further comprising a message generated based on an emotional state of the user.

[1796] "Application example 2 when combining emotion engines"

[1797] (Claim 1)

[1798] A means for inputting an image of an object that a user wishes to clean;

[1799] A means for analyzing the image by an image analysis means and recognizing the object and the type of dirt thereon;

[1800] A means for identifying an optimum cleaning method according to the type of object and dirt, as well as necessary tools and detergents, based on the recognition results;

[1801] means for providing a user with information about the cleaning method, tools, and detergent;

[1802] A means for periodically capturing and transmitting images of the interior of the vehicle by a camera installed in the autonomous driving vehicle;

[1803] A means of acquiring facial images of drivers and passengers and performing emotion analysis;

[1804] means for generating a cleaning procedure based on the analysis result including the acquired emotion information and providing an encouraging message to the user;

[1805] means for displaying said information via an in-vehicle display;

[1806] A system including:

[1807] (Claim 2)

[1808] The system of claim 1, wherein the image analysis means uses an image recognition algorithm that utilizes deep learning.

[1809] (Claim 3)

[1810] The system according to claim 1, wherein the cleaning method generated based on the recognition result includes specific instructions on how to use detergents and tools. [Explanation of symbols]

[1811] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting an image of an object that a user wishes to clean; A means for analyzing the image by an image analysis means and recognizing the object and the type of dirt thereon; A means for identifying an optimum cleaning method according to the type of object and dirt, as well as necessary tools and detergents, based on the recognition results; means for providing a user with information about the cleaning method, tools, and detergent; A system including:

2. The system according to claim 1 , wherein the image analysis means uses an image recognition algorithm that utilizes deep learning.

3. The system according to claim 1 , wherein the cleaning method generated based on the recognition result includes specific instructions on how to use a detergent and tools.

Images