Management device

Abstract

To provide a more convenient management device. [Solution] A management device for a cleaning service that collects clothing to be cleaned and delivers the cleaned clothing home comprises: an acquisition means for acquiring images of clothing taken by the customer or store clerk; a determination means for determining the category of clothing by examining the acquired images; a first determination means for determining the cleaning fee based on the determined images; a second determination means for determining the selectable cleaning options based on the determined images; a reception means for receiving input of the necessary options from the selectable cleaning options; a transmission means for selecting a store according to the customer's clothing collection location and sending a collection request to the store; and a management means for managing the cleaning schedule and location of the clothing after collection.

Description

Technical Field

[0001] This invention relates to a management device.

Background Art

[0002] Patent Document 1 below discloses a cleaning management system that rationalizes the management system of laundry in the cleaning industry and enables a cleaning operator to obtain information such as a cleaning history useful for cleaning at any time.

Prior Art Document

Patent Document

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of this invention is to provide a more convenient management device.

Means for Solving the Problems

[0005] According to an aspect of this invention, a management device for a cleaning service that performs collection of clothes to be cleaned and home delivery of the clothes after cleaning includes an acquisition means for acquiring an image of the clothes taken by a customer or a store clerk, a discrimination means for discriminating the category of the clothes by determining the acquired image, a first determination means for determining a cleaning fee based on the discriminated image, a second determination means for determining selectable cleaning options based on the discriminated image, a reception means for receiving an input of necessary options from the selectable cleaning options, a transmission means for selecting a store according to the collection location of the customer's clothes and transmitting a collection request to the store, and a management means for managing the schedule of cleaning the clothes after collection and the location where the cleaning is performed. [Brief explanation of the drawing]

[0006] [Figure 1] This is a schematic diagram showing the configuration of the cleaning service network system related to one embodiment of the present invention. [Figure 2] This is a block diagram showing the internal configuration of a customer terminal related to one embodiment of the present invention. [Figure 3] This is a sequence diagram showing the ordering process in the cleaning service system of the present invention. [Modes for carrying out the invention]

[0007] Figure 1 is a schematic diagram showing the configuration of a cleaning service network system according to one embodiment of the present invention.

[0008] This system is a network configuration that enables the efficient operation of cleaning services. A central server (1) is located at the center, and multiple terminals and PCs (personal computers) are interconnected via this server. The main components of the system are as follows: 1. Server (1) 2. Headquarters PC (2) 3. Customer terminal (3a, 3b) 4. Cleaning factory PC (4a, 4b) 5. Chain store PC (5a, 5b) The server (1), represented by a rectangle located in the center of Figure 1, is the core of the entire system. This server manages communication with all other components and provides the following functions: - Receiving and managing cleaning requests from customers - Issuance of collection (on-site pickup) and delivery instructions for chain stores. - Issuing work instructions to the cleaning factory - Tracking and managing progress in real time The headquarters PC (2), represented by a rectangle located at the top of Figure 1, indicates a terminal used in the central management department of a cleaning service company. The headquarters PC (2) is directly connected to the server (1) and has the following functions: - Monitoring and management of the entire system - Optimizing demand forecasting and staffing - Analysis and improvement of customer service quality The two small rectangles on the left side of Figure 1, representing customer terminals (3a, 3b), indicate devices used by users of the cleaning service. These can take various forms, such as smartphones, tablets, and personal computers. Through customer terminals (3a, 3b), customers can perform the following operations: - Request for collection of clothing to be cleaned - Specify pickup and delivery time - Check the progress of the cleaning - Providing service evaluations and feedback The two rectangles (4a, 4b) located in the upper right of Figure 1 represent the cleaning factory PCs used in factories that perform clothing cleaning. These PCs have the following functions: - Receipt of cleaning work instructions from server (1) - Input and report on the progress of the cleaning work. - Management of the operating status of cleaning equipment - Recording and reporting of quality control information The two large rectangles located in the lower right of Figure 1, representing chain store PCs (5a, 5b), indicate terminals used in stores that collect clothing from customers for cleaning and deliver the cleaned clothing back to the customers. These PCs have the following functions: - Receiving pickup and delivery instructions from server (1) - Managing collection and delivery schedules within the assigned area. - Real-time reporting of collection and delivery status - Managing customer communication records The basic flow of the cleaning service in this system is as follows: 1. The customer requests the collection of the clothes to be cleaned using the customer terminals (3a, 3b). 2. The server (1) receives the request, selects the optimal chain store, and sends a collection instruction to the chain store PCs (5a, 5b). 3. The person in charge at the chain store goes to the customer's home to collect the clothes based on the instruction. 4. The collected clothes are sent to the cleaning factory based on the instruction of the server (1). If there are multiple cleaning factories, the most suitable factory is automatically selected from the multiple cleaning factories based on the distance between the customer's home or the selected chain store and the cleaning factory (select the nearest one) or whether the cleaning factory can handle the cleaning options. 5. At the cleaning factory, work instructions are received through the cleaning factory PCs (4a, 4b), and the clothes are cleaned. 6. After the cleaning is completed, the server (1) sends a delivery instruction to the chain store PCs (5a, 5b) again. 7. The person in charge at the chain store delivers the cleaned clothes to the customer's home. 8. The customer provides service evaluation and feedback through the customer terminals (3a, 3b).

[0009] This series of processes is managed in real time by the server (1) and monitored through the head office PC (2).

[0010] The cleaning service network system shown in this embodiment has the following advantages: 1. Efficient operation: Centralized management enables optimal resource allocation and rapid response. 2. Improvement of customer satisfaction: High-quality service can be provided through real-time progress management and flexible scheduling. 3. Cost reduction: Operational costs can be reduced through the design of an efficient collection and delivery route and the optimization of work instructions. 4. Continuous improvement through data analysis: By analyzing accumulated data, it becomes possible to continuously improve the service. 5. Scalability: The system can be flexibly expanded in response to increases in the number of customers or the area covered.

[0011] Figure 2 is a block diagram showing the internal configuration of customer terminals (3a, 3b) according to one embodiment of the present invention.

[0012] The customer terminals (3a, 3b) are configured around a central processing unit (CPU) (21) and include the following main components: - Memory (22) - Mass storage (23) - Communications Department (24) - Camera (25) - Display (26) - Input devices (27) - Battery (28) These components are housed in a single enclosure and operate on power supplied by a battery (28).

[0013] The CPU (21) is located in the center of the diagram and is responsible for the core computational processing of the terminal. It performs tasks such as executing cleaning service applications, processing user input, and analyzing various sensor data.

[0014] Memory (22) is located in the upper left of the CPU (21) and holds temporary application data and running programs. This enables high-speed data access and improves terminal responsiveness.

[0015] The mass storage device (23) is located to the upper right of the CPU (21) and permanently stores the operating system, applications, user data, etc. It stores data that requires long-term retention, such as cleaning history and personal settings.

[0016] The communications unit (24) is located to the lower left of the CPU (21) and provides wireless communication functions such as Wi-Fi, cellular network, and Bluetooth. It is used for communication with the server (1) and for acquiring GPS location information.

[0017] The camera (25) is located to the lower right of the CPU (21) and provides image and video capture capabilities. It is used for taking photos of clothing to be cleaned and reading QR codes, among other things.

[0018] The display (26) is located above the CPU (21) and provides the user interface display and visual feedback. It features touchscreen functionality, enabling direct user interaction.

[0019] The input device (27) is located directly below the CPU (21) and includes a touchscreen, physical buttons, and a voice input device. It accepts instructions and information input from the user.

[0020] The battery (28) is prominently displayed at the bottom of the diagram and supplies power to the entire device. The dashed lines in the diagram indicate the power supply from the battery (28) to each component.

[0021] As shown in Figure 2, each component is directly connected to the CPU (21). This allows the CPU (21) to comprehensively control each component, enabling efficient data processing and information transfer.

[0022] When using the cleaning service, the customer terminals (3a, 3b) operate as follows: 1. The user enters a cleaning request via an input device (27). 2. The CPU (21) processes the input and, if necessary, takes a picture of the clothing with the camera (25). 3. Send request information to the server (1) via the communication unit (24). 4. Display the response from the server (1) on the display (26). 5. Save all data from the entire process to a large-capacity storage device (23). This series of operations is made possible by a stable power supply from the battery (28).

[0023] This configuration allows customers to use cleaning services intuitively and efficiently, while service providers can accurately understand customer needs and respond quickly.

[0024] The server and PC configurations are the same as those shown in the block diagram in Figure 2. Note that each component terminal and PC may be a highly portable device such as a smartphone, tablet, or portable PC, or a stationary device such as a desktop PC.

[0025] The configuration and operation of a cleaning service network system according to one embodiment of the present invention have been described in detail above. This configuration enables the realization of efficient cleaning services that provide high customer satisfaction.

[0026] The customer terminal contains a program module for AI (artificial intelligence) image recognition. This program module analyzes images captured by the camera to determine the type of clothing, material, degree of stains or damage, and outputs the results as text data. Details are as follows:

[0027] In image recognition, specifically, a computer analyzes an image or video, classifying, recognizing, and interpreting its content. This technology is based on deep learning, particularly convolutional neural networks (CNNs), and performs object detection, classification, and semantic segmentation in images with high accuracy.

[0028] The image recognition process consists of the following stages:

[0029] Image acquisition (100): Target image data is acquired using various sensors (e.g., cameras, scanners, etc.). The acquired image data has attributes such as resolution and color depth.

[0030] Preprocessing (110): The acquired image data is converted into a format suitable for processing by the AI ​​model. At this stage, processing such as noise reduction, resizing, brightness adjustment, color space conversion, and geometric distortion correction is performed.

[0031] Feature Extraction (120): Features are extracted from preprocessed image data using a deep learning model such as a CNN. In this invention, a multi-layered CNN is used to extract features hierarchically, from low-level features (edges, textures, etc.) to high-level features (object shapes, components, etc.) in the image. The CNN architecture used can be selected from (for example) ResNet, InceptionNet, EfficientNet, etc., and one optimized for a specific task can also be used.

[0032] Classification and Recognition (130): Based on the extracted features, the content of the image is classified and recognized using a support vector machine (SVM), random forest, or deep learning model. In particular, this invention achieves high-precision recognition with a small amount of training data by applying a transfer learning method using a pre-trained model. The recognition target can be the entire image or a specific region (ROI) within the image.

[0033] Post-processing (140): Convert the recognition results into a human-readable format (e.g., text, bounding boxes, segmentation masks, etc.).

[0034] Image recognition has the following characteristics:

[0035] High-precision object detection: Using object detection algorithms such as YOLO, Faster R-CNN, and SSD, the system accurately detects the location and type of objects in an image.

[0036] Semantic segmentation function: By assigning semantic labels to each pixel of an image, it enables a semantic understanding of the entire image.

[0037] Instance segmentation function: Precisely identifies individual objects and defines their respective contours.

[0038] Real-time processing: By using efficient algorithms and hardware, real-time image processing is enabled.

[0039] Image recognition offers the following advantages and new functionalities:

[0040] 1. Automatic image classification and organization: When a user takes and inputs an image, the AI ​​of this invention automatically classifies and organizes that image. For example, it may automatically classify clothing to be cleaned into categories and automatically calculate cleaning fees, etc. The photography may be done by the customer or by an employee of the cleaning shop. Customers (members) may be provided with a price list by downloading it or some other means, and the prices may be shown to the member, with the employee taking photos of the clothing at the time of collection. Categories include, for example, T-shirts, polo shirts, blouses, shirts, dresses, skirts, pants, jeans, jackets, coats, sweaters, cardigans, knitwear, sweatshirts, hoodies, sweaters, jumpers, blousons, down jackets, vests, scarves, stoles, gloves, hats, socks, tights, stockings, pajamas, loungewear, swimwear, running wear, yoga wear, dresses, suits, kimonos, yukatas, underwear, undergarments, bras, shorts, leggings, tights, overalls, dungarees, jumpsuits, tunics, camisoles, tank tops, dresses, skirts, shorts, culottes, caps, visors, belts, ties, bow ties, scarves, accessories, bags, shoes, boots, sandals, slippers, and raincoats. A basic cleaning fee is recorded for each item, along with any selectable options.

[0041] Available options include, for example, water-repellent, waterproof, stain-resistant, deodorizing, antibacterial, mildew-resistant, wrinkle-resistant, ironing, dry cleaning, wet cleaning, water washing, hand washing, spot cleaning, stain removal, bleaching, decolorization, dyeing, fulling, napping, pressing, shape retention, pilling removal, fray repair, zipper replacement, button attachment, hemming, length shortening, shoulder width adjustment, sleeve length adjustment, waist adjustment, size alteration, repair, restoration, mildew removal, stain removal, color fading correction, and shape correction. The available options are recorded according to the category of clothing.

[0042] AI image recognition technology can also be combined with communication software such as LINE. For example, AI image recognition could be used for communication with dry cleaning shops.

[0043] This feature allows users to search images in their chat history and albums using image content, in addition to text-based searches. For example, instead of typing "I want to find a picture of a ski suit top," the user can use the image as a search query to quickly find images of related clothing. This feature is also effective for ambiguous search queries that cannot be expressed using text alone.

[0044] Furthermore, AI understanding the content of images enables smarter image sharing. For example, if a user wants to share a specific type of image, the AI ​​will automatically select the appropriate image and suggest who to share it with. It will also be possible to automatically add information about people and places contained in the image before sharing it.

[0045] By applying image recognition technology, new communication functions can be added to communication software. For example, when a user sends a photo of clothing, AI can automatically recognize the type of clothing, the situation, and the outfit, and provide relevant information.

[0046] Furthermore, by linking with the AR functionality of communication software, the ability to overlay virtual objects onto the real world can be extended. For example, when a user identifies an object recognized by the AI ​​of this invention through communication software, a 3D model and additional information about that object can be displayed in AR. It can also be combined with a real-world person to provide an image of that person wearing clothes.

[0047] Companies can create new customer interactions by linking the AI ​​image recognition technology of the present invention with the official accounts of their communication software. For example, it can be used to recognize images of clothing and provide product information and purchase links, or to recognize receipts photographed by users and automate accounting processes.

[0048] These features play a significant role in improving user convenience and increasing engagement with communication software. The AI ​​image recognition technology of this invention can seamlessly integrate with communication software platforms to provide a more advanced and innovative communication experience. [Image recognition function on customer terminals and virtual closet] In one embodiment of the present invention, customer terminals (3a, 3b) are equipped with image recognition and virtual closet functions. This is achieved by installing a clothing cleaning application on the customer terminal. These functions enable users to efficiently manage their clothing and use cleaning services more conveniently. A specific example will be explained below.

[0049] The image recognition functionality of the customer terminals (3a, 3b) is implemented using the following components: - Camera (25): Takes pictures of clothing - CPU (21): Execution of image processing and recognition algorithms - Mass storage device (23): Storage of image recognition models and databases - Memory (22): Temporary data storage during image processing The operation is as follows: 1. The user takes a picture of the clothing using the camera (25). 2. The captured image data is sent to the CPU (21) for preprocessing (size adjustment, noise reduction, etc.). 3. The CPU (21) uses an image recognition model stored in the mass storage device (23) to identify the type, color, material, etc. of clothing. 4. The recognition results are temporarily stored in memory (22) and used for subsequent processing (such as registration in the virtual closet).

[0050] The virtual closet may allow users to download and register clothing data from manufacturers' websites, etc. It may also display clothing items that users do not yet own but plan to purchase, their current prices, and price fluctuation data.

[0051] The virtual closet function is implemented using the following components: - Mass storage device (23): Storage of virtual closet data - CPU (21): Data processing and management - Display (26): Virtual Closet Display - Input devices (27): Data manipulation by the user The virtual closet data includes the following information: - Clothing ID - Types of clothing - color - material - size - Purchase date - Cleaning history - Link to image data The virtual closet function works as follows: 1. Clothing information identified by the image recognition function is structured as virtual closet data by the CPU (21). 2. The structured data is stored in a mass storage device (23). 3. Users can view the contents of their virtual closet through the display (26). 4. Using the input device (27), it becomes possible to perform operations such as editing, deleting, and searching for clothing information.

[0052] Regarding data storage for virtual closets, the following two methods are possible: (1) In-device storage method - Virtual closet data is stored only on the mass storage device (23) of the customer terminal (3a, 3b). - Advantages: - Data privacy is highly protected. - Available even in offline environments - assignment: - There is a risk of data loss if the device is lost or damaged. (2) Server storage method - Virtual closet data is stored on an external server (1). - Operation: 1. Data generated on customer terminals (3a, 3b) is transmitted to the server (1) via the communication unit (24). 2. Server (1) receives the data and saves it to the database. 3. When a user accesses data, the data is retrieved from the server (1). - Advantages: - Data can be synchronized across multiple devices. - Easy data backup - assignment: - Internet connection required - Additional data security measures are required. Users can choose from these methods according to their needs. A hybrid method (storing data both on the device and on the server) is also available.

[0053] The virtual closet feature integrates with the cleaning service as follows: 1. When requesting cleaning services, users can select clothing from a virtual closet. 2. Information about the selected garments (material, color, etc.) will be automatically entered into the cleaning request form. 3. After cleaning is complete, the cleaning history of the garment will be automatically updated. 4. It is also possible to analyze clothing data in the virtual closet and notify the user of the recommended time for cleaning.

[0054] The image recognition function and virtual closet function of the present invention provide the following effects: 1. It makes it easier for users to manage their clothing and improves their experience using the cleaning service. 2. Input errors when requesting cleaning services will decrease, improving service quality. 3. By accumulating detailed data about users' clothing, it becomes possible to provide personalized services. 4. Automatic recording of cleaning history promotes timely cleaning, contributing to the extended lifespan of clothing.

[0055] The integration of the image recognition function and the virtual closet function described above is expected to significantly improve the convenience and efficiency of the cleaning service, leading to increased user satisfaction.

[0056] Next, we will explain the extended features and external site integration of the virtual closet.

[0057] In one embodiment of the present invention, the virtual closet function includes the ability to link with various websites on the internet, further streamlining the user's clothing management and enabling the valuation of clothing. This enhancement allows users to utilize their clothing more flexibly and understand its economic value.

[0058] The virtual closet feature enables integration with external sites such as the following: 1. Auction site 2. Buying and selling sites such as flea market apps 3. Used clothing buying website 4. Clothing donation website Integration with external sites is achieved through APIs (Application Programming Interfaces). API integration is implemented using the following configuration. - Communications Department (24): Responsible for communication with APIs of external websites. - CPU (21): Control of API communication, data processing - Mass storage device (23): Storage of configuration information and tokens necessary for API integration. The API integration works as follows: 1. From the cleaning app installed by the user, select the external site you want to connect with and perform authentication. 2. Authentication information (token, etc.) is encrypted and stored in a mass storage device (23). 3. Clothing data within the virtual closet is converted to a format compatible with external websites via an API. 4. The converted data is transmitted to an external site via the communication unit (24).

[0059] Specifically, the following types of integrations with external sites are possible: (Listing and selling function) 1. The user selects clothing items from the virtual closet and taps the "List Item" button. 2. Information about the selected clothing (images, description, size, etc.) will be automatically entered into the listing form. 3. The user enters any additional information (price, shipping method, etc.) as needed and completes the listing. (Automatic purchase price acquisition function) 1. If a user wants to know the buyback price of their clothing, they tap the "Buyback Price Check" button. 2. The system calls the API of the linked buyback site and sends the information about the relevant clothing. 3. The buyback price information returned from the buyback site will be displayed on the screen. 4. If the service is linked with multiple buyback websites, price comparison becomes possible. 5. You can also find out the total value (asset value) of all the clothing you own. You can also view the price fluctuations of each garment, and the total value of your clothing, from the past to the present, using graphs and other visuals. Furthermore, you can make predictions about future prices. (Donation function) 1. If a user wants to donate clothing, they tap the "Donate" button. 2. The system displays a list of linked donation sites. 3. Once the user selects a recipient for the donation, the clothing information will be automatically entered into the donation application form. 4. Users enter any additional information as needed and complete the donation process. (Clothing value assessment function) The clothing valuation function is implemented using the following components: - CPU(21): Execution of the valuation algorithm - Mass storage device (23): Storage of price database and evaluation model - Communications Department (24): Acquisition of external price information The operation is as follows: 1. The system performs a valuation of each garment regularly (e.g., once a week) or manually. 2. The following factors will be considered in the evaluation: - Clothing type, brand, material - Purchase price (user input) - Frequency of use, time elapsed since purchase - Cleaning history - Market price of similar products (obtained via API) 3. Based on these factors, the valuation algorithm calculates the current estimated value of each garment. 4. The calculated value is stored and linked to the data of each garment in the virtual closet. (Value indication) 1. Users can see the current estimated value of each garment within the virtual closet. 2. The total value of all clothing is also displayed, allowing users to understand the total value of their clothing assets. 3. Display a time-series graph of value fluctuations to visualize depreciation of clothing and value increases due to vintageization.

[0060] To implement these functions, it is recommended to add the following modules to the system configuration of the customer terminals (3a, 3b). 1. API Management Module: Manages API integration with various external sites. 2. Data Conversion Module: Optimizes virtual closet data for each external site. 3. Valuation Engine: Executes an algorithm to calculate the value of clothing. 4. Security Module: Responsible for the secure management of API authentication information and pricing data. The extended functions of the present invention provide the following effects: 1. Users will have a wider range of ways to utilize their clothing, making it easier to dispose of unwanted clothing and monetize it. 2. Visualizing the value of clothing improves users' awareness of asset management. 3. Automatic retrieval of purchase prices allows users to sell their clothes at the optimal time. 4. The donation function encourages participation in social contribution activities. 5. By integrating with various external websites, the value of the virtual closet will be significantly enhanced, and continued use by users can be expected.

[0061] This system has the following potential for future expansion: 1. Clothing coordination suggestion function utilizing AI (artificial intelligence) 2. Recording of clothing ownership and transaction history using blockchain technology 3. Virtual try-on function using AR (Augmented Reality) technology These enhancements give the present invention the potential to evolve into an even more advanced clothing management platform.

[0062] The above provides a detailed explanation of the extended features and external site integration of the virtual closet function. These features will enable users to manage their clothing more comprehensively and effectively, providing value beyond the scope of a cleaning service. Furthermore, the virtual closet may be made publicly accessible to third parties. This can be done through an original website or an existing website (web page or social media page). A "Like" button, inquiry button, and purchase request button may be provided for the entire closet, as well as for individual items within the closet. This allows third parties who view the closet and its items to send "Likes," inquire about the purchase location and price, or make purchases. The display / hide of the purchase request button may be set by the owner for each closet and item. The purchase request button will only be displayed for items that can be sold. Buying and selling may be conducted through an escrow service or as private transactions between individuals.

[0063] Furthermore, since AI-based image recognition can sometimes produce incorrect results, it is desirable to configure the device so that users can ultimately correct the results.

[0064] Next, the ordering process in the cleaning service system of the present invention will be described in detail based on the sequence diagram in Figure 3. 1. Order Initiation and Image Analysis (Steps 1-2) 1.1 The customer uses the camera (25) on the customer terminal (3a, 3b) to take an image of the clothing to be cleaned. 1.2 The CPU (21) of the customer terminals (3a, 3b) performs image analysis on the captured images. This analysis automatically determines the following items: - Type of clothing (shirt, trousers, dress, etc.) - Materials (cotton, wool, silk, etc.) - Degree of damage - Presence and location of tears or frays 1.3 The results of the image analysis are temporarily stored in memory (22) for use in subsequent processing. 2. Enter order details and submit to the server (Steps 3-4) 2.1 The customer enters the following information through the interface displayed on the display (26): - Cleaning options (special cleaning, whether or not ironing is included, etc.) - Desired pickup date and time - Collection method (home, designated location, etc.) 2.2 The CPU (21) of the customer terminals (3a, 3b) generates order data by combining the results of image analysis with customer input information. 2.3 The generated order data is sent to the server (1) via the communication unit (24). 3. Selecting a chain store and requesting pickup (Steps 5-7) 3.1 The server (1) processes the received order data and automatically selects the optimal chain store considering the following factors: - Customer location information - Congestion status of each chain store - Possibility of handling special cleaning requests 3.2 The server (1) sends a pickup request to the PCs (5a, 5b) of the selected chain store. 3.3 The chain store PC (5a, 5b) issues collection instructions to the person in charge based on the received collection request. 4. Collection of clothing and delivery to the factory (Steps 8-9) 4.1 The chain store representative will go to the customer's designated location at the instructed date and time to collect the clothing. 4.2 The collected clothing is properly packaged by chain store staff and sent to the cleaning factory. 5. Cleaning and return (Steps 10-12) 5.1 The factory performs cleaning on the received garments based on the order data. 5.2 Once cleaning is complete, the garments are returned from the factory to the chain store. 5.3 Chain store staff receive the cleaned clothing and deliver it to the customer's designated location. 6. Payment Processing (Steps 13-14) 6.1 After receiving the clothing, the customer uses their customer terminal (3a, 3b) to complete the payment online. 6.2 The server (1) processes the payment, and once the processing is complete, it sends a payment completion notification to the customer terminals (3a, 3b). 6.3 When a customer terminal (3a, 3b) receives a payment completion notification, it displays a completion message on its display (26).

[0065] This process allows customers to easily and efficiently use cleaning services using customer devices such as smartphones (3a, 3b). Furthermore, the use of image analysis technology enables proper processing at the cleaning factory, contributing to improved service quality. In addition, the automatic selection of the optimal chain store and an efficient collection and delivery system significantly improve operational efficiency for service providers.

[0066] (Design of a cleaning service management database) To efficiently operate the cleaning service system of the present invention, it is preferable to use a relational database as described below. This database is implemented on server (1) and is responsible for managing information for the entire system. 1. Table structure 1.1 Customers Table - Customer ID (primary key) - full name - address - telephone number - email address - Registration date - Point balance 1.2 Orders Table - Order ID (Primary Key) - Customer ID (external key) - Order date and time - Scheduled pickup date and time - Estimated delivery date and time - Order status (Waiting for pickup, Cleaning in progress, Waiting for delivery, Completed) - total amount 1.3 Order Details Table - Order details ID (primary key) - Order ID (external key) - Clothing type ID (external key) - Quantity - unit price - Option ID (Foreign Key) 1.4 Clothing Types Table - Clothing type ID (primary key) - Type name (shirt, trousers, dress, etc.) - Basic fee 1.5 Options Table - Option ID (Primary Key) - Option name (e.g., special cleaning, ironing) - Additional charges 1.6 Chain Stores Table - Chain store ID (primary key) - Store name - address - telephone number - Area of ​​responsibility 1.7 Employees Table - Employee ID (Primary Key) - Chain store ID (external key) - full name - post - contact address 1.8 Factories Table - Factory ID (Primary Key) - Factory name - address - telephone number - Processing capacity (maximum processing volume per day) 1.9 Schedule Table - Schedule ID (Primary Key) - Order ID (foreign key) - Chain store ID (external key) - Factory ID (external key) - Collection time - Estimated arrival time at the factory - Estimated time for cleaning completion - Estimated delivery time 1.10 Payments Table - Payment ID (Primary Key) - Order ID (foreign key) - Payment methods - Payment amount - Payment date - Payment status 2. Key Relationships - Customer table and order table: one-to-many (one customer can place multiple orders) - Order table and order details table: one-to-many (one order contains multiple clothing items) - Order table and schedule table: 1-to-1 (one schedule exists for each order) - Chain store table and employee table: one-to-many (multiple employees belong to one chain store) - Order table and payment table: 1-to-1 (one payment record exists for each order) 3. Index Design To execute queries efficiently, create indexes on the following columns: - Customer table: Customer ID, email address - Order table: Order date and time, Order status - Chain store table: assigned area - Schedule table: Pickup time, estimated delivery time 4. Examples of database usage 4.1 Order Processing 1. When a new order is placed, insert the information into the Orders, OrderDetails, and Schedules tables. 2. When updating the order status, update the corresponding record in the Orders table. 4.2 Schedule Management 1. Retrieve the pickup schedule for a specific day: Extract the record for the relevant day from the Schedules table. 2. Checking workload by chain store: Schedules table and summaries for each chain store 4.3 Sales Management 1. Sales summary by period: Summarize the total amount for the relevant period from the Orders table. 2. Sales analysis by customer: Analysis by joining the Orders table and the Customers table. 4.4 Inventory Management 1. Current number of garments being processed by factory: Aggregated by joining the Orders, Schedules, and Factories tables. 5. Data security and privacy protection 1. Customer personal information (Customers table) is stored encrypted. 2. Strict control of access rights: Implementing data access restrictions according to employee roles. 3. Develop regular backup and disaster recovery plans. This database design enables efficient operation of cleaning services, accurate information management, and prompt customer response. Furthermore, analyzing the accumulated data can be used for continuous service improvement and the development of new business strategies.

[0067] Furthermore, in order to efficiently operate the cleaning service system of the present invention and improve customer satisfaction, the following relational database design may be adopted. Here, customer evaluation and complaint management functions are added. 1.11 Customer Ratings Table - Evaluation ID (Primary Key) - Order ID (foreign key) - Customer ID (external key) - Chain store ID (external key) - Factory ID (external key) - Employee ID (Foreign key, refer to the Employees table) - Evaluation date - Overall rating (a number from 1 to 5) - Cleaning quality rating (number from 1 to 5) - Customer service rating (1-5 scale) - Delivery time evaluation (numerical value from 1 to 5) - comment 1.12 Complaints Table - Claim ID (Primary Key) - Order ID (foreign key) - Customer ID (external key) - Report date and time - Types of complaints (e.g., damaged clothing, delays, poor customer service) - Claim Details - Status (Not yet addressed, In progress, Resolved) - Responsible person ID (foreign key, refer to the Employees table) - Resolution date - Solution details - Compensation amount 1.13 Complaint Tracking Table - Tracking ID (Primary Key) - Claim ID (external key) - Update date and time - Updated content - Updater ID (foreign key, refer to the Employees table) 2. Key Relationships (Additional) - Order table and customer rating table: 1-to-1 (only one rating is possible for each order) - Order table and claim table: one-to-many (multiple claims can exist for a single order) - Claim table and claim tracking table: One-to-many (multiple tracking records can exist for a single claim) 3. Index Design (Additional) To ensure efficient query execution, create indexes on the following columns as well: - Customer rating table: Order ID, Rating date and time - Claim table: Order ID, reporting date and time, response status - Claim tracking table: Claim ID, update date and time 4. Examples of database usage (additional) 4.5 Managing Customer Reviews 1. Registering new ratings: After cleaning is complete, insert the rating information into the CustomerRatings table. 2. Analysis of evaluation statistics: The CustomerRatings table was aggregated to calculate the average evaluation by chain store, factory, and individual employee. 4.6 Claim Handling 1. Complaint Registration: When a customer submits a complaint, the information is inserted into the Complaints table. 2. Recording of complaint handling: Insert tracking information into the ComplaintTracking table as the complaint handling progresses. 3. Complaint Statistical Analysis: Aggregate the Complaints table and calculate the frequency of occurrence and average resolution time for each type of complaint. 5. Data security and privacy protection (additional information) 4. Strictly control access to evaluation data and complaint data, ensuring that only the minimum necessary employees have access. 5. To protect the confidentiality of claim information, detailed claim information will be stored encrypted. 6. Utilization of evaluation and claims management functions The following effects can be expected from extending this database design: 1. Continuous improvement of service quality: - Analyze customer feedback data to identify service weaknesses. - Quantitatively evaluate the performance of chain stores, factories, and individual employees. 2. Streamlining customer complaint handling: - Centralized management of complaints enables quick and appropriate responses. - Developing measures to prevent recurrence based on analysis of complaint history. 3. Improving the customer experience: - Providing personalized services using ratings and complaint information - Implementing service improvements based on customer feedback 4. Strengthening risk management: - Early detection of potential problems through analysis of complaint trends - Reducing financial risk through management of compensation amounts 5. Application to employee training: - Share the actions of highly-rated employees as best practices - Conducting practical training using customer complaint case studies. This expansion is expected to improve the quality of cleaning services and increase customer satisfaction, contributing to the creation of a competitive and sustainable business model. In the above-described embodiment, the invention can be considered not only as a system configuration, but also as an invention of the server portion only, an invention of the device portion only, an invention of a processing method, or a program that operates on each device.

[0068] The above-described embodiments and their elements (some configurations, some processes) can be combined or replaced to create new and different embodiments.

[0069] The embodiments described above should be considered in all respects to be illustrative and not restrictive. The scope of the present invention is indicated by the claims rather than by the foregoing description, and all modifications within the meaning and scope equivalent to the claims are intended to be included.

Claims

[Claim 1] A management device for a cleaning service that collects clothing to be cleaned and delivers the cleaned clothing to the customer, A means for acquiring images of clothing taken by customers or store employees, A discrimination means for determining the category of clothing by examining the acquired image, A first determination means for determining the cleaning fee based on the identified image, A second determination means for determining selectable cleaning options based on the identified image, A reception means for receiving input of the required option from the selectable cleaning options, A transmission means that selects a store according to the collection location of the customer's clothing and sends a collection request to the store, A management device comprising a management means for managing the cleaning schedule and location of the clothing after collection.

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