System

Abstract

A system is provided.SOLUTION: A system comprising: a sensor for measuring the weight, type, and degree of soiling of laundry; a server for analyzing data obtained from the sensor and calculating the amount and dispensing timing of detergent and softener; a terminal equipped with a dispenser for automatically dispensing detergent and softener based on instructions from the server; and a means for monitoring and controlling the operation of the terminal in real time via a smartphone app.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] Conventional washing machines require users to manually add detergent and fabric softener, making it difficult to ensure the correct amount and manage the timing of addition. This inconvenience leads to inconsistent washing results and raises concerns about the environmental impact of using excessive detergent and fabric softener. Furthermore, users are unable to monitor the progress of the wash in real time, making it difficult to wash efficiently. There was a need to solve these issues and improve the automation and efficiency of laundry. [Means for solving the problem]

[0005] The present invention is a system that includes a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on instructions from the server; and means for monitoring and controlling the terminal's operation in real time via a smartphone app. This frees users from the need to manually dispense detergent and fabric softener; the system automatically dispenses the appropriate amounts and at the correct timing, resulting in consistent washing results and preventing excessive waste of resources. The app also allows users to monitor the progress of the wash in real time and change settings as needed, achieving more efficient washing.

[0006] A "sensor" is a device for measuring the weight, type and soiling of laundry.

[0007] The "server" is a data processing device that analyzes data obtained from the sensors and calculates the optimal amounts of detergent and fabric softener and the timing of their addition.

[0008] A "dispenser" is a device that automatically dispenses detergent and fabric softener based on instructions from the server.

[0009] A "terminal" is a device that contains a dispenser and controls the operation of a washing machine.

[0010] A "smartphone app" is software that allows users to monitor and control the operation of a washing machine in real time using a smartphone.

[0011] "Weight" refers to the mass of the laundry, and is a value measured by a sensor.

[0012] "Type" refers to the material and fiber characteristics of the laundry, which are classified by the sensor.

[0013] "Level of soiling" indicates the degree of soiling of the laundry, and is a value evaluated by a sensor.

[0014] "Amount" refers to the amount of detergent and fabric softener to be used, as calculated by the server.

[0015] "Addition timing" refers to the optimal time to add detergent and fabric softener to the washing machine, and is determined by the server.

[0016] "Real-time" refers to the instant monitoring and reflection of the washing machine's operating status, which is achieved through a smartphone app. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

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

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

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

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

[0025] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0038] System Configuration

[0039] This invention is comprised of a system including a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the instructions; and means for monitoring and controlling the operation of the terminal in real time via a smartphone app.

[0040] System Operation

[0041] Data collection and analysis

[0042] First, the sensors measure the weight, type, and dirtiness of the laundry. The sensors perform highly accurate measurements and send the collected data to the device. The device then forwards this data to the server, where it is analyzed.

[0043] Calculating optimal dosage and timing

[0044] The server then uses the data it receives to run an algorithm that calculates the optimal amount of detergent and fabric softener to use. This algorithm calculates the optimal amount of detergent and fabric softener to maximize the effectiveness of the wash, for example, by adding more detergent if the laundry is heavily soiled. The server also considers the entire wash process and determines the optimal timing for adding detergent and fabric softener.

[0045] Automatic loading

[0046] Based on instructions from the server, the terminal controls the dispenser to automatically dispense the appropriate amounts of detergent and fabric softener, freeing the user from having to manually dispense detergent and ensuring that the right amount of detergent is used every time.

[0047] Real-time Monitoring and Control

[0048] Users can monitor the operation of their washing machine in real time using a smartphone app. The app displays the progress of the wash and the current operation phase, and users can change settings as needed. For example, they can easily increase the amount of detergent or add fabric softener during the wash.

[0049] Specific examples

[0050] Example 1: Lightly soiled laundry

[0051] The server receives the sensor data: "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0052] The server calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0053] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0054] Users can check the progress of their wash using a smartphone app and change settings as needed.

[0055] Example 2: Heavily soiled laundry

[0056] The server receives the sensor data: "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0057] The server calculates the amount of detergent for heavy soiling to be 100ml and the amount of fabric softener to be 50ml.

[0058] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0059] Users can check the progress of their wash using a smartphone app and change settings as needed.

[0060] This invention automatically dispenses the optimum amount and timing of detergent depending on the degree and type of soiling of the laundry, reducing the user's effort and achieving efficient and effective washing.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The sensors measure the weight, type and soiling of the laundry, each piece of information captured separately and sent to the device as a combined data packet.

[0064] Step 2:

[0065] The device sorts the received data packets and forwards them over the network link to the server, checking to ensure that no data is lost or sent incorrectly.

[0066] Step 3:

[0067] The server analyzes the data and selects the appropriate algorithm based on the weight, type and soiling of the laundry, for example, an algorithm for "light soiling" or "denim."

[0068] Step 4:

[0069] The server calculates the optimal amount of detergent and fabric softener based on the selected algorithm, using optimization techniques based on past data and laundry characteristics.

[0070] Step 5:

[0071] The server determines the calculated amounts of detergent and fabric softener and the timing of dispensing them. For example, it generates specific instructions such as "dispensing 50 ml of detergent 10 minutes after the start of washing" and "dispensing 20 ml of fabric softener 30 minutes after the start of washing."

[0072] Step 6:

[0073] The server sends these specific instructions back to the terminal, where it performs error checking to ensure the instructions were transmitted correctly.

[0074] Step 7:

[0075] The device then controls the built-in dispenser based on the received instructions, automatically dispensing the appropriate amount of detergent and fabric softener into the washing machine at the specified time.

[0076] Step 8:

[0077] Users monitor the progress of their wash in real time using a smartphone app, which displays information such as when to add detergent and fabric softener, and the current wash phase.

[0078] Step 9:

[0079] Users can change settings as needed through the app, for example, adjusting the amount of detergent or adding fabric softener.

[0080] Step 10:

[0081] The device constantly checks the progress of the laundry and reports any abnormalities to the server, which analyzes the data and sends an alert to the user if necessary.

[0082] Step 11:

[0083] When the laundry is complete, the device sends a completion notification to the server, which then forwards the information to the user's smartphone app, where the user receives the notification.

[0084] The above is the specific processing flow of the program.

[0085] Example 1

[0086] 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."

[0087] Conventional washing systems have difficulty automatically dispensing the optimal amount of detergent and fabric softener depending on the type of laundry and the degree of soiling, requiring manual dispensing. This reduces washing efficiency and prevents optimal cleaning results. Furthermore, there are few ways for users to monitor the progress of the washing process in real time and change settings as needed, resulting in a lack of convenience for users.

[0088] 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.

[0089] In this invention, the server includes a sensor that measures the weight, type, and degree of soiling of the laundry; an information processing device that analyzes the data and calculates the amount and timing of detergent and fabric softener addition; a chemical supply device that automatically adds detergent and fabric softener based on instructions from the information processing device; and a device that monitors and controls the device's operation in real time via mobile device software. This allows the optimal amount and timing of detergent addition based on the type and degree of soiling of the laundry to be automatically added, reducing user effort and enabling efficient and effective washing. Furthermore, the progress of the washing process can be monitored in real time and settings can be changed as needed, improving user convenience.

[0090] A "sensor" is a device for measuring the weight, type, and soiling level of laundry.

[0091] The "information processing device" is a computer device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of their addition.

[0092] The "chemical supply device" is a device that automatically dispenses detergent and fabric softener based on instructions from an information processing device.

[0093] "Software for mobile terminals" refers to application software for monitoring and controlling the operation of devices in real time using a mobile terminal.

[0094] A "detergent" is a cleaning agent used to clean stains.

[0095] "Fabric softener" is a chemical used to soften and scent laundry.

[0096] "Laundry type" refers to the classification of the material or construction of the items being washed.

[0097] "Level of dirt" refers to the amount and degree of dirt adhering to the laundry.

[0098] "Analysis" is the process of examining the data obtained from the sensors in detail to determine cleaning methods.

[0099] "Addition timing" refers to the timing at which detergent and fabric softener are added at an appropriate time during the washing process.

[0100] This invention is a system that includes a sensor that measures the weight, type, and degree of soiling of laundry, an information processing device that analyzes the measured data, a chemical supply device that automatically dispenses detergent and fabric softener based on the analysis results, and a means for monitoring and controlling the operation of the device in real time via software for a mobile device.

[0101] System configuration

[0102] sensor

[0103] The sensor is a device that accurately measures the weight, type, and degree of soiling of laundry. The sensor has the functions of measuring weight, identifying materials, and evaluating the degree of soiling using optical and chemical sensors. The data from this sensor is sent to the terminal.

[0104] Information processing device

[0105] The terminal transfers the data sent from the sensor to an information processing device (server) via a communications device. The information processing device analyzes the collected data and calculates the optimal amount of detergent and fabric softener. The analysis algorithm uses a generative AI model to derive optimal results based on past data and simulation results of washing effects. The information processing device also calculates the optimal timing to add detergent and fabric softener depending on the progress of the wash.

[0106] Chemical Supply Device

[0107] Based on instructions from the information processing device, the chemical supply device automatically dispenses the appropriate amounts of detergent and fabric softener at the required timing for each stage of the wash, eliminating the need for the user to do this manually.

[0108] Mobile device software

[0109] Mobile device software is an application that allows users to monitor and control the operation of the machine in real time using a mobile device such as a smartphone. This software allows users to check the progress of the wash, the amount of detergent and fabric softener used, and change settings as needed. For example, it is easy to adjust the amount of detergent or add fabric softener during the wash.

[0110] Specific examples

[0111] Example 1: Lightly soiled laundry

[0112] 1. The sensor measures "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0113] 2. The terminal transfers this data to the information processing device.

[0114] 3. The information processing device analyzes the data and calculates the amount of detergent for light soiling to be 50 ml and the amount of fabric softener to be 20 ml.

[0115] 4. The information processing device sends the calculation results to the terminal.

[0116] 5. The device dispenses detergent and fabric softener at the specified times.

[0117] 6. Users can check the progress of their wash on their smartphone app and change settings as needed.

[0118] Example 2: Heavily soiled laundry

[0119] 1. The sensor measures "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0120] 2. The terminal transfers this data to the information processing device.

[0121] 3. The information processing device analyzes the data and calculates the amount of detergent for heavy soiling to be 100 ml and the amount of fabric softener to be 50 ml.

[0122] 4. The information processing device sends the calculation results to the terminal.

[0123] 5. The device dispenses detergent and fabric softener at the specified times.

[0124] 6. Users can check the progress of their wash on their smartphone app and change settings as needed.

[0125] Prompt Sentence Examples

[0126] 1. "What is the optimal amount of detergent and fabric softener for lightly soiled cotton (3 kg) and when should I add them?"

[0127] 2. "Calculate the amount of detergent and fabric softener needed for heavily soiled denim (5 kg) and when to add them."

[0128] 3. "Please explain the procedures for data collection, analysis, automated input, and real-time monitoring in this system."

[0129] This system allows users to wash effectively and efficiently with the optimal amount and timing of detergent and fabric softener.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] The sensors measure the weight, type, and degree of soiling of the laundry. They collect this data and send it to the terminal. The sensors use a load cell to measure weight, a reflective light sensor to distinguish the material type, and a chemical sensor to evaluate the degree of soiling. The input is the laundry, and the output is data on "weight," "type," and "degree of soiling."

[0133] Step 2:

[0134] The terminal transfers the data received from the sensor to the information processing device. The terminal temporarily stores the data received from the sensor, encrypts it, and sends it to the information processing device (server) via the network. The input is the aforementioned "weight," "type," and "level of dirt" data, and the output is a notification of successful data transfer to the server.

[0135] Step 3:

[0136] The server analyzes the received data. Using the generative AI model, the server compares it with standard data for each type of laundry to determine the optimal amount of detergent and fabric softener, as well as the timing for adding them. Specifically, the server inputs the received data (weight, type, and level of soiling) into the analysis algorithm, and obtains the optimal amount of detergent, optimal amount of fabric softener, and timing for adding them as outputs.

[0137] Step 4:

[0138] The server sends the analysis results to the terminal. The server then sends the "optimum amount of detergent," "optimum amount of fabric softener," and "addition timing" obtained from the analysis results to the terminal in packet format. The input is the analysis results, and the output is confirmation of the results sent to the terminal.

[0139] Step 5:

[0140] The terminal controls the chemical supply device based on instructions from the server, and automatically dispenses detergent and fabric softener. Based on data from the server, the terminal dispenses the appropriate amount of detergent and fabric softener at a set time depending on the weight and degree of dirt of the laundry. The input is data on the "optimum amount of detergent," "optimum amount of fabric softener," and "dispensing timing," and the output is a notification that dispensing is complete.

[0141] Step 6:

[0142] Using the mobile device software, users can monitor the progress of their laundry in real time and change settings as needed. From the app, users can check the progress of the wash and the amount of detergent and fabric softener added, and change operations as needed. For example, it is possible to increase the amount of detergent during a wash. The input is the user's command to change the settings, and the output is the execution result based on the command.

[0143] At each step, the server, the terminal, and the user perform specific operations, which allows the entire system to work together and achieve effective and efficient laundry.

[0144] (Application example 1)

[0145] 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."

[0146] Traditional maintenance processes in the manufacturing industry require a lot of time and effort, often resulting in variations in work efficiency and quality. It is also difficult for managers to grasp the progress of maintenance in real time and issue instructions at the appropriate time. Furthermore, there was a need for a system that could automatically perform appropriate maintenance according to the degree of dirt and wear.

[0147] 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.

[0148] In this invention, the server includes a sensor that measures the weight, type, and condition of the object to be cleaned, a means for analyzing data obtained from the sensor and calculating the appropriate cleaning chemical and its dosage timing, a terminal equipped with a supply device that automatically dispenses the appropriate cleaning chemical based on instructions from the server, a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application, and a means for automatically performing maintenance on manufacturing equipment and production lines. This frees users from the traditional manual maintenance work and enables efficient and effective maintenance.

[0149] "Object to be cleaned" is any equipment or part of a manufacturing machine or production line that requires proper maintenance.

[0150] "Sensor" is a measuring device for measuring the weight, type and condition of the object being washed.

[0151] The "server" is a computer that analyzes data obtained from sensors and calculates the amount and timing of application of cleaning chemicals.

[0152] A "supply device" is a device that automatically dispenses cleaning chemicals based on instructions from the server.

[0153] A "terminal" is a computer device that controls a supply device according to instructions from a server.

[0154] A "mobile terminal application" is software that runs on a portable computer such as a smartphone or tablet and is used to monitor and control the operation of the terminal in real time.

[0155] "Maintenance of manufacturing equipment and production lines" refers to carrying out appropriate cleaning and repair work according to the degree of dirt and wear.

[0156] The "means for automatic execution" is a function that combines sensors, servers, supply devices, and terminals to execute maintenance work without human intervention.

[0157] System Configuration

[0158] This invention is a system for realizing automatic maintenance of manufacturing equipment and production lines in a factory environment. The system includes a sensor that captures the weight, type, and condition of the object to be cleaned, a server that analyzes data obtained from the sensor and calculates the appropriate amount and timing of supply of cleaning chemicals, a terminal equipped with a supply device that automatically supplies cleaning chemicals based on instructions from the server, and a mobile terminal application that monitors and controls the operation of the terminal in real time.

[0159] System Operation

[0160] Data collection and analysis

[0161] 1. The role of the sensor:

[0162] The sensors used include LiDAR sensors, cameras, and wear sensors, and measure the weight, type, and status of manufacturing equipment and production lines with high precision, and transmit the collected data to a terminal.

[0163] 2. Data Analysis:

[0164] The device transmits the data to a server, which runs data analysis algorithms that use software like Python and TensorFlow to calculate the optimal type and amount of cleaning chemicals and the optimal timing for cleaning based on soiling and friction parameters.

[0165] Automatic loading and maintenance work

[0166] 3. Feed device control:

[0167] The server's calculation results are sent to the terminal, which then controls the dispenser to dispense the appropriate cleaning chemicals at the specified amount and timing. The dispenser is controlled using software such as ROS (Robot Operating System) or Arduino IDE.

[0168] 4. Maintenance work:

[0169] The supply device automatically performs maintenance work on manufacturing equipment and production lines, cleaning and repairing them.

[0170] Real-time Monitoring and Control

[0171] 5. Mobile terminal applications:

[0172] Administrators (users) can monitor the system's operating status in real time using a mobile terminal application, which is developed using software such as Flutter and Firebase.

[0173] Managers can use the app to check the progress of maintenance and change settings as needed, such as adjusting cleaning timing or the amount of cleaning chemicals used.

[0174] Specific examples

[0175] Administrators can open a smartphone application to check the operating status of the conveyor belt cleaning robot in real time. For example, they can set the conveyor belt cleaning time to 3:00 p.m. and automatically start cleaning when the level of dirt exceeds 50%.

[0176] Prompt Sentence Examples

[0177] "If the conveyor belt cleaning robot detects that it is 50% dirty, add 100ml of cleaning liquid within 5 minutes of detecting the dirt and start cleaning. In addition, display the cleaning progress in real time on the smartphone app."

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] Data collection and transmission:

[0181] Sensors measure the weight, type, and condition of manufacturing equipment and production lines with high precision. For example, LiDAR sensors measure the surface area of ​​dirt, and wear sensors detect the depth of wear. The collected data is sent to a device as a signal. The input is the measurement data obtained from the sensor, and the output is the data sent to the device.

[0182] Step 2:

[0183] Data Analysis:

[0184] The device transmits the measurement data received from the sensor to a server. The server receives this data and analyzes it using Python and TensorFlow. The input is the measurement data sent from the device, and the output is a calculation result of the type, amount, and timing of application of cleaning chemicals. This analysis, for example, calculates the optimal amount of cleaning liquid based on the surface area of ​​the dirt and the depth of wear.

[0185] Step 3:

[0186] Send instructions:

[0187] The server sends the analysis results to the terminal. The input is the calculation result after data analysis, and the output is the instruction sent to the terminal. Specifically, the server packages the calculation results in JSON format or similar and sends them to the terminal via the network.

[0188] Step 4:

[0189] Automatic input:

[0190] The terminal receives instructions from the server and controls the dispenser to dispense the appropriate amount of cleaning chemicals. The input is the instruction from the server and the output is the action of the dispenser. In this step, ROS or Arduino IDE is used to control the dispenser, for example, to dispense the exact amount of cleaning liquid.

[0191] Step 5:

[0192] Real-time monitoring:

[0193] The user opens the mobile terminal application to monitor the system's operating status in real time. The input is the operating status data sent from the terminal to the application, and the output is the information displayed on the user's smartphone screen. Specifically, the application uses Firebase to update the real-time database and display the current maintenance progress to the user.

[0194] Step 6:

[0195] Changes made during the process:

[0196] The user can change the maintenance process midway through the application as needed. The input is the setting change made by the user in the application, and the output is the new setting information sent to the terminal and server. Specifically, when the user issues a command in the application to increase the amount of cleaning liquid, that information is transmitted to the dispenser via the server.

[0197] 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.

[0198] System Configuration

[0199] This invention is comprised of a system that includes a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the server's instructions; a means for monitoring and controlling the operation of the terminal in real time via a smartphone app; and an emotion engine that recognizes the user's emotions.

[0200] System Operation

[0201] Data collection and analysis

[0202] First, sensors measure the weight, type, and soiling of the laundry. This information is sent as a data packet to the device. The device then forwards the received data packet to a server, which analyzes the data and runs an algorithm to calculate the optimal amount of detergent and fabric softener.

[0203] Calculating optimal dosage and timing

[0204] The server then determines the optimal amount of detergent and fabric softener and the timing of dispensing based on the analyzed data. For example, it executes a process that determines the amount of detergent to use for light soiling and 100ml for heavy soiling.

[0205] Emotion Engine Operation

[0206] The smartphone app's built-in emotion engine analyzes the user's voice input and facial expressions to recognize their current emotions. The emotion engine determines the user's stress level, satisfaction level, excitement level, etc., and sends the results to the server.

[0207] Automatic loading

[0208] The terminal controls the dispenser based on instructions from the server, automatically dispensing the optimal amount of detergent and fabric softener, freeing the user from having to dispense detergent manually.

[0209] Real-time Monitoring and Control

[0210] Users can monitor the progress of their laundry in real time through a smartphone app. Furthermore, based on the analysis results of the emotion engine, the washing process can be adjusted according to the user's emotions. For example, if the user is feeling stressed, the settings can be changed to shorten the washing time.

[0211] Specific examples

[0212] Example 1: Lightly soiled laundry

[0213] The server receives the sensor data: "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0214] The server calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0215] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0216] Users can check the progress of their washing on their smartphone app and change settings as needed. If the emotion engine detects a comfortable state from the user's voice, it will maintain the washing process at the optimal settings to maintain that state.

[0217] Example 2: Heavily soiled laundry

[0218] The server receives the sensor data: "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0219] The server calculates the amount of detergent for heavy soiling to be 100ml and the amount of fabric softener to be 50ml.

[0220] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0221] The user checks the progress of the laundry using a smartphone app, and if the emotion engine detects a state of stress from the user's facial expression, it sets the laundry to finish earlier.

[0222] This invention not only automatically dispenses the optimum amount and timing of detergent depending on the degree of soiling and type of laundry, but also realizes a flexible washing process according to the user's feelings, reduces the user's effort, and provides efficient and effective washing.

[0223] The processing flow will be explained below.

[0224] Step 1:

[0225] The sensors measure the weight, type and soiling of the laundry, each piece of information captured separately and sent to the device as a data packet.

[0226] Step 2:

[0227] The device sorts the received data packets and forwards them to the server, checking to ensure that no data is lost or sent incorrectly.

[0228] Step 3:

[0229] The server analyzes the data and selects the appropriate algorithm based on the weight, type and soiling of the laundry, for example, an algorithm for "light soiling" or "denim."

[0230] Step 4:

[0231] The server calculates the optimal amount of detergent and fabric softener based on the selected algorithm, using optimization techniques based on past data and laundry characteristics.

[0232] Step 5:

[0233] The server determines the calculated amounts of detergent and fabric softener and the timing of dispensing them. For example, it generates specific instructions such as "dispensing 50 ml of detergent 10 minutes after the start of washing" and "dispensing 20 ml of fabric softener 30 minutes after the start of washing."

[0234] Step 6:

[0235] The server sends these specific instructions back to the terminal, where it performs error checking to ensure the instructions were transmitted correctly.

[0236] Step 7:

[0237] The device then controls the built-in dispenser based on the received instructions, automatically dispensing the appropriate amount of detergent and fabric softener into the washing machine at the specified time.

[0238] Step 8:

[0239] The smartphone app analyzes the user's voice input and facial expressions, and the emotion engine recognizes the user's emotions, determining, for example, whether the user is feeling stressed or relaxed.

[0240] Step 9:

[0241] The server receives the user's emotional data and optimizes the laundry process accordingly, for example, shortening the washing time or increasing the number of rinses if the user is feeling stressed.

[0242] Step 10:

[0243] Users can monitor the progress of their laundry in real time using a smartphone app, which displays information such as "detergent added," "rinsing," and "drying."

[0244] Step 11:

[0245] Through the app, users can make changes to the wash process as needed, for example, adding more detergent or adjusting the amount of fabric softener.

[0246] Step 12:

[0247] When the laundry is complete, the device sends a completion notification to the server, which then forwards the information to the user's smartphone app, where the user receives the notification.

[0248] The above is the specific processing flow of a system that combines an emotion engine.

[0249] Example 2

[0250] 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."

[0251] Conventional washing machine systems are often inefficient because the amount and timing of detergent and fabric softener dispense are manually controlled. Furthermore, they place a heavy burden on users because they are unable to flexibly respond to the user's emotions and lifestyle. Furthermore, there is a demand for automated systems that can optimally execute the washing process based on the weight, type, and soiling level of the laundry.

[0252] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0253] In this invention, the server includes a sensor that measures the weight, type, and degree of soiling of the laundry, a data processing device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them, a terminal equipped with a distribution device that automatically dispenses detergent and fabric softener based on instructions from the data processing device, means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application, and an emotion analysis device that analyzes the user's voice input and facial expressions to recognize the user's emotional state. This makes it possible to dispense the optimal amount of detergent and fabric softener based on the weight, type, and degree of soiling of the laundry, and also makes it possible to flexibly adjust the washing process according to the user's emotions and lifestyle.

[0254] A "sensor" is a device that measures the weight, type and soiling of laundry.

[0255] The "data processing device" is a device that analyzes the data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of their addition.

[0256] A "distribution device" is a device that automatically dispenses detergent and fabric softener based on instructions from a data processing device.

[0257] A "terminal" is a set of devices that has sensors and distribution devices and operates by integrating these functions.

[0258] A "mobile terminal application" is software that runs on a mobile terminal such as a smartphone or tablet and monitors and controls the terminal's operations in real time.

[0259] An "emotion analysis device" is a device that analyzes a user's voice input and facial expressions to recognize the user's emotional state.

[0260] A "detergent" is a chemical product used to remove stains from laundry.

[0261] "Fabric softener" is a chemical product used to soften and scent laundry.

[0262] An "algorithm" is a computational procedure for analyzing specific data and deriving an optimal result.

[0263] "Real time" refers to the time when user operations and system operations are reflected immediately.

[0264] System Configuration

[0265] This invention is a system that comprises a sensor that measures the weight, type and degree of soiling of laundry; a data processing device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a distribution device that automatically dispenses detergent and fabric softener based on the instructions; a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application; and an emotion analysis device that recognizes the user's emotions.

[0266] Data collection and analysis

[0267] First, sensors measure the weight, type, and degree of soiling of the laundry. This information is sent to the terminal as a data packet. Specifically, the weight obtained by the weight sensor and the type and degree of soiling obtained by the camera and soiling sensor are structured in JSON format. The terminal transfers the received data packet to a data processing device, which stores the data in a database (e.g., MySQL or PostgreSQL) and analyzes it. This analysis is performed using a Python data analysis library (e.g., Pandas or NumPy).

[0268] Calculating optimal dosage and timing

[0269] The data processing device determines the optimal amount of detergent and fabric softener and the timing of adding them based on the analyzed data. For example, using a machine learning model (TensorFlow or PyTorch), it receives the weight, type, and degree of soiling of the laundry as input and calculates the optimal output value. A specific example is a process that determines the amount of detergent to use for lightly soiled laundry and 100ml for heavily soiled laundry.

[0270] How the sentiment analysis engine works

[0271] When a user opens a smartphone app, the app captures voice input and facial expressions. Specifically, the smartphone's camera recognizes facial expressions and the microphone records audio. An emotion analysis engine within the app analyzes this data to determine the user's emotional state (e.g., stress, satisfaction, excitement). A natural language processing (NLP) library (e.g., NLTK or spaCy) is used for the analysis. The analysis results are sent to a data processing device.

[0272] Automatic loading

[0273] Based on the analysis results, the data processing device sends instructions to the terminal. According to these instructions, the terminal controls the dispenser to dispense the optimal amount of detergent and fabric softener at the specified time. For example, a control signal may be sent to the dispenser to cause the pump to dispense 50 ml of detergent.

[0274] Real-time Monitoring and Control

[0275] Users can check the progress of their laundry in real time using a smartphone app. The app displays the remaining time and progress of the wash and allows users to change settings as needed. For example, if they are feeling stressed, they can set the washing time to be shorter.

[0276] Specific prompt examples

[0277] This system uses sensors to measure the weight, type, and soiling level of laundry, and then calculates the optimal amount and timing of detergent and fabric softener based on that data. It also recognizes the user's emotions and optimizes the washing process accordingly. Please explain the specific settings and execution process.

[0278] Example: Lightly soiled laundry

[0279] 1. The data processing device receives the sensor data "Weight: 3 kg, Type: Cotton, Level of dirt: Light."

[0280] 2. The data processor analyzes and calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0281] 3. The data processing device transmits the calculation results to the terminal.

[0282] 4. The terminal controls the dispenser and dispenses detergent and fabric softener at the specified times.

[0283] 5. The user can check the progress on the smartphone app and change settings as needed. For example, if the washing machine determines that the washing conditions are comfortable based on voice input, the washing process will be optimized to maintain that condition.

[0284] As described above, this system not only automatically dispenses the optimal amount of detergent at the optimal timing depending on the type and degree of soiling of the laundry, but also realizes a flexible washing process that responds to the user's emotions, reducing the user's effort and providing efficient and effective washing.

[0285] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0286] Step 1:

[0287] The user puts laundry into the washing machine. At this moment, the system is in standby mode. The sensors are activated to measure the weight, type, and soiling level of the laundry. The data is first transmitted to the terminal.

[0288] Input: Weight, type, and degree of dirt of laundry

[0289] Output: Sensor data (JSON format)

[0290] Step 2:

[0291] The terminal receives the data obtained from the sensor. The received data is transferred directly to the data processing device. The HTTPS protocol is used for transfer to ensure data security.

[0292] Input: Sensor data (JSON format)

[0293] Output: Sensor data sent to the server

[0294] Step 3:

[0295] The data processor analyzes the received data, first storing it in a database, then preprocessing it using Python's Pandas and NumPy libraries, and then calculating the optimal amount and timing of detergent and fabric softener dosage using machine learning models (TensorFlow and PyTorch).

[0296] Input: Sensor data

[0297] Output: Data on optimal amounts of detergent and fabric softener and timing of addition

[0298] Step 4:

[0299] The data processor sends the calculation results to the terminal, which transmits them in real time to ensure the correct amount of detergent and fabric softener is dispensed early in the wash cycle.

[0300] Input: Data on optimal amounts of detergent and fabric softener and timing of addition

[0301] Output: Input instruction data sent to the terminal

[0302] Step 5:

[0303] The terminal controls the dispenser to dispense the optimal amount of detergent and fabric softener. Specifically, it sends a control signal to the dispenser, causing the pump to dispense 50 ml of detergent (for light soiling).

[0304] Input: Input instruction data

[0305] Output: Calculated amount of detergent and fabric softener dispensed

[0306] Step 6:

[0307] The user monitors the progress of the wash in real time using a smartphone app. The app receives progress data from the device and displays the remaining time and progress of the wash, allowing the user to change settings as needed.

[0308] Input: Progress data from the terminal

[0309] Output: Progress displayed on the smartphone app

[0310] Step 7:

[0311] A smartphone app works to recognize the user's emotions. It uses a camera to recognize facial expressions and a microphone to analyze voices to determine the user's emotional state. An emotion analysis engine processes the data using an NLP library (NLTK or spaCy) to determine the emotional state (e.g., stress, satisfaction, excitement). This data is sent to a data processing device.

[0312] Input: User's voice and facial expression data

[0313] Output: Sentiment analysis result data

[0314] Step 8:

[0315] The data processing device adjusts the washing process based on the user's emotional state, for example, if the user is feeling stressed, it sends instructions to the terminal to change settings to reduce the washing time.

[0316] Input: Sentiment analysis result data

[0317] Output: Washing process adjustment instructions

[0318] (Application example 2)

[0319] 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."

[0320] Modern laundry requires a lot of time and effort, and it is difficult to determine the appropriate amount of detergent and fabric softener for various laundry items. Furthermore, there is also the problem of users being unable to respond appropriately when they feel stressed during the wash. This makes it difficult to provide an efficient and effective laundry process.

[0321] The specific processing by the specific 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: a sensor that measures the weight, type, and degree of soiling of the laundry; means for analyzing data obtained from the sensor and calculating the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on instructions from the server; means for monitoring and controlling the operation of the terminal in real time via a mobile device application; an emotion analysis engine built into the mobile device application that recognizes the user's emotions; and means for adjusting the washing process based on the user's emotional state recognized by the emotion analysis engine. This not only enables the user to easily determine the appropriate amounts of detergent and fabric softener, but also enables the user to check the progress of the washing process through real-time monitoring, providing an optimal washing experience tailored to the user's emotions.

[0322] "Laundry" refers to all cloth products that require washing, such as clothes, towels, and sheets.

[0323] A "sensor" is a device that detects a physical phenomenon and generates data, and in this invention is a device for measuring the weight, type, and degree of soiling of laundry.

[0324] A "server" is a computer system that provides services to multiple terminals via a network, and in this invention has the function of analyzing data related to laundry and calculating the amount of detergent and fabric softener and the timing of their addition.

[0325] A "dispenser" is a device that dispenses a fixed amount of liquid, powder, etc., and in this invention, it plays a role in automatically dispensing detergent and fabric softener.

[0326] The term "terminal" refers to a device that can be directly operated by a user, and in this invention refers to a device including a dispenser installed in a washing machine.

[0327] A "mobile device application" is software that runs on a mobile electronic device such as a smartphone or tablet, and in this invention refers to an application for monitoring and controlling the progress of laundry in real time.

[0328] An "emotion analysis engine" refers to software or algorithms that analyze a user's voice input and facial expression data to recognize their emotional state.

[0329] A "detergent" is a chemical product used to remove stains from laundry.

[0330] A "softener" is a chemical product used to make laundry softer.

[0331] An "algorithm" is a set of procedures or rules for performing calculations or data analysis, and in this invention, it is used to calculate the optimal amount of detergent and fabric softener based on the type and degree of soiling of the laundry.

[0332] System Configuration

[0333] This invention is a system that comprises a sensor that measures the weight, type and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the server's instructions; a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application; and an emotion analysis engine that recognizes the user's emotions.

[0334] Program Generation

[0335] To realize this system, the following programs are required: First, a sensor measures the weight, type, and degree of soiling of the laundry and sends this data to the device as a data packet. The device then forwards the received data packet to a server, which analyzes the data. Next, the server calculates the optimal amount of detergent and fabric softener and the timing of dispensing them based on the analyzed data. Then, based on instructions from the server, the device controls the dispensers to automatically dispense the detergent and fabric softener. The progress of the wash is also monitored in real time via a mobile device application, and the washing process is adjusted according to the user's emotional state.

[0336] Processing Description

[0337] The system uses the following hardware and software:

[0338] Sensor: A device that measures the weight, type and soiling of laundry.

[0339] Server: A computer system that analyzes data collected from sensors and calculates the optimal amount of detergent and fabric softener.

[0340] Terminal: A device with dispensers that automatically dispense detergent and fabric softener.

[0341] Mobile device application: Software for monitoring and controlling the laundry progress in real time.

[0342] Emotion analysis engine: Software or algorithms that analyze a user's voice input and facial expression data to recognize their emotional state.

[0343] The data collected by the sensors is sent via the device to a server, where an analytical algorithm is used to calculate the optimal amount of detergent and fabric softener and the timing of dispensing. This algorithm takes into account the type of laundry and its degree of soiling. The calculation results are then sent to the device, which then automatically dispenses the detergent and fabric softener.

[0344] Furthermore, the progress of the laundry can be monitored in real time through a mobile application, and an emotion analysis engine can analyze the user's emotional state and adjust the laundry process accordingly. For example, if the user is feeling stressed, the washing time can be shortened.

[0345] Specific examples

[0346] For example, when a customer uses this system in a physical store, the process goes like this: First, sensors measure the weight, type, and dirtiness of the laundry. This data is sent to a server, which calculates the optimal amount of detergent and fabric softener. This information is sent to the device, which automatically dispenses the detergent and fabric softener at the appropriate time. The progress of the wash is then displayed in real time on the mobile device application. When the customer provides voice input or camera footage to the app, the emotion analysis engine recognizes the customer's emotional state, and the washing process is further optimized based on the results.

[0347] An example of a prompt sentence to be input to the generative AI model used is as follows:

[0348] "Write a program that calculates the optimal amount of detergent and fabric softener for a load of laundry. The program will send data from sensors that measure the weight, type, and soiling of the laundry to a server, which will analyze the data and determine the appropriate amounts. The washing machine will automatically dispense detergent and fabric softener, and the customer will be able to monitor and control it in real time through a mobile application. Also add the ability to use an emotion analysis engine to analyze the customer's emotional state and adjust the washing process as needed."

[0349] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0350] Step 1:

[0351] The sensor measures the weight, type, and soiling level of the laundry. The sensor is installed inside the washing machine and automatically starts measuring when laundry is put in. The measurement data is output from the sensor as weight (kg), type (cotton, denim, etc.), and soiling level (light, normal, heavy).

[0352] Input: Physical state of laundry

[0353] Output: Weight, type, and soiling data

[0354] Step 2:

[0355] The device receives data packets from the sensor and forwards them to the server. The device formats the data packets and sends them over the network to the server. This process ensures that the raw data from the sensor is delivered to the server in a format suitable for analysis.

[0356] Input: Weight, type, and soiling data from sensors

[0357] Output: Data packets transferred to the server

[0358] Step 3:

[0359] The server analyzes the received data and calculates the optimal amount of detergent and fabric softener and the timing of dispensing. The server uses specific algorithms to determine the amount of detergent and fabric softener needed based on the weight, type, and soiling of the laundry. It also calculates the optimal timing of dispensing.

[0360] Input: Data packets transmitted from the sensor

[0361] Output: Optimal detergent and fabric softener dosage and timing

[0362] Step 4:

[0363] The terminal controls the dispenser based on instructions from the server to automatically dispense detergent and fabric softener. The dispenser is installed inside the washing machine and is controlled to dispense the detergent and fabric softener accurately according to the optimal amounts received from the server.

[0364] Input: Instructions from the server on optimal amounts and timing of detergent and fabric softener

[0365] Output: Automatic detergent and fabric softener dispenser

[0366] Step 5:

[0367] Through the mobile application, users can monitor the progress of their laundry in real time. The app connects to the server and displays real-time laundry status information. Users can check the status of the washing process and change settings as needed.

[0368] Input: Status information of the laundry process from the server

[0369] Output: Real-time display on mobile application

[0370] Step 6:

[0371] An emotion analysis engine built into the mobile device application analyzes the user's voice input and facial expression data to recognize their emotional state. The emotion analysis engine receives the user's voice and facial expression data as input, analyzes them, and outputs their emotional state, such as stress level or satisfaction level.

[0372] Input: User voice input and facial expression data

[0373] Output: User's emotional state data

[0374] Step 7:

[0375] The server adjusts the washing process based on the emotional state data from the emotion analysis engine. For example, if the user is feeling stressed, the server adjusts the washing time to shorten the washing process. The server analyzes the emotional state data and sends instructions to the device to change the washing settings appropriately.

[0376] Input: Emotional state data

[0377] Output: Adjusted washing process settings

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Second embodiment]

[0382] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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."

[0394] System Configuration

[0395] This invention is comprised of a system including a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the instructions; and means for monitoring and controlling the operation of the terminal in real time via a smartphone app.

[0396] System Operation

[0397] Data collection and analysis

[0398] First, the sensors measure the weight, type, and dirtiness of the laundry. The sensors perform highly accurate measurements and send the collected data to the device. The device then forwards this data to the server, where it is analyzed.

[0399] Calculating optimal dosage and timing

[0400] The server then uses the data it receives to run an algorithm that calculates the optimal amount of detergent and fabric softener to use. This algorithm calculates the optimal amount of detergent and fabric softener to maximize the effectiveness of the wash, for example, by adding more detergent if the laundry is heavily soiled. The server also considers the entire wash process and determines the optimal timing for adding detergent and fabric softener.

[0401] Automatic loading

[0402] Based on instructions from the server, the terminal controls the dispenser to automatically dispense the appropriate amounts of detergent and fabric softener, freeing the user from having to manually dispense detergent and ensuring that the right amount of detergent is used every time.

[0403] Real-time Monitoring and Control

[0404] Users can monitor the operation of their washing machine in real time using a smartphone app. The app displays the progress of the wash and the current operation phase, and users can change settings as needed. For example, they can easily increase the amount of detergent or add fabric softener during the wash.

[0405] Specific examples

[0406] Example 1: Lightly soiled laundry

[0407] The server receives the sensor data: "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0408] The server calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0409] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0410] Users can check the progress of their wash using a smartphone app and change settings as needed.

[0411] Example 2: Heavily soiled laundry

[0412] The server receives the sensor data: "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0413] The server calculates the amount of detergent for heavy soiling to be 100ml and the amount of fabric softener to be 50ml.

[0414] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0415] Users can check the progress of their wash using a smartphone app and change settings as needed.

[0416] This invention automatically dispenses the optimum amount and timing of detergent depending on the degree and type of soiling of the laundry, reducing the user's effort and achieving efficient and effective washing.

[0417] The processing flow will be explained below.

[0418] Step 1:

[0419] The sensors measure the weight, type and soiling of the laundry, each piece of information captured separately and sent to the device as a combined data packet.

[0420] Step 2:

[0421] The device sorts the received data packets and forwards them over the network link to the server, checking to ensure that no data is lost or sent incorrectly.

[0422] Step 3:

[0423] The server analyzes the data and selects the appropriate algorithm based on the weight, type and soiling of the laundry, for example, an algorithm for "light soiling" or "denim."

[0424] Step 4:

[0425] The server calculates the optimal amount of detergent and fabric softener based on the selected algorithm, using optimization techniques based on past data and laundry characteristics.

[0426] Step 5:

[0427] The server determines the calculated amounts of detergent and fabric softener and the timing of dispensing them. For example, it generates specific instructions such as "dispensing 50 ml of detergent 10 minutes after the start of washing" and "dispensing 20 ml of fabric softener 30 minutes after the start of washing."

[0428] Step 6:

[0429] The server sends these specific instructions back to the terminal, where it performs error checking to ensure the instructions were transmitted correctly.

[0430] Step 7:

[0431] The device then controls the built-in dispenser based on the received instructions, automatically dispensing the appropriate amount of detergent and fabric softener into the washing machine at the specified time.

[0432] Step 8:

[0433] Users monitor the progress of their wash in real time using a smartphone app, which displays information such as when to add detergent and fabric softener, and the current wash phase.

[0434] Step 9:

[0435] Users can change settings as needed through the app, for example, adjusting the amount of detergent or adding fabric softener.

[0436] Step 10:

[0437] The device constantly checks the progress of the laundry and reports any abnormalities to the server, which analyzes the data and sends an alert to the user if necessary.

[0438] Step 11:

[0439] When the laundry is complete, the device sends a completion notification to the server, which then forwards the information to the user's smartphone app, where the user receives the notification.

[0440] The above is the specific processing flow of the program.

[0441] Example 1

[0442] 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."

[0443] Conventional washing systems have difficulty automatically dispensing the optimal amount of detergent and fabric softener depending on the type of laundry and the degree of soiling, requiring manual dispensing. This reduces washing efficiency and prevents optimal cleaning results. Furthermore, there are few ways for users to monitor the progress of the washing process in real time and change settings as needed, resulting in a lack of convenience for users.

[0444] 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.

[0445] In this invention, the server includes a sensor that measures the weight, type, and degree of soiling of the laundry; an information processing device that analyzes the data and calculates the amount and timing of detergent and fabric softener addition; a chemical supply device that automatically adds detergent and fabric softener based on instructions from the information processing device; and a device that monitors and controls the device's operation in real time via mobile device software. This allows the optimal amount and timing of detergent addition based on the type and degree of soiling of the laundry to be automatically added, reducing user effort and enabling efficient and effective washing. Furthermore, the progress of the washing process can be monitored in real time and settings can be changed as needed, improving user convenience.

[0446] A "sensor" is a device for measuring the weight, type, and soiling level of laundry.

[0447] The "information processing device" is a computer device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of their addition.

[0448] The "chemical supply device" is a device that automatically dispenses detergent and fabric softener based on instructions from an information processing device.

[0449] "Software for mobile terminals" refers to application software for monitoring and controlling the operation of devices in real time using a mobile terminal.

[0450] A "detergent" is a cleaning agent used to clean stains.

[0451] "Fabric softener" is a chemical used to soften and scent laundry.

[0452] "Laundry type" refers to the classification of the material or construction of the items being washed.

[0453] "Level of dirt" refers to the amount and degree of dirt adhering to the laundry.

[0454] "Analysis" is the process of examining the data obtained from the sensors in detail to determine cleaning methods.

[0455] "Addition timing" refers to the timing at which detergent and fabric softener are added at an appropriate time during the washing process.

[0456] This invention is a system that includes a sensor that measures the weight, type, and degree of soiling of laundry, an information processing device that analyzes the measured data, a chemical supply device that automatically dispenses detergent and fabric softener based on the analysis results, and a means for monitoring and controlling the operation of the device in real time via software for a mobile device.

[0457] System configuration

[0458] sensor

[0459] The sensor is a device that accurately measures the weight, type, and degree of soiling of laundry. The sensor has the functions of measuring weight, identifying materials, and evaluating the degree of soiling using optical and chemical sensors. The data from this sensor is sent to the terminal.

[0460] Information processing device

[0461] The terminal transfers the data sent from the sensor to an information processing device (server) via a communications device. The information processing device analyzes the collected data and calculates the optimal amount of detergent and fabric softener. The analysis algorithm uses a generative AI model to derive optimal results based on past data and simulation results of washing effects. The information processing device also calculates the optimal timing to add detergent and fabric softener depending on the progress of the wash.

[0462] Chemical Supply Device

[0463] Based on instructions from the information processing device, the chemical supply device automatically dispenses the appropriate amounts of detergent and fabric softener at the required timing for each stage of the wash, eliminating the need for the user to do this manually.

[0464] Mobile device software

[0465] Mobile device software is an application that allows users to monitor and control the operation of the machine in real time using a mobile device such as a smartphone. This software allows users to check the progress of the wash, the amount of detergent and fabric softener used, and change settings as needed. For example, it is easy to adjust the amount of detergent or add fabric softener during the wash.

[0466] Specific examples

[0467] Example 1: Lightly soiled laundry

[0468] 1. The sensor measures "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0469] 2. The terminal transfers this data to the information processing device.

[0470] 3. The information processing device analyzes the data and calculates the amount of detergent for light soiling to be 50 ml and the amount of fabric softener to be 20 ml.

[0471] 4. The information processing device sends the calculation results to the terminal.

[0472] 5. The device dispenses detergent and fabric softener at the specified times.

[0473] 6. Users can check the progress of their wash on their smartphone app and change settings as needed.

[0474] Example 2: Heavily soiled laundry

[0475] 1. The sensor measures "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0476] 2. The terminal transfers this data to the information processing device.

[0477] 3. The information processing device analyzes the data and calculates the amount of detergent for heavy soiling to be 100 ml and the amount of fabric softener to be 50 ml.

[0478] 4. The information processing device sends the calculation results to the terminal.

[0479] 5. The device dispenses detergent and fabric softener at the specified times.

[0480] 6. Users can check the progress of their wash on their smartphone app and change settings as needed.

[0481] Prompt Sentence Examples

[0482] 1. "What is the optimal amount of detergent and fabric softener for lightly soiled cotton (3 kg) and when should I add them?"

[0483] 2. "Calculate the amount of detergent and fabric softener needed for heavily soiled denim (5 kg) and when to add them."

[0484] 3. "Please explain the procedures for data collection, analysis, automated input, and real-time monitoring in this system."

[0485] This system allows users to wash effectively and efficiently with the optimal amount and timing of detergent and fabric softener.

[0486] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0487] Step 1:

[0488] The sensors measure the weight, type, and degree of soiling of the laundry. They collect this data and send it to the terminal. The sensors use a load cell to measure weight, a reflective light sensor to distinguish the material type, and a chemical sensor to evaluate the degree of soiling. The input is the laundry, and the output is data on "weight," "type," and "degree of soiling."

[0489] Step 2:

[0490] The terminal transfers the data received from the sensor to the information processing device. The terminal temporarily stores the data received from the sensor, encrypts it, and sends it to the information processing device (server) via the network. The input is the aforementioned "weight," "type," and "level of dirt" data, and the output is a notification of successful data transfer to the server.

[0491] Step 3:

[0492] The server analyzes the received data. Using the generative AI model, the server compares it with standard data for each type of laundry to determine the optimal amount of detergent and fabric softener, as well as the timing for adding them. Specifically, the server inputs the received data (weight, type, and level of soiling) into the analysis algorithm, and obtains the optimal amount of detergent, optimal amount of fabric softener, and timing for adding them as outputs.

[0493] Step 4:

[0494] The server sends the analysis results to the terminal. The server then sends the "optimum amount of detergent," "optimum amount of fabric softener," and "addition timing" obtained from the analysis results to the terminal in packet format. The input is the analysis results, and the output is confirmation of the results sent to the terminal.

[0495] Step 5:

[0496] The terminal controls the chemical supply device based on instructions from the server, and automatically dispenses detergent and fabric softener. Based on data from the server, the terminal dispenses the appropriate amount of detergent and fabric softener at a set time depending on the weight and degree of dirt of the laundry. The input is data on the "optimum amount of detergent," "optimum amount of fabric softener," and "dispensing timing," and the output is a notification that dispensing is complete.

[0497] Step 6:

[0498] Using the mobile device software, users can monitor the progress of their laundry in real time and change settings as needed. From the app, users can check the progress of the wash and the amount of detergent and fabric softener added, and change operations as needed. For example, it is possible to increase the amount of detergent during a wash. The input is the user's command to change the settings, and the output is the execution result based on the command.

[0499] At each step, the server, the terminal, and the user perform specific operations, which allows the entire system to work together and achieve effective and efficient laundry.

[0500] (Application example 1)

[0501] 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."

[0502] Traditional maintenance processes in the manufacturing industry require a lot of time and effort, often resulting in variations in work efficiency and quality. It is also difficult for managers to grasp the progress of maintenance in real time and issue instructions at the appropriate time. Furthermore, there was a need for a system that could automatically perform appropriate maintenance according to the degree of dirt and wear.

[0503] 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.

[0504] In this invention, the server includes a sensor that measures the weight, type, and condition of the object to be cleaned, a means for analyzing data obtained from the sensor and calculating the appropriate cleaning chemical and its dosage timing, a terminal equipped with a supply device that automatically dispenses the appropriate cleaning chemical based on instructions from the server, a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application, and a means for automatically performing maintenance on manufacturing equipment and production lines. This frees users from the traditional manual maintenance work and enables efficient and effective maintenance.

[0505] "Object to be cleaned" is any equipment or part of a manufacturing machine or production line that requires proper maintenance.

[0506] "Sensor" is a measuring device for measuring the weight, type and condition of the object being washed.

[0507] The "server" is a computer that analyzes data obtained from sensors and calculates the amount and timing of application of cleaning chemicals.

[0508] A "supply device" is a device that automatically dispenses cleaning chemicals based on instructions from the server.

[0509] A "terminal" is a computer device that controls a supply device according to instructions from a server.

[0510] A "mobile terminal application" is software that runs on a portable computer such as a smartphone or tablet and is used to monitor and control the operation of the terminal in real time.

[0511] "Maintenance of manufacturing equipment and production lines" refers to carrying out appropriate cleaning and repair work according to the degree of dirt and wear.

[0512] The "means for automatic execution" is a function that combines sensors, servers, supply devices, and terminals to execute maintenance work without human intervention.

[0513] System Configuration

[0514] This invention is a system for realizing automatic maintenance of manufacturing equipment and production lines in a factory environment. The system includes a sensor that captures the weight, type, and condition of the object to be cleaned, a server that analyzes data obtained from the sensor and calculates the appropriate amount and timing of supply of cleaning chemicals, a terminal equipped with a supply device that automatically supplies cleaning chemicals based on instructions from the server, and a mobile terminal application that monitors and controls the operation of the terminal in real time.

[0515] System Operation

[0516] Data collection and analysis

[0517] 1. The role of the sensor:

[0518] The sensors used include LiDAR sensors, cameras, and wear sensors, and measure the weight, type, and status of manufacturing equipment and production lines with high precision, and transmit the collected data to a terminal.

[0519] 2. Data Analysis:

[0520] The device transmits the data to a server, which runs data analysis algorithms that use software like Python and TensorFlow to calculate the optimal type and amount of cleaning chemicals and the optimal timing for cleaning based on soiling and friction parameters.

[0521] Automatic loading and maintenance work

[0522] 3. Feed device control:

[0523] The server's calculation results are sent to the terminal, which then controls the dispenser to dispense the appropriate cleaning chemicals at the specified amount and timing. The dispenser is controlled using software such as ROS (Robot Operating System) or Arduino IDE.

[0524] 4. Maintenance work:

[0525] The supply device automatically performs maintenance work on manufacturing equipment and production lines, cleaning and repairing them.

[0526] Real-time Monitoring and Control

[0527] 5. Mobile terminal applications:

[0528] Administrators (users) can monitor the system's operating status in real time using a mobile terminal application, which is developed using software such as Flutter and Firebase.

[0529] Managers can use the app to check the progress of maintenance and change settings as needed, such as adjusting cleaning timing or the amount of cleaning chemicals used.

[0530] Specific examples

[0531] Administrators can open a smartphone application to check the operating status of the conveyor belt cleaning robot in real time. For example, they can set the conveyor belt cleaning time to 3:00 p.m. and automatically start cleaning when the level of dirt exceeds 50%.

[0532] Prompt Sentence Examples

[0533] "If the conveyor belt cleaning robot detects that it is 50% dirty, add 100ml of cleaning liquid within 5 minutes of detecting the dirt and start cleaning. In addition, display the cleaning progress in real time on the smartphone app."

[0534] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0535] Step 1:

[0536] Data collection and transmission:

[0537] Sensors measure the weight, type, and condition of manufacturing equipment and production lines with high precision. For example, LiDAR sensors measure the surface area of ​​dirt, and wear sensors detect the depth of wear. The collected data is sent to a device as a signal. The input is the measurement data obtained from the sensor, and the output is the data sent to the device.

[0538] Step 2:

[0539] Data Analysis:

[0540] The device transmits the measurement data received from the sensor to a server. The server receives this data and analyzes it using Python and TensorFlow. The input is the measurement data sent from the device, and the output is a calculation result of the type, amount, and timing of application of cleaning chemicals. This analysis, for example, calculates the optimal amount of cleaning liquid based on the surface area of ​​the dirt and the depth of wear.

[0541] Step 3:

[0542] Send instructions:

[0543] The server sends the analysis results to the terminal. The input is the calculation result after data analysis, and the output is the instruction sent to the terminal. Specifically, the server packages the calculation results in JSON format or similar and sends them to the terminal via the network.

[0544] Step 4:

[0545] Automatic input:

[0546] The terminal receives instructions from the server and controls the dispenser to dispense the appropriate amount of cleaning chemicals. The input is the instruction from the server and the output is the action of the dispenser. In this step, ROS or Arduino IDE is used to control the dispenser, for example, to dispense the exact amount of cleaning liquid.

[0547] Step 5:

[0548] Real-time monitoring:

[0549] The user opens the mobile terminal application to monitor the system's operating status in real time. The input is the operating status data sent from the terminal to the application, and the output is the information displayed on the user's smartphone screen. Specifically, the application uses Firebase to update the real-time database and display the current maintenance progress to the user.

[0550] Step 6:

[0551] Changes made during the process:

[0552] The user can change the maintenance process midway through the application as needed. The input is the setting change made by the user in the application, and the output is the new setting information sent to the terminal and server. Specifically, when the user issues a command in the application to increase the amount of cleaning liquid, that information is transmitted to the dispenser via the server.

[0553] 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.

[0554] System Configuration

[0555] This invention is comprised of a system that includes a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the server's instructions; a means for monitoring and controlling the operation of the terminal in real time via a smartphone app; and an emotion engine that recognizes the user's emotions.

[0556] System Operation

[0557] Data collection and analysis

[0558] First, sensors measure the weight, type, and soiling of the laundry. This information is sent as a data packet to the device. The device then forwards the received data packet to a server, which analyzes the data and runs an algorithm to calculate the optimal amount of detergent and fabric softener.

[0559] Calculating optimal dosage and timing

[0560] The server then determines the optimal amount of detergent and fabric softener and the timing of dispensing based on the analyzed data. For example, it executes a process that determines the amount of detergent to use for light soiling and 100ml for heavy soiling.

[0561] Emotion Engine Operation

[0562] The smartphone app's built-in emotion engine analyzes the user's voice input and facial expressions to recognize their current emotions. The emotion engine determines the user's stress level, satisfaction level, excitement level, etc., and sends the results to the server.

[0563] Automatic loading

[0564] The terminal controls the dispenser based on instructions from the server, automatically dispensing the optimal amount of detergent and fabric softener, freeing the user from having to dispense detergent manually.

[0565] Real-time Monitoring and Control

[0566] Users can monitor the progress of their laundry in real time through a smartphone app. Furthermore, based on the analysis results of the emotion engine, the washing process can be adjusted according to the user's emotions. For example, if the user is feeling stressed, the settings can be changed to shorten the washing time.

[0567] Specific examples

[0568] Example 1: Lightly soiled laundry

[0569] The server receives the sensor data: "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0570] The server calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0571] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0572] Users can check the progress of their washing on their smartphone app and change settings as needed. If the emotion engine detects a comfortable state from the user's voice, it will maintain the washing process at the optimal settings to maintain that state.

[0573] Example 2: Heavily soiled laundry

[0574] The server receives the sensor data: "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0575] The server calculates the amount of detergent for heavy soiling to be 100ml and the amount of fabric softener to be 50ml.

[0576] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0577] The user checks the progress of the laundry using a smartphone app, and if the emotion engine detects a state of stress from the user's facial expression, it sets the laundry to finish earlier.

[0578] This invention not only automatically dispenses the optimum amount and timing of detergent depending on the degree of soiling and type of laundry, but also realizes a flexible washing process according to the user's feelings, reduces the user's effort, and provides efficient and effective washing.

[0579] The processing flow will be explained below.

[0580] Step 1:

[0581] The sensors measure the weight, type and soiling of the laundry, each piece of information captured separately and sent to the device as a data packet.

[0582] Step 2:

[0583] The device sorts the received data packets and forwards them to the server, checking to ensure that no data is lost or sent incorrectly.

[0584] Step 3:

[0585] The server analyzes the data and selects the appropriate algorithm based on the weight, type and soiling of the laundry, for example, an algorithm for "light soiling" or "denim."

[0586] Step 4:

[0587] The server calculates the optimal amount of detergent and fabric softener based on the selected algorithm, using optimization techniques based on past data and laundry characteristics.

[0588] Step 5:

[0589] The server determines the calculated amounts of detergent and fabric softener and the timing of dispensing them. For example, it generates specific instructions such as "dispensing 50 ml of detergent 10 minutes after the start of washing" and "dispensing 20 ml of fabric softener 30 minutes after the start of washing."

[0590] Step 6:

[0591] The server sends these specific instructions back to the terminal, where it performs error checking to ensure the instructions were transmitted correctly.

[0592] Step 7:

[0593] The device then controls the built-in dispenser based on the received instructions, automatically dispensing the appropriate amount of detergent and fabric softener into the washing machine at the specified time.

[0594] Step 8:

[0595] The smartphone app analyzes the user's voice input and facial expressions, and the emotion engine recognizes the user's emotions, determining, for example, whether the user is feeling stressed or relaxed.

[0596] Step 9:

[0597] The server receives the user's emotional data and optimizes the laundry process accordingly, for example, shortening the washing time or increasing the number of rinses if the user is feeling stressed.

[0598] Step 10:

[0599] Users can monitor the progress of their laundry in real time using a smartphone app, which displays information such as "detergent added," "rinsing," and "drying."

[0600] Step 11:

[0601] Through the app, users can make changes to the wash process as needed, for example, adding more detergent or adjusting the amount of fabric softener.

[0602] Step 12:

[0603] When the laundry is complete, the device sends a completion notification to the server, which then forwards the information to the user's smartphone app, where the user receives the notification.

[0604] The above is the specific processing flow of a system that combines an emotion engine.

[0605] Example 2

[0606] 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."

[0607] Conventional washing machine systems are often inefficient because the amount and timing of detergent and fabric softener dispense are manually controlled. Furthermore, they place a heavy burden on users because they are unable to flexibly respond to the user's emotions and lifestyle. Furthermore, there is a demand for automated systems that can optimally execute the washing process based on the weight, type, and soiling level of the laundry.

[0608] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0609] In this invention, the server includes a sensor that measures the weight, type, and degree of soiling of the laundry, a data processing device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them, a terminal equipped with a distribution device that automatically dispenses detergent and fabric softener based on instructions from the data processing device, means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application, and an emotion analysis device that analyzes the user's voice input and facial expressions to recognize the user's emotional state. This makes it possible to dispense the optimal amount of detergent and fabric softener based on the weight, type, and degree of soiling of the laundry, and also makes it possible to flexibly adjust the washing process according to the user's emotions and lifestyle.

[0610] A "sensor" is a device that measures the weight, type and soiling of laundry.

[0611] The "data processing device" is a device that analyzes the data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of their addition.

[0612] A "distribution device" is a device that automatically dispenses detergent and fabric softener based on instructions from a data processing device.

[0613] A "terminal" is a set of devices that has sensors and distribution devices and operates by integrating these functions.

[0614] A "mobile terminal application" is software that runs on a mobile terminal such as a smartphone or tablet and monitors and controls the terminal's operations in real time.

[0615] An "emotion analysis device" is a device that analyzes a user's voice input and facial expressions to recognize the user's emotional state.

[0616] A "detergent" is a chemical product used to remove stains from laundry.

[0617] "Fabric softener" is a chemical product used to soften and scent laundry.

[0618] An "algorithm" is a computational procedure for analyzing specific data and deriving an optimal result.

[0619] "Real time" refers to the time when user operations and system operations are reflected immediately.

[0620] System Configuration

[0621] This invention is a system that comprises a sensor that measures the weight, type and degree of soiling of laundry; a data processing device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a distribution device that automatically dispenses detergent and fabric softener based on the instructions; a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application; and an emotion analysis device that recognizes the user's emotions.

[0622] Data collection and analysis

[0623] First, sensors measure the weight, type, and degree of soiling of the laundry. This information is sent to the terminal as a data packet. Specifically, the weight obtained by the weight sensor and the type and degree of soiling obtained by the camera and soiling sensor are structured in JSON format. The terminal transfers the received data packet to a data processing device, which stores the data in a database (e.g., MySQL or PostgreSQL) and analyzes it. This analysis is performed using a Python data analysis library (e.g., Pandas or NumPy).

[0624] Calculating optimal dosage and timing

[0625] The data processing device determines the optimal amount of detergent and fabric softener and the timing of adding them based on the analyzed data. For example, using a machine learning model (TensorFlow or PyTorch), it receives the weight, type, and degree of soiling of the laundry as input and calculates the optimal output value. A specific example is a process that determines the amount of detergent to use for lightly soiled laundry and 100ml for heavily soiled laundry.

[0626] How the sentiment analysis engine works

[0627] When a user opens a smartphone app, the app captures voice input and facial expressions. Specifically, the smartphone's camera recognizes facial expressions and the microphone records audio. An emotion analysis engine within the app analyzes this data to determine the user's emotional state (e.g., stress, satisfaction, excitement). A natural language processing (NLP) library (e.g., NLTK or spaCy) is used for the analysis. The analysis results are sent to a data processing device.

[0628] Automatic loading

[0629] Based on the analysis results, the data processing device sends instructions to the terminal. According to these instructions, the terminal controls the dispenser to dispense the optimal amount of detergent and fabric softener at the specified time. For example, a control signal may be sent to the dispenser to cause the pump to dispense 50 ml of detergent.

[0630] Real-time Monitoring and Control

[0631] Users can check the progress of their laundry in real time using a smartphone app. The app displays the remaining time and progress of the wash and allows users to change settings as needed. For example, if they are feeling stressed, they can set the washing time to be shorter.

[0632] Specific prompt examples

[0633] This system uses sensors to measure the weight, type, and soiling level of laundry, and then calculates the optimal amount and timing of detergent and fabric softener based on that data. It also recognizes the user's emotions and optimizes the washing process accordingly. Please explain the specific settings and execution process.

[0634] Example: Lightly soiled laundry

[0635] 1. The data processing device receives the sensor data "Weight: 3 kg, Type: Cotton, Level of dirt: Light."

[0636] 2. The data processor analyzes and calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0637] 3. The data processing device transmits the calculation results to the terminal.

[0638] 4. The terminal controls the dispenser and dispenses detergent and fabric softener at the specified times.

[0639] 5. The user can check the progress on the smartphone app and change settings as needed. For example, if the washing machine determines that the washing conditions are comfortable based on voice input, the washing process will be optimized to maintain that condition.

[0640] As described above, this system not only automatically dispenses the optimal amount of detergent at the optimal timing depending on the type and degree of soiling of the laundry, but also realizes a flexible washing process that responds to the user's emotions, reducing the user's effort and providing efficient and effective washing.

[0641] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0642] Step 1:

[0643] The user puts laundry into the washing machine. At this moment, the system is in standby mode. The sensors are activated to measure the weight, type, and soiling level of the laundry. The data is first transmitted to the terminal.

[0644] Input: Weight, type, and degree of dirt of laundry

[0645] Output: Sensor data (JSON format)

[0646] Step 2:

[0647] The terminal receives the data obtained from the sensor. The received data is transferred directly to the data processing device. The HTTPS protocol is used for transfer to ensure data security.

[0648] Input: Sensor data (JSON format)

[0649] Output: Sensor data sent to the server

[0650] Step 3:

[0651] The data processor analyzes the received data, first storing it in a database, then preprocessing it using Python's Pandas and NumPy libraries, and then calculating the optimal amount and timing of detergent and fabric softener dosage using machine learning models (TensorFlow and PyTorch).

[0652] Input: Sensor data

[0653] Output: Data on optimal amounts of detergent and fabric softener and timing of addition

[0654] Step 4:

[0655] The data processor sends the calculation results to the terminal, which transmits them in real time to ensure the correct amount of detergent and fabric softener is dispensed early in the wash cycle.

[0656] Input: Data on optimal amounts of detergent and fabric softener and timing of addition

[0657] Output: Input instruction data sent to the terminal

[0658] Step 5:

[0659] The terminal controls the dispenser to dispense the optimal amount of detergent and fabric softener. Specifically, it sends a control signal to the dispenser, causing the pump to dispense 50 ml of detergent (for light soiling).

[0660] Input: Input instruction data

[0661] Output: Calculated amount of detergent and fabric softener dispensed

[0662] Step 6:

[0663] The user monitors the progress of the wash in real time using a smartphone app. The app receives progress data from the device and displays the remaining time and progress of the wash, allowing the user to change settings as needed.

[0664] Input: Progress data from the terminal

[0665] Output: Progress displayed on the smartphone app

[0666] Step 7:

[0667] A smartphone app works to recognize the user's emotions. It uses a camera to recognize facial expressions and a microphone to analyze voices to determine the user's emotional state. An emotion analysis engine processes the data using an NLP library (NLTK or spaCy) to determine the emotional state (e.g., stress, satisfaction, excitement). This data is sent to a data processing device.

[0668] Input: User's voice and facial expression data

[0669] Output: Sentiment analysis result data

[0670] Step 8:

[0671] The data processing device adjusts the washing process based on the user's emotional state, for example, if the user is feeling stressed, it sends instructions to the terminal to change settings to reduce the washing time.

[0672] Input: Sentiment analysis result data

[0673] Output: Washing process adjustment instructions

[0674] (Application example 2)

[0675] 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."

[0676] Modern laundry requires a lot of time and effort, and it is difficult to determine the appropriate amount of detergent and fabric softener for various laundry items. Furthermore, there is also the problem of users being unable to respond appropriately when they feel stressed during the wash. This makes it difficult to provide an efficient and effective laundry process.

[0677] The specific processing by the specific 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: a sensor that measures the weight, type, and degree of soiling of the laundry; means for analyzing data obtained from the sensor and calculating the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on instructions from the server; means for monitoring and controlling the operation of the terminal in real time via a mobile device application; an emotion analysis engine built into the mobile device application that recognizes the user's emotions; and means for adjusting the washing process based on the user's emotional state recognized by the emotion analysis engine. This not only enables the user to easily determine the appropriate amounts of detergent and fabric softener, but also enables the user to check the progress of the washing process through real-time monitoring, providing an optimal washing experience tailored to the user's emotions.

[0678] "Laundry" refers to all cloth products that require washing, such as clothes, towels, and sheets.

[0679] A "sensor" is a device that detects a physical phenomenon and generates data, and in this invention is a device for measuring the weight, type, and degree of soiling of laundry.

[0680] A "server" is a computer system that provides services to multiple terminals via a network, and in this invention has the function of analyzing data related to laundry and calculating the amount of detergent and fabric softener and the timing of their addition.

[0681] A "dispenser" is a device that dispenses a fixed amount of liquid, powder, etc., and in this invention, it plays a role in automatically dispensing detergent and fabric softener.

[0682] The term "terminal" refers to a device that can be directly operated by a user, and in this invention refers to a device including a dispenser installed in a washing machine.

[0683] A "mobile device application" is software that runs on a mobile electronic device such as a smartphone or tablet, and in this invention refers to an application for monitoring and controlling the progress of laundry in real time.

[0684] An "emotion analysis engine" refers to software or algorithms that analyze a user's voice input and facial expression data to recognize their emotional state.

[0685] A "detergent" is a chemical product used to remove stains from laundry.

[0686] A "softener" is a chemical product used to make laundry softer.

[0687] An "algorithm" is a set of procedures or rules for performing calculations or data analysis, and in this invention, it is used to calculate the optimal amount of detergent and fabric softener based on the type and degree of soiling of the laundry.

[0688] System Configuration

[0689] This invention is a system that comprises a sensor that measures the weight, type and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the server's instructions; a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application; and an emotion analysis engine that recognizes the user's emotions.

[0690] Program Generation

[0691] To realize this system, the following programs are required: First, a sensor measures the weight, type, and degree of soiling of the laundry and sends this data to the device as a data packet. The device then forwards the received data packet to a server, which analyzes the data. Next, the server calculates the optimal amount of detergent and fabric softener and the timing of dispensing them based on the analyzed data. Then, based on instructions from the server, the device controls the dispensers to automatically dispense the detergent and fabric softener. The progress of the wash is also monitored in real time via a mobile device application, and the washing process is adjusted according to the user's emotional state.

[0692] Processing Description

[0693] The system uses the following hardware and software:

[0694] Sensor: A device that measures the weight, type and soiling of laundry.

[0695] Server: A computer system that analyzes data collected from sensors and calculates the optimal amount of detergent and fabric softener.

[0696] Terminal: A device with dispensers that automatically dispense detergent and fabric softener.

[0697] Mobile device application: Software for monitoring and controlling the laundry progress in real time.

[0698] Emotion analysis engine: Software or algorithms that analyze a user's voice input and facial expression data to recognize their emotional state.

[0699] The data collected by the sensors is sent via the device to a server, where an analytical algorithm is used to calculate the optimal amount of detergent and fabric softener and the timing of dispensing. This algorithm takes into account the type of laundry and its degree of soiling. The calculation results are then sent to the device, which then automatically dispenses the detergent and fabric softener.

[0700] Furthermore, the progress of the laundry can be monitored in real time through a mobile application, and an emotion analysis engine can analyze the user's emotional state and adjust the laundry process accordingly. For example, if the user is feeling stressed, the washing time can be shortened.

[0701] Specific examples

[0702] For example, when a customer uses this system in a physical store, the process goes like this: First, sensors measure the weight, type, and dirtiness of the laundry. This data is sent to a server, which calculates the optimal amount of detergent and fabric softener. This information is sent to the device, which automatically dispenses the detergent and fabric softener at the appropriate time. The progress of the wash is then displayed in real time on the mobile device application. When the customer provides voice input or camera footage to the app, the emotion analysis engine recognizes the customer's emotional state, and the washing process is further optimized based on the results.

[0703] An example of a prompt sentence to be input to the generative AI model used is as follows:

[0704] "Write a program that calculates the optimal amount of detergent and fabric softener for a load of laundry. The program will send data from sensors that measure the weight, type, and soiling of the laundry to a server, which will analyze the data and determine the appropriate amounts. The washing machine will automatically dispense detergent and fabric softener, and the customer will be able to monitor and control it in real time through a mobile application. Also add the ability to use an emotion analysis engine to analyze the customer's emotional state and adjust the washing process as needed."

[0705] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0706] Step 1:

[0707] The sensor measures the weight, type, and soiling level of the laundry. The sensor is installed inside the washing machine and automatically starts measuring when laundry is put in. The measurement data is output from the sensor as weight (kg), type (cotton, denim, etc.), and soiling level (light, normal, heavy).

[0708] Input: Physical state of laundry

[0709] Output: Weight, type, and soiling data

[0710] Step 2:

[0711] The device receives data packets from the sensor and forwards them to the server. The device formats the data packets and sends them over the network to the server. This process ensures that the raw data from the sensor is delivered to the server in a format suitable for analysis.

[0712] Input: Weight, type, and soiling data from sensors

[0713] Output: Data packets transferred to the server

[0714] Step 3:

[0715] The server analyzes the received data and calculates the optimal amount of detergent and fabric softener and the timing of dispensing. The server uses specific algorithms to determine the amount of detergent and fabric softener needed based on the weight, type, and soiling of the laundry. It also calculates the optimal timing of dispensing.

[0716] Input: Data packets transmitted from the sensor

[0717] Output: Optimal detergent and fabric softener dosage and timing

[0718] Step 4:

[0719] The terminal controls the dispenser based on instructions from the server to automatically dispense detergent and fabric softener. The dispenser is installed inside the washing machine and is controlled to dispense the detergent and fabric softener accurately according to the optimal amounts received from the server.

[0720] Input: Instructions from the server on optimal amounts and timing of detergent and fabric softener

[0721] Output: Automatic detergent and fabric softener dispenser

[0722] Step 5:

[0723] Through the mobile application, users can monitor the progress of their laundry in real time. The app connects to the server and displays real-time laundry status information. Users can check the status of the washing process and change settings as needed.

[0724] Input: Status information of the laundry process from the server

[0725] Output: Real-time display on mobile application

[0726] Step 6:

[0727] An emotion analysis engine built into the mobile device application analyzes the user's voice input and facial expression data to recognize their emotional state. The emotion analysis engine receives the user's voice and facial expression data as input, analyzes them, and outputs their emotional state, such as stress level or satisfaction level.

[0728] Input: User voice input and facial expression data

[0729] Output: User's emotional state data

[0730] Step 7:

[0731] The server adjusts the washing process based on the emotional state data from the emotion analysis engine. For example, if the user is feeling stressed, the server adjusts the washing time to shorten the washing process. The server analyzes the emotional state data and sends instructions to the device to change the washing settings appropriately.

[0732] Input: Emotional state data

[0733] Output: Adjusted washing process settings

[0734] 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.

[0735] 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.

[0736] 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.

[0737] [Third embodiment]

[0738] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0739] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0740] 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).

[0741] 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.

[0742] 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.

[0743] 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).

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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."

[0750] System Configuration

[0751] This invention is comprised of a system including a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the instructions; and means for monitoring and controlling the operation of the terminal in real time via a smartphone app.

[0752] System Operation

[0753] Data collection and analysis

[0754] First, the sensors measure the weight, type, and dirtiness of the laundry. The sensors perform highly accurate measurements and send the collected data to the device. The device then forwards this data to the server, where it is analyzed.

[0755] Calculating optimal dosage and timing

[0756] The server then uses the data it receives to run an algorithm that calculates the optimal amount of detergent and fabric softener to use. This algorithm calculates the optimal amount of detergent and fabric softener to maximize the effectiveness of the wash, for example, by adding more detergent if the laundry is heavily soiled. The server also considers the entire wash process and determines the optimal timing for adding detergent and fabric softener.

[0757] Automatic loading

[0758] Based on instructions from the server, the terminal controls the dispenser to automatically dispense the appropriate amounts of detergent and fabric softener, freeing the user from having to manually dispense detergent and ensuring that the right amount of detergent is used every time.

[0759] Real-time Monitoring and Control

[0760] Users can monitor the operation of their washing machine in real time using a smartphone app. The app displays the progress of the wash and the current operation phase, and users can change settings as needed. For example, they can easily increase the amount of detergent or add fabric softener during the wash.

[0761] Specific examples

[0762] Example 1: Lightly soiled laundry

[0763] The server receives the sensor data: "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0764] The server calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0765] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0766] Users can check the progress of their wash using a smartphone app and change settings as needed.

[0767] Example 2: Heavily soiled laundry

[0768] The server receives the sensor data: "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0769] The server calculates the amount of detergent for heavy soiling to be 100ml and the amount of fabric softener to be 50ml.

[0770] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0771] Users can check the progress of their wash using a smartphone app and change settings as needed.

[0772] This invention automatically dispenses the optimum amount and timing of detergent depending on the degree and type of soiling of the laundry, reducing the user's effort and achieving efficient and effective washing.

[0773] The processing flow will be explained below.

[0774] Step 1:

[0775] The sensors measure the weight, type and soiling of the laundry, each piece of information captured separately and sent to the device as a combined data packet.

[0776] Step 2:

[0777] The device sorts the received data packets and forwards them over the network link to the server, checking to ensure that no data is lost or sent incorrectly.

[0778] Step 3:

[0779] The server analyzes the data and selects the appropriate algorithm based on the weight, type and soiling of the laundry, for example, an algorithm for "light soiling" or "denim."

[0780] Step 4:

[0781] The server calculates the optimal amount of detergent and fabric softener based on the selected algorithm, using optimization techniques based on past data and laundry characteristics.

[0782] Step 5:

[0783] The server determines the calculated amounts of detergent and fabric softener and the timing of dispensing them. For example, it generates specific instructions such as "dispensing 50 ml of detergent 10 minutes after the start of washing" and "dispensing 20 ml of fabric softener 30 minutes after the start of washing."

[0784] Step 6:

[0785] The server sends these specific instructions back to the terminal, where it performs error checking to ensure the instructions were transmitted correctly.

[0786] Step 7:

[0787] The device then controls the built-in dispenser based on the received instructions, automatically dispensing the appropriate amount of detergent and fabric softener into the washing machine at the specified time.

[0788] Step 8:

[0789] Users monitor the progress of their wash in real time using a smartphone app, which displays information such as when to add detergent and fabric softener, and the current wash phase.

[0790] Step 9:

[0791] Users can change settings as needed through the app, for example, adjusting the amount of detergent or adding fabric softener.

[0792] Step 10:

[0793] The device constantly checks the progress of the laundry and reports any abnormalities to the server, which analyzes the data and sends an alert to the user if necessary.

[0794] Step 11:

[0795] When the laundry is complete, the device sends a completion notification to the server, which then forwards the information to the user's smartphone app, where the user receives the notification.

[0796] The above is the specific processing flow of the program.

[0797] Example 1

[0798] 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."

[0799] Conventional washing systems have difficulty automatically dispensing the optimal amount of detergent and fabric softener depending on the type of laundry and the degree of soiling, requiring manual dispensing. This reduces washing efficiency and prevents optimal cleaning results. Furthermore, there are few ways for users to monitor the progress of the washing process in real time and change settings as needed, resulting in a lack of convenience for users.

[0800] 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.

[0801] In this invention, the server includes a sensor that measures the weight, type, and degree of soiling of the laundry; an information processing device that analyzes the data and calculates the amount and timing of detergent and fabric softener addition; a chemical supply device that automatically adds detergent and fabric softener based on instructions from the information processing device; and a device that monitors and controls the device's operation in real time via mobile device software. This allows the optimal amount and timing of detergent addition based on the type and degree of soiling of the laundry to be automatically added, reducing user effort and enabling efficient and effective washing. Furthermore, the progress of the washing process can be monitored in real time and settings can be changed as needed, improving user convenience.

[0802] A "sensor" is a device for measuring the weight, type, and soiling level of laundry.

[0803] The "information processing device" is a computer device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of their addition.

[0804] The "chemical supply device" is a device that automatically dispenses detergent and fabric softener based on instructions from an information processing device.

[0805] "Software for mobile terminals" refers to application software for monitoring and controlling the operation of devices in real time using a mobile terminal.

[0806] A "detergent" is a cleaning agent used to clean stains.

[0807] "Fabric softener" is a chemical used to soften and scent laundry.

[0808] "Laundry type" refers to the classification of the material or construction of the items being washed.

[0809] "Level of dirt" refers to the amount and degree of dirt adhering to the laundry.

[0810] "Analysis" is the process of examining the data obtained from the sensors in detail to determine cleaning methods.

[0811] "Addition timing" refers to the timing at which detergent and fabric softener are added at an appropriate time during the washing process.

[0812] This invention is a system that includes a sensor that measures the weight, type, and degree of soiling of laundry, an information processing device that analyzes the measured data, a chemical supply device that automatically dispenses detergent and fabric softener based on the analysis results, and a means for monitoring and controlling the operation of the device in real time via software for a mobile device.

[0813] System configuration

[0814] sensor

[0815] The sensor is a device that accurately measures the weight, type, and degree of soiling of laundry. The sensor has the functions of measuring weight, identifying materials, and evaluating the degree of soiling using optical and chemical sensors. The data from this sensor is sent to the terminal.

[0816] Information processing device

[0817] The terminal transfers the data sent from the sensor to an information processing device (server) via a communications device. The information processing device analyzes the collected data and calculates the optimal amount of detergent and fabric softener. The analysis algorithm uses a generative AI model to derive optimal results based on past data and simulation results of washing effects. The information processing device also calculates the optimal timing to add detergent and fabric softener depending on the progress of the wash.

[0818] Chemical Supply Device

[0819] Based on instructions from the information processing device, the chemical supply device automatically dispenses the appropriate amounts of detergent and fabric softener at the required timing for each stage of the wash, eliminating the need for the user to do this manually.

[0820] Mobile device software

[0821] Mobile device software is an application that allows users to monitor and control the operation of the machine in real time using a mobile device such as a smartphone. This software allows users to check the progress of the wash, the amount of detergent and fabric softener used, and change settings as needed. For example, it is easy to adjust the amount of detergent or add fabric softener during the wash.

[0822] Specific examples

[0823] Example 1: Lightly soiled laundry

[0824] 1. The sensor measures "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0825] 2. The terminal transfers this data to the information processing device.

[0826] 3. The information processing device analyzes the data and calculates the amount of detergent for light soiling to be 50 ml and the amount of fabric softener to be 20 ml.

[0827] 4. The information processing device sends the calculation results to the terminal.

[0828] 5. The device dispenses detergent and fabric softener at the specified times.

[0829] 6. Users can check the progress of their wash on their smartphone app and change settings as needed.

[0830] Example 2: Heavily soiled laundry

[0831] 1. The sensor measures "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0832] 2. The terminal transfers this data to the information processing device.

[0833] 3. The information processing device analyzes the data and calculates the amount of detergent for heavy soiling to be 100 ml and the amount of fabric softener to be 50 ml.

[0834] 4. The information processing device sends the calculation results to the terminal.

[0835] 5. The device dispenses detergent and fabric softener at the specified times.

[0836] 6. Users can check the progress of their wash on their smartphone app and change settings as needed.

[0837] Prompt Sentence Examples

[0838] 1. "What is the optimal amount of detergent and fabric softener for lightly soiled cotton (3 kg) and when should I add them?"

[0839] 2. "Calculate the amount of detergent and fabric softener needed for heavily soiled denim (5 kg) and when to add them."

[0840] 3. "Please explain the procedures for data collection, analysis, automated input, and real-time monitoring in this system."

[0841] This system allows users to wash effectively and efficiently with the optimal amount and timing of detergent and fabric softener.

[0842] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0843] Step 1:

[0844] The sensors measure the weight, type, and degree of soiling of the laundry. They collect this data and send it to the terminal. The sensors use a load cell to measure weight, a reflective light sensor to distinguish the material type, and a chemical sensor to evaluate the degree of soiling. The input is the laundry, and the output is data on "weight," "type," and "degree of soiling."

[0845] Step 2:

[0846] The terminal transfers the data received from the sensor to the information processing device. The terminal temporarily stores the data received from the sensor, encrypts it, and sends it to the information processing device (server) via the network. The input is the aforementioned "weight," "type," and "level of dirt" data, and the output is a notification of successful data transfer to the server.

[0847] Step 3:

[0848] The server analyzes the received data. Using the generative AI model, the server compares it with standard data for each type of laundry to determine the optimal amount of detergent and fabric softener, as well as the timing for adding them. Specifically, the server inputs the received data (weight, type, and level of soiling) into the analysis algorithm, and obtains the optimal amount of detergent, optimal amount of fabric softener, and timing for adding them as outputs.

[0849] Step 4:

[0850] The server sends the analysis results to the terminal. The server then sends the "optimum amount of detergent," "optimum amount of fabric softener," and "addition timing" obtained from the analysis results to the terminal in packet format. The input is the analysis results, and the output is confirmation of the results sent to the terminal.

[0851] Step 5:

[0852] The terminal controls the chemical supply device based on instructions from the server, and automatically dispenses detergent and fabric softener. Based on data from the server, the terminal dispenses the appropriate amount of detergent and fabric softener at a set time depending on the weight and degree of dirt of the laundry. The input is data on the "optimum amount of detergent," "optimum amount of fabric softener," and "dispensing timing," and the output is a notification that dispensing is complete.

[0853] Step 6:

[0854] Using the mobile device software, users can monitor the progress of their laundry in real time and change settings as needed. From the app, users can check the progress of the wash and the amount of detergent and fabric softener added, and change operations as needed. For example, it is possible to increase the amount of detergent during a wash. The input is the user's command to change the settings, and the output is the execution result based on the command.

[0855] At each step, the server, the terminal, and the user perform specific operations, which allows the entire system to work together and achieve effective and efficient laundry.

[0856] (Application example 1)

[0857] 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."

[0858] Traditional maintenance processes in the manufacturing industry require a lot of time and effort, often resulting in variations in work efficiency and quality. It is also difficult for managers to grasp the progress of maintenance in real time and issue instructions at the appropriate time. Furthermore, there was a need for a system that could automatically perform appropriate maintenance according to the degree of dirt and wear.

[0859] 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.

[0860] In this invention, the server includes a sensor that measures the weight, type, and condition of the object to be cleaned, a means for analyzing data obtained from the sensor and calculating the appropriate cleaning chemical and its dosage timing, a terminal equipped with a supply device that automatically dispenses the appropriate cleaning chemical based on instructions from the server, a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application, and a means for automatically performing maintenance on manufacturing equipment and production lines. This frees users from the traditional manual maintenance work and enables efficient and effective maintenance.

[0861] "Object to be cleaned" is any equipment or part of a manufacturing machine or production line that requires proper maintenance.

[0862] "Sensor" is a measuring device for measuring the weight, type and condition of the object being washed.

[0863] The "server" is a computer that analyzes data obtained from sensors and calculates the amount and timing of application of cleaning chemicals.

[0864] A "supply device" is a device that automatically dispenses cleaning chemicals based on instructions from the server.

[0865] A "terminal" is a computer device that controls a supply device according to instructions from a server.

[0866] A "mobile terminal application" is software that runs on a portable computer such as a smartphone or tablet and is used to monitor and control the operation of the terminal in real time.

[0867] "Maintenance of manufacturing equipment and production lines" refers to carrying out appropriate cleaning and repair work according to the degree of dirt and wear.

[0868] The "means for automatic execution" is a function that combines sensors, servers, supply devices, and terminals to execute maintenance work without human intervention.

[0869] System Configuration

[0870] This invention is a system for realizing automatic maintenance of manufacturing equipment and production lines in a factory environment. The system includes a sensor that captures the weight, type, and condition of the object to be cleaned, a server that analyzes data obtained from the sensor and calculates the appropriate amount and timing of supply of cleaning chemicals, a terminal equipped with a supply device that automatically supplies cleaning chemicals based on instructions from the server, and a mobile terminal application that monitors and controls the operation of the terminal in real time.

[0871] System Operation

[0872] Data collection and analysis

[0873] 1. The role of the sensor:

[0874] The sensors used include LiDAR sensors, cameras, and wear sensors, and measure the weight, type, and status of manufacturing equipment and production lines with high precision, and transmit the collected data to a terminal.

[0875] 2. Data Analysis:

[0876] The device transmits the data to a server, which runs data analysis algorithms that use software like Python and TensorFlow to calculate the optimal type and amount of cleaning chemicals and the optimal timing for cleaning based on soiling and friction parameters.

[0877] Automatic loading and maintenance work

[0878] 3. Feed device control:

[0879] The server's calculation results are sent to the terminal, which then controls the dispenser to dispense the appropriate cleaning chemicals at the specified amount and timing. The dispenser is controlled using software such as ROS (Robot Operating System) or Arduino IDE.

[0880] 4. Maintenance work:

[0881] The supply device automatically performs maintenance work on manufacturing equipment and production lines, cleaning and repairing them.

[0882] Real-time Monitoring and Control

[0883] 5. Mobile terminal applications:

[0884] Administrators (users) can monitor the system's operating status in real time using a mobile terminal application, which is developed using software such as Flutter and Firebase.

[0885] Managers can use the app to check the progress of maintenance and change settings as needed, such as adjusting cleaning timing or the amount of cleaning chemicals used.

[0886] Specific examples

[0887] Administrators can open a smartphone application to check the operating status of the conveyor belt cleaning robot in real time. For example, they can set the conveyor belt cleaning time to 3:00 p.m. and automatically start cleaning when the level of dirt exceeds 50%.

[0888] Prompt Sentence Examples

[0889] "If the conveyor belt cleaning robot detects that it is 50% dirty, add 100ml of cleaning liquid within 5 minutes of detecting the dirt and start cleaning. In addition, display the cleaning progress in real time on the smartphone app."

[0890] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0891] Step 1:

[0892] Data collection and transmission:

[0893] Sensors measure the weight, type, and condition of manufacturing equipment and production lines with high precision. For example, LiDAR sensors measure the surface area of ​​dirt, and wear sensors detect the depth of wear. The collected data is sent to a device as a signal. The input is the measurement data obtained from the sensor, and the output is the data sent to the device.

[0894] Step 2:

[0895] Data Analysis:

[0896] The device transmits the measurement data received from the sensor to a server. The server receives this data and analyzes it using Python and TensorFlow. The input is the measurement data sent from the device, and the output is a calculation result of the type, amount, and timing of application of cleaning chemicals. This analysis, for example, calculates the optimal amount of cleaning liquid based on the surface area of ​​the dirt and the depth of wear.

[0897] Step 3:

[0898] Send instructions:

[0899] The server sends the analysis results to the terminal. The input is the calculation result after data analysis, and the output is the instruction sent to the terminal. Specifically, the server packages the calculation results in JSON format or similar and sends them to the terminal via the network.

[0900] Step 4:

[0901] Automatic input:

[0902] The terminal receives instructions from the server and controls the dispenser to dispense the appropriate amount of cleaning chemicals. The input is the instruction from the server and the output is the action of the dispenser. In this step, ROS or Arduino IDE is used to control the dispenser, for example, to dispense the exact amount of cleaning liquid.

[0903] Step 5:

[0904] Real-time monitoring:

[0905] The user opens the mobile terminal application to monitor the system's operating status in real time. The input is the operating status data sent from the terminal to the application, and the output is the information displayed on the user's smartphone screen. Specifically, the application uses Firebase to update the real-time database and display the current maintenance progress to the user.

[0906] Step 6:

[0907] Changes made during the process:

[0908] The user can change the maintenance process midway through the application as needed. The input is the setting change made by the user in the application, and the output is the new setting information sent to the terminal and server. Specifically, when the user issues a command in the application to increase the amount of cleaning liquid, that information is transmitted to the dispenser via the server.

[0909] 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.

[0910] System Configuration

[0911] This invention is comprised of a system that includes a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the server's instructions; a means for monitoring and controlling the operation of the terminal in real time via a smartphone app; and an emotion engine that recognizes the user's emotions.

[0912] System Operation

[0913] Data collection and analysis

[0914] First, sensors measure the weight, type, and soiling of the laundry. This information is sent as a data packet to the device. The device then forwards the received data packet to a server, which analyzes the data and runs an algorithm to calculate the optimal amount of detergent and fabric softener.

[0915] Calculating optimal dosage and timing

[0916] The server then determines the optimal amount of detergent and fabric softener and the timing of dispensing based on the analyzed data. For example, it executes a process that determines the amount of detergent to use for light soiling and 100ml for heavy soiling.

[0917] Emotion Engine Operation

[0918] The smartphone app's built-in emotion engine analyzes the user's voice input and facial expressions to recognize their current emotions. The emotion engine determines the user's stress level, satisfaction level, excitement level, etc., and sends the results to the server.

[0919] Automatic loading

[0920] The terminal controls the dispenser based on instructions from the server, automatically dispensing the optimal amount of detergent and fabric softener, freeing the user from having to dispense detergent manually.

[0921] Real-time Monitoring and Control

[0922] Users can monitor the progress of their laundry in real time through a smartphone app. Furthermore, based on the analysis results of the emotion engine, the washing process can be adjusted according to the user's emotions. For example, if the user is feeling stressed, the settings can be changed to shorten the washing time.

[0923] Specific examples

[0924] Example 1: Lightly soiled laundry

[0925] The server receives the sensor data: "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[0926] The server calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0927] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0928] Users can check the progress of their washing on their smartphone app and change settings as needed. If the emotion engine detects a comfortable state from the user's voice, it will maintain the washing process at the optimal settings to maintain that state.

[0929] Example 2: Heavily soiled laundry

[0930] The server receives the sensor data: "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[0931] The server calculates the amount of detergent for heavy soiling to be 100ml and the amount of fabric softener to be 50ml.

[0932] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[0933] The user checks the progress of the laundry using a smartphone app, and if the emotion engine detects a state of stress from the user's facial expression, it sets the laundry to finish earlier.

[0934] This invention not only automatically dispenses the optimum amount and timing of detergent depending on the degree of soiling and type of laundry, but also realizes a flexible washing process according to the user's feelings, reduces the user's effort, and provides efficient and effective washing.

[0935] The processing flow will be explained below.

[0936] Step 1:

[0937] The sensors measure the weight, type and soiling of the laundry, each piece of information captured separately and sent to the device as a data packet.

[0938] Step 2:

[0939] The device sorts the received data packets and forwards them to the server, checking to ensure that no data is lost or sent incorrectly.

[0940] Step 3:

[0941] The server analyzes the data and selects the appropriate algorithm based on the weight, type and soiling of the laundry, for example, an algorithm for "light soiling" or "denim."

[0942] Step 4:

[0943] The server calculates the optimal amount of detergent and fabric softener based on the selected algorithm, using optimization techniques based on past data and laundry characteristics.

[0944] Step 5:

[0945] The server determines the calculated amounts of detergent and fabric softener and the timing of dispensing them. For example, it generates specific instructions such as "dispensing 50 ml of detergent 10 minutes after the start of washing" and "dispensing 20 ml of fabric softener 30 minutes after the start of washing."

[0946] Step 6:

[0947] The server sends these specific instructions back to the terminal, where it performs error checking to ensure the instructions were transmitted correctly.

[0948] Step 7:

[0949] The device then controls the built-in dispenser based on the received instructions, automatically dispensing the appropriate amount of detergent and fabric softener into the washing machine at the specified time.

[0950] Step 8:

[0951] The smartphone app analyzes the user's voice input and facial expressions, and the emotion engine recognizes the user's emotions, determining, for example, whether the user is feeling stressed or relaxed.

[0952] Step 9:

[0953] The server receives the user's emotional data and optimizes the laundry process accordingly, for example, shortening the washing time or increasing the number of rinses if the user is feeling stressed.

[0954] Step 10:

[0955] Users can monitor the progress of their laundry in real time using a smartphone app, which displays information such as "detergent added," "rinsing," and "drying."

[0956] Step 11:

[0957] Through the app, users can make changes to the wash process as needed, for example, adding more detergent or adjusting the amount of fabric softener.

[0958] Step 12:

[0959] When the laundry is complete, the device sends a completion notification to the server, which then forwards the information to the user's smartphone app, where the user receives the notification.

[0960] The above is the specific processing flow of a system that combines an emotion engine.

[0961] Example 2

[0962] 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."

[0963] Conventional washing machine systems are often inefficient because the amount and timing of detergent and fabric softener dispense are manually controlled. Furthermore, they place a heavy burden on users because they are unable to flexibly respond to the user's emotions and lifestyle. Furthermore, there is a demand for automated systems that can optimally execute the washing process based on the weight, type, and soiling level of the laundry.

[0964] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0965] In this invention, the server includes a sensor that measures the weight, type, and degree of soiling of the laundry, a data processing device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them, a terminal equipped with a distribution device that automatically dispenses detergent and fabric softener based on instructions from the data processing device, means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application, and an emotion analysis device that analyzes the user's voice input and facial expressions to recognize the user's emotional state. This makes it possible to dispense the optimal amount of detergent and fabric softener based on the weight, type, and degree of soiling of the laundry, and also makes it possible to flexibly adjust the washing process according to the user's emotions and lifestyle.

[0966] A "sensor" is a device that measures the weight, type and soiling of laundry.

[0967] The "data processing device" is a device that analyzes the data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of their addition.

[0968] A "distribution device" is a device that automatically dispenses detergent and fabric softener based on instructions from a data processing device.

[0969] A "terminal" is a set of devices that has sensors and distribution devices and operates by integrating these functions.

[0970] A "mobile terminal application" is software that runs on a mobile terminal such as a smartphone or tablet and monitors and controls the terminal's operations in real time.

[0971] An "emotion analysis device" is a device that analyzes a user's voice input and facial expressions to recognize the user's emotional state.

[0972] A "detergent" is a chemical product used to remove stains from laundry.

[0973] "Fabric softener" is a chemical product used to soften and scent laundry.

[0974] An "algorithm" is a computational procedure for analyzing specific data and deriving an optimal result.

[0975] "Real time" refers to the time when user operations and system operations are reflected immediately.

[0976] System Configuration

[0977] This invention is a system that comprises a sensor that measures the weight, type and degree of soiling of laundry; a data processing device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a distribution device that automatically dispenses detergent and fabric softener based on the instructions; a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application; and an emotion analysis device that recognizes the user's emotions.

[0978] Data collection and analysis

[0979] First, sensors measure the weight, type, and degree of soiling of the laundry. This information is sent to the terminal as a data packet. Specifically, the weight obtained by the weight sensor and the type and degree of soiling obtained by the camera and soiling sensor are structured in JSON format. The terminal transfers the received data packet to a data processing device, which stores the data in a database (e.g., MySQL or PostgreSQL) and analyzes it. This analysis is performed using a Python data analysis library (e.g., Pandas or NumPy).

[0980] Calculating optimal dosage and timing

[0981] The data processing device determines the optimal amount of detergent and fabric softener and the timing of adding them based on the analyzed data. For example, using a machine learning model (TensorFlow or PyTorch), it receives the weight, type, and degree of soiling of the laundry as input and calculates the optimal output value. A specific example is a process that determines the amount of detergent to use for lightly soiled laundry and 100ml for heavily soiled laundry.

[0982] How the sentiment analysis engine works

[0983] When a user opens a smartphone app, the app captures voice input and facial expressions. Specifically, the smartphone's camera recognizes facial expressions and the microphone records audio. An emotion analysis engine within the app analyzes this data to determine the user's emotional state (e.g., stress, satisfaction, excitement). A natural language processing (NLP) library (e.g., NLTK or spaCy) is used for the analysis. The analysis results are sent to a data processing device.

[0984] Automatic loading

[0985] Based on the analysis results, the data processing device sends instructions to the terminal. According to these instructions, the terminal controls the dispenser to dispense the optimal amount of detergent and fabric softener at the specified time. For example, a control signal may be sent to the dispenser to cause the pump to dispense 50 ml of detergent.

[0986] Real-time Monitoring and Control

[0987] Users can check the progress of their laundry in real time using a smartphone app. The app displays the remaining time and progress of the wash and allows users to change settings as needed. For example, if they are feeling stressed, they can set the washing time to be shorter.

[0988] Specific prompt examples

[0989] This system uses sensors to measure the weight, type, and soiling level of laundry, and then calculates the optimal amount and timing of detergent and fabric softener based on that data. It also recognizes the user's emotions and optimizes the washing process accordingly. Please explain the specific settings and execution process.

[0990] Example: Lightly soiled laundry

[0991] 1. The data processing device receives the sensor data "Weight: 3 kg, Type: Cotton, Level of dirt: Light."

[0992] 2. The data processor analyzes and calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[0993] 3. The data processing device transmits the calculation results to the terminal.

[0994] 4. The terminal controls the dispenser and dispenses detergent and fabric softener at the specified times.

[0995] 5. The user can check the progress on the smartphone app and change settings as needed. For example, if the washing machine determines that the washing conditions are comfortable based on voice input, the washing process will be optimized to maintain that condition.

[0996] As described above, this system not only automatically dispenses the optimal amount of detergent at the optimal timing depending on the type and degree of soiling of the laundry, but also realizes a flexible washing process that responds to the user's emotions, reducing the user's effort and providing efficient and effective washing.

[0997] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0998] Step 1:

[0999] The user puts laundry into the washing machine. At this moment, the system is in standby mode. The sensors are activated to measure the weight, type, and soiling level of the laundry. The data is first transmitted to the terminal.

[1000] Input: Weight, type, and degree of dirt of laundry

[1001] Output: Sensor data (JSON format)

[1002] Step 2:

[1003] The terminal receives the data obtained from the sensor. The received data is transferred directly to the data processing device. The HTTPS protocol is used for transfer to ensure data security.

[1004] Input: Sensor data (JSON format)

[1005] Output: Sensor data sent to the server

[1006] Step 3:

[1007] The data processor analyzes the received data, first storing it in a database, then preprocessing it using Python's Pandas and NumPy libraries, and then calculating the optimal amount and timing of detergent and fabric softener dosage using machine learning models (TensorFlow and PyTorch).

[1008] Input: Sensor data

[1009] Output: Data on optimal amounts of detergent and fabric softener and timing of addition

[1010] Step 4:

[1011] The data processor sends the calculation results to the terminal, which transmits them in real time to ensure the correct amount of detergent and fabric softener is dispensed early in the wash cycle.

[1012] Input: Data on optimal amounts of detergent and fabric softener and timing of addition

[1013] Output: Input instruction data sent to the terminal

[1014] Step 5:

[1015] The terminal controls the dispenser to dispense the optimal amount of detergent and fabric softener. Specifically, it sends a control signal to the dispenser, causing the pump to dispense 50 ml of detergent (for light soiling).

[1016] Input: Input instruction data

[1017] Output: Calculated amount of detergent and fabric softener dispensed

[1018] Step 6:

[1019] The user monitors the progress of the wash in real time using a smartphone app. The app receives progress data from the device and displays the remaining time and progress of the wash, allowing the user to change settings as needed.

[1020] Input: Progress data from the terminal

[1021] Output: Progress displayed on the smartphone app

[1022] Step 7:

[1023] A smartphone app works to recognize the user's emotions. It uses a camera to recognize facial expressions and a microphone to analyze voices to determine the user's emotional state. An emotion analysis engine processes the data using an NLP library (NLTK or spaCy) to determine the emotional state (e.g., stress, satisfaction, excitement). This data is sent to a data processing device.

[1024] Input: User's voice and facial expression data

[1025] Output: Sentiment analysis result data

[1026] Step 8:

[1027] The data processing device adjusts the washing process based on the user's emotional state, for example, if the user is feeling stressed, it sends instructions to the terminal to change settings to reduce the washing time.

[1028] Input: Sentiment analysis result data

[1029] Output: Washing process adjustment instructions

[1030] (Application example 2)

[1031] 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."

[1032] Modern laundry requires a lot of time and effort, and it is difficult to determine the appropriate amount of detergent and fabric softener for various laundry items. Furthermore, there is also the problem of users being unable to respond appropriately when they feel stressed during the wash. This makes it difficult to provide an efficient and effective laundry process.

[1033] The specific processing by the specific 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: a sensor that measures the weight, type, and degree of soiling of the laundry; means for analyzing data obtained from the sensor and calculating the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on instructions from the server; means for monitoring and controlling the operation of the terminal in real time via a mobile device application; an emotion analysis engine built into the mobile device application that recognizes the user's emotions; and means for adjusting the washing process based on the user's emotional state recognized by the emotion analysis engine. This not only enables the user to easily determine the appropriate amounts of detergent and fabric softener, but also enables the user to check the progress of the washing process through real-time monitoring, providing an optimal washing experience tailored to the user's emotions.

[1034] "Laundry" refers to all cloth products that require washing, such as clothes, towels, and sheets.

[1035] A "sensor" is a device that detects a physical phenomenon and generates data, and in this invention is a device for measuring the weight, type, and degree of soiling of laundry.

[1036] A "server" is a computer system that provides services to multiple terminals via a network, and in this invention has the function of analyzing data related to laundry and calculating the amount of detergent and fabric softener and the timing of their addition.

[1037] A "dispenser" is a device that dispenses a fixed amount of liquid, powder, etc., and in this invention, it plays a role in automatically dispensing detergent and fabric softener.

[1038] The term "terminal" refers to a device that can be directly operated by a user, and in this invention refers to a device including a dispenser installed in a washing machine.

[1039] A "mobile device application" is software that runs on a mobile electronic device such as a smartphone or tablet, and in this invention refers to an application for monitoring and controlling the progress of laundry in real time.

[1040] An "emotion analysis engine" refers to software or algorithms that analyze a user's voice input and facial expression data to recognize their emotional state.

[1041] A "detergent" is a chemical product used to remove stains from laundry.

[1042] A "softener" is a chemical product used to make laundry softer.

[1043] An "algorithm" is a set of procedures or rules for performing calculations or data analysis, and in this invention, it is used to calculate the optimal amount of detergent and fabric softener based on the type and degree of soiling of the laundry.

[1044] System Configuration

[1045] This invention is a system that comprises a sensor that measures the weight, type and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the server's instructions; a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application; and an emotion analysis engine that recognizes the user's emotions.

[1046] Program Generation

[1047] To realize this system, the following programs are required: First, a sensor measures the weight, type, and degree of soiling of the laundry and sends this data to the device as a data packet. The device then forwards the received data packet to a server, which analyzes the data. Next, the server calculates the optimal amount of detergent and fabric softener and the timing of dispensing them based on the analyzed data. Then, based on instructions from the server, the device controls the dispensers to automatically dispense the detergent and fabric softener. The progress of the wash is also monitored in real time via a mobile device application, and the washing process is adjusted according to the user's emotional state.

[1048] Processing Description

[1049] The system uses the following hardware and software:

[1050] Sensor: A device that measures the weight, type and soiling of laundry.

[1051] Server: A computer system that analyzes data collected from sensors and calculates the optimal amount of detergent and fabric softener.

[1052] Terminal: A device with dispensers that automatically dispense detergent and fabric softener.

[1053] Mobile device application: Software for monitoring and controlling the laundry progress in real time.

[1054] Emotion analysis engine: Software or algorithms that analyze a user's voice input and facial expression data to recognize their emotional state.

[1055] The data collected by the sensors is sent via the device to a server, where an analytical algorithm is used to calculate the optimal amount of detergent and fabric softener and the timing of dispensing. This algorithm takes into account the type of laundry and its degree of soiling. The calculation results are then sent to the device, which then automatically dispenses the detergent and fabric softener.

[1056] Furthermore, the progress of the laundry can be monitored in real time through a mobile application, and an emotion analysis engine can analyze the user's emotional state and adjust the laundry process accordingly. For example, if the user is feeling stressed, the washing time can be shortened.

[1057] Specific examples

[1058] For example, when a customer uses this system in a physical store, the process goes like this: First, sensors measure the weight, type, and dirtiness of the laundry. This data is sent to a server, which calculates the optimal amount of detergent and fabric softener. This information is sent to the device, which automatically dispenses the detergent and fabric softener at the appropriate time. The progress of the wash is then displayed in real time on the mobile device application. When the customer provides voice input or camera footage to the app, the emotion analysis engine recognizes the customer's emotional state, and the washing process is further optimized based on the results.

[1059] An example of a prompt sentence to be input to the generative AI model used is as follows:

[1060] "Write a program that calculates the optimal amount of detergent and fabric softener for a load of laundry. The program will send data from sensors that measure the weight, type, and soiling of the laundry to a server, which will analyze the data and determine the appropriate amounts. The washing machine will automatically dispense detergent and fabric softener, and the customer will be able to monitor and control it in real time through a mobile application. Also add the ability to use an emotion analysis engine to analyze the customer's emotional state and adjust the washing process as needed."

[1061] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1062] Step 1:

[1063] The sensor measures the weight, type, and soiling level of the laundry. The sensor is installed inside the washing machine and automatically starts measuring when laundry is put in. The measurement data is output from the sensor as weight (kg), type (cotton, denim, etc.), and soiling level (light, normal, heavy).

[1064] Input: Physical state of laundry

[1065] Output: Weight, type, and soiling data

[1066] Step 2:

[1067] The device receives data packets from the sensor and forwards them to the server. The device formats the data packets and sends them over the network to the server. This process ensures that the raw data from the sensor is delivered to the server in a format suitable for analysis.

[1068] Input: Weight, type, and soiling data from sensors

[1069] Output: Data packets transferred to the server

[1070] Step 3:

[1071] The server analyzes the received data and calculates the optimal amount of detergent and fabric softener and the timing of dispensing. The server uses specific algorithms to determine the amount of detergent and fabric softener needed based on the weight, type, and soiling of the laundry. It also calculates the optimal timing of dispensing.

[1072] Input: Data packets transmitted from the sensor

[1073] Output: Optimal detergent and fabric softener dosage and timing

[1074] Step 4:

[1075] The terminal controls the dispenser based on instructions from the server to automatically dispense detergent and fabric softener. The dispenser is installed inside the washing machine and is controlled to dispense the detergent and fabric softener accurately according to the optimal amounts received from the server.

[1076] Input: Instructions from the server on optimal amounts and timing of detergent and fabric softener

[1077] Output: Automatic detergent and fabric softener dispenser

[1078] Step 5:

[1079] Through the mobile application, users can monitor the progress of their laundry in real time. The app connects to the server and displays real-time laundry status information. Users can check the status of the washing process and change settings as needed.

[1080] Input: Status information of the laundry process from the server

[1081] Output: Real-time display on mobile application

[1082] Step 6:

[1083] An emotion analysis engine built into the mobile device application analyzes the user's voice input and facial expression data to recognize their emotional state. The emotion analysis engine receives the user's voice and facial expression data as input, analyzes them, and outputs their emotional state, such as stress level or satisfaction level.

[1084] Input: User voice input and facial expression data

[1085] Output: User's emotional state data

[1086] Step 7:

[1087] The server adjusts the washing process based on the emotional state data from the emotion analysis engine. For example, if the user is feeling stressed, the server adjusts the washing time to shorten the washing process. The server analyzes the emotional state data and sends instructions to the device to change the washing settings appropriately.

[1088] Input: Emotional state data

[1089] Output: Adjusted washing process settings

[1090] 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.

[1091] 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.

[1092] 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.

[1093] [Fourth embodiment]

[1094] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1095] 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.

[1096] 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).

[1097] 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.

[1098] 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.

[1099] 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).

[1100] 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.

[1101] 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.

[1102] 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.

[1103] 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.

[1104] 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.

[1105] 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.

[1106] 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."

[1107] System Configuration

[1108] This invention is comprised of a system including a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the instructions; and means for monitoring and controlling the operation of the terminal in real time via a smartphone app.

[1109] System Operation

[1110] Data collection and analysis

[1111] First, the sensors measure the weight, type, and dirtiness of the laundry. The sensors perform highly accurate measurements and send the collected data to the device. The device then forwards this data to the server, where it is analyzed.

[1112] Calculating optimal dosage and timing

[1113] The server then uses the data it receives to run an algorithm that calculates the optimal amount of detergent and fabric softener to use. This algorithm calculates the optimal amount of detergent and fabric softener to maximize the effectiveness of the wash, for example, by adding more detergent if the laundry is heavily soiled. The server also considers the entire wash process and determines the optimal timing for adding detergent and fabric softener.

[1114] Automatic loading

[1115] Based on instructions from the server, the terminal controls the dispenser to automatically dispense the appropriate amounts of detergent and fabric softener, freeing the user from having to manually dispense detergent and ensuring that the right amount of detergent is used every time.

[1116] Real-time Monitoring and Control

[1117] Users can monitor the operation of their washing machine in real time using a smartphone app. The app displays the progress of the wash and the current operation phase, and users can change settings as needed. For example, they can easily increase the amount of detergent or add fabric softener during the wash.

[1118] Specific examples

[1119] Example 1: Lightly soiled laundry

[1120] The server receives the sensor data: "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[1121] The server calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[1122] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[1123] Users can check the progress of their wash using a smartphone app and change settings as needed.

[1124] Example 2: Heavily soiled laundry

[1125] The server receives the sensor data: "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[1126] The server calculates the amount of detergent for heavy soiling to be 100ml and the amount of fabric softener to be 50ml.

[1127] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[1128] Users can check the progress of their wash using a smartphone app and change settings as needed.

[1129] This invention automatically dispenses the optimum amount and timing of detergent depending on the degree and type of soiling of the laundry, reducing the user's effort and achieving efficient and effective washing.

[1130] The processing flow will be explained below.

[1131] Step 1:

[1132] The sensors measure the weight, type and soiling of the laundry, each piece of information captured separately and sent to the device as a combined data packet.

[1133] Step 2:

[1134] The device sorts the received data packets and forwards them over the network link to the server, checking to ensure that no data is lost or sent incorrectly.

[1135] Step 3:

[1136] The server analyzes the data and selects the appropriate algorithm based on the weight, type and soiling of the laundry, for example, an algorithm for "light soiling" or "denim."

[1137] Step 4:

[1138] The server calculates the optimal amount of detergent and fabric softener based on the selected algorithm, using optimization techniques based on past data and laundry characteristics.

[1139] Step 5:

[1140] The server determines the calculated amounts of detergent and fabric softener and the timing of dispensing them. For example, it generates specific instructions such as "dispensing 50 ml of detergent 10 minutes after the start of washing" and "dispensing 20 ml of fabric softener 30 minutes after the start of washing."

[1141] Step 6:

[1142] The server sends these specific instructions back to the terminal, where it performs error checking to ensure the instructions were transmitted correctly.

[1143] Step 7:

[1144] The device then controls the built-in dispenser based on the received instructions, automatically dispensing the appropriate amount of detergent and fabric softener into the washing machine at the specified time.

[1145] Step 8:

[1146] Users monitor the progress of their wash in real time using a smartphone app, which displays information such as when to add detergent and fabric softener, and the current wash phase.

[1147] Step 9:

[1148] Users can change settings as needed through the app, for example, adjusting the amount of detergent or adding fabric softener.

[1149] Step 10:

[1150] The device constantly checks the progress of the laundry and reports any abnormalities to the server, which analyzes the data and sends an alert to the user if necessary.

[1151] Step 11:

[1152] When the laundry is complete, the device sends a completion notification to the server, which then forwards the information to the user's smartphone app, where the user receives the notification.

[1153] The above is the specific processing flow of the program.

[1154] Example 1

[1155] 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."

[1156] Conventional washing systems have difficulty automatically dispensing the optimal amount of detergent and fabric softener depending on the type of laundry and the degree of soiling, requiring manual dispensing. This reduces washing efficiency and prevents optimal cleaning results. Furthermore, there are few ways for users to monitor the progress of the washing process in real time and change settings as needed, resulting in a lack of convenience for users.

[1157] 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.

[1158] In this invention, the server includes a sensor that measures the weight, type, and degree of soiling of the laundry; an information processing device that analyzes the data and calculates the amount and timing of detergent and fabric softener addition; a chemical supply device that automatically adds detergent and fabric softener based on instructions from the information processing device; and a device that monitors and controls the device's operation in real time via mobile device software. This allows the optimal amount and timing of detergent addition based on the type and degree of soiling of the laundry to be automatically added, reducing user effort and enabling efficient and effective washing. Furthermore, the progress of the washing process can be monitored in real time and settings can be changed as needed, improving user convenience.

[1159] A "sensor" is a device for measuring the weight, type, and soiling level of laundry.

[1160] The "information processing device" is a computer device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of their addition.

[1161] The "chemical supply device" is a device that automatically dispenses detergent and fabric softener based on instructions from an information processing device.

[1162] "Software for mobile terminals" refers to application software for monitoring and controlling the operation of devices in real time using a mobile terminal.

[1163] A "detergent" is a cleaning agent used to clean stains.

[1164] "Fabric softener" is a chemical used to soften and scent laundry.

[1165] "Laundry type" refers to the classification of the material or construction of the items being washed.

[1166] "Level of dirt" refers to the amount and degree of dirt adhering to the laundry.

[1167] "Analysis" is the process of examining the data obtained from the sensors in detail to determine cleaning methods.

[1168] "Addition timing" refers to the timing at which detergent and fabric softener are added at an appropriate time during the washing process.

[1169] This invention is a system that includes a sensor that measures the weight, type, and degree of soiling of laundry, an information processing device that analyzes the measured data, a chemical supply device that automatically dispenses detergent and fabric softener based on the analysis results, and a means for monitoring and controlling the operation of the device in real time via software for a mobile device.

[1170] System configuration

[1171] sensor

[1172] The sensor is a device that accurately measures the weight, type, and degree of soiling of laundry. The sensor has the functions of measuring weight, identifying materials, and evaluating the degree of soiling using optical and chemical sensors. The data from this sensor is sent to the terminal.

[1173] Information processing device

[1174] The terminal transfers the data sent from the sensor to an information processing device (server) via a communications device. The information processing device analyzes the collected data and calculates the optimal amount of detergent and fabric softener. The analysis algorithm uses a generative AI model to derive optimal results based on past data and simulation results of washing effects. The information processing device also calculates the optimal timing to add detergent and fabric softener depending on the progress of the wash.

[1175] Chemical Supply Device

[1176] Based on instructions from the information processing device, the chemical supply device automatically dispenses the appropriate amounts of detergent and fabric softener at the required timing for each stage of the wash, eliminating the need for the user to do this manually.

[1177] Mobile device software

[1178] Mobile device software is an application that allows users to monitor and control the operation of the machine in real time using a mobile device such as a smartphone. This software allows users to check the progress of the wash, the amount of detergent and fabric softener used, and change settings as needed. For example, it is easy to adjust the amount of detergent or add fabric softener during the wash.

[1179] Specific examples

[1180] Example 1: Lightly soiled laundry

[1181] 1. The sensor measures "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[1182] 2. The terminal transfers this data to the information processing device.

[1183] 3. The information processing device analyzes the data and calculates the amount of detergent for light soiling to be 50 ml and the amount of fabric softener to be 20 ml.

[1184] 4. The information processing device sends the calculation results to the terminal.

[1185] 5. The device dispenses detergent and fabric softener at the specified times.

[1186] 6. Users can check the progress of their wash on their smartphone app and change settings as needed.

[1187] Example 2: Heavily soiled laundry

[1188] 1. The sensor measures "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[1189] 2. The terminal transfers this data to the information processing device.

[1190] 3. The information processing device analyzes the data and calculates the amount of detergent for heavy soiling to be 100 ml and the amount of fabric softener to be 50 ml.

[1191] 4. The information processing device sends the calculation results to the terminal.

[1192] 5. The device dispenses detergent and fabric softener at the specified times.

[1193] 6. Users can check the progress of their wash on their smartphone app and change settings as needed.

[1194] Prompt Sentence Examples

[1195] 1. "What is the optimal amount of detergent and fabric softener for lightly soiled cotton (3 kg) and when should I add them?"

[1196] 2. "Calculate the amount of detergent and fabric softener needed for heavily soiled denim (5 kg) and when to add them."

[1197] 3. "Please explain the procedures for data collection, analysis, automated input, and real-time monitoring in this system."

[1198] This system allows users to wash effectively and efficiently with the optimal amount and timing of detergent and fabric softener.

[1199] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1200] Step 1:

[1201] The sensors measure the weight, type, and degree of soiling of the laundry. They collect this data and send it to the terminal. The sensors use a load cell to measure weight, a reflective light sensor to distinguish the material type, and a chemical sensor to evaluate the degree of soiling. The input is the laundry, and the output is data on "weight," "type," and "degree of soiling."

[1202] Step 2:

[1203] The terminal transfers the data received from the sensor to the information processing device. The terminal temporarily stores the data received from the sensor, encrypts it, and sends it to the information processing device (server) via the network. The input is the aforementioned "weight," "type," and "level of dirt" data, and the output is a notification of successful data transfer to the server.

[1204] Step 3:

[1205] The server analyzes the received data. Using the generative AI model, the server compares it with standard data for each type of laundry to determine the optimal amount of detergent and fabric softener, as well as the timing for adding them. Specifically, the server inputs the received data (weight, type, and level of soiling) into the analysis algorithm, and obtains the optimal amount of detergent, optimal amount of fabric softener, and timing for adding them as outputs.

[1206] Step 4:

[1207] The server sends the analysis results to the terminal. The server then sends the "optimum amount of detergent," "optimum amount of fabric softener," and "addition timing" obtained from the analysis results to the terminal in packet format. The input is the analysis results, and the output is confirmation of the results sent to the terminal.

[1208] Step 5:

[1209] The terminal controls the chemical supply device based on instructions from the server, and automatically dispenses detergent and fabric softener. Based on data from the server, the terminal dispenses the appropriate amount of detergent and fabric softener at a set time depending on the weight and degree of dirt of the laundry. The input is data on the "optimum amount of detergent," "optimum amount of fabric softener," and "dispensing timing," and the output is a notification that dispensing is complete.

[1210] Step 6:

[1211] Using the mobile device software, users can monitor the progress of their laundry in real time and change settings as needed. From the app, users can check the progress of the wash and the amount of detergent and fabric softener added, and change operations as needed. For example, it is possible to increase the amount of detergent during a wash. The input is the user's command to change the settings, and the output is the execution result based on the command.

[1212] At each step, the server, the terminal, and the user perform specific operations, which allows the entire system to work together and achieve effective and efficient laundry.

[1213] (Application example 1)

[1214] 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."

[1215] Traditional maintenance processes in the manufacturing industry require a lot of time and effort, often resulting in variations in work efficiency and quality. It is also difficult for managers to grasp the progress of maintenance in real time and issue instructions at the appropriate time. Furthermore, there was a need for a system that could automatically perform appropriate maintenance according to the degree of dirt and wear.

[1216] 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.

[1217] In this invention, the server includes a sensor that measures the weight, type, and condition of the object to be cleaned, a means for analyzing data obtained from the sensor and calculating the appropriate cleaning chemical and its dosage timing, a terminal equipped with a supply device that automatically dispenses the appropriate cleaning chemical based on instructions from the server, a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application, and a means for automatically performing maintenance on manufacturing equipment and production lines. This frees users from the traditional manual maintenance work and enables efficient and effective maintenance.

[1218] "Object to be cleaned" is any equipment or part of a manufacturing machine or production line that requires proper maintenance.

[1219] "Sensor" is a measuring device for measuring the weight, type and condition of the object being washed.

[1220] The "server" is a computer that analyzes data obtained from sensors and calculates the amount and timing of application of cleaning chemicals.

[1221] A "supply device" is a device that automatically dispenses cleaning chemicals based on instructions from the server.

[1222] A "terminal" is a computer device that controls a supply device according to instructions from a server.

[1223] A "mobile terminal application" is software that runs on a portable computer such as a smartphone or tablet and is used to monitor and control the operation of the terminal in real time.

[1224] "Maintenance of manufacturing equipment and production lines" refers to carrying out appropriate cleaning and repair work according to the degree of dirt and wear.

[1225] The "means for automatic execution" is a function that combines sensors, servers, supply devices, and terminals to execute maintenance work without human intervention.

[1226] System Configuration

[1227] This invention is a system for realizing automatic maintenance of manufacturing equipment and production lines in a factory environment. The system includes a sensor that captures the weight, type, and condition of the object to be cleaned, a server that analyzes data obtained from the sensor and calculates the appropriate amount and timing of supply of cleaning chemicals, a terminal equipped with a supply device that automatically supplies cleaning chemicals based on instructions from the server, and a mobile terminal application that monitors and controls the operation of the terminal in real time.

[1228] System Operation

[1229] Data collection and analysis

[1230] 1. The role of the sensor:

[1231] The sensors used include LiDAR sensors, cameras, and wear sensors, and measure the weight, type, and status of manufacturing equipment and production lines with high precision, and transmit the collected data to a terminal.

[1232] 2. Data Analysis:

[1233] The device transmits the data to a server, which runs data analysis algorithms that use software like Python and TensorFlow to calculate the optimal type and amount of cleaning chemicals and the optimal timing for cleaning based on soiling and friction parameters.

[1234] Automatic loading and maintenance work

[1235] 3. Feed device control:

[1236] The server's calculation results are sent to the terminal, which then controls the dispenser to dispense the appropriate cleaning chemicals at the specified amount and timing. The dispenser is controlled using software such as ROS (Robot Operating System) or Arduino IDE.

[1237] 4. Maintenance work:

[1238] The supply device automatically performs maintenance work on manufacturing equipment and production lines, cleaning and repairing them.

[1239] Real-time Monitoring and Control

[1240] 5. Mobile terminal applications:

[1241] Administrators (users) can monitor the system's operating status in real time using a mobile terminal application, which is developed using software such as Flutter and Firebase.

[1242] Managers can use the app to check the progress of maintenance and change settings as needed, such as adjusting cleaning timing or the amount of cleaning chemicals used.

[1243] Specific examples

[1244] Administrators can open a smartphone application to check the operating status of the conveyor belt cleaning robot in real time. For example, they can set the conveyor belt cleaning time to 3:00 p.m. and automatically start cleaning when the level of dirt exceeds 50%.

[1245] Prompt Sentence Examples

[1246] "If the conveyor belt cleaning robot detects that it is 50% dirty, add 100ml of cleaning liquid within 5 minutes of detecting the dirt and start cleaning. In addition, display the cleaning progress in real time on the smartphone app."

[1247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1248] Step 1:

[1249] Data collection and transmission:

[1250] Sensors measure the weight, type, and condition of manufacturing equipment and production lines with high precision. For example, LiDAR sensors measure the surface area of ​​dirt, and wear sensors detect the depth of wear. The collected data is sent to a device as a signal. The input is the measurement data obtained from the sensor, and the output is the data sent to the device.

[1251] Step 2:

[1252] Data Analysis:

[1253] The device transmits the measurement data received from the sensor to a server. The server receives this data and analyzes it using Python and TensorFlow. The input is the measurement data sent from the device, and the output is a calculation result of the type, amount, and timing of application of cleaning chemicals. This analysis, for example, calculates the optimal amount of cleaning liquid based on the surface area of ​​the dirt and the depth of wear.

[1254] Step 3:

[1255] Send instructions:

[1256] The server sends the analysis results to the terminal. The input is the calculation result after data analysis, and the output is the instruction sent to the terminal. Specifically, the server packages the calculation results in JSON format or similar and sends them to the terminal via the network.

[1257] Step 4:

[1258] Automatic input:

[1259] The terminal receives instructions from the server and controls the dispenser to dispense the appropriate amount of cleaning chemicals. The input is the instruction from the server and the output is the action of the dispenser. In this step, ROS or Arduino IDE is used to control the dispenser, for example, to dispense the exact amount of cleaning liquid.

[1260] Step 5:

[1261] Real-time monitoring:

[1262] The user opens the mobile terminal application to monitor the system's operating status in real time. The input is the operating status data sent from the terminal to the application, and the output is the information displayed on the user's smartphone screen. Specifically, the application uses Firebase to update the real-time database and display the current maintenance progress to the user.

[1263] Step 6:

[1264] Changes made during the process:

[1265] The user can change the maintenance process midway through the application as needed. The input is the setting change made by the user in the application, and the output is the new setting information sent to the terminal and server. Specifically, when the user issues a command in the application to increase the amount of cleaning liquid, that information is transmitted to the dispenser via the server.

[1266] 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.

[1267] System Configuration

[1268] This invention is comprised of a system that includes a sensor that measures the weight, type, and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the server's instructions; a means for monitoring and controlling the operation of the terminal in real time via a smartphone app; and an emotion engine that recognizes the user's emotions.

[1269] System Operation

[1270] Data collection and analysis

[1271] First, sensors measure the weight, type, and soiling of the laundry. This information is sent as a data packet to the device. The device then forwards the received data packet to a server, which analyzes the data and runs an algorithm to calculate the optimal amount of detergent and fabric softener.

[1272] Calculating optimal dosage and timing

[1273] The server then determines the optimal amount of detergent and fabric softener and the timing of dispensing based on the analyzed data. For example, it executes a process that determines the amount of detergent to use for light soiling and 100ml for heavy soiling.

[1274] Emotion Engine Operation

[1275] The smartphone app's built-in emotion engine analyzes the user's voice input and facial expressions to recognize their current emotions. The emotion engine determines the user's stress level, satisfaction level, excitement level, etc., and sends the results to the server.

[1276] Automatic loading

[1277] The terminal controls the dispenser based on instructions from the server, automatically dispensing the optimal amount of detergent and fabric softener, freeing the user from having to dispense detergent manually.

[1278] Real-time Monitoring and Control

[1279] Users can monitor the progress of their laundry in real time through a smartphone app. Furthermore, based on the analysis results of the emotion engine, the washing process can be adjusted according to the user's emotions. For example, if the user is feeling stressed, the settings can be changed to shorten the washing time.

[1280] Specific examples

[1281] Example 1: Lightly soiled laundry

[1282] The server receives the sensor data: "Weight: 3kg, Type: Cotton, Level of dirt: Light."

[1283] The server calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[1284] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[1285] Users can check the progress of their washing on their smartphone app and change settings as needed. If the emotion engine detects a comfortable state from the user's voice, it will maintain the washing process at the optimal settings to maintain that state.

[1286] Example 2: Heavily soiled laundry

[1287] The server receives the sensor data: "Weight: 5kg, Type: Denim, Level of dirt: Heavy."

[1288] The server calculates the amount of detergent for heavy soiling to be 100ml and the amount of fabric softener to be 50ml.

[1289] The server sends the calculation results to the terminal, which then dispenses detergent and fabric softener at the specified times.

[1290] The user checks the progress of the laundry using a smartphone app, and if the emotion engine detects a state of stress from the user's facial expression, it sets the laundry to finish earlier.

[1291] This invention not only automatically dispenses the optimum amount and timing of detergent depending on the degree of soiling and type of laundry, but also realizes a flexible washing process according to the user's feelings, reduces the user's effort, and provides efficient and effective washing.

[1292] The processing flow will be explained below.

[1293] Step 1:

[1294] The sensors measure the weight, type and soiling of the laundry, each piece of information captured separately and sent to the device as a data packet.

[1295] Step 2:

[1296] The device sorts the received data packets and forwards them to the server, checking to ensure that no data is lost or sent incorrectly.

[1297] Step 3:

[1298] The server analyzes the data and selects the appropriate algorithm based on the weight, type and soiling of the laundry, for example, an algorithm for "light soiling" or "denim."

[1299] Step 4:

[1300] The server calculates the optimal amount of detergent and fabric softener based on the selected algorithm, using optimization techniques based on past data and laundry characteristics.

[1301] Step 5:

[1302] The server determines the calculated amounts of detergent and fabric softener and the timing of dispensing them. For example, it generates specific instructions such as "dispensing 50 ml of detergent 10 minutes after the start of washing" and "dispensing 20 ml of fabric softener 30 minutes after the start of washing."

[1303] Step 6:

[1304] The server sends these specific instructions back to the terminal, where it performs error checking to ensure the instructions were transmitted correctly.

[1305] Step 7:

[1306] The device then controls the built-in dispenser based on the received instructions, automatically dispensing the appropriate amount of detergent and fabric softener into the washing machine at the specified time.

[1307] Step 8:

[1308] The smartphone app analyzes the user's voice input and facial expressions, and the emotion engine recognizes the user's emotions, determining, for example, whether the user is feeling stressed or relaxed.

[1309] Step 9:

[1310] The server receives the user's emotional data and optimizes the laundry process accordingly, for example, shortening the washing time or increasing the number of rinses if the user is feeling stressed.

[1311] Step 10:

[1312] Users can monitor the progress of their laundry in real time using a smartphone app, which displays information such as "detergent added," "rinsing," and "drying."

[1313] Step 11:

[1314] Through the app, users can make changes to the wash process as needed, for example, adding more detergent or adjusting the amount of fabric softener.

[1315] Step 12:

[1316] When the laundry is complete, the device sends a completion notification to the server, which then forwards the information to the user's smartphone app, where the user receives the notification.

[1317] The above is the specific processing flow of a system that combines an emotion engine.

[1318] Example 2

[1319] 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."

[1320] Conventional washing machine systems are often inefficient because the amount and timing of detergent and fabric softener dispense are manually controlled. Furthermore, they place a heavy burden on users because they are unable to flexibly respond to the user's emotions and lifestyle. Furthermore, there is a demand for automated systems that can optimally execute the washing process based on the weight, type, and soiling level of the laundry.

[1321] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1322] In this invention, the server includes a sensor that measures the weight, type, and degree of soiling of the laundry, a data processing device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them, a terminal equipped with a distribution device that automatically dispenses detergent and fabric softener based on instructions from the data processing device, means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application, and an emotion analysis device that analyzes the user's voice input and facial expressions to recognize the user's emotional state. This makes it possible to dispense the optimal amount of detergent and fabric softener based on the weight, type, and degree of soiling of the laundry, and also makes it possible to flexibly adjust the washing process according to the user's emotions and lifestyle.

[1323] A "sensor" is a device that measures the weight, type and soiling of laundry.

[1324] The "data processing device" is a device that analyzes the data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of their addition.

[1325] A "distribution device" is a device that automatically dispenses detergent and fabric softener based on instructions from a data processing device.

[1326] A "terminal" is a set of devices that has sensors and distribution devices and operates by integrating these functions.

[1327] A "mobile terminal application" is software that runs on a mobile terminal such as a smartphone or tablet and monitors and controls the terminal's operations in real time.

[1328] An "emotion analysis device" is a device that analyzes a user's voice input and facial expressions to recognize the user's emotional state.

[1329] A "detergent" is a chemical product used to remove stains from laundry.

[1330] "Fabric softener" is a chemical product used to soften and scent laundry.

[1331] An "algorithm" is a computational procedure for analyzing specific data and deriving an optimal result.

[1332] "Real time" refers to the time when user operations and system operations are reflected immediately.

[1333] System Configuration

[1334] This invention is a system that comprises a sensor that measures the weight, type and degree of soiling of laundry; a data processing device that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a distribution device that automatically dispenses detergent and fabric softener based on the instructions; a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application; and an emotion analysis device that recognizes the user's emotions.

[1335] Data collection and analysis

[1336] First, sensors measure the weight, type, and degree of soiling of the laundry. This information is sent to the terminal as a data packet. Specifically, the weight obtained by the weight sensor and the type and degree of soiling obtained by the camera and soiling sensor are structured in JSON format. The terminal transfers the received data packet to a data processing device, which stores the data in a database (e.g., MySQL or PostgreSQL) and analyzes it. This analysis is performed using a Python data analysis library (e.g., Pandas or NumPy).

[1337] Calculating optimal dosage and timing

[1338] The data processing device determines the optimal amount of detergent and fabric softener and the timing of adding them based on the analyzed data. For example, using a machine learning model (TensorFlow or PyTorch), it receives the weight, type, and degree of soiling of the laundry as input and calculates the optimal output value. A specific example is a process that determines the amount of detergent to use for lightly soiled laundry and 100ml for heavily soiled laundry.

[1339] How the sentiment analysis engine works

[1340] When a user opens a smartphone app, the app captures voice input and facial expressions. Specifically, the smartphone's camera recognizes facial expressions and the microphone records audio. An emotion analysis engine within the app analyzes this data to determine the user's emotional state (e.g., stress, satisfaction, excitement). A natural language processing (NLP) library (e.g., NLTK or spaCy) is used for the analysis. The analysis results are sent to a data processing device.

[1341] Automatic loading

[1342] Based on the analysis results, the data processing device sends instructions to the terminal. According to these instructions, the terminal controls the dispenser to dispense the optimal amount of detergent and fabric softener at the specified time. For example, a control signal may be sent to the dispenser to cause the pump to dispense 50 ml of detergent.

[1343] Real-time Monitoring and Control

[1344] Users can check the progress of their laundry in real time using a smartphone app. The app displays the remaining time and progress of the wash and allows users to change settings as needed. For example, if they are feeling stressed, they can set the washing time to be shorter.

[1345] Specific prompt examples

[1346] This system uses sensors to measure the weight, type, and soiling level of laundry, and then calculates the optimal amount and timing of detergent and fabric softener based on that data. It also recognizes the user's emotions and optimizes the washing process accordingly. Please explain the specific settings and execution process.

[1347] Example: Lightly soiled laundry

[1348] 1. The data processing device receives the sensor data "Weight: 3 kg, Type: Cotton, Level of dirt: Light."

[1349] 2. The data processor analyzes and calculates the amount of detergent for light soiling to be 50ml and the amount of fabric softener to be 20ml.

[1350] 3. The data processing device transmits the calculation results to the terminal.

[1351] 4. The terminal controls the dispenser and dispenses detergent and fabric softener at the specified times.

[1352] 5. The user can check the progress on the smartphone app and change settings as needed. For example, if the washing machine determines that the washing conditions are comfortable based on voice input, the washing process will be optimized to maintain that condition.

[1353] As described above, this system not only automatically dispenses the optimal amount of detergent at the optimal timing depending on the type and degree of soiling of the laundry, but also realizes a flexible washing process that responds to the user's emotions, reducing the user's effort and providing efficient and effective washing.

[1354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1355] Step 1:

[1356] The user puts laundry into the washing machine. At this moment, the system is in standby mode. The sensors are activated to measure the weight, type, and soiling level of the laundry. The data is first transmitted to the terminal.

[1357] Input: Weight, type, and degree of dirt of laundry

[1358] Output: Sensor data (JSON format)

[1359] Step 2:

[1360] The terminal receives the data obtained from the sensor. The received data is transferred directly to the data processing device. The HTTPS protocol is used for transfer to ensure data security.

[1361] Input: Sensor data (JSON format)

[1362] Output: Sensor data sent to the server

[1363] Step 3:

[1364] The data processor analyzes the received data, first storing it in a database, then preprocessing it using Python's Pandas and NumPy libraries, and then calculating the optimal amount and timing of detergent and fabric softener dosage using machine learning models (TensorFlow and PyTorch).

[1365] Input: Sensor data

[1366] Output: Data on optimal amounts of detergent and fabric softener and timing of addition

[1367] Step 4:

[1368] The data processor sends the calculation results to the terminal, which transmits them in real time to ensure the correct amount of detergent and fabric softener is dispensed early in the wash cycle.

[1369] Input: Data on optimal amounts of detergent and fabric softener and timing of addition

[1370] Output: Input instruction data sent to the terminal

[1371] Step 5:

[1372] The terminal controls the dispenser to dispense the optimal amount of detergent and fabric softener. Specifically, it sends a control signal to the dispenser, causing the pump to dispense 50 ml of detergent (for light soiling).

[1373] Input: Input instruction data

[1374] Output: Calculated amount of detergent and fabric softener dispensed

[1375] Step 6:

[1376] The user monitors the progress of the wash in real time using a smartphone app. The app receives progress data from the device and displays the remaining time and progress of the wash, allowing the user to change settings as needed.

[1377] Input: Progress data from the terminal

[1378] Output: Progress displayed on the smartphone app

[1379] Step 7:

[1380] A smartphone app works to recognize the user's emotions. It uses a camera to recognize facial expressions and a microphone to analyze voices to determine the user's emotional state. An emotion analysis engine processes the data using an NLP library (NLTK or spaCy) to determine the emotional state (e.g., stress, satisfaction, excitement). This data is sent to a data processing device.

[1381] Input: User's voice and facial expression data

[1382] Output: Sentiment analysis result data

[1383] Step 8:

[1384] The data processing device adjusts the washing process based on the user's emotional state, for example, if the user is feeling stressed, it sends instructions to the terminal to change settings to reduce the washing time.

[1385] Input: Sentiment analysis result data

[1386] Output: Washing process adjustment instructions

[1387] (Application example 2)

[1388] 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."

[1389] Modern laundry requires a lot of time and effort, and it is difficult to determine the appropriate amount of detergent and fabric softener for various laundry items. Furthermore, there is also the problem of users being unable to respond appropriately when they feel stressed during the wash. This makes it difficult to provide an efficient and effective laundry process.

[1390] The specific processing by the specific 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: a sensor that measures the weight, type, and degree of soiling of the laundry; means for analyzing data obtained from the sensor and calculating the amount of detergent and fabric softener and the timing of dispensing; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on instructions from the server; means for monitoring and controlling the operation of the terminal in real time via a mobile device application; an emotion analysis engine built into the mobile device application that recognizes the user's emotions; and means for adjusting the washing process based on the user's emotional state recognized by the emotion analysis engine. This not only enables the user to easily determine the appropriate amounts of detergent and fabric softener, but also enables the user to check the progress of the washing process through real-time monitoring, providing an optimal washing experience tailored to the user's emotions.

[1391] "Laundry" refers to all cloth products that require washing, such as clothes, towels, and sheets.

[1392] A "sensor" is a device that detects a physical phenomenon and generates data, and in this invention is a device for measuring the weight, type, and degree of soiling of laundry.

[1393] A "server" is a computer system that provides services to multiple terminals via a network, and in this invention has the function of analyzing data related to laundry and calculating the amount of detergent and fabric softener and the timing of their addition.

[1394] A "dispenser" is a device that dispenses a fixed amount of liquid, powder, etc., and in this invention, it plays a role in automatically dispensing detergent and fabric softener.

[1395] The term "terminal" refers to a device that can be directly operated by a user, and in this invention refers to a device including a dispenser installed in a washing machine.

[1396] A "mobile device application" is software that runs on a mobile electronic device such as a smartphone or tablet, and in this invention refers to an application for monitoring and controlling the progress of laundry in real time.

[1397] An "emotion analysis engine" refers to software or algorithms that analyze a user's voice input and facial expression data to recognize their emotional state.

[1398] A "detergent" is a chemical product used to remove stains from laundry.

[1399] A "softener" is a chemical product used to make laundry softer.

[1400] An "algorithm" is a set of procedures or rules for performing calculations or data analysis, and in this invention, it is used to calculate the optimal amount of detergent and fabric softener based on the type and degree of soiling of the laundry.

[1401] System Configuration

[1402] This invention is a system that comprises a sensor that measures the weight, type and degree of soiling of laundry; a server that analyzes data obtained from the sensor and calculates the amount of detergent and fabric softener and the timing of dispensing them; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on the server's instructions; a means for monitoring and controlling the operation of the terminal in real time via a mobile terminal application; and an emotion analysis engine that recognizes the user's emotions.

[1403] Program Generation

[1404] To realize this system, the following programs are required: First, a sensor measures the weight, type, and degree of soiling of the laundry and sends this data to the device as a data packet. The device then forwards the received data packet to a server, which analyzes the data. Next, the server calculates the optimal amount of detergent and fabric softener and the timing of dispensing them based on the analyzed data. Then, based on instructions from the server, the device controls the dispensers to automatically dispense the detergent and fabric softener. The progress of the wash is also monitored in real time via a mobile device application, and the washing process is adjusted according to the user's emotional state.

[1405] Processing Description

[1406] The system uses the following hardware and software:

[1407] Sensor: A device that measures the weight, type and soiling of laundry.

[1408] Server: A computer system that analyzes data collected from sensors and calculates the optimal amount of detergent and fabric softener.

[1409] Terminal: A device with dispensers that automatically dispense detergent and fabric softener.

[1410] Mobile device application: Software for monitoring and controlling the laundry progress in real time.

[1411] Emotion analysis engine: Software or algorithms that analyze a user's voice input and facial expression data to recognize their emotional state.

[1412] The data collected by the sensors is sent via the device to a server, where an analytical algorithm is used to calculate the optimal amount of detergent and fabric softener and the timing of dispensing. This algorithm takes into account the type of laundry and its degree of soiling. The calculation results are then sent to the device, which then automatically dispenses the detergent and fabric softener.

[1413] Furthermore, the progress of the laundry can be monitored in real time through a mobile application, and an emotion analysis engine can analyze the user's emotional state and adjust the laundry process accordingly. For example, if the user is feeling stressed, the washing time can be shortened.

[1414] Specific examples

[1415] For example, when a customer uses this system in a physical store, the process goes like this: First, sensors measure the weight, type, and dirtiness of the laundry. This data is sent to a server, which calculates the optimal amount of detergent and fabric softener. This information is sent to the device, which automatically dispenses the detergent and fabric softener at the appropriate time. The progress of the wash is then displayed in real time on the mobile device application. When the customer provides voice input or camera footage to the app, the emotion analysis engine recognizes the customer's emotional state, and the washing process is further optimized based on the results.

[1416] An example of a prompt sentence to be input to the generative AI model used is as follows:

[1417] "Write a program that calculates the optimal amount of detergent and fabric softener for a load of laundry. The program will send data from sensors that measure the weight, type, and soiling of the laundry to a server, which will analyze the data and determine the appropriate amounts. The washing machine will automatically dispense detergent and fabric softener, and the customer will be able to monitor and control it in real time through a mobile application. Also add the ability to use an emotion analysis engine to analyze the customer's emotional state and adjust the washing process as needed."

[1418] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1419] Step 1:

[1420] The sensor measures the weight, type, and soiling level of the laundry. The sensor is installed inside the washing machine and automatically starts measuring when laundry is put in. The measurement data is output from the sensor as weight (kg), type (cotton, denim, etc.), and soiling level (light, normal, heavy).

[1421] Input: Physical state of laundry

[1422] Output: Weight, type, and soiling data

[1423] Step 2:

[1424] The device receives data packets from the sensor and forwards them to the server. The device formats the data packets and sends them over the network to the server. This process ensures that the raw data from the sensor is delivered to the server in a format suitable for analysis.

[1425] Input: Weight, type, and soiling data from sensors

[1426] Output: Data packets transferred to the server

[1427] Step 3:

[1428] The server analyzes the received data and calculates the optimal amount of detergent and fabric softener and the timing of dispensing. The server uses specific algorithms to determine the amount of detergent and fabric softener needed based on the weight, type, and soiling of the laundry. It also calculates the optimal timing of dispensing.

[1429] Input: Data packets transmitted from the sensor

[1430] Output: Optimal detergent and fabric softener dosage and timing

[1431] Step 4:

[1432] The terminal controls the dispenser based on instructions from the server to automatically dispense detergent and fabric softener. The dispenser is installed inside the washing machine and is controlled to dispense the detergent and fabric softener accurately according to the optimal amounts received from the server.

[1433] Input: Instructions from the server on optimal amounts and timing of detergent and fabric softener

[1434] Output: Automatic detergent and fabric softener dispenser

[1435] Step 5:

[1436] Through the mobile application, users can monitor the progress of their laundry in real time. The app connects to the server and displays real-time laundry status information. Users can check the status of the washing process and change settings as needed.

[1437] Input: Status information of the laundry process from the server

[1438] Output: Real-time display on mobile application

[1439] Step 6:

[1440] An emotion analysis engine built into the mobile device application analyzes the user's voice input and facial expression data to recognize their emotional state. The emotion analysis engine receives the user's voice and facial expression data as input, analyzes them, and outputs their emotional state, such as stress level or satisfaction level.

[1441] Input: User voice input and facial expression data

[1442] Output: User's emotional state data

[1443] Step 7:

[1444] The server adjusts the washing process based on the emotional state data from the emotion analysis engine. For example, if the user is feeling stressed, the server adjusts the washing time to shorten the washing process. The server analyzes the emotional state data and sends instructions to the device to change the washing settings appropriately.

[1445] Input: Emotional state data

[1446] Output: Adjusted washing process settings

[1447] 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.

[1448] 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.

[1449] 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 robot 414.

[1450] 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.

[1451] FIG. 9 illustrates 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 behaviors 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.

[1452] 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.

[1453] 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).

[1454] 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.

[1455] 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."

[1456] 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.

[1457] 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).

[1458] 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.

[1459] 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.

[1460] 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.

[1461] 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.

[1462] 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.

[1463] 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.

[1464] 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.

[1465] 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.

[1466] 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.

[1467] 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.

[1468] The following is further disclosed regarding the above embodiment.

[1469] (Claim 1)

[1470] a sensor that measures the weight, type and soiling of laundry;

[1471] a server that analyzes data obtained from the sensors and calculates the amounts of detergent and fabric softener and the timing of their addition;

[1472] a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on instructions from the server;

[1473] means for monitoring and controlling the operation of the terminal in real time via a smartphone app;

[1474] A system including:

[1475] (Claim 2)

[1476] 10. The system of claim 1, wherein the server includes an algorithm that calculates optimal amounts of detergent and fabric softener based on the type and soiling of laundry.

[1477] (Claim 3)

[1478] 10. The system of claim 1, wherein the smartphone app provides real-time monitoring of the laundry's progress and provides a means for the user to modify the laundry process mid-stream.

[1479] "Example 1"

[1480] (Claim 1)

[1481] a sensor that measures the weight, type and soiling of laundry;

[1482] an information processing device that analyzes data obtained from the sensor and calculates the amounts of detergent and fabric softener and the timing of their addition;

[1483] a device including a chemical supply device that automatically dispenses detergent and fabric softener based on instructions from the information processing device;

[1484] means for monitoring and controlling the operation of said device in real time via software on a mobile terminal;

[1485] A system including:

[1486] (Claim 2)

[1487] 2. The system of claim 1, wherein the information processing device comprises an algorithm for calculating optimal amounts of detergent and fabric softener based on the type and degree of soiling of laundry.

[1488] (Claim 3)

[1489] 10. The system of claim 1, wherein the mobile device software includes means for monitoring the progress of the laundry in real time and allowing the user to change the laundry process mid-process.

[1490] "Application Example 1"

[1491] (Claim 1)

[1492] a sensor that measures the weight, type and condition of the object being washed;

[1493] a server that analyzes the data obtained from the sensors and calculates the appropriate cleaning chemicals and their dosage timing;

[1494] a terminal equipped with a dispenser that automatically dispenses appropriate cleaning chemicals based on instructions from the server;

[1495] means for monitoring and controlling the operation of said terminal in real time via a mobile terminal application;

[1496] A means for automatically performing maintenance on manufacturing equipment and production lines;

[1497] A system including:

[1498] (Claim 2)

[1499] 10. The system of claim 1, wherein the server comprises an algorithm that calculates optimal amounts of cleaning chemicals based on the condition of the object.

[1500] (Claim 3)

[1501] 2. The system according to claim 1, wherein the mobile terminal application comprises means for monitoring the progress of maintenance in real time and enabling an administrator to change the work process midway.

[1502] "Example 2: Combining Emotion Engines"

[1503] (Claim 1)

[1504] a sensor that measures the weight, type and soiling of laundry;

[1505] a data processing device that analyzes data obtained from the sensor and calculates the amounts of detergent and fabric softener and the timing of their addition;

[1506] a terminal equipped with a dispensing device that automatically dispenses detergent and fabric softener based on instructions from the data processing device;

[1507] means for monitoring and controlling the operation of said terminal in real time via a mobile terminal application;

[1508] an emotion analysis device that analyzes a user's voice input and facial expression to recognize the user's emotional state;

[1509] A system including:

[1510] (Claim 2)

[1511] 10. The system of claim 1, wherein the data processing device includes an algorithm for calculating optimal amounts of detergent and fabric softener based on the type and soiling level of laundry.

[1512] (Claim 3)

[1513] 10. The system of claim 1, wherein the mobile terminal application provides real-time monitoring of the progress of the laundry and includes means for allowing the user to modify the laundry process mid-stream.

[1514] "Application example 2 when combining emotion engines"

[1515] (Claim 1)

[1516] a sensor that measures the weight, type and soiling of laundry;

[1517] a server that analyzes data obtained from the sensors and calculates the amounts of detergent and fabric softener and the timing of their addition;

[1518] a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on instructions from the server;

[1519] means for monitoring and controlling the operation of said terminal in real time via a mobile terminal application;

[1520] an emotion analysis engine built into the mobile terminal application that recognizes the user's emotions;

[1521] means for adjusting a laundry process based on the emotional state of the user recognized by the emotion analysis engine;

[1522] A system including:

[1523] (Claim 2)

[1524] 10. The system of claim 1, wherein the server includes an algorithm that calculates optimal amounts of detergent and fabric softener based on the type and soiling of laundry.

[1525] (Claim 3)

[1526] 10. The system of claim 1, wherein the mobile device application provides real-time monitoring of the progress of the laundry and provides a means for the user to modify the laundry process mid-stream. [Explanation of symbols]

[1527] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a sensor that measures the weight, type and soiling of laundry; a server that analyzes data obtained from the sensors and calculates the amounts of detergent and fabric softener and the timing of their addition; a terminal equipped with a dispenser that automatically dispenses detergent and fabric softener based on instructions from the server; means for monitoring and controlling the operation of the terminal in real time via a smartphone app; A system including:

2. 10. The system of claim 1, wherein the server includes an algorithm that calculates optimal amounts of detergent and fabric softener based on the type and soiling of laundry.

3. 10. The system of claim 1, wherein the smartphone app provides real-time monitoring of laundry progress and provides a means for the user to modify the laundry process mid-stream.

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