System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an image captured by a user; means for analyzing the received image to identify a required cleaning tool and an efficient cleaning procedure; means for providing the identified cleaning tool and cleaning procedure to the user; means for suggesting a new interior arrangement based on an input of the user; and means for generating and transmitting an ale message to the user after the cleaning is completed.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] For many people, cleaning is a time-consuming and labor-intensive task. It is also difficult to know how to clean efficiently and to choose the right cleaning tools. Maintaining daily cleaning is a major challenge, especially for busy business people, families busy with housework and childcare, and seniors who are experiencing declining physical strength. Furthermore, when redecorating, it can be difficult to determine the best interior layout. To solve these problems, a system that is easy for users to use is needed. [Means for solving the problem]

[0005] This invention solves the above problem by providing a system that receives images taken by users and performs image analysis. Specifically, the system analyzes the received images directly, identifies the cleaning tools and efficient cleaning procedures needed for the room, and suggests them to the user. It also includes a function to suggest new interior layouts and items based on the user's preferences. Furthermore, upon completion of cleaning, a dedicated character provides a message of encouragement, boosting the user's motivation. This system allows users to clean and redecorate easily and enjoyably.

[0006] "User" refers to a user of the system.

[0007] "Image" refers to photographic data of a room taken by a user.

[0008] "Means for receiving" refers to a means having a function for uploading images taken by a user to a server.

[0009] "Means for analyzing" refers to means capable of processing received images and identifying room conditions and recognized objects.

[0010] "Cleaning supplies" refers to the tools and products needed to clean a room.

[0011] "Cleaning procedures" refer to the methods and processes for cleaning efficiently and effectively.

[0012] "Means to provide" refers to a means that has the function of notifying users of the specified cleaning tools and cleaning procedures.

[0013] "Interior arrangement" refers to the specific arrangement and layout of furniture and decorations in a room.

[0014] An "encouragement message" refers to a message of encouragement or support for users.

[0015] "Machine learning model" refers to an algorithm used in image analysis that learns patterns and features from data.

[0016] "Usage history" refers to a record of the cleaning tools and cleaning procedures used by the user in the past.

[0017] "Desired cleaning level" refers to the thoroughness and quality of cleaning desired by the user.

[0018] "Simulation Image" refers to a visual projection of the proposed interior layout. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The system for implementing this invention starts when a user takes a photo of a room using a terminal and uploads it to a server. The system clearly divides the roles of the server, terminal, and user, and suggests efficient cleaning and interior layout.

[0041] System Overview

[0042] The system includes the following main measures:

[0043] Means for receiving images

[0044] A means of analyzing images

[0045] A means of identifying and providing cleaning supplies and procedures

[0046] A means of proposing new interior layouts

[0047] A means of generating and sending a message

[0048] Image receiving means

[0049] First, the user takes a photo of the room using a device such as a smartphone or tablet. The captured image data is then saved on the device. The user then launches the "Magical Makeover" app, selects the captured image, and uploads it. The image data is then sent to the server on the device.

[0050] Image analysis methods

[0051] The server analyzes the received image data. During the image analysis process, machine learning models are used to recognize objects in the photo (furniture, miscellaneous items, trash, etc.). Based on the results of this analysis, the server determines the condition of the room and identifies areas that need cleaning.

[0052] Cleaning equipment and procedures

[0053] The server then selects the optimal cleaning tools and efficient cleaning procedures based on the analysis results, taking into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[0054] Interior layout proposal method

[0055] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The server creates a list of items that can be purchased within the user's budget and a simulation image of the new layout, and sends them to the device.

[0056] How to generate cheer messages

[0057] When a user completes a cleaning task and reports the results within the app, the server receives the information. Based on the report, the server generates an encouraging message from a dedicated character and sends it to the device.

[0058] Specific examples

[0059] Prerequisites

[0060] A user wants to clean the living room.

[0061] The user takes a photo and uploads it to the server through the app.

[0062] Step 1: Take a photo and upload it

[0063] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0064] Step 2: Image analysis

[0065] The server analyzes the photos and identifies stains on the carpet, scattered magazines, etc.

[0066] Step 3: Submit a proposal

[0067] The server suggests specific cleaners and brushes for stained carpets and recommends using organizers for cluttered magazines.

[0068] The server estimates the time required to complete the request to be 30 minutes.

[0069] Step 4: Interior design proposal

[0070] The user inputs their desired redecoration (a relaxing space).

[0071] The server suggests new couch and plant placements and provides simulated images.

[0072] Step 5: Message of encouragement

[0073] The user completes the cleaning and reports it.

[0074] The server generates an encouraging message saying, "You did a great job! We're looking forward to your next cleaning!" and sends it to the device.

[0075] As described above, this system provides a series of procedures and functions to assist users in cleaning and interior arrangement, allowing them to clean efficiently and enjoyably and rearrange their rooms.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] The user uses the device's camera to take a photo of the room from an appropriate angle so that the entire room can be seen.

[0079] Step 2:

[0080] The user launches the "Magical Makeover" app on their device and selects the image they have taken. The user then uses the file selection function within the app to select the desired photo data.

[0081] Step 3:

[0082] The terminal creates a request to upload the selected image data to the server. The terminal transmits the image data to the server.

[0083] Step 4:

[0084] The server receives the image data and stores it in a directory for analysis. The server starts the image analysis process.

[0085] Step 5:

[0086] The server uses machine learning models to recognize objects in the images, determining the location and type of furniture, miscellaneous items, trash, etc., and records this information in a database.

[0087] Step 6:

[0088] The server selects the necessary cleaning tools based on the analysis results, and creates an optimal cleaning tool list taking into account the user's past usage history and desired cleaning level.

[0089] Step 7:

[0090] The server generates an efficient cleaning procedure. The server creates a step-by-step procedure based on how to use cleaning tools and calculates the estimated time required for cleaning.

[0091] Step 8:

[0092] The server sends the recommendation in a response to the device, providing the device with suggested cleaning tools, procedures, and required time.

[0093] Step 9:

[0094] The user confirms the suggestions on the device and starts cleaning. The user proceeds with the cleaning by following the suggested steps.

[0095] Step 10:

[0096] After the user has completed cleaning, they can report it by pressing the "Cleaning Completed" button in the app. The user will then send a completion notification.

[0097] Step 11:

[0098] The server receives the completion notification and records it in the database. The server then generates a cheer message from the dedicated character.

[0099] Step 12:

[0100] The server sends the generated cheer message to the terminal, which notifies the user of the received cheer message and displays it.

[0101] Step 13:

[0102] Users can see the encouraging message and feel a sense of accomplishment, which increases their motivation for the next cleaning.

[0103] Example 1

[0104] 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."

[0105] Conventional cleaning support systems are inefficient because they require users to manually create cleaning plans and select the necessary tools. Furthermore, user satisfaction is low due to a lack of interior layout suggestions and encouraging messages to maintain motivation. There is a need for a system that solves these problems and allows users to clean easily and efficiently and enjoy redecorating their rooms.

[0106] 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.

[0107] In this invention, the server includes a means for receiving images taken by a user, a means for analyzing the received images and recognizing objects in the images to determine the condition of the room, and a means for identifying optimal cleaning tools and an efficient cleaning procedure. This allows the user to easily grasp the condition of the room and automatically receive an efficient cleaning plan. The server also includes a means for providing the user with the identified cleaning tools and cleaning procedure, a means for proposing a new interior layout based on the user's input, and a means for generating and sending a message of encouragement to the user after cleaning is completed. This allows the user to know the appropriate cleaning tools and procedures and receive a message of encouragement to maintain motivation after cleaning. Furthermore, when redecorating, the server can receive suggestions for interior layouts based on the user's budget and preferences.

[0108] "User" refers to an individual who uses this system to receive suggestions for cleaning and interior design for their room.

[0109] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[0110] "Server" refers to the central system that analyzes received image data, suggests cleaning procedures, generates encouraging messages, etc.

[0111] "Means for receiving images" refers to the function of the server receiving image data sent from the terminal.

[0112] "Means for analyzing images" refers to the function of analyzing received image data using a machine learning model, recognizing objects in the image, and determining the condition of the room.

[0113] "Means for identifying optimal cleaning tools and procedures" refers to a function for determining tools and procedures for efficient cleaning based on the analysis results.

[0114] "Means to suggest new interior layouts" refers to a function that suggests new interior layouts based on the user's wishes and budget.

[0115] "Means for generating and sending encouraging messages to users" refers to the function of creating and sending messages to encourage and motivate users after cleaning is completed.

[0116] A "machine learning model" refers to the algorithms and network structures used when performing image analysis, and examples include YOLO and ResNet.

[0117] "Cleaning equipment" refers to the tools and materials used when cleaning (e.g., vacuum cleaners, mops, detergents, etc.).

[0118] "Cleaning procedures" refer to the steps and methods required to clean efficiently.

[0119] "Interior arrangement" refers to the arrangement of furniture and decorations within a room.

[0120] "Encouragement messages" refer to words or messages of encouragement sent to users who have finished cleaning.

[0121] This invention begins when a user takes a photo of a room using a terminal and uploads the image data to a server. This system clearly divides the roles of the server, terminal, and user, and provides suggestions for efficient cleaning and interior layout. Detailed embodiments are described below.

[0122] Hardware and Software Configuration

[0123] User device: refers to a device such as a smartphone, tablet, or PC on which the "Magical Makeover" app is installed.

[0124] Server: Refers to the central system that performs image processing and data analysis, and is equipped with machine learning models (e.g., YOLO, ResNet, etc.) for image analysis.

[0125] Communications network: Refers to the infrastructure for sending and receiving data between terminals and servers.

[0126] Specific operation procedures and data processing

[0127] 1. Image receiving method: The user takes a photo of the room using the device's camera. The user selects the photo within the app and uploads it to the server. The device then sends this image data to the server.

[0128] 2. Image analysis: The server analyzes the image data it receives. Using a machine learning model, it recognizes objects in the image, such as furniture, miscellaneous items, and trash, and assigns an identification tag to each. Based on the results of this analysis, it determines the condition of the room and identifies areas that need cleaning.

[0129] 3. Method for identifying cleaning tools and cleaning procedures: The server selects the optimal cleaning tools and efficient cleaning procedures based on the analysis results. The server also takes into account the user's past usage history and desired cleaning level. The server combines this information and provides it to the user along with an estimate of the required time.

[0130] 4. Interior layout proposal method: The user inputs their desired redecorating needs. The server generates a proposal for a new interior layout based on the user's input information. It creates a list of items that can be purchased within the user's budget and a simulation image of the new layout, and sends these to the terminal.

[0131] 5. Means for generating encouraging messages: After the user completes cleaning, they report the results within the app. The server receives this information, generates encouraging messages, and sends them to the device.

[0132] Specific examples

[0133] Prerequisites

[0134] A user wants to clean the living room.

[0135] The user takes a photo and uploads it to the server through the app.

[0136] Step 1: Take a photo and upload it

[0137] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0138] Step 2: Image analysis

[0139] The server analyzes the photos and identifies stains on the carpet, scattered magazines, etc.

[0140] Step 3: Submit a proposal

[0141] The server suggests a specific cleaner and brush for the stained carpet and recommends using a magazine organizer for the scattered magazines. The server estimates the process will take 30 minutes.

[0142] Step 4: Interior design proposal

[0143] The user inputs their desired redecorating needs (a relaxing space), and the server proposes new couch and plant placements and provides simulation images.

[0144] Step 5: Message of encouragement

[0145] When the user completes cleaning and reports it, the server generates an encouraging message saying, "You did a great job! We look forward to your next cleaning!" and sends it to the device.

[0146] Example prompts for generative AI models

[0147] 1. Image analysis prompt

[0148] Analyze photos of rooms and identify each object, such as furniture, household items, and trash. Use this information to suggest areas that need cleaning.

[0149] 2. Cleaning Prompt

[0150] Based on the analyzed image information of the room, please suggest the cleaning procedure and necessary cleaning tools, along with the required time.

[0151] 3. Prompt for interior design proposal

[0152] Please propose a new interior layout based on the user's desire for a relaxing space. Please also provide a list of items that can be purchased within the user's budget and simulation images.

[0153] This invention allows users to clean efficiently and enjoyably redecorate their rooms. The system allows users to easily grasp the condition of their rooms and receive optimal cleaning plans and procedures. It also helps users maintain motivation through encouraging messages and creates new spaces through suggestions for interior layout.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Step 1:

[0156] The user takes a photo of the room using the device's camera. Then, the user launches the "Magical Makeover" app, selects the image, and uploads it. The device then sends the image data to the server.

[0157] Input: Photo of the room

[0158] Output: Image data sent to the server

[0159] Step 2:

[0160] The server analyzes the received image data. During the image analysis process, machine learning models (e.g., YOLO, ResNet, etc.) are used to recognize furniture, miscellaneous items, trash, etc. in the image. As a result of this recognition, each object is assigned an identification tag.

[0161] Input: Image data

[0162] Output: Analysis results with identification tags

[0163] Step 3:

[0164] The server uses the analysis results to determine the condition of the room and identify areas that need cleaning, such as stains on the carpet or scattered magazines. Based on this information, the server notifies the user.

[0165] Input: Analysis results

[0166] Output: Identifying areas that need cleaning and notifying the user

[0167] Step 4:

[0168] The server then uses the analysis results to identify the optimal cleaning tools and efficient cleaning procedures, taking into account the user's past usage history and desired cleaning level. For example, the server may suggest using specific detergents and brushes and provide cleaning instructions, including an estimate of the time required.

[0169] Input: Analysis results, user usage history, cleaning level

[0170] Output: Suggested cleaning materials and procedures

[0171] Step 5:

[0172] The server generates a new interior layout proposal based on the user's input information (budget, preferences, etc.), creates a list of items that can be purchased within the budget, and a simulation image of the new layout, and sends these to the terminal.

[0173] Input: User input information

[0174] Output: interior layout proposal, item list, simulation image

[0175] Step 6:

[0176] After the user completes the cleaning, they report the results within the app. The server receives the information and generates an encouraging message based on the report. The message is generated in the form of a dedicated character and sent to the device.

[0177] Input: Report of cleaning completion

[0178] Output: Generate and send a cheer message

[0179] (Application example 1)

[0180] 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."

[0181] In conventional cleaning support systems and interior design suggestion systems, users take photos of their rooms and are given suggestions on cleaning methods and interior layouts, but they do not offer support for purchasing in physical stores or suggest specific products. As a result, users are forced to find out where to purchase the suggested interior items themselves, which creates an inefficient purchasing experience.

[0182] 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.

[0183] In this invention, the server includes means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and an efficient cleaning procedure, means for providing the identified cleaning tools and procedure to the user, means for proposing a new interior layout based on the user's input, means for searching for products based on the suggestions and presenting candidates available for purchase, and means for generating and sending a message of encouragement to the user after cleaning is completed. This allows the user to not only clean efficiently but also purchase the suggested interior products on the spot.

[0184] The "receiving means" refers to the device and process for acquiring and storing image data captured by the user.

[0185] The "analyzing means" refers to devices and programs that perform topological and morphological analysis of the received image data to identify objects in the image and areas that need cleaning.

[0186] The "means for specifying" refers to a device and a program for selecting necessary cleaning tools and efficient cleaning procedures based on the analysis results.

[0187] "Means for providing" means devices and mechanisms for informing or displaying information to users about identified cleaning tools and procedures.

[0188] The "means for proposing" is a device and a program for generating and proposing a new interior layout in accordance with information input by the user.

[0189] The "searching means" refers to a device and program for searching a database for available products based on the proposed interior layout.

[0190] "Presenting means" refers to devices and mechanisms for notifying or displaying information about searched products to users.

[0191] The "means for generating an encouraging message" refers to a device and a program for generating and sending an encouraging message to a user after the cleaning is completed.

[0192] This embodiment of the present invention is a system for improving user experience in a physical store. A series of processes from a user taking an image of a room using a smartphone to receiving suggestions for cleaning and interior layout will be described.

[0193] System Overview

[0194] The system includes the following elements:

[0195] 1. Means of receiving images

[0196] 2. Means of analyzing images

[0197] 3. A means of identifying necessary cleaning supplies and efficient cleaning procedures.

[0198] 4. Means of providing users with identified cleaning supplies and procedures

[0199] 5. A method for suggesting new interior layouts based on user input

[0200] 6. A way to search for products based on suggestions and present available options for purchase

[0201] 7. A method for generating and sending a cheer message to the user after cleaning is complete

[0202] Image receiving means

[0203] Users take photos of the room using their smartphone camera and upload the images to the server via a dedicated app.

[0204] Image analysis methods

[0205] The server analyzes the received images using machine learning models such as TensorFlow and PyTorch, and this analysis identifies furniture in the photo and areas that need cleaning (such as dirt on the floor or scattered magazines).

[0206] Cleaning equipment and procedures

[0207] Based on the analysis, the server identifies the optimal cleaning tools (e.g., specific detergents and brushes) and efficient cleaning procedures, taking into account the user's past cleaning history and desired cleaning level. The server provides this information to the user, along with an estimate of the required time.

[0208] Interior layout proposal method

[0209] When a user wishes to redecorate, the server will suggest interior layouts. For example, if the user desires a "modern living room," the server will generate a new layout based on this request and provide a list of related products along with simulation images.

[0210] Example prompt: "Suggest a décor arrangement that fits a modern theme in the living room."

[0211] Product search and presentation methods

[0212] Based on the proposed interior layout, the server searches for available products from a database of physical stores, allowing users to view detailed information about these products and purchase them on the spot.

[0213] How to generate cheer messages

[0214] When a user completes cleaning and reports the results to the app, the server generates an encouraging message based on the results, such as "Have a wonderful time in your new home!"

[0215] With the above configuration, this system provides users with comprehensive support, suggesting efficient cleaning and interior layout, and even allowing them to purchase products on the spot.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] Users take photos of the room using their smartphone camera and upload them to the server via a dedicated app.

[0219] Input: A photo of the room taken with a smartphone

[0220] Output: Image data uploaded to the server

[0221] Specific operation: The user launches the dedicated app, presses the "Take Photo" button to take a picture of the room, and then presses the "Upload Image" button to send the image data to the server.

[0222] Step 2:

[0223] The server uses machine learning models (TensorFlow or PyTorch) to acquire and analyze the images received.

[0224] Input: Room image data uploaded by the user

[0225] Output: Identification of objects and areas that need cleaning in the image

[0226] Specific operation: The server inputs the received image data into a machine learning model to identify furniture, miscellaneous items, and areas that need cleaning. As a result of the analysis, it outputs specific object categories and their location information.

[0227] Step 3:

[0228] Based on the analysis results, the server identifies the necessary cleaning tools and efficient cleaning procedures, and provides them to the user along with an estimate of the time required.

[0229] Input: Image analysis results, user's past cleaning history, desired cleaning level

[0230] Output: List of cleaning supplies, cleaning procedure, and time required

[0231] Specific operation: The server selects the optimal cleaning tools (e.g., specific detergents, brushes, etc.) based on the analysis results, generates an efficient cleaning procedure, estimates the required time, and notifies the user of this information.

[0232] Step 4:

[0233] The user inputs their desired redecorating needs (e.g., "modern living room"), and the server makes suggestions for new interior layouts.

[0234] Input: User's redecorating intentions and budget information

[0235] Output: Simulation images of new interior layout, list of potential purchases

[0236] How it works: The user inputs their desired redecorating needs into the app, and the server generates a new interior layout based on that information. The generated interior layout is presented to the user as a simulation image.

[0237] Step 5:

[0238] Based on the suggestions, the server searches a database of physical stores and presents candidates for products that the user can purchase.

[0239] Input: New interior layout proposals, physical store inventory database

[0240] Output: A list of products available for purchase

[0241] Specific operation: The server searches the physical store database for products that match the proposed interior, and presents a list of detailed information about the products available for purchase to the user.

[0242] Step 6:

[0243] When a user completes a cleaning task and reports it to the app, the server generates and sends an encouraging message.

[0244] Input: User's cleaning completion report

[0245] Output: Cheers message

[0246] How it works: The user reports their cleaning completion through the app, and the server uses the AI ​​model to generate a message of encouragement based on the report. The message is then sent to the user in the form of a special character.

[0247] As described above, this system assists users in cleaning and arranging their rooms, providing a consistent shopping experience similar to that of a physical store.

[0248] 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.

[0249] The system for implementing this invention has the function of listening to photos of a room taken by the user, suggesting cleaning tools and cleaning procedures based on those photos, and even recommending new interior layouts. It also incorporates an emotion engine that recognizes the user's emotions, aiming to improve the user's motivation by providing encouraging messages that correspond to the user's emotions. The specific operation of this system is described below.

[0250] System Overview

[0251] The system includes the following main measures:

[0252] Means for receiving images

[0253] A means of analyzing images

[0254] A means of identifying and providing cleaning supplies and procedures

[0255] A means of proposing new interior layouts

[0256] A means to generate and send a cheer message to users after cleaning is completed

[0257] Emotion engine that recognizes user emotions

[0258] Image receiving means

[0259] The user takes a photo of the room using the device. The captured image data is saved on the device, and then the user opens the "Magical Makeover" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[0260] Image analysis methods

[0261] The server analyzes the received image data. The image analysis process uses machine learning models to recognize objects in the photo (furniture, miscellaneous items, trash, etc.). Based on the analysis results, the server determines the condition of the room and identifies areas that need cleaning.

[0262] Cleaning equipment and procedures

[0263] The server then uses the analysis results to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[0264] Interior layout proposal method

[0265] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The proposal includes a list of items that can be purchased within the user's budget and a simulation image of the new layout.

[0266] Emotion Engine

[0267] The system incorporates an emotion engine to recognize the user's emotions, which is determined through facial and voice analysis and provides appropriate feedback.

[0268] How to generate cheer messages

[0269] When the cleaning is complete, the user reports the results within the app. The server receives this report and uses an emotion engine to analyze the user's emotions and generates a cheer message from a dedicated character. This cheer message is customized according to the user's emotions.

[0270] Specific examples

[0271] Prerequisites

[0272] A user wants to clean the living room.

[0273] The user takes a photo and uploads it to the server through the app.

[0274] Step 1: Take a photo and upload it

[0275] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0276] Step 2: Image analysis

[0277] The server analyzes the photos and identifies things like stains on the carpet or scattered magazines.

[0278] Step 3: Submit a proposal

[0279] The server suggests specific cleaners and brushes for stained carpets and recommends using organizers for cluttered magazines.

[0280] The server estimates the time required to complete the request to be 30 minutes.

[0281] Step 4: Interior design proposal

[0282] The user inputs their desired redecoration (a relaxing space).

[0283] The server suggests new couch and plant placements and provides simulated images.

[0284] Step 5: Emotion Recognition and Encouragement

[0285] The user completes the cleaning and reports it.

[0286] The server receives the report and analyzes the user's emotions using an emotion engine.

[0287] If the user feels tired, the system will encourage them by saying, "You're tired! Get plenty of rest today!" If the user is feeling positive, it will generate an encouraging message such as, "Perfect! I'm looking forward to your next challenge!" and send it to the device.

[0288] The system assists users in cleaning and interior arrangement, and also provides emotional feedback to enhance the overall user experience.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] The user takes a photo of the room using the device. The user uses the camera function to take a photo from an appropriate angle so that the entire room can be seen.

[0292] Step 2:

[0293] The user launches the "Magical Makeover" app on their device and selects the image they have taken. The user then uses the file selection function within the app to select the desired photo data.

[0294] Step 3:

[0295] The device creates a request to upload the selected image data to the server. The device sends the image data to the server.

[0296] Step 4:

[0297] The server receives the image data and stores it in a directory for analysis. The server starts the image analysis process.

[0298] Step 5:

[0299] The server uses machine learning models to recognize objects in the images, determining the location and type of furniture, miscellaneous items, trash, etc., and records this information in a database.

[0300] Step 6:

[0301] The server selects the necessary cleaning tools based on the analysis results, and creates an optimal cleaning tool list taking into account the user's past usage history and desired cleaning level.

[0302] Step 7:

[0303] The server generates an efficient cleaning procedure. The server creates a step-by-step procedure based on how to use cleaning tools and calculates the estimated time required for cleaning.

[0304] Step 8:

[0305] The server sends the recommendation in a response to the device, providing the device with suggested cleaning tools, procedures, and required time.

[0306] Step 9:

[0307] The user confirms the suggestions on the device and starts cleaning. The user then follows the suggested steps to proceed with the cleaning.

[0308] Step 10:

[0309] After the user has completed cleaning, they can report it by pressing the "Cleaning Completed" button in the app. The user will then send a completion notification.

[0310] Step 11:

[0311] The server receives the completion notification and records it in the database. The server then activates the emotion engine to recognize the user's emotion.

[0312] Step 12:

[0313] The server uses an emotion engine to analyze the user's emotions, identify emotions from the user's facial expressions and voice, and record the results.

[0314] Step 13:

[0315] The server generates a message of encouragement based on the user's emotions. If the user is tired, it generates a message such as "You're tired! Get plenty of rest today!", and if the user is positive, it generates a message such as "Perfect! Looking forward to the next challenge!".

[0316] Step 14:

[0317] The server sends the generated cheer message to the terminal, which notifies the user of the received cheer message and displays it.

[0318] Step 15:

[0319] Users can see the encouraging message and feel a sense of accomplishment, which increases their motivation for the next cleaning.

[0320] Example 2

[0321] 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."

[0322] Existing cleaning support systems have the problem that they do not take into account the user's emotional state, which can lead to a decrease in motivation for cleaning work. Furthermore, while there are systems that support both cleaning and interior layout suggestions, there is a lack of a means to provide these services in a unified manner. This makes it difficult for users to efficiently perform both cleaning and redecorating.

[0323] 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 means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and an efficient cleaning procedure, means for providing the identified cleaning tools and cleaning procedure to the user, means for proposing a new interior layout based on the user's input, means for generating and sending a cheer message to the user after cleaning is completed, and an emotion engine for recognizing the user's emotions. This not only enables the user to efficiently clean and rearrange the room, but also makes it easier for them to stay motivated during the process.

[0324] "User" refers to an individual or organization who wishes to use this system to clean and arrange the interior of a room.

[0325] "Means for receiving captured images" refers to a function or process by which the server receives image data captured by a user on a terminal via a network.

[0326] "Means for analyzing received images and identifying necessary cleaning tools and efficient cleaning procedures" refers to the function or process in which the server analyzes the received image data using machine learning models or other analytical technologies and selects the optimal cleaning tools and procedures based on the results.

[0327] "Means for providing the identified cleaning tools and cleaning procedures to the user" refers to the function or process by which the server provides the user with the cleaning tools and cleaning procedures identified as a result of the analysis through display, notification, etc.

[0328] "Means for suggesting new interior layouts based on user input" refers to a function or process that automatically generates and suggests new interior layouts taking into account input information from the user (e.g., budget and preferences).

[0329] "Means for generating and sending a cheer message to the user after cleaning is completed" refers to a function or process in which the server generates a cheer message and sends it to the user after the user reports that the cleaning job is completed.

[0330] An "emotion engine" refers to a software or hardware component that recognizes a user's emotions and generates feedback or messages accordingly.

[0331] The system for implementing this invention receives photos of a room taken by the user, and based on those photos, suggests cleaning tools and procedures, and even recommends new interior layouts. It also incorporates an emotion engine that recognizes the user's emotions, aiming to improve the user's motivation by providing encouraging messages that correspond to the user's emotions.

[0332] The system includes the following main measures:

[0333] Means for receiving images

[0334] A means of analyzing images

[0335] A means of identifying and providing cleaning supplies and procedures

[0336] A means of proposing new interior layouts

[0337] A means to generate and send a cheer message to users after cleaning is completed

[0338] Emotion engine that recognizes user emotions

[0339] The user takes a photo of the room using the device. The captured image data is saved on the device, and then the user opens the "Magical Makeover" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[0340] The server analyzes the received image data. This process uses machine learning models (e.g., YOLO or ResNet) to recognize objects in the photo (furniture, household items, trash, etc.). Based on the analysis results, the server determines the condition of the room and identifies areas that need cleaning.

[0341] The server then uses the analysis results to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[0342] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The proposal includes a list of items that can be purchased within the user's budget and a simulation image of the new layout. The simulation image is generated using computer graphics technology and simulation software.

[0343] The system incorporates an emotion engine to recognize the user's emotions, which is determined through facial and voice analysis and provides appropriate feedback.

[0344] When the cleaning is complete, the user reports the results within the app. The server then uses an emotion engine to analyze the user's emotions and generates a cheer message from a dedicated character. This cheer message is customized based on the user's emotions and sent to the device.

[0345] Specific examples

[0346] Prerequisites

[0347] A user wants to clean the living room.

[0348] The user takes a photo and uploads it to the server through the app.

[0349] Step 1: Take a photo and upload it

[0350] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0351] Example prompt sentence:

[0352] Describe in detail a scenario where a user takes a photo of their living room and sends it to a server through your app.

[0353] Step 2: Image analysis

[0354] The server analyzes the photos and identifies things like stains on the carpet or scattered magazines.

[0355] Example prompt sentence:

[0356] Analyze the photos received by the server and describe any stains or objects identified.

[0357] Step 3: Submit a proposal

[0358] The server suggests a specific cleaner and brush for stained carpets, recommends using organizers for cluttered magazines, and estimates the cleaning time to be 30 minutes.

[0359] Example prompt sentence:

[0360] Describe the process by which the server suggests cleaning supplies and procedures and estimates the time required.

[0361] Step 4: Interior design proposal

[0362] The user inputs their desired redecorating needs (a relaxing space), and the server proposes new couch and plant placements and provides simulation images.

[0363] Example prompt sentence:

[0364] Describe the process by which the server proposes new interior layouts based on the user's preferences.

[0365] Step 5: Emotion recognition and cheer message generation

[0366] After the user completes the cleaning task and reports it within the app, the server uses an emotion engine to analyze the user's emotions, generates a cheer message based on the user's emotions, and sends it to the device.

[0367] Example prompt sentence:

[0368] Please explain the process by which the server uses the emotion engine to generate a cheer message and send it to the user after cleaning is complete.

[0369] The system aims to assist users with cleaning and interior arrangement, and provide emotional feedback to improve the overall user experience.

[0370] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0371] Step 1:

[0372] The user takes a photo of the room and saves the image data on the device. Specifically, the photo is taken using the smartphone's camera app, and the image data is saved in the "Magical Makeover" app's gallery.

[0373] Input: User action to take a picture of the room

[0374] Output: Image data stored on the device

[0375] Step 2:

[0376] The user opens the "Magical Makeover" app, selects a photo they have taken, and then presses the upload button on the app, causing the device to send the image data to the server. HTTP or HTTPS is used as the communication protocol.

[0377] Input: User selection and upload of photo

[0378] Output: Image data sent to the server

[0379] Step 3:

[0380] The server analyzes the received image data and uses a machine learning model (e.g., YOLO or ResNet) to recognize objects in the photo (furniture, household items, trash, etc.). This analysis process also identifies the location and type of each object.

[0381] Input: Image data sent to the server

[0382] Output: List of analyzed objects and their positions

[0383] Step 4:

[0384] The server then uses image analysis to identify the necessary cleaning tools and efficient cleaning procedures, taking into account the user's past usage history and desired cleaning level. For example, if dirt is detected on the carpet, it will suggest the appropriate detergent and brush, and estimate the required time.

[0385] Input: List of analyzed objects and their locations, user's past cleaning history, desired cleaning level

[0386] Output: A list of recommended cleaning supplies and procedures, and the cleaning time required.

[0387] Step 5:

[0388] The server provides the user with the identified cleaning supplies and procedures, specifically by displaying a list of recommended cleaning supplies and procedures through the app and informing them of the required time.

[0389] Input: A list of recommended cleaning supplies and procedures, and the time required for cleaning.

[0390] Output: A list of cleaning supplies and procedures provided to the user, along with a notification of the required time.

[0391] Step 6:

[0392] The user enters their desired redecorating needs into the app (optional), including desired conditions such as a relaxing space and a modern design.

[0393] Input: User inputs desired redecorating conditions

[0394] Output: Condition data for interior layout proposals

[0395] Step 7:

[0396] The server generates new interior layout proposals based on the user's desired conditions. Specifically, it provides a list of furniture and decorations that can be purchased within the user's budget, along with simulation images of the new layout. The simulation images are generated using CG technology and simulation software.

[0397] Input: Condition data for interior layout proposal

[0398] Output: Proposed interior layout (item list and simulation images)

[0399] Step 8:

[0400] When the user completes cleaning, they report it within the app. Specifically, they press the "Report Completion" button in the app to send the cleaning completion information to the server.

[0401] Input: User reports completion of cleaning

[0402] Output: Cleaning completion information sent to the server

[0403] Step 9:

[0404] The server receives the report and analyzes the user's emotions using an emotion engine. The emotion engine analyzes the user's facial photo and voice data to determine the user's emotional state. Based on the results, a cheer message from a dedicated character is generated and sent to the device.

[0405] Input: Cleaning completion information sent to the server, facial photos and voice data

[0406] Output: Emotion-based cheer message

[0407] This allows the system to assist users with cleaning and interior arrangement, and even provide emotional feedback to improve the overall user experience.

[0408] (Application example 2)

[0409] 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."

[0410] In physical stores, there are challenges such as a lack of specific procedures and methods for selecting tools to enable employees to clean the store efficiently and effectively, a lack of means to maintain employee motivation, and the difficulty of changing the store layout and optimizing display placement.

[0411] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and efficient cleaning procedures, means for providing the identified cleaning tools and cleaning procedures to the user, means for proposing a new interior layout based on the user's input, means for generating and sending a cheer message to the user after cleaning is completed, means for employees to take photos of the interior space of the physical store and analyze areas within the store that need cleaning, means for providing employees with cleaning tools and cleaning procedures based on the analysis results, and means for recognizing the employee's emotions after cleaning is completed and generating and sending a cheer message. This enables employees to clean efficiently and maintain their motivation while working.

[0412] "User" refers to a member of the public or a store employee who uses this system.

[0413] "Images" refers to photographs and videos taken with a smartphone or other imaging device.

[0414] "Means of receiving" refers to the technology or method for importing image data from an external server or application.

[0415] "Means for analyzing" refers to techniques or methods for analyzing received image data using machine learning models or image processing algorithms.

[0416] "Cleaning equipment" refers to tools used for cleaning, such as vacuum cleaners, mops, brushes, and detergents.

[0417] "Cleaning procedures" refer to the specific steps and processes for cleaning efficiently.

[0418] "New interior layout" refers to proposals for redesigning the layout of furniture and decorations in a room or store to create a better space.

[0419] An "encouragement message" is a message of encouragement sent to boost the user's enthusiasm and motivation.

[0420] "Employees" refers to staff and clerks working in the store.

[0421] A "machine learning model" is an algorithm or framework that allows a computer to learn from data and make predictions or classifications based on that data.

[0422] "Means for recognizing emotions" refers to technologies and methods that analyze the facial expressions and voices of users and employees to identify their emotional state.

[0423] A "server" is a computer system that processes data and provides services over a network.

[0424] The "Clean Captain" system for implementing this invention aims to help store employees efficiently clean the store and improve their motivation. The specific operation of this system is described below.

[0425] System Overview

[0426] The system includes the following main measures:

[0427] Means for receiving images

[0428] A means of analyzing images

[0429] A means of identifying and providing cleaning supplies and procedures

[0430] A means of proposing new interior layouts

[0431] A means to generate and send a cheer message to users after cleaning is completed

[0432] A method for employees to take photos of the interior space of a physical store and analyze areas that need cleaning.

[0433] A means of providing employees with cleaning supplies and procedures based on the analysis results

[0434] A means of recognizing employee emotions and generating and sending encouraging messages

[0435] Image receiving means

[0436] Employees use their smartphones to take photos of the store. The captured image data is saved on the smartphone, and then the employee opens the "Clean Captain" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[0437] Image analysis methods

[0438] The server analyzes the received image data. The image analysis process uses machine learning models to recognize objects in the photo (furniture, products, trash, etc.). Based on the analysis results, the server identifies specific areas in the store that need cleaning. The machine learning models used are deep learning frameworks such as Keras.

[0439] Cleaning equipment and procedures

[0440] The server uses the analysis to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the employee's past usage history and desired cleaning level. The server compiles this information and provides it to the employee along with an estimate of the required time.

[0441] Interior layout proposal method

[0442] When an employee wants to rearrange the store or arrange new product displays, the server generates a proposal for a new layout based on the employee's input information (budget, desired layout, etc.) The proposal includes a list of items that can be purchased within the budget and a simulation image of the new layout.

[0443] How to generate cheer messages

[0444] When cleaning is complete, employees report their results within the app. The server receives this report and uses an emotion engine to analyze the employee's emotions and generate a message of encouragement. The emotion engine determines the employee's emotions through facial and voice analysis, and provides appropriate feedback.

[0445] Specific examples

[0446] Prerequisites

[0447] Employees want to clean the store.

[0448] Employees take photos and upload them to a server via the app.

[0449] Step 1: Take a photo and upload it

[0450] Employees take photos of the store using their smartphone cameras and upload them through the app.

[0451] Step 2: Image analysis

[0452] The server analyzes the photos to determine product placement and litter levels.

[0453] Step 3: Submit a proposal

[0454] The server will suggest specific garbage bags and vacuum cleaners for cluttered trash, and also suggest ways to organize product placement.

[0455] The server estimates the required time.

[0456] Step 4: Interior layout proposal

[0457] If an employee wants to arrange a new product display, the system will propose a new layout and provide a simulation image.

[0458] Step 5: Emotion Recognition and Encouragement

[0459] Employees complete the cleaning and report back.

[0460] The server receives the reports and analyzes the employee's emotions using an emotion engine.

[0461] If an employee feels tired, the system will encourage them by saying, "You've worked hard! Make sure you get plenty of rest today!" If the employee is feeling positive, it will generate an encouraging message such as, "Perfect! I'm looking forward to your next challenge!" and send it to the device.

[0462] The system helps employees efficiently clean and rearrange the store, and also provides emotional feedback to improve the overall work experience.

[0463] Prompt Sentence Examples

[0464] "Using this sophisticated cleaning support app, you can take photos of your store and receive recommendations for the best cleaning tools and procedures based on the analysis results. After the cleaning is complete, the app will use emotion recognition to provide you with a message of encouragement tailored to your emotions."

[0465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0466] Step 1:

[0467] A user takes a photo of the inside of a store using the smartphone camera and uploads the image data to the server via the app. The input is the image data taken by the user, and the output is the image data sent to the server. In this processing step, the image data is sent to the cloud server using the smartphone camera or the app's upload function.

[0468] Step 2:

[0469] The server receives the uploaded image data and begins the image analysis process. The input is the image data sent by the user, and the output is the analyzed data, which is feature data of objects and areas in the image. The server uses a machine learning model (e.g., Keras) and an image recognition algorithm to identify objects and cleaning areas in the store.

[0470] Step 3:

[0471] The server identifies cleaning tools and procedures based on the analysis results. The input is the analyzed feature data, and the output is the optimal cleaning tool list and procedure manual. This processing step also takes into account the user's past usage history and desired cleaning level data, and suggests the tools (e.g., vacuum cleaner, mop) and procedures (e.g., floor sweeping, garbage disposal) required for cleaning.

[0472] Step 4:

[0473] The server generates proposals for new interior layouts. The inputs are the user's desired and budget data and analysis results, and the output is a simulated image of the new interior layout and a list of items. The server uses simulation software to generate proposals for new layouts and product display arrangements.

[0474] Step 5:

[0475] The user cleans and reports the results in the app. The input is the cleaning completion report that the user enters into the app, and the output is the report data to the server. In this processing step, the user reports the completion of cleaning and the data is sent to the server.

[0476] Step 6:

[0477] The server receives the cleaning completion report and analyzes the user's emotions using an emotion recognition engine. The input is the user's facial expression and voice data after cleaning, and the output is the user's emotional data. The server uses an emotion recognition algorithm to identify emotions such as fatigue and joy.

[0478] Step 7:

[0479] The server generates a message of encouragement based on the user's emotions and sends it to the smartphone. The input is the user's emotional data, and the output is the message of encouragement. The server generates the optimal message based on the user's emotional state (e.g., "You're tired! Get plenty of rest today!") and sends it to the smartphone. In this processing step, a generative AI model is used to dynamically generate a message based on the user's emotions.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] [Second embodiment]

[0484] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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).

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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."

[0496] The system for implementing this invention starts when a user takes a photo of a room using a terminal and uploads it to a server. The system clearly divides the roles of the server, terminal, and user, and suggests efficient cleaning and interior layout.

[0497] System Overview

[0498] The system includes the following main measures:

[0499] Means for receiving images

[0500] A means of analyzing images

[0501] A means of identifying and providing cleaning supplies and procedures

[0502] A means of proposing new interior layouts

[0503] A means of generating and sending a message

[0504] Image receiving means

[0505] First, the user takes a photo of the room using a device such as a smartphone or tablet. The captured image data is then saved on the device. The user then launches the "Magical Makeover" app, selects the captured image, and uploads it. The image data is then sent to the server on the device.

[0506] Image analysis methods

[0507] The server analyzes the received image data. During the image analysis process, machine learning models are used to recognize objects in the photo (furniture, miscellaneous items, trash, etc.). Based on the results of this analysis, the server determines the condition of the room and identifies areas that need cleaning.

[0508] Cleaning equipment and procedures

[0509] The server then selects the optimal cleaning tools and efficient cleaning procedures based on the analysis results, taking into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[0510] Interior layout proposal method

[0511] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The server creates a list of items that can be purchased within the user's budget and a simulation image of the new layout, and sends them to the device.

[0512] How to generate cheer messages

[0513] When a user completes a cleaning task and reports the results within the app, the server receives the information. Based on the report, the server generates an encouraging message from a dedicated character and sends it to the device.

[0514] Specific examples

[0515] Prerequisites

[0516] A user wants to clean the living room.

[0517] The user takes a photo and uploads it to the server through the app.

[0518] Step 1: Take a photo and upload it

[0519] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0520] Step 2: Image analysis

[0521] The server analyzes the photos and identifies stains on the carpet, scattered magazines, etc.

[0522] Step 3: Submit a proposal

[0523] The server suggests specific cleaners and brushes for stained carpets and recommends using organizers for cluttered magazines.

[0524] The server estimates the time required to complete the request to be 30 minutes.

[0525] Step 4: Interior design proposal

[0526] The user inputs their desired redecoration (a relaxing space).

[0527] The server suggests new couch and plant placements and provides simulated images.

[0528] Step 5: Message of encouragement

[0529] The user completes the cleaning and reports it.

[0530] The server generates an encouraging message saying, "You did a great job! We're looking forward to your next cleaning!" and sends it to the device.

[0531] As described above, this system provides a series of procedures and functions to assist users in cleaning and interior arrangement, allowing them to clean efficiently and enjoyably and rearrange their rooms.

[0532] The processing flow will be explained below.

[0533] Step 1:

[0534] The user uses the device's camera to take a photo of the room from an appropriate angle so that the entire room can be seen.

[0535] Step 2:

[0536] The user launches the "Magical Makeover" app on their device and selects the image they have taken. The user then uses the file selection function within the app to select the desired photo data.

[0537] Step 3:

[0538] The terminal creates a request to upload the selected image data to the server. The terminal transmits the image data to the server.

[0539] Step 4:

[0540] The server receives the image data and stores it in a directory for analysis. The server starts the image analysis process.

[0541] Step 5:

[0542] The server uses machine learning models to recognize objects in the images, determining the location and type of furniture, miscellaneous items, trash, etc., and records this information in a database.

[0543] Step 6:

[0544] The server selects the necessary cleaning tools based on the analysis results, and creates an optimal cleaning tool list taking into account the user's past usage history and desired cleaning level.

[0545] Step 7:

[0546] The server generates an efficient cleaning procedure. The server creates a step-by-step procedure based on how to use cleaning tools and calculates the estimated time required for cleaning.

[0547] Step 8:

[0548] The server sends the recommendation in a response to the device, providing the device with suggested cleaning tools, procedures, and required time.

[0549] Step 9:

[0550] The user confirms the suggestions on the device and starts cleaning. The user proceeds with the cleaning by following the suggested steps.

[0551] Step 10:

[0552] After the user has completed cleaning, they can report it by pressing the "Cleaning Completed" button in the app. The user will then send a completion notification.

[0553] Step 11:

[0554] The server receives the completion notification and records it in the database. The server then generates a cheer message from the dedicated character.

[0555] Step 12:

[0556] The server sends the generated cheer message to the terminal, which notifies the user of the received cheer message and displays it.

[0557] Step 13:

[0558] Users can see the encouraging message and feel a sense of accomplishment, which increases their motivation for the next cleaning.

[0559] Example 1

[0560] 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."

[0561] Conventional cleaning support systems are inefficient because they require users to manually create cleaning plans and select the necessary tools. Furthermore, user satisfaction is low due to a lack of interior layout suggestions and encouraging messages to maintain motivation. There is a need for a system that solves these problems and allows users to clean easily and efficiently and enjoy redecorating their rooms.

[0562] 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.

[0563] In this invention, the server includes a means for receiving images taken by a user, a means for analyzing the received images and recognizing objects in the images to determine the condition of the room, and a means for identifying optimal cleaning tools and an efficient cleaning procedure. This allows the user to easily grasp the condition of the room and automatically receive an efficient cleaning plan. The server also includes a means for providing the user with the identified cleaning tools and cleaning procedure, a means for proposing a new interior layout based on the user's input, and a means for generating and sending a message of encouragement to the user after cleaning is completed. This allows the user to know the appropriate cleaning tools and procedures and receive a message of encouragement to maintain motivation after cleaning. Furthermore, when redecorating, the server can receive suggestions for interior layouts based on the user's budget and preferences.

[0564] "User" refers to an individual who uses this system to receive suggestions for cleaning and interior design for their room.

[0565] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[0566] "Server" refers to the central system that analyzes received image data, suggests cleaning procedures, generates encouraging messages, etc.

[0567] "Means for receiving images" refers to the function of the server receiving image data sent from the terminal.

[0568] "Means for analyzing images" refers to the function of analyzing received image data using a machine learning model, recognizing objects in the image, and determining the condition of the room.

[0569] "Means for identifying optimal cleaning tools and procedures" refers to a function for determining tools and procedures for efficient cleaning based on the analysis results.

[0570] "Means to suggest new interior layouts" refers to a function that suggests new interior layouts based on the user's wishes and budget.

[0571] "Means for generating and sending encouraging messages to users" refers to the function of creating and sending messages to encourage and motivate users after cleaning is completed.

[0572] A "machine learning model" refers to the algorithms and network structures used when performing image analysis, and examples include YOLO and ResNet.

[0573] "Cleaning equipment" refers to the tools and materials used when cleaning (e.g., vacuum cleaners, mops, detergents, etc.).

[0574] "Cleaning procedures" refer to the steps and methods required to clean efficiently.

[0575] "Interior arrangement" refers to the arrangement of furniture and decorations within a room.

[0576] "Encouragement messages" refer to words or messages of encouragement sent to users who have finished cleaning.

[0577] This invention begins when a user takes a photo of a room using a terminal and uploads the image data to a server. This system clearly divides the roles of the server, terminal, and user, and provides suggestions for efficient cleaning and interior layout. Detailed embodiments are described below.

[0578] Hardware and Software Configuration

[0579] User device: refers to a device such as a smartphone, tablet, or PC on which the "Magical Makeover" app is installed.

[0580] Server: Refers to the central system that performs image processing and data analysis, and is equipped with machine learning models (e.g., YOLO, ResNet, etc.) for image analysis.

[0581] Communications network: Refers to the infrastructure for sending and receiving data between terminals and servers.

[0582] Specific operation procedures and data processing

[0583] 1. Image receiving method: The user takes a photo of the room using the device's camera. The user selects the photo within the app and uploads it to the server. The device then sends this image data to the server.

[0584] 2. Image analysis: The server analyzes the image data it receives. Using a machine learning model, it recognizes objects in the image, such as furniture, miscellaneous items, and trash, and assigns an identification tag to each. Based on the results of this analysis, it determines the condition of the room and identifies areas that need cleaning.

[0585] 3. Method for identifying cleaning tools and cleaning procedures: The server selects the optimal cleaning tools and efficient cleaning procedures based on the analysis results. The server also takes into account the user's past usage history and desired cleaning level. The server combines this information and provides it to the user along with an estimate of the required time.

[0586] 4. Interior layout proposal method: The user inputs their desired redecorating needs. The server generates a proposal for a new interior layout based on the user's input information. It creates a list of items that can be purchased within the user's budget and a simulation image of the new layout, and sends these to the terminal.

[0587] 5. Means for generating encouraging messages: After the user completes cleaning, they report the results within the app. The server receives this information, generates encouraging messages, and sends them to the device.

[0588] Specific examples

[0589] Prerequisites

[0590] A user wants to clean the living room.

[0591] The user takes a photo and uploads it to the server through the app.

[0592] Step 1: Take a photo and upload it

[0593] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0594] Step 2: Image analysis

[0595] The server analyzes the photos and identifies stains on the carpet, scattered magazines, etc.

[0596] Step 3: Submit a proposal

[0597] The server suggests a specific cleaner and brush for the stained carpet and recommends using a magazine organizer for the scattered magazines. The server estimates the process will take 30 minutes.

[0598] Step 4: Interior design proposal

[0599] The user inputs their desired redecorating needs (a relaxing space), and the server proposes new couch and plant placements and provides simulation images.

[0600] Step 5: Message of encouragement

[0601] When the user completes cleaning and reports it, the server generates an encouraging message saying, "You did a great job! We look forward to your next cleaning!" and sends it to the device.

[0602] Example prompts for generative AI models

[0603] 1. Image analysis prompt

[0604] Analyze photos of rooms and identify each object, such as furniture, household items, and trash. Use this information to suggest areas that need cleaning.

[0605] 2. Cleaning Prompt

[0606] Based on the analyzed image information of the room, please suggest the cleaning procedure and necessary cleaning tools, along with the required time.

[0607] 3. Prompt for interior design proposal

[0608] Please propose a new interior layout based on the user's desire for a relaxing space. Please also provide a list of items that can be purchased within the user's budget and simulation images.

[0609] This invention allows users to clean efficiently and enjoyably redecorate their rooms. The system allows users to easily grasp the condition of their rooms and receive optimal cleaning plans and procedures. It also helps users maintain motivation through encouraging messages and creates new spaces through suggestions for interior layout.

[0610] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0611] Step 1:

[0612] The user takes a photo of the room using the device's camera. Then, the user launches the "Magical Makeover" app, selects the image, and uploads it. The device then sends the image data to the server.

[0613] Input: Photo of the room

[0614] Output: Image data sent to the server

[0615] Step 2:

[0616] The server analyzes the received image data. During the image analysis process, machine learning models (e.g., YOLO, ResNet, etc.) are used to recognize furniture, miscellaneous items, trash, etc. in the image. As a result of this recognition, each object is assigned an identification tag.

[0617] Input: Image data

[0618] Output: Analysis results with identification tags

[0619] Step 3:

[0620] The server uses the analysis results to determine the condition of the room and identify areas that need cleaning, such as stains on the carpet or scattered magazines. Based on this information, the server notifies the user.

[0621] Input: Analysis results

[0622] Output: Identifying areas that need cleaning and notifying the user

[0623] Step 4:

[0624] The server then uses the analysis results to identify the optimal cleaning tools and efficient cleaning procedures, taking into account the user's past usage history and desired cleaning level. For example, the server may suggest using specific detergents and brushes and provide cleaning instructions, including an estimate of the time required.

[0625] Input: Analysis results, user usage history, cleaning level

[0626] Output: Suggested cleaning materials and procedures

[0627] Step 5:

[0628] The server generates a new interior layout proposal based on the user's input information (budget, preferences, etc.), creates a list of items that can be purchased within the budget, and a simulation image of the new layout, and sends these to the terminal.

[0629] Input: User input information

[0630] Output: interior layout proposal, item list, simulation image

[0631] Step 6:

[0632] After the user completes the cleaning, they report the results within the app. The server receives the information and generates an encouraging message based on the report. The message is generated in the form of a dedicated character and sent to the device.

[0633] Input: Report of cleaning completion

[0634] Output: Generate and send a cheer message

[0635] (Application example 1)

[0636] 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."

[0637] In conventional cleaning support systems and interior design suggestion systems, users take photos of their rooms and are given suggestions on cleaning methods and interior layouts, but they do not offer support for purchasing in physical stores or suggest specific products. As a result, users are forced to find out where to purchase the suggested interior items themselves, which creates an inefficient purchasing experience.

[0638] 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.

[0639] In this invention, the server includes means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and an efficient cleaning procedure, means for providing the identified cleaning tools and procedure to the user, means for proposing a new interior layout based on the user's input, means for searching for products based on the suggestions and presenting candidates available for purchase, and means for generating and sending a message of encouragement to the user after cleaning is completed. This allows the user to not only clean efficiently but also purchase the suggested interior products on the spot.

[0640] The "receiving means" refers to the device and process for acquiring and storing image data captured by the user.

[0641] The "analyzing means" refers to devices and programs that perform topological and morphological analysis of the received image data to identify objects in the image and areas that need cleaning.

[0642] The "means for specifying" refers to a device and a program for selecting necessary cleaning tools and efficient cleaning procedures based on the analysis results.

[0643] "Means for providing" means devices and mechanisms for informing or displaying information to users about identified cleaning tools and procedures.

[0644] The "means for proposing" is a device and a program for generating and proposing a new interior layout in accordance with information input by the user.

[0645] The "searching means" refers to a device and program for searching a database for available products based on the proposed interior layout.

[0646] "Presenting means" refers to devices and mechanisms for notifying or displaying information about searched products to users.

[0647] The "means for generating an encouraging message" refers to a device and a program for generating and sending an encouraging message to a user after the cleaning is completed.

[0648] This embodiment of the present invention is a system for improving user experience in a physical store. A series of processes from a user taking an image of a room using a smartphone to receiving suggestions for cleaning and interior layout will be described.

[0649] System Overview

[0650] The system includes the following elements:

[0651] 1. Means of receiving images

[0652] 2. Means of analyzing images

[0653] 3. A means of identifying necessary cleaning supplies and efficient cleaning procedures.

[0654] 4. Means of providing users with identified cleaning supplies and procedures

[0655] 5. A method for suggesting new interior layouts based on user input

[0656] 6. A way to search for products based on suggestions and present available options for purchase

[0657] 7. A method for generating and sending a cheer message to the user after cleaning is complete

[0658] Image receiving means

[0659] Users take photos of the room using their smartphone camera and upload the images to the server via a dedicated app.

[0660] Image analysis methods

[0661] The server analyzes the received images using machine learning models such as TensorFlow and PyTorch, and this analysis identifies furniture in the photo and areas that need cleaning (such as dirt on the floor or scattered magazines).

[0662] Cleaning equipment and procedures

[0663] Based on the analysis, the server identifies the optimal cleaning tools (e.g., specific detergents and brushes) and efficient cleaning procedures, taking into account the user's past cleaning history and desired cleaning level. The server provides this information to the user, along with an estimate of the required time.

[0664] Interior layout proposal method

[0665] When a user wishes to redecorate, the server will suggest interior layouts. For example, if the user desires a "modern living room," the server will generate a new layout based on this request and provide a list of related products along with simulation images.

[0666] Example prompt: "Suggest a décor arrangement that fits a modern theme in the living room."

[0667] Product search and presentation methods

[0668] Based on the proposed interior layout, the server searches for available products from a database of physical stores, allowing users to view detailed information about these products and purchase them on the spot.

[0669] How to generate cheer messages

[0670] When a user completes cleaning and reports the results to the app, the server generates an encouraging message based on the results, such as "Have a wonderful time in your new home!"

[0671] With the above configuration, this system provides users with comprehensive support, suggesting efficient cleaning and interior layout, and even allowing them to purchase products on the spot.

[0672] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0673] Step 1:

[0674] Users take photos of the room using their smartphone camera and upload them to the server via a dedicated app.

[0675] Input: A photo of the room taken with a smartphone

[0676] Output: Image data uploaded to the server

[0677] Specific operation: The user launches the dedicated app, presses the "Take Photo" button to take a picture of the room, and then presses the "Upload Image" button to send the image data to the server.

[0678] Step 2:

[0679] The server uses machine learning models (TensorFlow or PyTorch) to acquire and analyze the images received.

[0680] Input: Room image data uploaded by the user

[0681] Output: Identification of objects and areas that need cleaning in the image

[0682] Specific operation: The server inputs the received image data into a machine learning model to identify furniture, miscellaneous items, and areas that need cleaning. As a result of the analysis, it outputs specific object categories and their location information.

[0683] Step 3:

[0684] Based on the analysis results, the server identifies the necessary cleaning tools and efficient cleaning procedures, and provides them to the user along with an estimate of the time required.

[0685] Input: Image analysis results, user's past cleaning history, desired cleaning level

[0686] Output: List of cleaning supplies, cleaning procedure, and time required

[0687] Specific operation: The server selects the optimal cleaning tools (e.g., specific detergents, brushes, etc.) based on the analysis results, generates an efficient cleaning procedure, estimates the required time, and notifies the user of this information.

[0688] Step 4:

[0689] The user inputs their desired redecorating needs (e.g., "modern living room"), and the server makes suggestions for new interior layouts.

[0690] Input: User's redecorating intentions and budget information

[0691] Output: Simulation images of new interior layout, list of potential purchases

[0692] How it works: The user inputs their desired redecorating needs into the app, and the server generates a new interior layout based on that information. The generated interior layout is presented to the user as a simulation image.

[0693] Step 5:

[0694] Based on the suggestions, the server searches a database of physical stores and presents candidates for products that the user can purchase.

[0695] Input: New interior layout proposals, physical store inventory database

[0696] Output: A list of products available for purchase

[0697] Specific operation: The server searches the physical store database for products that match the proposed interior, and presents a list of detailed information about the products available for purchase to the user.

[0698] Step 6:

[0699] When a user completes a cleaning task and reports it to the app, the server generates and sends an encouraging message.

[0700] Input: User's cleaning completion report

[0701] Output: Cheers message

[0702] How it works: The user reports their cleaning completion through the app, and the server uses the AI ​​model to generate a message of encouragement based on the report. The message is then sent to the user in the form of a special character.

[0703] As described above, this system assists users in cleaning and arranging their rooms, providing a consistent shopping experience similar to that of a physical store.

[0704] 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.

[0705] The system for implementing this invention has the function of listening to photos of a room taken by the user, suggesting cleaning tools and cleaning procedures based on those photos, and even recommending new interior layouts. It also incorporates an emotion engine that recognizes the user's emotions, aiming to improve the user's motivation by providing encouraging messages that correspond to the user's emotions. The specific operation of this system is described below.

[0706] System Overview

[0707] The system includes the following main measures:

[0708] Means for receiving images

[0709] A means of analyzing images

[0710] A means of identifying and providing cleaning supplies and procedures

[0711] A means of proposing new interior layouts

[0712] A means to generate and send a cheer message to users after cleaning is completed

[0713] Emotion engine that recognizes user emotions

[0714] Image receiving means

[0715] The user takes a photo of the room using the device. The captured image data is saved on the device, and then the user opens the "Magical Makeover" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[0716] Image analysis methods

[0717] The server analyzes the received image data. The image analysis process uses machine learning models to recognize objects in the photo (furniture, miscellaneous items, trash, etc.). Based on the analysis results, the server determines the condition of the room and identifies areas that need cleaning.

[0718] Cleaning equipment and procedures

[0719] The server then uses the analysis results to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[0720] Interior layout proposal method

[0721] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The proposal includes a list of items that can be purchased within the user's budget and a simulation image of the new layout.

[0722] Emotion Engine

[0723] The system incorporates an emotion engine to recognize the user's emotions, which is determined through facial and voice analysis and provides appropriate feedback.

[0724] How to generate cheer messages

[0725] When the cleaning is complete, the user reports the results within the app. The server receives this report and uses an emotion engine to analyze the user's emotions and generates a cheer message from a dedicated character. This cheer message is customized according to the user's emotions.

[0726] Specific examples

[0727] Prerequisites

[0728] A user wants to clean the living room.

[0729] The user takes a photo and uploads it to the server through the app.

[0730] Step 1: Take a photo and upload it

[0731] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0732] Step 2: Image analysis

[0733] The server analyzes the photos and identifies things like stains on the carpet or scattered magazines.

[0734] Step 3: Submit a proposal

[0735] The server suggests specific cleaners and brushes for stained carpets and recommends using organizers for cluttered magazines.

[0736] The server estimates the time required to complete the request to be 30 minutes.

[0737] Step 4: Interior design proposal

[0738] The user inputs their desired redecoration (a relaxing space).

[0739] The server suggests new couch and plant placements and provides simulated images.

[0740] Step 5: Emotion Recognition and Encouragement

[0741] The user completes the cleaning and reports it.

[0742] The server receives the report and analyzes the user's emotions using an emotion engine.

[0743] If the user feels tired, the system will encourage them by saying, "You're tired! Get plenty of rest today!" If the user is feeling positive, it will generate an encouraging message such as, "Perfect! I'm looking forward to your next challenge!" and send it to the device.

[0744] The system assists users in cleaning and interior arrangement, and also provides emotional feedback to enhance the overall user experience.

[0745] The processing flow will be explained below.

[0746] Step 1:

[0747] The user takes a photo of the room using the device. The user uses the camera function to take a photo from an appropriate angle so that the entire room can be seen.

[0748] Step 2:

[0749] The user launches the "Magical Makeover" app on their device and selects the image they have taken. The user then uses the file selection function within the app to select the desired photo data.

[0750] Step 3:

[0751] The device creates a request to upload the selected image data to the server. The device sends the image data to the server.

[0752] Step 4:

[0753] The server receives the image data and stores it in a directory for analysis. The server starts the image analysis process.

[0754] Step 5:

[0755] The server uses machine learning models to recognize objects in the images, determining the location and type of furniture, miscellaneous items, trash, etc., and records this information in a database.

[0756] Step 6:

[0757] The server selects the necessary cleaning tools based on the analysis results, and creates an optimal cleaning tool list taking into account the user's past usage history and desired cleaning level.

[0758] Step 7:

[0759] The server generates an efficient cleaning procedure. The server creates a step-by-step procedure based on how to use cleaning tools and calculates the estimated time required for cleaning.

[0760] Step 8:

[0761] The server sends the recommendation in a response to the device, providing the device with suggested cleaning tools, procedures, and required time.

[0762] Step 9:

[0763] The user confirms the suggestions on the device and starts cleaning. The user then follows the suggested steps to proceed with the cleaning.

[0764] Step 10:

[0765] After the user has completed cleaning, they can report it by pressing the "Cleaning Completed" button in the app. The user will then send a completion notification.

[0766] Step 11:

[0767] The server receives the completion notification and records it in the database. The server then activates the emotion engine to recognize the user's emotion.

[0768] Step 12:

[0769] The server uses an emotion engine to analyze the user's emotions, identify emotions from the user's facial expressions and voice, and record the results.

[0770] Step 13:

[0771] The server generates a message of encouragement based on the user's emotions. If the user is tired, it generates a message such as "You're tired! Get plenty of rest today!", and if the user is positive, it generates a message such as "Perfect! Looking forward to the next challenge!".

[0772] Step 14:

[0773] The server sends the generated cheer message to the terminal, which notifies the user of the received cheer message and displays it.

[0774] Step 15:

[0775] Users can see the encouraging message and feel a sense of accomplishment, which increases their motivation for the next cleaning.

[0776] Example 2

[0777] 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."

[0778] Existing cleaning support systems have the problem that they do not take into account the user's emotional state, which can lead to a decrease in motivation for cleaning work. Furthermore, while there are systems that support both cleaning and interior layout suggestions, there is a lack of a means to provide these services in a unified manner. This makes it difficult for users to efficiently perform both cleaning and redecorating.

[0779] 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 means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and an efficient cleaning procedure, means for providing the identified cleaning tools and cleaning procedure to the user, means for proposing a new interior layout based on the user's input, means for generating and sending a cheer message to the user after cleaning is completed, and an emotion engine for recognizing the user's emotions. This not only enables the user to efficiently clean and rearrange the room, but also makes it easier for them to stay motivated during the process.

[0780] "User" refers to an individual or organization who wishes to use this system to clean and arrange the interior of a room.

[0781] "Means for receiving captured images" refers to a function or process by which the server receives image data captured by a user on a terminal via a network.

[0782] "Means for analyzing received images and identifying necessary cleaning tools and efficient cleaning procedures" refers to the function or process in which the server analyzes the received image data using machine learning models or other analytical technologies and selects the optimal cleaning tools and procedures based on the results.

[0783] "Means for providing the identified cleaning tools and cleaning procedures to the user" refers to the function or process by which the server provides the user with the cleaning tools and cleaning procedures identified as a result of the analysis through display, notification, etc.

[0784] "Means for suggesting new interior layouts based on user input" refers to a function or process that automatically generates and suggests new interior layouts taking into account input information from the user (e.g., budget and preferences).

[0785] "Means for generating and sending a cheer message to the user after cleaning is completed" refers to a function or process in which the server generates a cheer message and sends it to the user after the user reports that the cleaning job is completed.

[0786] An "emotion engine" refers to a software or hardware component that recognizes a user's emotions and generates feedback or messages accordingly.

[0787] The system for implementing this invention receives photos of a room taken by the user, and based on those photos, suggests cleaning tools and procedures, and even recommends new interior layouts. It also incorporates an emotion engine that recognizes the user's emotions, aiming to improve the user's motivation by providing encouraging messages that correspond to the user's emotions.

[0788] The system includes the following main measures:

[0789] Means for receiving images

[0790] A means of analyzing images

[0791] A means of identifying and providing cleaning supplies and procedures

[0792] A means of proposing new interior layouts

[0793] A means to generate and send a cheer message to users after cleaning is completed

[0794] Emotion engine that recognizes user emotions

[0795] The user takes a photo of the room using the device. The captured image data is saved on the device, and then the user opens the "Magical Makeover" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[0796] The server analyzes the received image data. This process uses machine learning models (e.g., YOLO or ResNet) to recognize objects in the photo (furniture, household items, trash, etc.). Based on the analysis results, the server determines the condition of the room and identifies areas that need cleaning.

[0797] The server then uses the analysis results to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[0798] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The proposal includes a list of items that can be purchased within the user's budget and a simulation image of the new layout. The simulation image is generated using computer graphics technology and simulation software.

[0799] The system incorporates an emotion engine to recognize the user's emotions, which is determined through facial and voice analysis and provides appropriate feedback.

[0800] When the cleaning is complete, the user reports the results within the app. The server then uses an emotion engine to analyze the user's emotions and generates a cheer message from a dedicated character. This cheer message is customized based on the user's emotions and sent to the device.

[0801] Specific examples

[0802] Prerequisites

[0803] A user wants to clean the living room.

[0804] The user takes a photo and uploads it to the server through the app.

[0805] Step 1: Take a photo and upload it

[0806] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0807] Example prompt sentence:

[0808] Describe in detail a scenario where a user takes a photo of their living room and sends it to a server through your app.

[0809] Step 2: Image analysis

[0810] The server analyzes the photos and identifies things like stains on the carpet or scattered magazines.

[0811] Example prompt sentence:

[0812] Analyze the photos received by the server and describe any stains or objects identified.

[0813] Step 3: Submit a proposal

[0814] The server suggests a specific cleaner and brush for stained carpets, recommends using organizers for cluttered magazines, and estimates the cleaning time to be 30 minutes.

[0815] Example prompt sentence:

[0816] Describe the process by which the server suggests cleaning supplies and procedures and estimates the time required.

[0817] Step 4: Interior design proposal

[0818] The user inputs their desired redecorating needs (a relaxing space), and the server proposes new couch and plant placements and provides simulation images.

[0819] Example prompt sentence:

[0820] Describe the process by which the server proposes new interior layouts based on the user's preferences.

[0821] Step 5: Emotion recognition and cheer message generation

[0822] After the user completes the cleaning task and reports it within the app, the server uses an emotion engine to analyze the user's emotions, generates a cheer message based on the user's emotions, and sends it to the device.

[0823] Example prompt sentence:

[0824] Please explain the process by which the server uses the emotion engine to generate a cheer message and send it to the user after cleaning is complete.

[0825] The system aims to assist users with cleaning and interior arrangement, and provide emotional feedback to improve the overall user experience.

[0826] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0827] Step 1:

[0828] The user takes a photo of the room and saves the image data on the device. Specifically, the photo is taken using the smartphone's camera app, and the image data is saved in the "Magical Makeover" app's gallery.

[0829] Input: User action to take a picture of the room

[0830] Output: Image data stored on the device

[0831] Step 2:

[0832] The user opens the "Magical Makeover" app, selects a photo they have taken, and then presses the upload button on the app, causing the device to send the image data to the server. HTTP or HTTPS is used as the communication protocol.

[0833] Input: User selection and upload of photo

[0834] Output: Image data sent to the server

[0835] Step 3:

[0836] The server analyzes the received image data and uses a machine learning model (e.g., YOLO or ResNet) to recognize objects in the photo (furniture, household items, trash, etc.). This analysis process also identifies the location and type of each object.

[0837] Input: Image data sent to the server

[0838] Output: List of analyzed objects and their positions

[0839] Step 4:

[0840] The server then uses image analysis to identify the necessary cleaning tools and efficient cleaning procedures, taking into account the user's past usage history and desired cleaning level. For example, if dirt is detected on the carpet, it will suggest the appropriate detergent and brush, and estimate the required time.

[0841] Input: List of analyzed objects and their locations, user's past cleaning history, desired cleaning level

[0842] Output: A list of recommended cleaning supplies and procedures, and the cleaning time required.

[0843] Step 5:

[0844] The server provides the user with the identified cleaning supplies and procedures, specifically by displaying a list of recommended cleaning supplies and procedures through the app and informing them of the required time.

[0845] Input: A list of recommended cleaning supplies and procedures, and the time required for cleaning.

[0846] Output: A list of cleaning supplies and procedures provided to the user, along with a notification of the required time.

[0847] Step 6:

[0848] The user enters their desired redecorating needs into the app (optional), including desired conditions such as a relaxing space and a modern design.

[0849] Input: User inputs desired redecorating conditions

[0850] Output: Condition data for interior layout proposals

[0851] Step 7:

[0852] The server generates new interior layout proposals based on the user's desired conditions. Specifically, it provides a list of furniture and decorations that can be purchased within the user's budget, along with simulation images of the new layout. The simulation images are generated using CG technology and simulation software.

[0853] Input: Condition data for interior layout proposal

[0854] Output: Proposed interior layout (item list and simulation images)

[0855] Step 8:

[0856] When the user completes cleaning, they report it within the app. Specifically, they press the "Report Completion" button in the app to send the cleaning completion information to the server.

[0857] Input: User reports completion of cleaning

[0858] Output: Cleaning completion information sent to the server

[0859] Step 9:

[0860] The server receives the report and analyzes the user's emotions using an emotion engine. The emotion engine analyzes the user's facial photo and voice data to determine the user's emotional state. Based on the results, a cheer message from a dedicated character is generated and sent to the device.

[0861] Input: Cleaning completion information sent to the server, facial photos and voice data

[0862] Output: Emotion-based cheer message

[0863] This allows the system to assist users with cleaning and interior arrangement, and even provide emotional feedback to improve the overall user experience.

[0864] (Application example 2)

[0865] 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."

[0866] In physical stores, there are challenges such as a lack of specific procedures and methods for selecting tools to enable employees to clean the store efficiently and effectively, a lack of means to maintain employee motivation, and the difficulty of changing the store layout and optimizing display placement.

[0867] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and efficient cleaning procedures, means for providing the identified cleaning tools and cleaning procedures to the user, means for proposing a new interior layout based on the user's input, means for generating and sending a cheer message to the user after cleaning is completed, means for employees to take photos of the interior space of the physical store and analyze areas within the store that need cleaning, means for providing employees with cleaning tools and cleaning procedures based on the analysis results, and means for recognizing the employee's emotions after cleaning is completed and generating and sending a cheer message. This enables employees to clean efficiently and maintain their motivation while working.

[0868] "User" refers to a member of the public or a store employee who uses this system.

[0869] "Images" refers to photographs and videos taken with a smartphone or other imaging device.

[0870] "Means of receiving" refers to the technology or method for importing image data from an external server or application.

[0871] "Means for analyzing" refers to techniques or methods for analyzing received image data using machine learning models or image processing algorithms.

[0872] "Cleaning equipment" refers to tools used for cleaning, such as vacuum cleaners, mops, brushes, and detergents.

[0873] "Cleaning procedures" refer to the specific steps and processes for cleaning efficiently.

[0874] "New interior layout" refers to proposals for redesigning the layout of furniture and decorations in a room or store to create a better space.

[0875] An "encouragement message" is a message of encouragement sent to boost the user's enthusiasm and motivation.

[0876] "Employees" refers to staff and clerks working in the store.

[0877] A "machine learning model" is an algorithm or framework that allows a computer to learn from data and make predictions or classifications based on that data.

[0878] "Means for recognizing emotions" refers to technologies and methods that analyze the facial expressions and voices of users and employees to identify their emotional state.

[0879] A "server" is a computer system that processes data and provides services over a network.

[0880] The "Clean Captain" system for implementing this invention aims to help store employees efficiently clean the store and improve their motivation. The specific operation of this system is described below.

[0881] System Overview

[0882] The system includes the following main measures:

[0883] Means for receiving images

[0884] A means of analyzing images

[0885] A means of identifying and providing cleaning supplies and procedures

[0886] A means of proposing new interior layouts

[0887] A means to generate and send a cheer message to users after cleaning is completed

[0888] A method for employees to take photos of the interior space of a physical store and analyze areas that need cleaning.

[0889] A means of providing employees with cleaning supplies and procedures based on the analysis results

[0890] A means of recognizing employee emotions and generating and sending encouraging messages

[0891] Image receiving means

[0892] Employees use their smartphones to take photos of the store. The captured image data is saved on the smartphone, and then the employee opens the "Clean Captain" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[0893] Image analysis methods

[0894] The server analyzes the received image data. The image analysis process uses machine learning models to recognize objects in the photo (furniture, products, trash, etc.). Based on the analysis results, the server identifies specific areas in the store that need cleaning. The machine learning models used are deep learning frameworks such as Keras.

[0895] Cleaning equipment and procedures

[0896] The server uses the analysis to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the employee's past usage history and desired cleaning level. The server compiles this information and provides it to the employee along with an estimate of the required time.

[0897] Interior layout proposal method

[0898] When an employee wants to rearrange the store or arrange new product displays, the server generates a proposal for a new layout based on the employee's input information (budget, desired layout, etc.) The proposal includes a list of items that can be purchased within the budget and a simulation image of the new layout.

[0899] How to generate cheer messages

[0900] When cleaning is complete, employees report their results within the app. The server receives this report and uses an emotion engine to analyze the employee's emotions and generate a message of encouragement. The emotion engine determines the employee's emotions through facial and voice analysis, and provides appropriate feedback.

[0901] Specific examples

[0902] Prerequisites

[0903] Employees want to clean the store.

[0904] Employees take photos and upload them to a server via the app.

[0905] Step 1: Take a photo and upload it

[0906] Employees take photos of the store using their smartphone cameras and upload them through the app.

[0907] Step 2: Image analysis

[0908] The server analyzes the photos to determine product placement and litter levels.

[0909] Step 3: Submit a proposal

[0910] The server will suggest specific garbage bags and vacuum cleaners for cluttered trash, and also suggest ways to organize product placement.

[0911] The server estimates the required time.

[0912] Step 4: Interior layout proposal

[0913] If an employee wants to arrange a new product display, the system will propose a new layout and provide a simulation image.

[0914] Step 5: Emotion Recognition and Encouragement

[0915] Employees complete the cleaning and report back.

[0916] The server receives the reports and analyzes the employee's emotions using an emotion engine.

[0917] If an employee feels tired, the system will encourage them by saying, "You've worked hard! Make sure you get plenty of rest today!" If the employee is feeling positive, it will generate an encouraging message such as, "Perfect! I'm looking forward to your next challenge!" and send it to the device.

[0918] The system helps employees efficiently clean and rearrange the store, and also provides emotional feedback to improve the overall work experience.

[0919] Prompt Sentence Examples

[0920] "Using this sophisticated cleaning support app, you can take photos of your store and receive recommendations for the best cleaning tools and procedures based on the analysis results. After the cleaning is complete, the app will use emotion recognition to provide you with a message of encouragement tailored to your emotions."

[0921] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0922] Step 1:

[0923] A user takes a photo of the inside of a store using the smartphone camera and uploads the image data to the server via the app. The input is the image data taken by the user, and the output is the image data sent to the server. In this processing step, the image data is sent to the cloud server using the smartphone camera or the app's upload function.

[0924] Step 2:

[0925] The server receives the uploaded image data and begins the image analysis process. The input is the image data sent by the user, and the output is the analyzed data, which is feature data of objects and areas in the image. The server uses a machine learning model (e.g., Keras) and an image recognition algorithm to identify objects and cleaning areas in the store.

[0926] Step 3:

[0927] The server identifies cleaning tools and procedures based on the analysis results. The input is the analyzed feature data, and the output is the optimal cleaning tool list and procedure manual. This processing step also takes into account the user's past usage history and desired cleaning level data, and suggests the tools (e.g., vacuum cleaner, mop) and procedures (e.g., floor sweeping, garbage disposal) required for cleaning.

[0928] Step 4:

[0929] The server generates proposals for new interior layouts. The inputs are the user's desired and budget data and analysis results, and the output is a simulated image of the new interior layout and a list of items. The server uses simulation software to generate proposals for new layouts and product display arrangements.

[0930] Step 5:

[0931] The user cleans and reports the results in the app. The input is the cleaning completion report that the user enters into the app, and the output is the report data to the server. In this processing step, the user reports the completion of cleaning and the data is sent to the server.

[0932] Step 6:

[0933] The server receives the cleaning completion report and analyzes the user's emotions using an emotion recognition engine. The input is the user's facial expression and voice data after cleaning, and the output is the user's emotional data. The server uses an emotion recognition algorithm to identify emotions such as fatigue and joy.

[0934] Step 7:

[0935] The server generates a message of encouragement based on the user's emotions and sends it to the smartphone. The input is the user's emotional data, and the output is the message of encouragement. The server generates the optimal message based on the user's emotional state (e.g., "You're tired! Get plenty of rest today!") and sends it to the smartphone. In this processing step, a generative AI model is used to dynamically generate a message based on the user's emotions.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] [Third embodiment]

[0940] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0941] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0942] 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).

[0943] 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.

[0944] 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.

[0945] 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).

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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."

[0952] The system for implementing this invention starts when a user takes a photo of a room using a terminal and uploads it to a server. The system clearly divides the roles of the server, terminal, and user, and suggests efficient cleaning and interior layout.

[0953] System Overview

[0954] The system includes the following main measures:

[0955] Means for receiving images

[0956] A means of analyzing images

[0957] A means of identifying and providing cleaning supplies and procedures

[0958] A means of proposing new interior layouts

[0959] A means of generating and sending a message

[0960] Image receiving means

[0961] First, the user takes a photo of the room using a device such as a smartphone or tablet. The captured image data is then saved on the device. The user then launches the "Magical Makeover" app, selects the captured image, and uploads it. The image data is then sent to the server on the device.

[0962] Image analysis methods

[0963] The server analyzes the received image data. During the image analysis process, machine learning models are used to recognize objects in the photo (furniture, miscellaneous items, trash, etc.). Based on the results of this analysis, the server determines the condition of the room and identifies areas that need cleaning.

[0964] Cleaning equipment and procedures

[0965] The server then selects the optimal cleaning tools and efficient cleaning procedures based on the analysis results, taking into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[0966] Interior layout proposal method

[0967] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The server creates a list of items that can be purchased within the user's budget and a simulation image of the new layout, and sends them to the device.

[0968] How to generate cheer messages

[0969] When a user completes a cleaning task and reports the results within the app, the server receives the information. Based on the report, the server generates an encouraging message from a dedicated character and sends it to the device.

[0970] Specific examples

[0971] Prerequisites

[0972] A user wants to clean the living room.

[0973] The user takes a photo and uploads it to the server through the app.

[0974] Step 1: Take a photo and upload it

[0975] The user takes a photo of their living room with their device's camera and uploads it through the app.

[0976] Step 2: Image analysis

[0977] The server analyzes the photos and identifies stains on the carpet, scattered magazines, etc.

[0978] Step 3: Submit a proposal

[0979] The server suggests specific cleaners and brushes for stained carpets and recommends using organizers for cluttered magazines.

[0980] The server estimates the time required to complete the request to be 30 minutes.

[0981] Step 4: Interior design proposal

[0982] The user inputs their desired redecoration (a relaxing space).

[0983] The server suggests new couch and plant placements and provides simulated images.

[0984] Step 5: Message of encouragement

[0985] The user completes the cleaning and reports it.

[0986] The server generates an encouraging message saying, "You did a great job! We're looking forward to your next cleaning!" and sends it to the device.

[0987] As described above, this system provides a series of procedures and functions to assist users in cleaning and interior arrangement, allowing them to clean efficiently and enjoyably and rearrange their rooms.

[0988] The processing flow will be explained below.

[0989] Step 1:

[0990] The user uses the device's camera to take a photo of the room from an appropriate angle so that the entire room can be seen.

[0991] Step 2:

[0992] The user launches the "Magical Makeover" app on their device and selects the image they have taken. The user then uses the file selection function within the app to select the desired photo data.

[0993] Step 3:

[0994] The terminal creates a request to upload the selected image data to the server. The terminal transmits the image data to the server.

[0995] Step 4:

[0996] The server receives the image data and stores it in a directory for analysis. The server starts the image analysis process.

[0997] Step 5:

[0998] The server uses machine learning models to recognize objects in the images, determining the location and type of furniture, miscellaneous items, trash, etc., and records this information in a database.

[0999] Step 6:

[1000] The server selects the necessary cleaning tools based on the analysis results, and creates an optimal cleaning tool list taking into account the user's past usage history and desired cleaning level.

[1001] Step 7:

[1002] The server generates an efficient cleaning procedure. The server creates a step-by-step procedure based on how to use cleaning tools and calculates the estimated time required for cleaning.

[1003] Step 8:

[1004] The server sends the recommendation in a response to the device, providing the device with suggested cleaning tools, procedures, and required time.

[1005] Step 9:

[1006] The user confirms the suggestions on the device and starts cleaning. The user proceeds with the cleaning by following the suggested steps.

[1007] Step 10:

[1008] After the user has completed cleaning, they can report it by pressing the "Cleaning Completed" button in the app. The user will then send a completion notification.

[1009] Step 11:

[1010] The server receives the completion notification and records it in the database. The server then generates a cheer message from the dedicated character.

[1011] Step 12:

[1012] The server sends the generated cheer message to the terminal, which notifies the user of the received cheer message and displays it.

[1013] Step 13:

[1014] Users can see the encouraging message and feel a sense of accomplishment, which increases their motivation for the next cleaning.

[1015] Example 1

[1016] 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."

[1017] Conventional cleaning support systems are inefficient because they require users to manually create cleaning plans and select the necessary tools. Furthermore, user satisfaction is low due to a lack of interior layout suggestions and encouraging messages to maintain motivation. There is a need for a system that solves these problems and allows users to clean easily and efficiently and enjoy redecorating their rooms.

[1018] 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.

[1019] In this invention, the server includes a means for receiving images taken by a user, a means for analyzing the received images and recognizing objects in the images to determine the condition of the room, and a means for identifying optimal cleaning tools and an efficient cleaning procedure. This allows the user to easily grasp the condition of the room and automatically receive an efficient cleaning plan. The server also includes a means for providing the user with the identified cleaning tools and cleaning procedure, a means for proposing a new interior layout based on the user's input, and a means for generating and sending a message of encouragement to the user after cleaning is completed. This allows the user to know the appropriate cleaning tools and procedures and receive a message of encouragement to maintain motivation after cleaning. Furthermore, when redecorating, the server can receive suggestions for interior layouts based on the user's budget and preferences.

[1020] "User" refers to an individual who uses this system to receive suggestions for cleaning and interior design for their room.

[1021] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[1022] "Server" refers to the central system that analyzes received image data, suggests cleaning procedures, generates encouraging messages, etc.

[1023] "Means for receiving images" refers to the function of the server receiving image data sent from the terminal.

[1024] "Means for analyzing images" refers to the function of analyzing received image data using a machine learning model, recognizing objects in the image, and determining the condition of the room.

[1025] "Means for identifying optimal cleaning tools and procedures" refers to a function for determining tools and procedures for efficient cleaning based on the analysis results.

[1026] "Means to suggest new interior layouts" refers to a function that suggests new interior layouts based on the user's wishes and budget.

[1027] "Means for generating and sending encouraging messages to users" refers to the function of creating and sending messages to encourage and motivate users after cleaning is completed.

[1028] A "machine learning model" refers to the algorithms and network structures used when performing image analysis, and examples include YOLO and ResNet.

[1029] "Cleaning equipment" refers to the tools and materials used when cleaning (e.g., vacuum cleaners, mops, detergents, etc.).

[1030] "Cleaning procedures" refer to the steps and methods required to clean efficiently.

[1031] "Interior arrangement" refers to the arrangement of furniture and decorations within a room.

[1032] "Encouragement messages" refer to words or messages of encouragement sent to users who have finished cleaning.

[1033] This invention begins when a user takes a photo of a room using a terminal and uploads the image data to a server. This system clearly divides the roles of the server, terminal, and user, and provides suggestions for efficient cleaning and interior layout. Detailed embodiments are described below.

[1034] Hardware and Software Configuration

[1035] User device: refers to a device such as a smartphone, tablet, or PC on which the "Magical Makeover" app is installed.

[1036] Server: Refers to the central system that performs image processing and data analysis, and is equipped with machine learning models (e.g., YOLO, ResNet, etc.) for image analysis.

[1037] Communications network: Refers to the infrastructure for sending and receiving data between terminals and servers.

[1038] Specific operation procedures and data processing

[1039] 1. Image receiving method: The user takes a photo of the room using the device's camera. The user selects the photo within the app and uploads it to the server. The device then sends this image data to the server.

[1040] 2. Image analysis: The server analyzes the image data it receives. Using a machine learning model, it recognizes objects in the image, such as furniture, miscellaneous items, and trash, and assigns an identification tag to each. Based on the results of this analysis, it determines the condition of the room and identifies areas that need cleaning.

[1041] 3. Method for identifying cleaning tools and cleaning procedures: The server selects the optimal cleaning tools and efficient cleaning procedures based on the analysis results. The server also takes into account the user's past usage history and desired cleaning level. The server combines this information and provides it to the user along with an estimate of the required time.

[1042] 4. Interior layout proposal method: The user inputs their desired redecorating needs. The server generates a proposal for a new interior layout based on the user's input information. It creates a list of items that can be purchased within the user's budget and a simulation image of the new layout, and sends these to the terminal.

[1043] 5. Means for generating encouraging messages: After the user completes cleaning, they report the results within the app. The server receives this information, generates encouraging messages, and sends them to the device.

[1044] Specific examples

[1045] Prerequisites

[1046] A user wants to clean the living room.

[1047] The user takes a photo and uploads it to the server through the app.

[1048] Step 1: Take a photo and upload it

[1049] The user takes a photo of their living room with their device's camera and uploads it through the app.

[1050] Step 2: Image analysis

[1051] The server analyzes the photos and identifies stains on the carpet, scattered magazines, etc.

[1052] Step 3: Submit a proposal

[1053] The server suggests a specific cleaner and brush for the stained carpet and recommends using a magazine organizer for the scattered magazines. The server estimates the process will take 30 minutes.

[1054] Step 4: Interior design proposal

[1055] The user inputs their desired redecorating needs (a relaxing space), and the server proposes new couch and plant placements and provides simulation images.

[1056] Step 5: Message of encouragement

[1057] When the user completes cleaning and reports it, the server generates an encouraging message saying, "You did a great job! We look forward to your next cleaning!" and sends it to the device.

[1058] Example prompts for generative AI models

[1059] 1. Image analysis prompt

[1060] Analyze photos of rooms and identify each object, such as furniture, household items, and trash. Use this information to suggest areas that need cleaning.

[1061] 2. Cleaning Prompt

[1062] Based on the analyzed image information of the room, please suggest the cleaning procedure and necessary cleaning tools, along with the required time.

[1063] 3. Prompt for interior design proposal

[1064] Please propose a new interior layout based on the user's desire for a relaxing space. Please also provide a list of items that can be purchased within the user's budget and simulation images.

[1065] This invention allows users to clean efficiently and enjoyably redecorate their rooms. The system allows users to easily grasp the condition of their rooms and receive optimal cleaning plans and procedures. It also helps users maintain motivation through encouraging messages and creates new spaces through suggestions for interior layout.

[1066] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1067] Step 1:

[1068] The user takes a photo of the room using the device's camera. Then, the user launches the "Magical Makeover" app, selects the image, and uploads it. The device then sends the image data to the server.

[1069] Input: Photo of the room

[1070] Output: Image data sent to the server

[1071] Step 2:

[1072] The server analyzes the received image data. During the image analysis process, machine learning models (e.g., YOLO, ResNet, etc.) are used to recognize furniture, miscellaneous items, trash, etc. in the image. As a result of this recognition, each object is assigned an identification tag.

[1073] Input: Image data

[1074] Output: Analysis results with identification tags

[1075] Step 3:

[1076] The server uses the analysis results to determine the condition of the room and identify areas that need cleaning, such as stains on the carpet or scattered magazines. Based on this information, the server notifies the user.

[1077] Input: Analysis results

[1078] Output: Identifying areas that need cleaning and notifying the user

[1079] Step 4:

[1080] The server then uses the analysis results to identify the optimal cleaning tools and efficient cleaning procedures, taking into account the user's past usage history and desired cleaning level. For example, the server may suggest using specific detergents and brushes and provide cleaning instructions, including an estimate of the time required.

[1081] Input: Analysis results, user usage history, cleaning level

[1082] Output: Suggested cleaning materials and procedures

[1083] Step 5:

[1084] The server generates a new interior layout proposal based on the user's input information (budget, preferences, etc.), creates a list of items that can be purchased within the budget, and a simulation image of the new layout, and sends these to the terminal.

[1085] Input: User input information

[1086] Output: interior layout proposal, item list, simulation image

[1087] Step 6:

[1088] After the user completes the cleaning, they report the results within the app. The server receives the information and generates an encouraging message based on the report. The message is generated in the form of a dedicated character and sent to the device.

[1089] Input: Report of cleaning completion

[1090] Output: Generate and send a cheer message

[1091] (Application example 1)

[1092] 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."

[1093] In conventional cleaning support systems and interior design suggestion systems, users take photos of their rooms and are given suggestions on cleaning methods and interior layouts, but they do not offer support for purchasing in physical stores or suggest specific products. As a result, users are forced to find out where to purchase the suggested interior items themselves, which creates an inefficient purchasing experience.

[1094] 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.

[1095] In this invention, the server includes means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and an efficient cleaning procedure, means for providing the identified cleaning tools and procedure to the user, means for proposing a new interior layout based on the user's input, means for searching for products based on the suggestions and presenting candidates available for purchase, and means for generating and sending a message of encouragement to the user after cleaning is completed. This allows the user to not only clean efficiently but also purchase the suggested interior products on the spot.

[1096] The "receiving means" refers to the device and process for acquiring and storing image data captured by the user.

[1097] The "analyzing means" refers to devices and programs that perform topological and morphological analysis of the received image data to identify objects in the image and areas that need cleaning.

[1098] The "means for specifying" refers to a device and a program for selecting necessary cleaning tools and efficient cleaning procedures based on the analysis results.

[1099] "Means for providing" means devices and mechanisms for informing or displaying information to users about identified cleaning tools and procedures.

[1100] The "means for proposing" is a device and a program for generating and proposing a new interior layout in accordance with information input by the user.

[1101] The "searching means" refers to a device and program for searching a database for available products based on the proposed interior layout.

[1102] "Presenting means" refers to devices and mechanisms for notifying or displaying information about searched products to users.

[1103] The "means for generating an encouraging message" refers to a device and a program for generating and sending an encouraging message to a user after the cleaning is completed.

[1104] This embodiment of the present invention is a system for improving user experience in a physical store. A series of processes from a user taking an image of a room using a smartphone to receiving suggestions for cleaning and interior layout will be described.

[1105] System Overview

[1106] The system includes the following elements:

[1107] 1. Means of receiving images

[1108] 2. Means of analyzing images

[1109] 3. A means of identifying necessary cleaning supplies and efficient cleaning procedures.

[1110] 4. Means of providing users with identified cleaning supplies and procedures

[1111] 5. A method for suggesting new interior layouts based on user input

[1112] 6. A way to search for products based on suggestions and present available options for purchase

[1113] 7. A method for generating and sending a cheer message to the user after cleaning is complete

[1114] Image receiving means

[1115] Users take photos of the room using their smartphone camera and upload the images to the server via a dedicated app.

[1116] Image analysis methods

[1117] The server analyzes the received images using machine learning models such as TensorFlow and PyTorch, and this analysis identifies furniture in the photo and areas that need cleaning (such as dirt on the floor or scattered magazines).

[1118] Cleaning equipment and procedures

[1119] Based on the analysis, the server identifies the optimal cleaning tools (e.g., specific detergents and brushes) and efficient cleaning procedures, taking into account the user's past cleaning history and desired cleaning level. The server provides this information to the user, along with an estimate of the required time.

[1120] Interior layout proposal method

[1121] When a user wishes to redecorate, the server will suggest interior layouts. For example, if the user desires a "modern living room," the server will generate a new layout based on this request and provide a list of related products along with simulation images.

[1122] Example prompt: "Suggest a décor arrangement that fits a modern theme in the living room."

[1123] Product search and presentation methods

[1124] Based on the proposed interior layout, the server searches for available products from a database of physical stores, allowing users to view detailed information about these products and purchase them on the spot.

[1125] How to generate cheer messages

[1126] When a user completes cleaning and reports the results to the app, the server generates an encouraging message based on the results, such as "Have a wonderful time in your new home!"

[1127] With the above configuration, this system provides users with comprehensive support, suggesting efficient cleaning and interior layout, and even allowing them to purchase products on the spot.

[1128] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1129] Step 1:

[1130] Users take photos of the room using their smartphone camera and upload them to the server via a dedicated app.

[1131] Input: A photo of the room taken with a smartphone

[1132] Output: Image data uploaded to the server

[1133] Specific operation: The user launches the dedicated app, presses the "Take Photo" button to take a picture of the room, and then presses the "Upload Image" button to send the image data to the server.

[1134] Step 2:

[1135] The server uses machine learning models (TensorFlow or PyTorch) to acquire and analyze the images received.

[1136] Input: Room image data uploaded by the user

[1137] Output: Identification of objects and areas that need cleaning in the image

[1138] Specific operation: The server inputs the received image data into a machine learning model to identify furniture, miscellaneous items, and areas that need cleaning. As a result of the analysis, it outputs specific object categories and their location information.

[1139] Step 3:

[1140] Based on the analysis results, the server identifies the necessary cleaning tools and efficient cleaning procedures, and provides them to the user along with an estimate of the time required.

[1141] Input: Image analysis results, user's past cleaning history, desired cleaning level

[1142] Output: List of cleaning supplies, cleaning procedure, and time required

[1143] Specific operation: The server selects the optimal cleaning tools (e.g., specific detergents, brushes, etc.) based on the analysis results, generates an efficient cleaning procedure, estimates the required time, and notifies the user of this information.

[1144] Step 4:

[1145] The user inputs their desired redecorating needs (e.g., "modern living room"), and the server makes suggestions for new interior layouts.

[1146] Input: User's redecorating intentions and budget information

[1147] Output: Simulation images of new interior layout, list of potential purchases

[1148] How it works: The user inputs their desired redecorating needs into the app, and the server generates a new interior layout based on that information. The generated interior layout is presented to the user as a simulation image.

[1149] Step 5:

[1150] Based on the suggestions, the server searches a database of physical stores and presents candidates for products that the user can purchase.

[1151] Input: New interior layout proposals, physical store inventory database

[1152] Output: A list of products available for purchase

[1153] Specific operation: The server searches the physical store database for products that match the proposed interior, and presents a list of detailed information about the products available for purchase to the user.

[1154] Step 6:

[1155] When a user completes a cleaning task and reports it to the app, the server generates and sends an encouraging message.

[1156] Input: User's cleaning completion report

[1157] Output: Cheers message

[1158] How it works: The user reports their cleaning completion through the app, and the server uses the AI ​​model to generate a message of encouragement based on the report. The message is then sent to the user in the form of a special character.

[1159] As described above, this system assists users in cleaning and arranging their rooms, providing a consistent shopping experience similar to that of a physical store.

[1160] 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.

[1161] The system for implementing this invention has the function of listening to photos of a room taken by the user, suggesting cleaning tools and cleaning procedures based on those photos, and even recommending new interior layouts. It also incorporates an emotion engine that recognizes the user's emotions, aiming to improve the user's motivation by providing encouraging messages that correspond to the user's emotions. The specific operation of this system is described below.

[1162] System Overview

[1163] The system includes the following main measures:

[1164] Means for receiving images

[1165] A means of analyzing images

[1166] A means of identifying and providing cleaning supplies and procedures

[1167] A means of proposing new interior layouts

[1168] A means to generate and send a cheer message to users after cleaning is completed

[1169] Emotion engine that recognizes user emotions

[1170] Image receiving means

[1171] The user takes a photo of the room using the device. The captured image data is saved on the device, and then the user opens the "Magical Makeover" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[1172] Image analysis methods

[1173] The server analyzes the received image data. The image analysis process uses machine learning models to recognize objects in the photo (furniture, miscellaneous items, trash, etc.). Based on the analysis results, the server determines the condition of the room and identifies areas that need cleaning.

[1174] Cleaning equipment and procedures

[1175] The server then uses the analysis results to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[1176] Interior layout proposal method

[1177] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The proposal includes a list of items that can be purchased within the user's budget and a simulation image of the new layout.

[1178] Emotion Engine

[1179] The system incorporates an emotion engine to recognize the user's emotions, which is determined through facial and voice analysis and provides appropriate feedback.

[1180] How to generate cheer messages

[1181] When the cleaning is complete, the user reports the results within the app. The server receives this report and uses an emotion engine to analyze the user's emotions and generates a cheer message from a dedicated character. This cheer message is customized according to the user's emotions.

[1182] Specific examples

[1183] Prerequisites

[1184] A user wants to clean the living room.

[1185] The user takes a photo and uploads it to the server through the app.

[1186] Step 1: Take a photo and upload it

[1187] The user takes a photo of their living room with their device's camera and uploads it through the app.

[1188] Step 2: Image analysis

[1189] The server analyzes the photos and identifies things like stains on the carpet or scattered magazines.

[1190] Step 3: Submit a proposal

[1191] The server suggests specific cleaners and brushes for stained carpets and recommends using organizers for cluttered magazines.

[1192] The server estimates the time required to complete the request to be 30 minutes.

[1193] Step 4: Interior design proposal

[1194] The user inputs their desired redecoration (a relaxing space).

[1195] The server suggests new couch and plant placements and provides simulated images.

[1196] Step 5: Emotion Recognition and Encouragement

[1197] The user completes the cleaning and reports it.

[1198] The server receives the report and analyzes the user's emotions using an emotion engine.

[1199] If the user feels tired, the system will encourage them by saying, "You're tired! Get plenty of rest today!" If the user is feeling positive, it will generate an encouraging message such as, "Perfect! I'm looking forward to your next challenge!" and send it to the device.

[1200] The system assists users in cleaning and interior arrangement, and also provides emotional feedback to enhance the overall user experience.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] The user takes a photo of the room using the device. The user uses the camera function to take a photo from an appropriate angle so that the entire room can be seen.

[1204] Step 2:

[1205] The user launches the "Magical Makeover" app on their device and selects the image they have taken. The user then uses the file selection function within the app to select the desired photo data.

[1206] Step 3:

[1207] The device creates a request to upload the selected image data to the server. The device sends the image data to the server.

[1208] Step 4:

[1209] The server receives the image data and stores it in a directory for analysis. The server starts the image analysis process.

[1210] Step 5:

[1211] The server uses machine learning models to recognize objects in the images, determining the location and type of furniture, miscellaneous items, trash, etc., and records this information in a database.

[1212] Step 6:

[1213] The server selects the necessary cleaning tools based on the analysis results, and creates an optimal cleaning tool list taking into account the user's past usage history and desired cleaning level.

[1214] Step 7:

[1215] The server generates an efficient cleaning procedure. The server creates a step-by-step procedure based on how to use cleaning tools and calculates the estimated time required for cleaning.

[1216] Step 8:

[1217] The server sends the recommendation in a response to the device, providing the device with suggested cleaning tools, procedures, and required time.

[1218] Step 9:

[1219] The user confirms the suggestions on the device and starts cleaning. The user then follows the suggested steps to proceed with the cleaning.

[1220] Step 10:

[1221] After the user has completed cleaning, they can report it by pressing the "Cleaning Completed" button in the app. The user will then send a completion notification.

[1222] Step 11:

[1223] The server receives the completion notification and records it in the database. The server then activates the emotion engine to recognize the user's emotion.

[1224] Step 12:

[1225] The server uses an emotion engine to analyze the user's emotions, identify emotions from the user's facial expressions and voice, and record the results.

[1226] Step 13:

[1227] The server generates a message of encouragement based on the user's emotions. If the user is tired, it generates a message such as "You're tired! Get plenty of rest today!", and if the user is positive, it generates a message such as "Perfect! Looking forward to the next challenge!".

[1228] Step 14:

[1229] The server sends the generated cheer message to the terminal, which notifies the user of the received cheer message and displays it.

[1230] Step 15:

[1231] Users can see the encouraging message and feel a sense of accomplishment, which increases their motivation for the next cleaning.

[1232] Example 2

[1233] 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."

[1234] Existing cleaning support systems have the problem that they do not take into account the user's emotional state, which can lead to a decrease in motivation for cleaning work. Furthermore, while there are systems that support both cleaning and interior layout suggestions, there is a lack of a means to provide these services in a unified manner. This makes it difficult for users to efficiently perform both cleaning and redecorating.

[1235] 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 means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and an efficient cleaning procedure, means for providing the identified cleaning tools and cleaning procedure to the user, means for proposing a new interior layout based on the user's input, means for generating and sending a cheer message to the user after cleaning is completed, and an emotion engine for recognizing the user's emotions. This not only enables the user to efficiently clean and rearrange the room, but also makes it easier for them to stay motivated during the process.

[1236] "User" refers to an individual or organization who wishes to use this system to clean and arrange the interior of a room.

[1237] "Means for receiving captured images" refers to a function or process by which the server receives image data captured by a user on a terminal via a network.

[1238] "Means for analyzing received images and identifying necessary cleaning tools and efficient cleaning procedures" refers to the function or process in which the server analyzes the received image data using machine learning models or other analytical technologies and selects the optimal cleaning tools and procedures based on the results.

[1239] "Means for providing the identified cleaning tools and cleaning procedures to the user" refers to the function or process by which the server provides the user with the cleaning tools and cleaning procedures identified as a result of the analysis through display, notification, etc.

[1240] "Means for suggesting new interior layouts based on user input" refers to a function or process that automatically generates and suggests new interior layouts taking into account input information from the user (e.g., budget and preferences).

[1241] "Means for generating and sending a cheer message to the user after cleaning is completed" refers to a function or process in which the server generates a cheer message and sends it to the user after the user reports that the cleaning job is completed.

[1242] An "emotion engine" refers to a software or hardware component that recognizes a user's emotions and generates feedback or messages accordingly.

[1243] The system for implementing this invention receives photos of a room taken by the user, and based on those photos, suggests cleaning tools and procedures, and even recommends new interior layouts. It also incorporates an emotion engine that recognizes the user's emotions, aiming to improve the user's motivation by providing encouraging messages that correspond to the user's emotions.

[1244] The system includes the following main measures:

[1245] Means for receiving images

[1246] A means of analyzing images

[1247] A means of identifying and providing cleaning supplies and procedures

[1248] A means of proposing new interior layouts

[1249] A means to generate and send a cheer message to users after cleaning is completed

[1250] Emotion engine that recognizes user emotions

[1251] The user takes a photo of the room using the device. The captured image data is saved on the device, and then the user opens the "Magical Makeover" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[1252] The server analyzes the received image data. This process uses machine learning models (e.g., YOLO or ResNet) to recognize objects in the photo (furniture, household items, trash, etc.). Based on the analysis results, the server determines the condition of the room and identifies areas that need cleaning.

[1253] The server then uses the analysis results to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[1254] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The proposal includes a list of items that can be purchased within the user's budget and a simulation image of the new layout. The simulation image is generated using computer graphics technology and simulation software.

[1255] The system incorporates an emotion engine to recognize the user's emotions, which is determined through facial and voice analysis and provides appropriate feedback.

[1256] When the cleaning is complete, the user reports the results within the app. The server then uses an emotion engine to analyze the user's emotions and generates a cheer message from a dedicated character. This cheer message is customized based on the user's emotions and sent to the device.

[1257] Specific examples

[1258] Prerequisites

[1259] A user wants to clean the living room.

[1260] The user takes a photo and uploads it to the server through the app.

[1261] Step 1: Take a photo and upload it

[1262] The user takes a photo of their living room with their device's camera and uploads it through the app.

[1263] Example prompt sentence:

[1264] Describe in detail a scenario where a user takes a photo of their living room and sends it to a server through your app.

[1265] Step 2: Image analysis

[1266] The server analyzes the photos and identifies things like stains on the carpet or scattered magazines.

[1267] Example prompt sentence:

[1268] Analyze the photos received by the server and describe any stains or objects identified.

[1269] Step 3: Submit a proposal

[1270] The server suggests a specific cleaner and brush for stained carpets, recommends using organizers for cluttered magazines, and estimates the cleaning time to be 30 minutes.

[1271] Example prompt sentence:

[1272] Describe the process by which the server suggests cleaning supplies and procedures and estimates the time required.

[1273] Step 4: Interior design proposal

[1274] The user inputs their desired redecorating needs (a relaxing space), and the server proposes new couch and plant placements and provides simulation images.

[1275] Example prompt sentence:

[1276] Describe the process by which the server proposes new interior layouts based on the user's preferences.

[1277] Step 5: Emotion recognition and cheer message generation

[1278] After the user completes the cleaning task and reports it within the app, the server uses an emotion engine to analyze the user's emotions, generates a cheer message based on the user's emotions, and sends it to the device.

[1279] Example prompt sentence:

[1280] Please explain the process by which the server uses the emotion engine to generate a cheer message and send it to the user after cleaning is complete.

[1281] The system aims to assist users with cleaning and interior arrangement, and provide emotional feedback to improve the overall user experience.

[1282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1283] Step 1:

[1284] The user takes a photo of the room and saves the image data on the device. Specifically, the photo is taken using the smartphone's camera app, and the image data is saved in the "Magical Makeover" app's gallery.

[1285] Input: User action to take a picture of the room

[1286] Output: Image data stored on the device

[1287] Step 2:

[1288] The user opens the "Magical Makeover" app, selects a photo they have taken, and then presses the upload button on the app, causing the device to send the image data to the server. HTTP or HTTPS is used as the communication protocol.

[1289] Input: User selection and upload of photo

[1290] Output: Image data sent to the server

[1291] Step 3:

[1292] The server analyzes the received image data and uses a machine learning model (e.g., YOLO or ResNet) to recognize objects in the photo (furniture, household items, trash, etc.). This analysis process also identifies the location and type of each object.

[1293] Input: Image data sent to the server

[1294] Output: List of analyzed objects and their positions

[1295] Step 4:

[1296] The server then uses image analysis to identify the necessary cleaning tools and efficient cleaning procedures, taking into account the user's past usage history and desired cleaning level. For example, if dirt is detected on the carpet, it will suggest the appropriate detergent and brush, and estimate the required time.

[1297] Input: List of analyzed objects and their locations, user's past cleaning history, desired cleaning level

[1298] Output: A list of recommended cleaning supplies and procedures, and the cleaning time required.

[1299] Step 5:

[1300] The server provides the user with the identified cleaning supplies and procedures, specifically by displaying a list of recommended cleaning supplies and procedures through the app and informing them of the required time.

[1301] Input: A list of recommended cleaning supplies and procedures, and the time required for cleaning.

[1302] Output: A list of cleaning supplies and procedures provided to the user, along with a notification of the required time.

[1303] Step 6:

[1304] The user enters their desired redecorating needs into the app (optional), including desired conditions such as a relaxing space and a modern design.

[1305] Input: User inputs desired redecorating conditions

[1306] Output: Condition data for interior layout proposals

[1307] Step 7:

[1308] The server generates new interior layout proposals based on the user's desired conditions. Specifically, it provides a list of furniture and decorations that can be purchased within the user's budget, along with simulation images of the new layout. The simulation images are generated using CG technology and simulation software.

[1309] Input: Condition data for interior layout proposal

[1310] Output: Proposed interior layout (item list and simulation images)

[1311] Step 8:

[1312] When the user completes cleaning, they report it within the app. Specifically, they press the "Report Completion" button in the app to send the cleaning completion information to the server.

[1313] Input: User reports completion of cleaning

[1314] Output: Cleaning completion information sent to the server

[1315] Step 9:

[1316] The server receives the report and analyzes the user's emotions using an emotion engine. The emotion engine analyzes the user's facial photo and voice data to determine the user's emotional state. Based on the results, a cheer message from a dedicated character is generated and sent to the device.

[1317] Input: Cleaning completion information sent to the server, facial photos and voice data

[1318] Output: Emotion-based cheer message

[1319] This allows the system to assist users with cleaning and interior arrangement, and even provide emotional feedback to improve the overall user experience.

[1320] (Application example 2)

[1321] 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."

[1322] In physical stores, there are challenges such as a lack of specific procedures and methods for selecting tools to enable employees to clean the store efficiently and effectively, a lack of means to maintain employee motivation, and the difficulty of changing the store layout and optimizing display placement.

[1323] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and efficient cleaning procedures, means for providing the identified cleaning tools and cleaning procedures to the user, means for proposing a new interior layout based on the user's input, means for generating and sending a cheer message to the user after cleaning is completed, means for employees to take photos of the interior space of the physical store and analyze areas within the store that need cleaning, means for providing employees with cleaning tools and cleaning procedures based on the analysis results, and means for recognizing the employee's emotions after cleaning is completed and generating and sending a cheer message. This enables employees to clean efficiently and maintain their motivation while working.

[1324] "User" refers to a member of the public or a store employee who uses this system.

[1325] "Images" refers to photographs and videos taken with a smartphone or other imaging device.

[1326] "Means of receiving" refers to the technology or method for importing image data from an external server or application.

[1327] "Means for analyzing" refers to techniques or methods for analyzing received image data using machine learning models or image processing algorithms.

[1328] "Cleaning equipment" refers to tools used for cleaning, such as vacuum cleaners, mops, brushes, and detergents.

[1329] "Cleaning procedures" refer to the specific steps and processes for cleaning efficiently.

[1330] "New interior layout" refers to proposals for redesigning the layout of furniture and decorations in a room or store to create a better space.

[1331] An "encouragement message" is a message of encouragement sent to boost the user's enthusiasm and motivation.

[1332] "Employees" refers to staff and clerks working in the store.

[1333] A "machine learning model" is an algorithm or framework that allows a computer to learn from data and make predictions or classifications based on that data.

[1334] "Means for recognizing emotions" refers to technologies and methods that analyze the facial expressions and voices of users and employees to identify their emotional state.

[1335] A "server" is a computer system that processes data and provides services over a network.

[1336] The "Clean Captain" system for implementing this invention aims to help store employees efficiently clean the store and improve their motivation. The specific operation of this system is described below.

[1337] System Overview

[1338] The system includes the following main measures:

[1339] Means for receiving images

[1340] A means of analyzing images

[1341] A means of identifying and providing cleaning supplies and procedures

[1342] A means of proposing new interior layouts

[1343] A means to generate and send a cheer message to users after cleaning is completed

[1344] A method for employees to take photos of the interior space of a physical store and analyze areas that need cleaning.

[1345] A means of providing employees with cleaning supplies and procedures based on the analysis results

[1346] A means of recognizing employee emotions and generating and sending encouraging messages

[1347] Image receiving means

[1348] Employees use their smartphones to take photos of the store. The captured image data is saved on the smartphone, and then the employee opens the "Clean Captain" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[1349] Image analysis methods

[1350] The server analyzes the received image data. The image analysis process uses machine learning models to recognize objects in the photo (furniture, products, trash, etc.). Based on the analysis results, the server identifies specific areas in the store that need cleaning. The machine learning models used are deep learning frameworks such as Keras.

[1351] Cleaning equipment and procedures

[1352] The server uses the analysis to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the employee's past usage history and desired cleaning level. The server compiles this information and provides it to the employee along with an estimate of the required time.

[1353] Interior layout proposal method

[1354] When an employee wants to rearrange the store or arrange new product displays, the server generates a proposal for a new layout based on the employee's input information (budget, desired layout, etc.) The proposal includes a list of items that can be purchased within the budget and a simulation image of the new layout.

[1355] How to generate cheer messages

[1356] When cleaning is complete, employees report their results within the app. The server receives this report and uses an emotion engine to analyze the employee's emotions and generate a message of encouragement. The emotion engine determines the employee's emotions through facial and voice analysis, and provides appropriate feedback.

[1357] Specific examples

[1358] Prerequisites

[1359] Employees want to clean the store.

[1360] Employees take photos and upload them to a server via the app.

[1361] Step 1: Take a photo and upload it

[1362] Employees take photos of the store using their smartphone cameras and upload them through the app.

[1363] Step 2: Image analysis

[1364] The server analyzes the photos to determine product placement and litter levels.

[1365] Step 3: Submit a proposal

[1366] The server will suggest specific garbage bags and vacuum cleaners for cluttered trash, and also suggest ways to organize product placement.

[1367] The server estimates the required time.

[1368] Step 4: Interior layout proposal

[1369] If an employee wants to arrange a new product display, the system will propose a new layout and provide a simulation image.

[1370] Step 5: Emotion Recognition and Encouragement

[1371] Employees complete the cleaning and report back.

[1372] The server receives the reports and analyzes the employee's emotions using an emotion engine.

[1373] If an employee feels tired, the system will encourage them by saying, "You've worked hard! Make sure you get plenty of rest today!" If the employee is feeling positive, it will generate an encouraging message such as, "Perfect! I'm looking forward to your next challenge!" and send it to the device.

[1374] The system helps employees efficiently clean and rearrange the store, and also provides emotional feedback to improve the overall work experience.

[1375] Prompt Sentence Examples

[1376] "Using this sophisticated cleaning support app, you can take photos of your store and receive recommendations for the best cleaning tools and procedures based on the analysis results. After the cleaning is complete, the app will use emotion recognition to provide you with a message of encouragement tailored to your emotions."

[1377] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1378] Step 1:

[1379] A user takes a photo of the inside of a store using the smartphone camera and uploads the image data to the server via the app. The input is the image data taken by the user, and the output is the image data sent to the server. In this processing step, the image data is sent to the cloud server using the smartphone camera or the app's upload function.

[1380] Step 2:

[1381] The server receives the uploaded image data and begins the image analysis process. The input is the image data sent by the user, and the output is the analyzed data, which is feature data of objects and areas in the image. The server uses a machine learning model (e.g., Keras) and an image recognition algorithm to identify objects and cleaning areas in the store.

[1382] Step 3:

[1383] The server identifies cleaning tools and procedures based on the analysis results. The input is the analyzed feature data, and the output is the optimal cleaning tool list and procedure manual. This processing step also takes into account the user's past usage history and desired cleaning level data, and suggests the tools (e.g., vacuum cleaner, mop) and procedures (e.g., floor sweeping, garbage disposal) required for cleaning.

[1384] Step 4:

[1385] The server generates proposals for new interior layouts. The inputs are the user's desired and budget data and analysis results, and the output is a simulated image of the new interior layout and a list of items. The server uses simulation software to generate proposals for new layouts and product display arrangements.

[1386] Step 5:

[1387] The user cleans and reports the results in the app. The input is the cleaning completion report that the user enters into the app, and the output is the report data to the server. In this processing step, the user reports the completion of cleaning and the data is sent to the server.

[1388] Step 6:

[1389] The server receives the cleaning completion report and analyzes the user's emotions using an emotion recognition engine. The input is the user's facial expression and voice data after cleaning, and the output is the user's emotional data. The server uses an emotion recognition algorithm to identify emotions such as fatigue and joy.

[1390] Step 7:

[1391] The server generates a message of encouragement based on the user's emotions and sends it to the smartphone. The input is the user's emotional data, and the output is the message of encouragement. The server generates the optimal message based on the user's emotional state (e.g., "You're tired! Get plenty of rest today!") and sends it to the smartphone. In this processing step, a generative AI model is used to dynamically generate a message based on the user's emotions.

[1392] 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.

[1393] 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.

[1394] 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.

[1395] [Fourth embodiment]

[1396] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1397] 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.

[1398] 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).

[1399] 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.

[1400] 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.

[1401] 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).

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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."

[1409] The system for implementing this invention starts when a user takes a photo of a room using a terminal and uploads it to a server. The system clearly divides the roles of the server, terminal, and user, and suggests efficient cleaning and interior layout.

[1410] System Overview

[1411] The system includes the following main measures:

[1412] Means for receiving images

[1413] A means of analyzing images

[1414] A means of identifying and providing cleaning supplies and procedures

[1415] A means of proposing new interior layouts

[1416] A means of generating and sending a message

[1417] Image receiving means

[1418] First, the user takes a photo of the room using a device such as a smartphone or tablet. The captured image data is then saved on the device. The user then launches the "Magical Makeover" app, selects the captured image, and uploads it. The image data is then sent to the server on the device.

[1419] Image analysis methods

[1420] The server analyzes the received image data. During the image analysis process, machine learning models are used to recognize objects in the photo (furniture, miscellaneous items, trash, etc.). Based on the results of this analysis, the server determines the condition of the room and identifies areas that need cleaning.

[1421] Cleaning equipment and procedures

[1422] The server then selects the optimal cleaning tools and efficient cleaning procedures based on the analysis results, taking into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[1423] Interior layout proposal method

[1424] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The server creates a list of items that can be purchased within the user's budget and a simulation image of the new layout, and sends them to the device.

[1425] How to generate cheer messages

[1426] When a user completes a cleaning task and reports the results within the app, the server receives the information. Based on the report, the server generates an encouraging message from a dedicated character and sends it to the device.

[1427] Specific examples

[1428] Prerequisites

[1429] A user wants to clean the living room.

[1430] The user takes a photo and uploads it to the server through the app.

[1431] Step 1: Take a photo and upload it

[1432] The user takes a photo of their living room with their device's camera and uploads it through the app.

[1433] Step 2: Image analysis

[1434] The server analyzes the photos and identifies stains on the carpet, scattered magazines, etc.

[1435] Step 3: Submit a proposal

[1436] The server suggests specific cleaners and brushes for stained carpets and recommends using organizers for cluttered magazines.

[1437] The server estimates the time required to complete the request to be 30 minutes.

[1438] Step 4: Interior design proposal

[1439] The user inputs their desired redecoration (a relaxing space).

[1440] The server suggests new couch and plant placements and provides simulated images.

[1441] Step 5: Message of encouragement

[1442] The user completes the cleaning and reports it.

[1443] The server generates an encouraging message saying, "You did a great job! We're looking forward to your next cleaning!" and sends it to the device.

[1444] As described above, this system provides a series of procedures and functions to assist users in cleaning and interior arrangement, allowing them to clean efficiently and enjoyably and rearrange their rooms.

[1445] The processing flow will be explained below.

[1446] Step 1:

[1447] The user uses the device's camera to take a photo of the room from an appropriate angle so that the entire room can be seen.

[1448] Step 2:

[1449] The user launches the "Magical Makeover" app on their device and selects the image they have taken. The user then uses the file selection function within the app to select the desired photo data.

[1450] Step 3:

[1451] The terminal creates a request to upload the selected image data to the server. The terminal transmits the image data to the server.

[1452] Step 4:

[1453] The server receives the image data and stores it in a directory for analysis. The server starts the image analysis process.

[1454] Step 5:

[1455] The server uses machine learning models to recognize objects in the images, determining the location and type of furniture, miscellaneous items, trash, etc., and records this information in a database.

[1456] Step 6:

[1457] The server selects the necessary cleaning tools based on the analysis results, and creates an optimal cleaning tool list taking into account the user's past usage history and desired cleaning level.

[1458] Step 7:

[1459] The server generates an efficient cleaning procedure. The server creates a step-by-step procedure based on how to use cleaning tools and calculates the estimated time required for cleaning.

[1460] Step 8:

[1461] The server sends the recommendation in a response to the device, providing the device with suggested cleaning tools, procedures, and required time.

[1462] Step 9:

[1463] The user confirms the suggestions on the device and starts cleaning. The user proceeds with the cleaning by following the suggested steps.

[1464] Step 10:

[1465] After the user has completed cleaning, they can report it by pressing the "Cleaning Completed" button in the app. The user will then send a completion notification.

[1466] Step 11:

[1467] The server receives the completion notification and records it in the database. The server then generates a cheer message from the dedicated character.

[1468] Step 12:

[1469] The server sends the generated cheer message to the terminal, which notifies the user of the received cheer message and displays it.

[1470] Step 13:

[1471] Users can see the encouraging message and feel a sense of accomplishment, which increases their motivation for the next cleaning.

[1472] Example 1

[1473] 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."

[1474] Conventional cleaning support systems are inefficient because they require users to manually create cleaning plans and select the necessary tools. Furthermore, user satisfaction is low due to a lack of interior layout suggestions and encouraging messages to maintain motivation. There is a need for a system that solves these problems and allows users to clean easily and efficiently, allowing them to enjoy redecorating their rooms.

[1475] 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.

[1476] In this invention, the server includes a means for receiving images taken by a user, a means for analyzing the received images and recognizing objects in the images to determine the condition of the room, and a means for identifying optimal cleaning tools and an efficient cleaning procedure. This allows the user to easily grasp the condition of the room and automatically receive an efficient cleaning plan. The server also includes a means for providing the user with the identified cleaning tools and cleaning procedure, a means for proposing a new interior layout based on the user's input, and a means for generating and sending a message of encouragement to the user after cleaning is completed. This allows the user to know the appropriate cleaning tools and procedures and receive a message of encouragement to maintain motivation after cleaning. Furthermore, when redecorating, the server can receive suggestions for interior layouts based on the user's budget and preferences.

[1477] "User" refers to an individual who uses this system to receive suggestions for cleaning and interior design for their room.

[1478] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[1479] "Server" refers to the central system that analyzes received image data, suggests cleaning procedures, generates encouraging messages, etc.

[1480] "Means for receiving images" refers to the function of the server receiving image data sent from the terminal.

[1481] "Means for analyzing images" refers to the function of analyzing received image data using a machine learning model, recognizing objects in the image, and determining the condition of the room.

[1482] "Means for identifying optimal cleaning tools and procedures" refers to a function for determining tools and procedures for efficient cleaning based on the analysis results.

[1483] "Means to suggest new interior layouts" refers to a function that suggests new interior layouts based on the user's wishes and budget.

[1484] "Means for generating and sending encouraging messages to users" refers to the function of creating and sending messages to encourage and motivate users after cleaning is completed.

[1485] A "machine learning model" refers to the algorithms and network structures used when performing image analysis, and examples include YOLO and ResNet.

[1486] "Cleaning equipment" refers to the tools and materials used when cleaning (e.g., vacuum cleaners, mops, detergents, etc.).

[1487] "Cleaning procedures" refer to the steps and methods required to clean efficiently.

[1488] "Interior arrangement" refers to the arrangement of furniture and decorations within a room.

[1489] "Encouragement messages" refer to words or messages of encouragement sent to users who have finished cleaning.

[1490] This invention begins when a user takes a photo of a room using a terminal and uploads the image data to a server. This system clearly divides the roles of the server, terminal, and user, and provides suggestions for efficient cleaning and interior layout. Detailed embodiments are described below.

[1491] Hardware and Software Configuration

[1492] User device: refers to a device such as a smartphone, tablet, or PC on which the "Magical Makeover" app is installed.

[1493] Server: Refers to the central system that performs image processing and data analysis, and is equipped with machine learning models (e.g., YOLO, ResNet, etc.) for image analysis.

[1494] Communications network: Refers to the infrastructure for sending and receiving data between terminals and servers.

[1495] Specific operation procedures and data processing

[1496] 1. Image receiving method: The user takes a photo of the room using the device's camera. The user selects the photo within the app and uploads it to the server. The device then sends this image data to the server.

[1497] 2. Image analysis: The server analyzes the image data it receives. Using a machine learning model, it recognizes objects in the image, such as furniture, miscellaneous items, and trash, and assigns an identification tag to each. Based on the results of this analysis, it determines the condition of the room and identifies areas that need cleaning.

[1498] 3. Method for identifying cleaning tools and cleaning procedures: The server selects the optimal cleaning tools and efficient cleaning procedures based on the analysis results. The server also takes into account the user's past usage history and desired cleaning level. The server combines this information and provides it to the user along with an estimate of the required time.

[1499] 4. Interior layout proposal method: The user inputs their desired redecorating needs. The server generates a proposal for a new interior layout based on the user's input information. It creates a list of items that can be purchased within the user's budget and a simulation image of the new layout, and sends these to the terminal.

[1500] 5. Means for generating encouraging messages: After the user completes cleaning, they report the results within the app. The server receives this information, generates encouraging messages, and sends them to the device.

[1501] Specific examples

[1502] Prerequisites

[1503] A user wants to clean the living room.

[1504] The user takes a photo and uploads it to the server through the app.

[1505] Step 1: Take a photo and upload it

[1506] The user takes a photo of their living room with their device's camera and uploads it through the app.

[1507] Step 2: Image analysis

[1508] The server analyzes the photos and identifies stains on the carpet, scattered magazines, etc.

[1509] Step 3: Submit a proposal

[1510] The server suggests a specific cleaner and brush for the stained carpet and recommends using a magazine organizer for the scattered magazines. The server estimates the process will take 30 minutes.

[1511] Step 4: Interior design proposal

[1512] The user inputs their desired redecorating needs (a relaxing space), and the server proposes new couch and plant placements and provides simulation images.

[1513] Step 5: Message of encouragement

[1514] When the user completes cleaning and reports it, the server generates an encouraging message saying, "You did a great job! We look forward to your next cleaning!" and sends it to the device.

[1515] Example prompts for generative AI models

[1516] 1. Image analysis prompt

[1517] Analyze photos of rooms and identify each object, such as furniture, household items, and trash. Use this information to suggest areas that need cleaning.

[1518] 2. Cleaning Prompt

[1519] Based on the analyzed image information of the room, please suggest the cleaning procedure and necessary cleaning tools, along with the required time.

[1520] 3. Prompt for interior design proposal

[1521] Please propose a new interior layout based on the user's desire for a relaxing space. Please also provide a list of items that can be purchased within the user's budget and simulation images.

[1522] This invention allows users to clean efficiently and enjoyably redecorate their rooms. The system allows users to easily grasp the condition of their rooms and receive optimal cleaning plans and procedures. It also helps users maintain motivation through encouraging messages and creates new spaces through suggestions for interior layout.

[1523] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1524] Step 1:

[1525] The user takes a photo of the room using the device's camera. Then, the user launches the "Magical Makeover" app, selects the image, and uploads it. The device then sends the image data to the server.

[1526] Input: Photo of the room

[1527] Output: Image data sent to the server

[1528] Step 2:

[1529] The server analyzes the received image data. During the image analysis process, machine learning models (e.g., YOLO, ResNet, etc.) are used to recognize furniture, miscellaneous items, trash, etc. in the image. As a result of this recognition, each object is assigned an identification tag.

[1530] Input: Image data

[1531] Output: Analysis results with identification tags

[1532] Step 3:

[1533] The server uses the analysis results to determine the condition of the room and identify areas that need cleaning, such as stains on the carpet or scattered magazines. Based on this information, the server notifies the user.

[1534] Input: Analysis results

[1535] Output: Identifying areas that need cleaning and notifying the user

[1536] Step 4:

[1537] The server then uses the analysis results to identify the optimal cleaning tools and efficient cleaning procedures, taking into account the user's past usage history and desired cleaning level. For example, the server may suggest using specific detergents and brushes and provide cleaning instructions, including an estimate of the time required.

[1538] Input: Analysis results, user usage history, cleaning level

[1539] Output: Suggested cleaning materials and procedures

[1540] Step 5:

[1541] The server generates a new interior layout proposal based on the user's input information (budget, preferences, etc.), creates a list of items that can be purchased within the budget, and a simulation image of the new layout, and sends these to the terminal.

[1542] Input: User input information

[1543] Output: interior layout proposal, item list, simulation image

[1544] Step 6:

[1545] After the user completes the cleaning, they report the results within the app. The server receives the information and generates an encouraging message based on the report. The message is generated in the form of a dedicated character and sent to the device.

[1546] Input: Report of cleaning completion

[1547] Output: Generate and send a cheer message

[1548] (Application example 1)

[1549] 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."

[1550] In conventional cleaning support systems and interior design suggestion systems, users take photos of their rooms and are given suggestions on cleaning methods and interior layouts, but they do not offer support for purchasing in physical stores or suggest specific products. As a result, users are forced to find out where to purchase the suggested interior items themselves, which creates an inefficient purchasing experience.

[1551] 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.

[1552] In this invention, the server includes means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and an efficient cleaning procedure, means for providing the identified cleaning tools and procedure to the user, means for proposing a new interior layout based on the user's input, means for searching for products based on the suggestions and presenting candidates available for purchase, and means for generating and sending a message of encouragement to the user after cleaning is completed. This allows the user to not only clean efficiently but also purchase the suggested interior products on the spot.

[1553] The "receiving means" refers to the device and process for acquiring and storing image data captured by the user.

[1554] The "analyzing means" refers to devices and programs that perform topological and morphological analysis of the received image data to identify objects in the image and areas that need cleaning.

[1555] The "means for specifying" refers to a device and a program for selecting necessary cleaning tools and efficient cleaning procedures based on the analysis results.

[1556] "Means for providing" means devices and mechanisms for informing or displaying information to users about identified cleaning tools and procedures.

[1557] The "means for proposing" is a device and a program for generating and proposing a new interior layout in accordance with information input by the user.

[1558] The "searching means" refers to a device and program for searching a database for available products based on the proposed interior layout.

[1559] "Presenting means" refers to devices and mechanisms for notifying or displaying information about searched products to users.

[1560] The "means for generating an encouraging message" refers to a device and a program for generating and sending an encouraging message to a user after the cleaning is completed.

[1561] This embodiment of the present invention is a system for improving user experience in a physical store. A series of processes from a user taking an image of a room using a smartphone to receiving suggestions for cleaning and interior layout will be described.

[1562] System Overview

[1563] The system includes the following elements:

[1564] 1. Means of receiving images

[1565] 2. Means of analyzing images

[1566] 3. A means of identifying necessary cleaning supplies and efficient cleaning procedures.

[1567] 4. Means of providing users with identified cleaning supplies and procedures

[1568] 5. A method for suggesting new interior layouts based on user input

[1569] 6. A way to search for products based on suggestions and present available options for purchase

[1570] 7. A method for generating and sending a cheer message to the user after cleaning is complete

[1571] Image receiving means

[1572] Users take photos of the room using their smartphone camera and upload the images to the server via a dedicated app.

[1573] Image analysis methods

[1574] The server analyzes the received images using machine learning models such as TensorFlow and PyTorch, and this analysis identifies furniture in the photo and areas that need cleaning (such as dirt on the floor or scattered magazines).

[1575] Cleaning equipment and procedures

[1576] Based on the analysis, the server identifies the optimal cleaning tools (e.g., specific detergents and brushes) and efficient cleaning procedures, taking into account the user's past cleaning history and desired cleaning level. The server provides this information to the user, along with an estimate of the required time.

[1577] Interior layout proposal method

[1578] When a user wishes to redecorate, the server will suggest interior layouts. For example, if the user desires a "modern living room," the server will generate a new layout based on this request and provide a list of related products along with simulation images.

[1579] Example prompt: "Suggest a décor arrangement that fits a modern theme in the living room."

[1580] Product search and presentation methods

[1581] Based on the proposed interior layout, the server searches for available products from a database of physical stores, allowing users to view detailed information about these products and purchase them on the spot.

[1582] How to generate cheer messages

[1583] When a user completes cleaning and reports the results to the app, the server generates an encouraging message based on the results, such as "Have a wonderful time in your new home!"

[1584] With the above configuration, this system provides users with comprehensive support, suggesting efficient cleaning and interior layout, and even allowing them to purchase products on the spot.

[1585] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1586] Step 1:

[1587] Users take photos of the room using their smartphone camera and upload them to the server via a dedicated app.

[1588] Input: A photo of the room taken with a smartphone

[1589] Output: Image data uploaded to the server

[1590] Specific operation: The user launches the dedicated app, presses the "Take Photo" button to take a picture of the room, and then presses the "Upload Image" button to send the image data to the server.

[1591] Step 2:

[1592] The server uses machine learning models (TensorFlow or PyTorch) to acquire and analyze the images received.

[1593] Input: Room image data uploaded by the user

[1594] Output: Identification of objects and areas that need cleaning in the image

[1595] Specific operation: The server inputs the received image data into a machine learning model to identify furniture, miscellaneous items, and areas that need cleaning. As a result of the analysis, it outputs specific object categories and their location information.

[1596] Step 3:

[1597] Based on the analysis results, the server identifies the necessary cleaning tools and efficient cleaning procedures, and provides them to the user along with an estimate of the time required.

[1598] Input: Image analysis results, user's past cleaning history, desired cleaning level

[1599] Output: List of cleaning supplies, cleaning procedure, and time required

[1600] Specific operation: The server selects the optimal cleaning tools (e.g., specific detergents, brushes, etc.) based on the analysis results, generates an efficient cleaning procedure, estimates the required time, and notifies the user of this information.

[1601] Step 4:

[1602] The user inputs their desired redecorating needs (e.g., "modern living room"), and the server makes suggestions for new interior layouts.

[1603] Input: User's redecorating intentions and budget information

[1604] Output: Simulation images of new interior layout, list of potential purchases

[1605] How it works: The user inputs their desired redecorating needs into the app, and the server generates a new interior layout based on that information. The generated interior layout is presented to the user as a simulation image.

[1606] Step 5:

[1607] Based on the suggestions, the server searches a database of physical stores and presents candidates for products that the user can purchase.

[1608] Input: New interior layout proposals, physical store inventory database

[1609] Output: A list of products available for purchase

[1610] Specific operation: The server searches the physical store database for products that match the proposed interior, and presents a list of detailed information about the products available for purchase to the user.

[1611] Step 6:

[1612] When a user completes a cleaning task and reports it to the app, the server generates and sends an encouraging message.

[1613] Input: User's cleaning completion report

[1614] Output: Cheers message

[1615] How it works: The user reports their cleaning completion through the app, and the server uses the AI ​​model to generate a message of encouragement based on the report. The message is then sent to the user in the form of a special character.

[1616] As described above, this system assists users in cleaning and arranging their rooms, providing a consistent shopping experience similar to that of a physical store.

[1617] 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.

[1618] The system for implementing this invention has the function of listening to photos of a room taken by the user, suggesting cleaning tools and cleaning procedures based on those photos, and even recommending new interior layouts. It also incorporates an emotion engine that recognizes the user's emotions, aiming to improve the user's motivation by providing encouraging messages that correspond to the user's emotions. The specific operation of this system is described below.

[1619] System Overview

[1620] The system includes the following main measures:

[1621] Means for receiving images

[1622] A means of analyzing images

[1623] A means of identifying and providing cleaning supplies and procedures

[1624] A means of proposing new interior layouts

[1625] A means to generate and send a cheer message to users after cleaning is completed

[1626] Emotion engine that recognizes user emotions

[1627] Image receiving means

[1628] The user takes a photo of the room using the device. The captured image data is saved on the device, and then the user opens the "Magical Makeover" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[1629] Image analysis methods

[1630] The server analyzes the received image data. The image analysis process uses machine learning models to recognize objects in the photo (furniture, miscellaneous items, trash, etc.). Based on the analysis results, the server determines the condition of the room and identifies areas that need cleaning.

[1631] Cleaning equipment and procedures

[1632] The server then uses the analysis results to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[1633] Interior layout proposal method

[1634] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The proposal includes a list of items that can be purchased within the user's budget and a simulation image of the new layout.

[1635] Emotion Engine

[1636] The system incorporates an emotion engine to recognize the user's emotions, which is determined through facial and voice analysis and provides appropriate feedback.

[1637] How to generate cheer messages

[1638] When the cleaning is complete, the user reports the results within the app. The server receives this report and uses an emotion engine to analyze the user's emotions and generates a cheer message from a dedicated character. This cheer message is customized according to the user's emotions.

[1639] Specific examples

[1640] Prerequisites

[1641] A user wants to clean the living room.

[1642] The user takes a photo and uploads it to the server through the app.

[1643] Step 1: Take a photo and upload it

[1644] The user takes a photo of their living room with their device's camera and uploads it through the app.

[1645] Step 2: Image analysis

[1646] The server analyzes the photos and identifies things like stains on the carpet or scattered magazines.

[1647] Step 3: Submit a proposal

[1648] The server suggests specific cleaners and brushes for stained carpets and recommends using organizers for cluttered magazines.

[1649] The server estimates the time required to complete the request to be 30 minutes.

[1650] Step 4: Interior design proposal

[1651] The user inputs their desired redecoration (a relaxing space).

[1652] The server suggests new couch and plant placements and provides simulated images.

[1653] Step 5: Emotion Recognition and Encouragement

[1654] The user completes the cleaning and reports it.

[1655] The server receives the report and analyzes the user's emotions using an emotion engine.

[1656] If the user feels tired, the system will encourage them by saying, "You're tired! Get plenty of rest today!" If the user is feeling positive, it will generate an encouraging message such as, "Perfect! I'm looking forward to your next challenge!" and send it to the device.

[1657] The system assists users in cleaning and interior arrangement, and also provides emotional feedback to enhance the overall user experience.

[1658] The processing flow will be explained below.

[1659] Step 1:

[1660] The user takes a photo of the room using the device. The user uses the camera function to take a photo from an appropriate angle so that the entire room can be seen.

[1661] Step 2:

[1662] The user launches the "Magical Makeover" app on their device and selects the image they have taken. The user then uses the file selection function within the app to select the desired photo data.

[1663] Step 3:

[1664] The device creates a request to upload the selected image data to the server. The device sends the image data to the server.

[1665] Step 4:

[1666] The server receives the image data and stores it in a directory for analysis. The server starts the image analysis process.

[1667] Step 5:

[1668] The server uses machine learning models to recognize objects in the images, determining the location and type of furniture, miscellaneous items, trash, etc., and records this information in a database.

[1669] Step 6:

[1670] The server selects the necessary cleaning tools based on the analysis results, and creates an optimal cleaning tool list taking into account the user's past usage history and desired cleaning level.

[1671] Step 7:

[1672] The server generates an efficient cleaning procedure. The server creates a step-by-step procedure based on how to use cleaning tools and calculates the estimated time required for cleaning.

[1673] Step 8:

[1674] The server sends the recommendation in a response to the device, providing the device with suggested cleaning tools, procedures, and required time.

[1675] Step 9:

[1676] The user confirms the suggestions on the device and starts cleaning. The user then follows the suggested steps to proceed with the cleaning.

[1677] Step 10:

[1678] After the user has completed cleaning, they can report it by pressing the "Cleaning Completed" button in the app. The user will then send a completion notification.

[1679] Step 11:

[1680] The server receives the completion notification and records it in the database. The server then activates the emotion engine to recognize the user's emotion.

[1681] Step 12:

[1682] The server uses an emotion engine to analyze the user's emotions, identify emotions from the user's facial expressions and voice, and record the results.

[1683] Step 13:

[1684] The server generates a message of encouragement based on the user's emotions. If the user is tired, it generates a message such as "You're tired! Get plenty of rest today!", and if the user is positive, it generates a message such as "Perfect! Looking forward to the next challenge!".

[1685] Step 14:

[1686] The server sends the generated cheer message to the terminal, which notifies the user of the received cheer message and displays it.

[1687] Step 15:

[1688] Users can see the encouraging message and feel a sense of accomplishment, which increases their motivation for the next cleaning.

[1689] Example 2

[1690] 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."

[1691] Existing cleaning support systems have the problem that they do not take into account the user's emotional state, which can lead to a decrease in motivation for cleaning work. Furthermore, while there are systems that support both cleaning and interior layout suggestions, there is a lack of a means to provide these services in a unified manner. This makes it difficult for users to efficiently perform both cleaning and redecorating.

[1692] 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 means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and an efficient cleaning procedure, means for providing the identified cleaning tools and cleaning procedure to the user, means for proposing a new interior layout based on the user's input, means for generating and sending a cheer message to the user after cleaning is completed, and an emotion engine for recognizing the user's emotions. This not only enables the user to efficiently clean and rearrange the room, but also makes it easier for them to stay motivated during the process.

[1693] "User" refers to an individual or organization who wishes to use this system to clean and arrange the interior of a room.

[1694] "Means for receiving captured images" refers to a function or process by which the server receives image data captured by a user on a terminal via a network.

[1695] "Means for analyzing received images and identifying necessary cleaning tools and efficient cleaning procedures" refers to the function or process in which the server analyzes the received image data using machine learning models or other analytical technologies and selects the optimal cleaning tools and procedures based on the results.

[1696] "Means for providing the identified cleaning tools and cleaning procedures to the user" refers to the function or process by which the server provides the user with the cleaning tools and cleaning procedures identified as a result of the analysis through display, notification, etc.

[1697] "Means for suggesting new interior layouts based on user input" refers to a function or process that automatically generates and suggests new interior layouts taking into account input information from the user (e.g., budget and preferences).

[1698] "Means for generating and sending a cheer message to the user after cleaning is completed" refers to a function or process in which the server generates a cheer message and sends it to the user after the user reports that the cleaning job is completed.

[1699] An "emotion engine" refers to a software or hardware component that recognizes a user's emotions and generates feedback or messages accordingly.

[1700] The system for implementing this invention receives photos of a room taken by the user, and based on those photos, suggests cleaning tools and procedures, and even recommends new interior layouts. It also incorporates an emotion engine that recognizes the user's emotions, aiming to improve the user's motivation by providing encouraging messages that correspond to the user's emotions.

[1701] The system includes the following main measures:

[1702] Means for receiving images

[1703] A means of analyzing images

[1704] A means of identifying and providing cleaning supplies and procedures

[1705] A means of proposing new interior layouts

[1706] A means to generate and send a cheer message to users after cleaning is completed

[1707] Emotion engine that recognizes user emotions

[1708] The user takes a photo of the room using the device. The captured image data is saved on the device, and then the user opens the "Magical Makeover" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[1709] The server analyzes the received image data. This process uses machine learning models (e.g., YOLO or ResNet) to recognize objects in the photo (furniture, household items, trash, etc.). Based on the analysis results, the server determines the condition of the room and identifies areas that need cleaning.

[1710] The server then uses the analysis results to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the user's past usage history and desired cleaning level. The server then compiles this information and provides it to the user along with an estimate of the required time.

[1711] When a user wishes to rearrange their home, the server generates a proposal for a new interior layout based on the user's input information (budget, preferences, etc.). The proposal includes a list of items that can be purchased within the user's budget and a simulation image of the new layout. The simulation image is generated using computer graphics technology and simulation software.

[1712] The system incorporates an emotion engine to recognize the user's emotions, which is determined through facial and voice analysis and provides appropriate feedback.

[1713] When the cleaning is complete, the user reports the results within the app. The server then uses an emotion engine to analyze the user's emotions and generates a cheer message from a dedicated character. This cheer message is customized based on the user's emotions and sent to the device.

[1714] Specific examples

[1715] Prerequisites

[1716] A user wants to clean the living room.

[1717] The user takes a photo and uploads it to the server through the app.

[1718] Step 1: Take a photo and upload it

[1719] The user takes a photo of their living room with their device's camera and uploads it through the app.

[1720] Example prompt sentence:

[1721] Describe in detail a scenario where a user takes a photo of their living room and sends it to a server through your app.

[1722] Step 2: Image analysis

[1723] The server analyzes the photos and identifies things like stains on the carpet or scattered magazines.

[1724] Example prompt sentence:

[1725] Analyze the photos received by the server and describe any stains or objects identified.

[1726] Step 3: Submit a proposal

[1727] The server suggests a specific cleaner and brush for stained carpets, recommends using organizers for cluttered magazines, and estimates the cleaning time to be 30 minutes.

[1728] Example prompt sentence:

[1729] Describe the process by which the server suggests cleaning supplies and procedures and estimates the time required.

[1730] Step 4: Interior design proposal

[1731] The user inputs their desired redecorating needs (a relaxing space), and the server proposes new couch and plant placements and provides simulation images.

[1732] Example prompt sentence:

[1733] Describe the process by which the server proposes new interior layouts based on the user's preferences.

[1734] Step 5: Emotion recognition and cheer message generation

[1735] After the user completes the cleaning task and reports it within the app, the server uses an emotion engine to analyze the user's emotions, generates a cheer message based on the user's emotions, and sends it to the device.

[1736] Example prompt sentence:

[1737] Please explain the process by which the server uses the emotion engine to generate a cheer message and send it to the user after cleaning is complete.

[1738] The system aims to assist users with cleaning and interior arrangement, and provide emotional feedback to improve the overall user experience.

[1739] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1740] Step 1:

[1741] The user takes a photo of the room and saves the image data on the device. Specifically, the photo is taken using the smartphone's camera app, and the image data is saved in the "Magical Makeover" app's gallery.

[1742] Input: User action to take a picture of the room

[1743] Output: Image data stored on the device

[1744] Step 2:

[1745] The user opens the "Magical Makeover" app, selects a photo they have taken, and then presses the upload button on the app, causing the device to send the image data to the server. HTTP or HTTPS is used as the communication protocol.

[1746] Input: User selection and upload of photo

[1747] Output: Image data sent to the server

[1748] Step 3:

[1749] The server analyzes the received image data and uses a machine learning model (e.g., YOLO or ResNet) to recognize objects in the photo (furniture, household items, trash, etc.). This analysis process also identifies the location and type of each object.

[1750] Input: Image data sent to the server

[1751] Output: List of analyzed objects and their positions

[1752] Step 4:

[1753] The server then uses image analysis to identify the necessary cleaning tools and efficient cleaning procedures, taking into account the user's past usage history and desired cleaning level. For example, if dirt is detected on the carpet, it will suggest the appropriate detergent and brush, and estimate the required time.

[1754] Input: List of analyzed objects and their locations, user's past cleaning history, desired cleaning level

[1755] Output: A list of recommended cleaning supplies and procedures, and the cleaning time required.

[1756] Step 5:

[1757] The server provides the user with the identified cleaning supplies and procedures, specifically by displaying a list of recommended cleaning supplies and procedures through the app and informing them of the required time.

[1758] Input: A list of recommended cleaning supplies and procedures, and the time required for cleaning.

[1759] Output: A list of cleaning supplies and procedures provided to the user, along with a notification of the required time.

[1760] Step 6:

[1761] The user enters their desired redecorating needs into the app (optional), including desired conditions such as a relaxing space and a modern design.

[1762] Input: User inputs desired redecorating conditions

[1763] Output: Condition data for interior layout proposals

[1764] Step 7:

[1765] The server generates new interior layout proposals based on the user's desired conditions. Specifically, it provides a list of furniture and decorations that can be purchased within the user's budget, along with simulation images of the new layout. The simulation images are generated using CG technology and simulation software.

[1766] Input: Condition data for interior layout proposal

[1767] Output: Proposed interior layout (item list and simulation images)

[1768] Step 8:

[1769] When the user completes cleaning, they report it within the app. Specifically, they press the "Report Completion" button in the app to send the cleaning completion information to the server.

[1770] Input: User reports completion of cleaning

[1771] Output: Cleaning completion information sent to the server

[1772] Step 9:

[1773] The server receives the report and analyzes the user's emotions using an emotion engine. The emotion engine analyzes the user's facial photo and voice data to determine the user's emotional state. Based on the results, a cheer message from a dedicated character is generated and sent to the device.

[1774] Input: Cleaning completion information sent to the server, facial photos and voice data

[1775] Output: Emotion-based cheer message

[1776] This allows the system to assist users with cleaning and interior arrangement, and even provide emotional feedback to improve the overall user experience.

[1777] (Application example 2)

[1778] 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."

[1779] In physical stores, there are challenges such as a lack of specific procedures and methods for selecting tools to enable employees to clean the store efficiently and effectively, a lack of ways to maintain employee motivation, and the difficulty of changing the store layout and optimizing display placement.

[1780] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving images taken by a user, means for analyzing the received images and identifying necessary cleaning tools and efficient cleaning procedures, means for providing the identified cleaning tools and cleaning procedures to the user, means for proposing a new interior layout based on the user's input, means for generating and sending a cheer message to the user after cleaning is completed, means for employees to take photos of the interior space of the physical store and analyze areas within the store that need cleaning, means for providing employees with cleaning tools and cleaning procedures based on the analysis results, and means for recognizing the employee's emotions after cleaning is completed and generating and sending a cheer message. This allows employees to clean efficiently and maintain their motivation while working.

[1781] "User" refers to a member of the public or a store employee who uses this system.

[1782] "Images" refers to photographs and videos taken with a smartphone or other imaging device.

[1783] "Means of receiving" refers to the technology or method for importing image data from an external server or application.

[1784] "Means for analyzing" refers to techniques or methods for analyzing received image data using machine learning models or image processing algorithms.

[1785] "Cleaning equipment" refers to tools used for cleaning, such as vacuum cleaners, mops, brushes, and detergents.

[1786] "Cleaning procedures" refer to the specific steps and processes for cleaning efficiently.

[1787] "New interior layout" refers to proposals for redesigning the layout of furniture and decorations in a room or store to create a better space.

[1788] An "encouragement message" is a message of encouragement sent to boost the user's enthusiasm and motivation.

[1789] "Employees" refers to staff and clerks working in the store.

[1790] A "machine learning model" is an algorithm or framework that allows a computer to learn from data and make predictions or classifications based on that data.

[1791] "Means for recognizing emotions" refers to technologies and methods that analyze the facial expressions and voices of users and employees to identify their emotional state.

[1792] A "server" is a computer system that processes data and provides services over a network.

[1793] The "Clean Captain" system for implementing this invention aims to help store employees efficiently clean the store and improve their motivation. The specific operation of this system is described below.

[1794] System Overview

[1795] The system includes the following main measures:

[1796] Means for receiving images

[1797] A means of analyzing images

[1798] A means of identifying and providing cleaning supplies and procedures

[1799] A means of proposing new interior layouts

[1800] A means to generate and send a cheer message to users after cleaning is completed

[1801] A method for employees to take photos of the interior space of a physical store and analyze areas that need cleaning.

[1802] A means of providing employees with cleaning supplies and procedures based on the analysis results

[1803] A means of recognizing employee emotions and generating and sending encouraging messages

[1804] Image receiving means

[1805] Employees use their smartphones to take photos of the store. The captured image data is saved on the smartphone, and then the employee opens the "Clean Captain" app, selects the image, and uploads it to the server. The device then sends the image data to the server.

[1806] Image analysis methods

[1807] The server analyzes the received image data. The image analysis process uses machine learning models to recognize objects in the photo (furniture, products, trash, etc.). Based on the analysis results, the server identifies specific areas in the store that need cleaning. The machine learning models used are deep learning frameworks such as Keras.

[1808] Cleaning equipment and procedures

[1809] The server uses the analysis to select the most appropriate cleaning tools and efficient cleaning procedures. This process also takes into account the employee's past usage history and desired cleaning level. The server compiles this information and provides it to the employee along with an estimate of the required time.

[1810] Interior layout proposal method

[1811] When an employee wants to rearrange the store or arrange new product displays, the server generates a proposal for a new layout based on the employee's input information (budget, desired layout, etc.) The proposal includes a list of items that can be purchased within the budget and a simulation image of the new layout.

[1812] How to generate cheer messages

[1813] When cleaning is complete, employees report their results within the app. The server receives this report and uses an emotion engine to analyze the employee's emotions and generate a message of encouragement. The emotion engine determines the employee's emotions through facial and voice analysis, and provides appropriate feedback.

[1814] Specific examples

[1815] Prerequisites

[1816] Employees want to clean the store.

[1817] Employees take photos and upload them to a server via the app.

[1818] Step 1: Take a photo and upload it

[1819] Employees take photos of the store using their smartphone cameras and upload them through the app.

[1820] Step 2: Image analysis

[1821] The server analyzes the photos to determine product placement and litter levels.

[1822] Step 3: Submit a proposal

[1823] The server will suggest specific garbage bags and vacuum cleaners for cluttered trash, and also suggest ways to organize product placement.

[1824] The server estimates the required time.

[1825] Step 4: Interior layout proposal

[1826] If an employee wants to arrange a new product display, the system will propose a new layout and provide a simulation image.

[1827] Step 5: Emotion Recognition and Encouragement

[1828] Employees complete the cleaning and report back.

[1829] The server receives the reports and analyzes the employee's emotions using an emotion engine.

[1830] If an employee feels tired, the system will encourage them by saying, "You've worked hard! Make sure you get plenty of rest today!" If the employee is feeling positive, it will generate an encouraging message such as, "Perfect! I'm looking forward to your next challenge!" and send it to the device.

[1831] The system helps employees efficiently clean and rearrange the store, and also provides emotional feedback to improve the overall work experience.

[1832] Prompt Sentence Examples

[1833] "Using this sophisticated cleaning support app, you can take photos of your store and receive recommendations for the best cleaning tools and procedures based on the analysis results. After the cleaning is complete, the app will use emotion recognition to provide you with a message of encouragement tailored to your emotions."

[1834] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1835] Step 1:

[1836] A user takes a photo of the inside of a store using the smartphone camera and uploads the image data to the server via the app. The input is the image data taken by the user, and the output is the image data sent to the server. In this processing step, the image data is sent to the cloud server using the smartphone camera or the app's upload function.

[1837] Step 2:

[1838] The server receives the uploaded image data and begins the image analysis process. The input is the image data sent by the user, and the output is the analyzed data, which is feature data of objects and areas in the image. The server uses a machine learning model (e.g., Keras) and an image recognition algorithm to identify objects and cleaning areas in the store.

[1839] Step 3:

[1840] The server identifies cleaning tools and procedures based on the analysis results. The input is the analyzed feature data, and the output is the optimal cleaning tool list and procedure manual. This processing step also takes into account the user's past usage history and desired cleaning level data, and suggests the tools (e.g., vacuum cleaner, mop) and procedures (e.g., floor sweeping, garbage disposal) required for cleaning.

[1841] Step 4:

[1842] The server generates proposals for new interior layouts. The inputs are the user's desired and budget data and analysis results, and the output is a simulated image of the new interior layout and a list of items. The server uses simulation software to generate proposals for new layouts and product display arrangements.

[1843] Step 5:

[1844] The user cleans and reports the results in the app. The input is the cleaning completion report that the user enters into the app, and the output is the report data to the server. In this processing step, the user reports the completion of cleaning and the data is sent to the server.

[1845] Step 6:

[1846] The server receives the cleaning completion report and analyzes the user's emotions using an emotion recognition engine. The input is the user's facial expression and voice data after cleaning, and the output is the user's emotional data. The server uses an emotion recognition algorithm to identify emotions such as fatigue and joy.

[1847] Step 7:

[1848] The server generates a message of encouragement based on the user's emotions and sends it to the smartphone. The input is the user's emotional data, and the output is the message of encouragement. The server generates the optimal message based on the user's emotional state (e.g., "You're tired! Get plenty of rest today!") and sends it to the smartphone. In this processing step, a generative AI model is used to dynamically generate a message based on the user's emotions.

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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).

[1856] 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.

[1857] 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."

[1858] 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.

[1859] 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).

[1860] 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.

[1861] 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.

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] The following is further disclosed regarding the above embodiment.

[1871] (Claim 1)

[1872] means for receiving an image taken by a user;

[1873] means for analyzing the received images to identify necessary cleaning equipment and efficient cleaning procedures;

[1874] a means of providing patrons with identified cleaning supplies and procedures;

[1875] a means for suggesting new interior layouts based on user input;

[1876] A means for generating and sending a cheer message to the user after the cleaning is completed;

[1877] A system including:

[1878] (Claim 2)

[1879] 10. The system of claim 1, wherein the means for analyzing the received images uses a machine learning model to identify each object.

[1880] (Claim 3)

[1881] 2. The system of claim 1, wherein the means for identifying cleaning tools and cleaning procedures is based on the user's past usage history and desired cleaning level.

[1882] "Example 1"

[1883] (Claim 1)

[1884] means for receiving an image taken by a user;

[1885] means for analyzing the received image and recognizing objects in the image to determine the state of the room;

[1886] A means of identifying optimal cleaning equipment and efficient cleaning procedures;

[1887] a means of providing patrons with identified cleaning supplies and procedures;

[1888] a means for suggesting new interior layouts based on user input;

[1889] A means for generating and sending a cheer message to the user after the cleaning is completed;

[1890] A system including:

[1891] (Claim 2)

[1892] 10. The system of claim 1, wherein the means for analyzing the received images uses a machine learning model to identify each object.

[1893] (Claim 3)

[1894] 2. The system of claim 1, wherein the means for identifying cleaning tools and cleaning procedures is based on the user's past usage history and desired cleaning level.

[1895] "Application Example 1"

[1896] (Claim 1)

[1897] means for receiving an image taken by a user;

[1898] means for analyzing the received images to identify necessary cleaning equipment and efficient cleaning procedures;

[1899] a means of providing patrons with identified cleaning supplies and procedures;

[1900] a means for suggesting new interior layouts based on user input;

[1901] A means for searching for products based on the suggestions and presenting candidates that can be purchased;

[1902] A means for generating and sending a cheer message to the user after the cleaning is completed;

[1903] A system including:

[1904] (Claim 2)

[1905] 10. The system of claim 1, wherein the means for analyzing the received images uses a machine learning model to identify each object.

[1906] (Claim 3)

[1907] 2. The system of claim 1, wherein the means for identifying cleaning tools and cleaning procedures is based on the user's past usage history and desired cleaning level.

[1908] "Example 2: Combining Emotion Engines"

[1909] (Claim 1)

[1910] means for receiving an image taken by a user;

[1911] means for analyzing the received images to identify necessary cleaning equipment and efficient cleaning procedures;

[1912] a means of providing patrons with identified cleaning supplies and procedures;

[1913] a means for suggesting new interior layouts based on user input;

[1914] A means for generating and sending a cheer message to the user after the cleaning is completed;

[1915] an emotion engine for recognizing the user's emotions;

[1916] A system including:

[1917] (Claim 2)

[1918] 10. The system of claim 1, wherein the means for analyzing the received images uses a machine learning model to identify each object.

[1919] (Claim 3)

[1920] 2. The system of claim 1, wherein the means for identifying cleaning tools and cleaning procedures is based on the user's past usage history and desired cleaning level.

[1921] "Application example 2 when combining emotion engines"

[1922] (Claim 1)

[1923] means for receiving an image taken by a user;

[1924] means for analyzing the received images to identify necessary cleaning equipment and efficient cleaning procedures;

[1925] a means of providing patrons with identified cleaning supplies and procedures;

[1926] a means for suggesting new interior layouts based on user input;

[1927] A means for generating and sending a cheer message to the user after the cleaning is completed;

[1928] A method for employees to take photos of the interior space of physical stores and analyze areas that need cleaning within the store.

[1929] a means of providing employees with cleaning supplies and procedures based on the analysis;

[1930] A means to recognize employees' emotions after cleaning is completed and generate and send cheer messages;

[1931] A system including:

[1932] (Claim 2)

[1933] 10. The system of claim 1, wherein the means for analyzing the received images uses a machine learning model to identify each object.

[1934] (Claim 3)

[1935] 2. The system of claim 1, wherein the means for identifying cleaning tools and cleaning procedures is based on the user's past usage history and desired cleaning level. [Explanation of symbols]

[1936] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving an image taken by a user; means for analyzing the received images to identify necessary cleaning equipment and efficient cleaning procedures; a means of providing patrons with identified cleaning supplies and procedures; a means for suggesting new interior layouts based on user input; A means for generating and sending a cheer message to the user after the cleaning is completed; A system including:

2. 10. The system of claim 1, wherein the means for analyzing the received images uses a machine learning model to identify each object.

3. 2. The system of claim 1, wherein the means for specifying cleaning tools and cleaning procedures is based on the user's past usage history and desired cleaning level.

Images