system

Abstract

We provide the system. [Solution] An input method for entering information about living spaces and residents' lifestyle data, A planning means for formulating an optimal cleaning plan based on the input means, Control means for controlling a cleaning machine that executes the cleaning plan formulated by the aforementioned planning means, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern shared households and households with busy lifestyles, it is difficult to secure time for cleaning, and there is a need for a system to perform cleaning efficiently and effectively. In such households, there is a problem that it is difficult to appropriately manage the cleaning schedule and know the optimal cleaning method according to each room.

Means for Solving the Problems

[0005] This invention is a system that formulates an optimal cleaning plan based on information about the living space and the residents' daily routines, and controls cleaning machines to execute that plan. This system detects dirt in real time and performs focused cleaning as needed. Furthermore, an assistant function that receives voice or text instructions from the user provides an efficient cleaning sequence and method, enabling effective cleaning without requiring much time or effort.

[0006] "Information about the living space" refers to physical layout information within a residence, such as the number and arrangement of rooms and the placement of furniture.

[0007] "Resident lifestyle data" refers to data on residents' daily behavior patterns and time zones, including information such as wake-up times, departure times, and return-home times.

[0008] "Input means" refers to interfaces or devices that users use to provide information to a system, and includes apps and sensors.

[0009] "Planning method" refers to an algorithm or processing device that creates an optimal cleaning plan based on input information about the living space and lifestyle data.

[0010] "Control means" refers to a set of hardware or software used to operate cleaning machines based on a formulated cleaning plan.

[0011] "Cleaning machine" refers to a mechanical device that performs cleaning automatically, and includes cleaning robots and the like.

[0012] "Dirt detection" refers to the use of sensors, cameras, etc., to identify the location and condition of dust, dirt, and other debris present on floors, furniture, etc.

[0013] The "assistant function" refers to the software's ability to provide cleaning advice and instructions in response to user requests. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system designed to streamline cleaning within homes for busy households. The system effectively performs cleaning tasks by formulating an optimal cleaning plan based on information about the living space and residents' lifestyle data, and by automatically controlling the cleaning machine. An embodiment of this system is described below.

[0036] Program processing:

[0037] 1. User registration and information entry

[0038] Users input information about their living space through a dedicated app. This includes the number of rooms and the arrangement of furniture.

[0039] Users set their own daily routines. This data is sent to and stored on the server.

[0040] 2. Developing an optimal cleaning plan

[0041] The server analyzes the received information. Using AI algorithms, it develops an optimal cleaning plan that takes into account the layout of the residence and the user's daily routine.

[0042] For example, you could set up a schedule to clean during times when people are away from home on weekdays.

[0043] 3. Control of the vacuum cleaner

[0044] The server sends the formulated plan to the cleaning machine. The cleaning machine (terminal) automatically starts cleaning based on the plan.

[0045] The vacuum cleaner uses sensors to detect dirt in real time and focuses on cleaning areas with heavy soiling.

[0046] 4. User Instructions and Assistant Functions

[0047] Users can give voice commands to the system using smart speakers or apps.

[0048] The server recognizes the instructions and uses its assistant function to suggest the optimal cleaning procedure to the user. For example, it might give instructions such as, "First clean the floor, then wipe the shelves."

[0049] 5. Data accumulation and plan improvement

[0050] Once the cleaning is complete, the results of the cleaning are sent to the server.

[0051] The server analyzes this data and uses it to plan future cleaning sessions. This allows for continuous improvement in cleaning efficiency.

[0052] The system described above allows users to significantly reduce the effort required for cleaning and perform cleaning efficiently. For example, by using the time when the house is normally empty, the vacuum cleaner can automatically clean, making it possible to keep the home clean at all times.

[0053] The following describes the processing flow.

[0054] Step 1:

[0055] Users input information about their living space and daily routine through a dedicated app. Specifically, they input details such as the number of rooms, furniture arrangement, and their usual wake-up and outing times.

[0056] Step 2:

[0057] The server receives input data from users and stores it in a database. This data is used to understand the layout of the residence and the user's lifestyle.

[0058] Step 3:

[0059] The server uses AI algorithms to create an optimal cleaning plan based on the living space and the residents' lifestyles. For example, it might schedule cleaning for times when the residents are away.

[0060] Step 4:

[0061] The server transmits the formulated cleaning plan to the cleaning machine, which acts as the terminal. The plan includes information such as the rooms to be cleaned, the start time, and the areas to focus on.

[0062] Step 5:

[0063] The device (vacuum cleaner) automatically starts cleaning based on the received plan. It uses built-in sensors to monitor the room's condition in real time, identify areas with heavy soiling, and clean those areas intensively.

[0064] Step 6:

[0065] Users can send real-time cleaning instructions to the server using smart speakers or apps as needed. For example, they might say, "Please clean the living room."

[0066] Step 7:

[0067] The server analyzes user instructions and uses its assistant function to transmit the optimal cleaning procedure to the terminal. It also provides the user with cleaning advice.

[0068] Step 8:

[0069] Once cleaning is complete, the device reports the cleaning results to the server. This report includes information such as the time taken for cleaning, the efficiency of the cleaning, and the location of any detected dirt.

[0070] Step 9:

[0071] The server records the received cleaning results in a database and uses them to improve future cleaning plans. This allows the system to continuously improve cleaning efficiency.

[0072] (Example 1)

[0073] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0074] In modern homes, efficiently and effectively cleaning living spaces is a time-consuming and physically demanding task for many people. Furthermore, conventional cleaning devices and systems rely on fixed cleaning plans, making them unable to adapt to real-time changes in conditions. The challenge lies in improving cleaning efficiency and providing flexible cleaning plans tailored to user needs.

[0075] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0076] In this invention, the server includes information acquisition means for inputting information about the living space and movement data of the occupants, planning processing means for formulating an optimal cleaning plan using an artificial intelligence algorithm, and device control means for transmitting the cleaning plan generated by the planning processing means to a cleaning device via communication technology and controlling the device. This makes it possible to dynamically and efficiently perform cleaning optimized for the user's living situation.

[0077] "Living space" refers to the physical space in which an individual or family conducts their daily life, and includes the environment within which rooms, furniture, and home appliances are located.

[0078] "Dynamic data" refers to information that reflects a user's daily routine and behavioral patterns, including data on daily activity time, movement, and location.

[0079] "Information acquisition means" refers to devices and interfaces used to input or collect data on a user's living space and movements, and specifically includes smartphone apps and sensors.

[0080] "Artificial intelligence algorithms" refer to mathematical and programmatic methods for analyzing data and automatically generating optimal cleaning plans.

[0081] "Planning and processing means" refers to a process or system function that creates an optimal cleaning plan based on acquired information.

[0082] "Communication technology" refers to the technology used to send and receive information between specific devices, and describes the mechanism for transferring data via a network environment.

[0083] "Cleaning equipment" refers to mechanical devices used to clean living spaces, and specifically includes robotic vacuum cleaners and automatic cleaning machines.

[0084] "Device control means" refers to system functions for managing and coordinating the operation of the cleaning device, and this includes programs and interfaces for instructing the execution of the cleaning plan.

[0085] "Dynamic control function" refers to a function that adjusts the operation of the cleaning device according to the real-time situation in order to perform cleaning efficiently.

[0086] "Voice recognition technology" refers to the technology that converts a user's voice commands into a digital format that a machine can understand.

[0087] A "learning algorithm" refers to a programmatic method that analyzes trends and optimizes based on past cleaning data to improve the planning of future cleaning.

[0088] This invention is implemented as a smart cleaning system for streamlining the maintenance and management of living spaces. This system utilizes dynamic data provided by the user to generate and execute an optimized cleaning plan.

[0089] First, users input information about their living space through a dedicated mobile application. This information includes room layout, size, furniture placement, and the user's daily routine. This information is transmitted to a server via the internet and stored in a cloud database.

[0090] The server uses artificial intelligence algorithms to develop an optimal cleaning plan based on the received information. This process utilizes Google® Cloud AI and AWS® machine learning services to perform data analysis quickly and accurately. The generated cleaning plan takes into account the layout of the living space and includes dynamic routes for efficient cleaning.

[0091] Next, the server sends the cleaning plan to the cleaning device. The cleaning device receives instructions from the server via Wi-Fi and automatically starts cleaning, using its built-in LiDAR sensors and cameras to avoid obstacles. This cleaning device has a dynamic control function that detects dirt in real time and focuses cleaning on areas that are particularly dirty.

[0092] Users can communicate voice commands or text instructions to the server via smart speakers or apps. Using voice recognition technology, the server understands user instructions and suggests the optimal cleaning procedure. After cleaning is complete, clean data is sent to the server to help improve future cleaning plans. The server analyzes this data and uses machine learning algorithms to continuously improve cleaning efficiency.

[0093] For example, if the living room is to be cleaned every Friday at 3 PM, the user can input a prompt message to the server instructing it to "start cleaning the living room at 3 PM on Friday." The server then creates a plan based on this instruction and has the cleaning device execute it.

[0094] This system enables efficient cleaning with minimal time and physical burden on the user.

[0095] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0096] Step 1:

[0097] Users open a dedicated application and input their living information and lifestyle data. Specifically, they enter the number and size of rooms, furniture arrangement, and daily activity times into the app. This data is sent to a server via the cloud. The input data includes numerical information, location information, and text information about time periods, which the server stores as structured data in preparation for subsequent processing.

[0098] Step 2:

[0099] The server uses an AI algorithm to analyze the received residential information and lifestyle data, and then formulates an optimal cleaning plan. Specifically, it uses a generative AI model to calculate the optimal cleaning route and timing, and designs an efficient work sequence. The input is the residential layout and lifestyle patterns, and the output is a cleaning plan that includes the cleaning area, route, and time schedule.

[0100] Step 3:

[0101] The server transmits the formulated cleaning plan to the cleaning device. Specifically, instructions such as the cleaning start time and cleaning route are sent to the cleaning device (terminal) via Wi-Fi. The input instruction data is transmitted in a command format that the terminal can decipher, and the terminal starts its operation based on this.

[0102] Step 4:

[0103] The device cleans by scanning the environment in real time according to the received plan. Specifically, it uses LiDAR sensors and cameras to scan the living space, avoiding obstacles and detecting dirt. The input is scan data obtained from the sensors, and the device adjusts its cleaning route based on this data to perform efficient cleaning. The output is fed back to the server as cleaning progress and completion data.

[0104] Step 5:

[0105] Once cleaning is complete, the terminal sends the cleaning results to the server. Specifically, it sends data such as the degree of dirt detected during cleaning, the time taken for cleaning, and the power used. The server analyzes this data and uses it to improve the next cleaning plan. The input is the cleaning result data, and the output is the optimized cleaning parameters generated after analysis.

[0106] Step 6:

[0107] Users can provide voice or text instructions to the server via smart speakers or apps as needed. The server uses voice recognition technology to interpret user requests and perform optimal suggestions or immediate cleaning actions. The input is the user's voice command, which the server analyzes and outputs the necessary actions and suggestions.

[0108] (Application Example 1)

[0109] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0110] In today's busy lifestyle, efficient and effective cleaning of homes is crucial. However, conventional cleaning equipment and methods struggle to flexibly adapt to the layout of homes and the lifestyles of residents, leading to increased effort and time spent on cleaning. Furthermore, the lack of feedback mechanisms to utilize the results of cleaning for future improvements makes it difficult to create more efficient cleaning plans.

[0111] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0112] In this invention, the server includes information input means for inputting the configuration of the living area and lifestyle patterns; plan generation means for generating an optimal work plan corresponding to the living area based on the information input means; operation means for operating an automatic cleaning device that carries out the work plan generated by the plan generation means; and result feedback means for acquiring the cleaning results after the automatic cleaning device has been operated and reflecting them in the next work plan. This makes it possible to clean the residence automatically and efficiently in accordance with the user's lifestyle.

[0113] A "residential area" is a zone that includes spaces and rooms where residents live and carry out their activities.

[0114] "Lifestyle patterns" refer to the habitual patterns and time-based flow of daily activities of residents, and efficient work plans are developed based on these patterns.

[0115] "Information input means" refers to methods and technologies for collecting data on residents and housing information and transmitting it to a computer.

[0116] "Plan generation means" refers to methods and techniques for constructing an optimal work plan based on the input information.

[0117] An "automatic cleaning device" refers to equipment that performs cleaning tasks based on a set plan without requiring manual operation.

[0118] "Operating means" refers to methods or techniques for controlling an automatic cleaning device and causing it to perform specific actions.

[0119] "Results feedback methods" refer to methods and techniques for collecting the results of cleaning work and providing analysis and information to improve future work.

[0120] In this embodiment, the server provides an input means for receiving information on the configuration of the living area and lifestyle patterns. Users input living space information via a dedicated application using a smartphone or tablet. The server also functions as a plan generation means based on the input information in a cloud computing environment, generating an optimal work plan using an AI algorithm. This can utilize machine learning services provided by the cloud provider.

[0121] The automated cleaning device, acting as the terminal, receives instructions from a server via Wi-Fi or Bluetooth and performs specific cleaning actions using its control mechanisms. For example, the device automatically cleans a room, taking into account the time when residents are away during the day. At this time, the device uses sensors to identify dirt in real time and can perform focused cleaning as needed.

[0122] Once cleaning is complete, the terminal sends the cleaning results to the server, which then uses a results feedback mechanism to incorporate the findings into the next work plan. This includes analyzing the cleaning performance data and identifying areas for improvement.

[0123] As a concrete example, if a user enters into the app, "Please clean the living room for one hour starting at 2 PM while I'm out," the AI ​​model will generate a cleaning plan based on this prompt. After the cleaning is complete, the user receives a notification saying, "Cleaning complete. We will optimize the next plan."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] Users input information about their living area (number of rooms, furniture arrangement) and lifestyle patterns (daily routine, time spent outside) using a dedicated app. This data is sent to and stored on a server. The entered information is used as base data necessary for subsequent plan generation.

[0127] Step 2:

[0128] Based on the residential area information and lifestyle pattern data received by the server, data analysis is performed using an AI model. In this process, the data acquired in step 1 is processed by an algorithm to generate a cleaning plan optimized for the residence. The output includes a proposal for specific cleaning dates and routes.

[0129] Step 3:

[0130] The server generates a cleaning plan and sends it to the automated cleaning device, which acts as the terminal. The terminal receives this plan via Wi-Fi or Bluetooth and, using its internal control system, begins operating according to the plan. Based on the distributed plan, the terminal calculates and executes an efficient movement path.

[0131] Step 4:

[0132] When the device performs cleaning, it uses sensors to detect dirt. Real-time identification of dirt and determination of priority cleaning areas are performed, and the cleaning intensity and path are dynamically adjusted. The output generates a history of highly efficient cleaning operations.

[0133] Step 5:

[0134] Once cleaning is complete, the terminal sends cleaning results and history data to the server. The server uses a results feedback mechanism to analyze this data and use it to plan future cleanings. This improves the accuracy and efficiency of cleaning plans.

[0135] Step 6:

[0136] Users receive notifications through the application regarding cleaning results and the next cleaning schedule. Feedback tailored to the user's lifestyle is provided, and they can modify the plan or add instructions as needed.

[0137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0138] This invention combines a system for efficiently cleaning within a residence with a function that recognizes and reflects the user's emotions. The system formulates a cleaning plan based on information about the living space and residents' lifestyle data, and further uses an emotion engine to propose and execute cleaning that takes the user's emotions into consideration.

[0139] Program processing:

[0140] 1. Data entry and analysis

[0141] Users input information about their living space through a dedicated app. Their daily routines are also registered in the app.

[0142] The server receives this information and begins analyzing it to formulate the optimal cleaning plan.

[0143] 2. Implementation of emotion recognition function

[0144] The emotion engine analyzes the user's voice and video to identify their current emotional state. For example, it might determine if the user is tired or relaxed.

[0145] This emotional data will be a crucial element for the server to adjust its cleaning plan.

[0146] 3. Optimizing the cleaning plan

[0147] The server adjusts the cleaning process and timing based on emotional data. For example, if a user is feeling stressed, it may postpone cleaning or provide relaxation-focused advice.

[0148] 4. Control of the vacuum cleaner

[0149] The server sends the cleaning plan to the terminal (vacuum cleaner). The vacuum cleaner automatically starts cleaning according to the plan.

[0150] The vacuum cleaner uses sensors to detect dirt in real time and cleans those areas intensively.

[0151] 5. User Interaction

[0152] Users can send voice commands to the server via smart speakers or apps.

[0153] The server analyzes these instructions and uses its assistant function to suggest the optimal cleaning sequence and method to the user. For example, it might offer specific advice such as, "You seem tired, so I recommend a quick and effective cleaning."

[0154] This allows the system to provide a comfortable living environment by enabling flexible cleaning that adapts to the user's lifestyle and emotional state. For example, if the system determines that the user is feeling very tired after returning home from work, it could offer a cleaning plan that can be completed in a short time, or suggest a function to play relaxing music.

[0155] The following describes the processing flow.

[0156] Step 1:

[0157] The user launches a dedicated app and enters information about their living space (e.g., number of rooms, furniture arrangement) and their daily routine (e.g., wake-up time, return-home time). This data is then sent to the server.

[0158] Step 2:

[0159] The server analyzes the received residential information and lifestyle data, and uses an AI algorithm to formulate a cleaning plan, including the optimal timing for cleaning. This plan is later transmitted to the terminal.

[0160] Step 3:

[0161] The emotion engine analyzes the user's voice and video in real time to identify their current emotional state. For example, it can collect emotional data such as whether the user is tired or stressed.

[0162] Step 4:

[0163] The server adjusts the cleaning plan based on emotional data. For example, if the user is feeling fatigued, it will suggest shortening the cleaning time to reduce the burden of cleaning.

[0164] Step 5:

[0165] The server sends the adjusted cleaning plan to the terminal (cleaning machine). The terminal follows the instructions and automatically starts cleaning at the specified time.

[0166] Step 6:

[0167] The vacuum cleaner uses built-in sensors to detect dirt in real time, identify areas with heavy soiling, and focus its cleaning efforts on those areas.

[0168] Step 7:

[0169] Users can send additional instructions to the server via voice or app. The server analyzes these instructions and further adjusts the cleaning plan as needed.

[0170] Step 8:

[0171] Once cleaning is complete, the terminal reports the cleaning results and implementation data to the server. The server receives this data, records it for future cleaning planning, and optimizes the system based on the accumulated data to achieve more efficient cleaning.

[0172] (Example 2)

[0173] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0174] The present invention aims to provide a system that can respond to individual user needs and changing circumstances that cannot be addressed by conventional technologies, by offering an efficient and flexible cleaning plan for cleaning work in living environments, taking into account the user's emotional state and the characteristics of the living space. In particular, it aims to reduce the burden felt by the user and realize a comfortable living environment.

[0175] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0176] In this invention, the server includes data acquisition means for inputting data on the characteristics of the living space and lifestyle habits; planning means for formulating a dynamic cleaning plan that corresponds to the user's emotional state, utilizing an emotion recognition function based on the data acquisition means; and management means for executing the cleaning plan formulated by the planning means and controlling a cleaning device that includes adjustments based on emotions. This enables optimal cleaning according to the user's emotions and circumstances.

[0177] 1. "Characteristics of the living space" refers to physical and structural information within a residence, such as room size, furniture arrangement, and type of flooring.

[0178] 2. "Lifestyle data" refers to information including the user's daily behavior patterns, how they spend their time, their wake-up and bedtime, and when they are at home and when they are out.

[0179] 3. "Data acquisition means" refers to means used to collect data on the characteristics of living spaces and lifestyle habits from users, and includes smartphone applications and sensors.

[0180] 4. "Emotion recognition function" refers to technology that analyzes and identifies the emotional state from the user's voice and video, and is a function for understanding the user's current psychological and emotional state.

[0181] 5. A "dynamic cleaning plan" refers to a plan that determines the most appropriate cleaning content and timing at any given time, based on the user's emotional state and lifestyle.

[0182] 6. "Formulation methods" refer to processes and algorithms used to plan the content, procedures, and timing of cleaning based on collected data.

[0183] 7. "Control means" refers to a control device or system used to cause a cleaning device to perform a planned cleaning or to make adjustments as necessary.

[0184] 8. "Cleaning equipment" refers to machines or facilities that automatically clean within a user's residence, and specifically includes cleaning robots.

[0185] This system aims to efficiently clean the user's living space. The entire system operates primarily through the collaboration of three parties: the server, the terminal, and the user.

[0186] First, users input information about their living space and lifestyle data using a smartphone app. This includes room size, furniture arrangement, wake-up time, and bedtime. The app then sends the data obtained from the user to a server. The app can be implemented on a platform that runs on a common mobile operating system.

[0187] The server receives this data and uses advanced machine learning algorithms to formulate the optimal cleaning plan. By incorporating emotion recognition capabilities, it analyzes the user's current emotional state from their voice and video data. Specifically, it can utilize a software module that integrates an AI module for emotion analysis.

[0188] The server utilizes emotional data to develop a dynamic cleaning plan tailored to the user's state. For example, if the user is stressed, it can postpone cleaning or suggest a shorter cleaning plan. The server then transmits the developed cleaning plan to the cleaning device via Wi-Fi or Bluetooth.

[0189] The cleaning device, acting as a terminal, automatically starts cleaning based on the cleaning plan received from the server. The device is equipped with high-precision sensors that monitor the floor condition in real time and select the optimal cleaning method as needed. This allows, for example, to focus cleaning on areas with significant dirt.

[0190] Furthermore, users can send voice commands to the server via smart speakers or apps. In response to the user's commands, the server suggests the most suitable cleaning method. For example, if a user requests, "I want a quick and effective cleaning today," the server might respond, "I recommend a quick cleaning mode in the living room."

[0191] For example, if a user inputs "I'm tired," the system will automatically play relaxing music and provide a plan to do some light cleaning. An example of how the generating AI model would be used in this case would be a prompt message like this: "Generate a program that suggests the most suitable short cleaning when the user is in a fatigued state. The data to be used will include the layout of the living space, furniture placement information, and emotional data obtained by analyzing the voice."

[0192] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0193] Step 1:

[0194] Users input information about their living space and lifestyle habits using a smartphone app. This includes information such as room size, furniture arrangement, wake-up time, and bedtime. The data entered by the user is immediately sent to a cloud server. The input data is used as the basic data for the living space model.

[0195] Step 2:

[0196] The server receives living space information and lifestyle data sent by the user. Based on the received data, a machine learning algorithm is used to generate an initial cleaning plan. During this process, the data is cleansed and processed, and transformed into input for the algorithm to plan the statistically optimal cleaning sequence. As output, a rough cleaning schedule for each day of the week is formulated.

[0197] Step 3:

[0198] The emotion recognition engine on the server begins analyzing voice and video data from the user. The primary input is the voice the user sends to their smart speaker. The engine analyzes this voice data and identifies the user's emotional state, classifying it as "fatigue," "relaxation," or "stress." This results in the output of real-time emotional state data.

[0199] Step 4:

[0200] The server dynamically adjusts the initially generated cleaning plan based on emotional state data. It reflects the results of the emotional analysis in the cleaning schedule; for example, if the user is experiencing stress, it changes or reduces the cleaning date and time. This process also includes automatic checks and balance adjustments, generating a newly adjusted cleaning plan as output.

[0201] Step 5:

[0202] The server sends a new cleaning plan to the cleaning device. The cleaning device, acting as the terminal, receives this plan and acts as instructed. The cleaning device constantly monitors the environment with its sensors and performs cleaning according to the plan. Here, a sensor system that detects dirt and obstacles collects data in real time and determines priority cleaning areas.

[0203] Step 6:

[0204] Users can send voice commands directly to the server via smart speakers or apps. For example, they might say, "Please finish cleaning in under 30 minutes." This user input is interpreted by the server and processed as a prompt to determine the most efficient cleaning method. The server responds with a voice suggestion and outputs a quick cleaning option to the user.

[0205] (Application Example 2)

[0206] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0207] Conventional cleaning systems for homes, which do not take into account the user's emotional state, have the problem of not being able to perform flexible cleaning that adapts to their lifestyle. Furthermore, when cleaning is performed without considering when the user wants to relax or is tired, it is difficult to maintain a comfortable living environment.

[0208] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0209] In this invention, the server includes an input means for inputting information about the living space and residents' lifestyle data, a planning means for formulating an optimal cleaning plan, and an emotion recognition means for recognizing the user's emotional state and reflecting it in the plan. This makes it possible to flexibly adjust the timing and content of cleaning according to the user's emotional state, thereby maintaining a comfortable living environment.

[0210] "Residential space" refers to the physical area where an individual or family lives, and includes the indoor environment, including furniture and fixtures.

[0211] "Lifestyle data" refers to information about residents' daily routines, behavioral patterns, and activities.

[0212] "Input means" refers to a function or device for collecting information about living spaces and lifestyle data and providing it to the system.

[0213] "Planning method" refers to the function or process for formulating an optimal cleaning plan based on the collected information.

[0214] "Emotion recognition means" refers to technology that analyzes a user's voice and video data to identify the user's current emotional state.

[0215] A "cleaning machine" is a device designed to clean automatically, removing dirt from floors and furniture without human intervention.

[0216] "Control means" refers to the functions or devices that operate the cleaning machine in order to carry out the planned cleaning action.

[0217] "Relaxation suggestions" refer to providing advice and actions aimed at reducing stress and promoting relaxation, taking into account the user's emotional state.

[0218] The system for realizing this invention includes the following main elements: The user inputs information about their living space and lifestyle data using a smartphone app. This information is transmitted to a server via an input means. The server analyzes the collected data and uses a planning means to formulate a cleaning plan. This planning process incorporates an emotion recognition means that analyzes the user's voice and video data to recognize their emotional state, thereby reflecting the user's emotions in the cleaning plan.

[0219] The server transmits the formulated cleaning plan to the vacuum cleaner. The vacuum cleaner starts cleaning according to the plan via a control system, detects dirt in real time, and focuses cleaning as needed. Users can give direct instructions via voice or text through smart speakers or applications. The assistant function analyzes this and provides the optimal cleaning sequence and relaxation suggestions.

[0220] For example, if a user returns home after a long day of work and is deemed tired, a plan for quick and effective cleaning is offered, along with a suggestion to play relaxation music. Furthermore, by using a generative AI model and inputting prompts such as "Suggest the best cleaning plan based on my emotional state today," cleaning activities tailored to the user's needs are executed.

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] Users input information about their living space and lifestyle data using a smartphone app. This input data is then stored on the device. This input includes, for example, room layout, furniture arrangement, number of residents, and daily lifestyle patterns. The user's voice and facial expressions are also collected as video data.

[0224] Step 2:

[0225] The device transmits the collected input data to a server via the internet. The server analyzes the received data. This analysis uses a generative AI model to integrate the input lifestyle data with emotional judgments to evaluate the user's current needs and emotional state. In this process, the data is interpreted to determine the optimal cleaning method.

[0226] Step 3:

[0227] The server develops an optimal cleaning plan based on the evaluation results. Using emotion recognition, it creates different plans depending on whether the user wants to relax, clean quickly, or request a thorough cleaning. The output is an optimized cleaning schedule and specific cleaning tasks.

[0228] Step 4:

[0229] The server transmits the formulated cleaning plan to the vacuum cleaner. The vacuum cleaner acts based on the specified plan, using room sensors to detect dirt and focusing on removing it. It adapts to the plan in real time and adjusts its operation as needed.

[0230] Step 5:

[0231] Users can send instructions via voice or text through smart speakers or apps. The server analyzes these instructions using its assistant function and updates the cleaning plan as needed. It also outputs additional information, such as relaxation suggestions, and provides advice tailored to the user's emotions and needs.

[0232] Step 6:

[0233] As a final output, the user receives a summary of the cleaning performed and related suggestions. If the prompt "Suggest the best cleaning plan based on today's emotional state" is entered, the user interface will display the most suitable cleaning results and relaxation suggestions.

[0234] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0235] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0236] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0240] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0241] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0242] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0243] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0244] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0245] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0246] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0247] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0248] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0249] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0250] This invention is a system designed to streamline cleaning within homes for busy households. The system effectively performs cleaning tasks by formulating an optimal cleaning plan based on information about the living space and residents' lifestyle data, and by automatically controlling the cleaning machine. An embodiment of this system is described below.

[0251] Program processing:

[0252] 1. User registration and information entry

[0253] Users input information about their living space through a dedicated app. This includes the number of rooms and the arrangement of furniture.

[0254] Users set their own daily routines. This data is sent to and stored on the server.

[0255] 2. Developing an optimal cleaning plan

[0256] The server analyzes the received information. Using AI algorithms, it develops an optimal cleaning plan that takes into account the layout of the residence and the user's daily routine.

[0257] For example, you could set up a schedule to clean during times when people are away from home on weekdays.

[0258] 3. Control of the vacuum cleaner

[0259] The server sends the formulated plan to the cleaning machine. The cleaning machine (terminal) automatically starts cleaning based on the plan.

[0260] The vacuum cleaner uses sensors to detect dirt in real time and focuses on cleaning areas with heavy soiling.

[0261] 4. User Instructions and Assistant Functions

[0262] Users can give voice commands to the system using smart speakers or apps.

[0263] The server recognizes the instructions and uses its assistant function to suggest the optimal cleaning procedure to the user. For example, it might give instructions such as, "First clean the floor, then wipe the shelves."

[0264] 5. Data accumulation and plan improvement

[0265] Once the cleaning is complete, the results of the cleaning are sent to the server.

[0266] The server analyzes this data and uses it to plan future cleaning sessions. This allows for continuous improvement in cleaning efficiency.

[0267] The system described above allows users to significantly reduce the effort required for cleaning and perform cleaning efficiently. For example, by using the time when the house is normally empty, the vacuum cleaner can automatically clean, making it possible to keep the home clean at all times.

[0268] The following describes the processing flow.

[0269] Step 1:

[0270] Users input information about their living space and daily routine through a dedicated app. Specifically, they input details such as the number of rooms, furniture arrangement, and their usual wake-up and outing times.

[0271] Step 2:

[0272] The server receives input data from users and stores it in a database. This data is used to understand the layout of the residence and the user's lifestyle.

[0273] Step 3:

[0274] The server uses AI algorithms to create an optimal cleaning plan based on the living space and the residents' lifestyles. For example, it might schedule cleaning for times when the residents are away.

[0275] Step 4:

[0276] The server transmits the formulated cleaning plan to the cleaning machine, which acts as the terminal. The plan includes information such as the rooms to be cleaned, the start time, and the areas to focus on.

[0277] Step 5:

[0278] The device (vacuum cleaner) automatically starts cleaning based on the received plan. It uses built-in sensors to monitor the room's condition in real time, identify areas with heavy soiling, and clean those areas intensively.

[0279] Step 6:

[0280] The user can send real-time cleaning instructions to the server using a smart speaker or an app as needed. For example, give an instruction like "Please clean the living room."

[0281] Step 7:

[0282] The server analyzes the instructions from the user and uses the assistant function to transmit the optimal cleaning procedure to the terminal. Also, provide the user with advice on cleaning.

[0283] Step 8:

[0284] When the cleaning is completed, the terminal reports the cleaning results to the server. This report includes the time required for cleaning, the efficiency of cleaning, the location of the detected dirt, etc.

[0285] Step 9:

[0286] The server records the received cleaning results in the database and utilizes them to improve the cleaning plan for subsequent times. Thereby, the system continuously improves the efficiency of cleaning.

[0287] (Example 1)

[0288] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In modern households, efficiently and effectively cleaning the living space is a time-consuming and physically burdensome task for many people. Also, in conventional cleaning devices and systems, cleaning is performed based on a fixed cleaning plan, and there is a problem that they cannot respond to real-time situation changes. Furthermore, the issue is how to improve cleaning efficiency and provide a flexible cleaning plan that meets the needs of users.

[0290] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0291] In this invention, the server includes information acquisition means for inputting information about the living space and movement data of the occupants, planning processing means for formulating an optimal cleaning plan using an artificial intelligence algorithm, and device control means for transmitting the cleaning plan generated by the planning processing means to a cleaning device via communication technology and controlling the device. This makes it possible to dynamically and efficiently perform cleaning optimized for the user's living situation.

[0292] "Living space" refers to the physical space in which an individual or family conducts their daily life, and includes the environment within which rooms, furniture, and home appliances are located.

[0293] "Dynamic data" refers to information that reflects a user's daily routine and behavioral patterns, including data on daily activity time, movement, and location.

[0294] "Information acquisition means" refers to devices and interfaces used to input or collect data on a user's living space and movements, and specifically includes smartphone apps and sensors.

[0295] "Artificial intelligence algorithms" refer to mathematical and programmatic methods for analyzing data and automatically generating optimal cleaning plans.

[0296] "Planning and processing means" refers to a process or system function that creates an optimal cleaning plan based on acquired information.

[0297] "Communication technology" refers to the technology used to send and receive information between specific devices, and describes the mechanism for transferring data via a network environment.

[0298] "Cleaning equipment" refers to mechanical devices used to clean living spaces, and specifically includes robotic vacuum cleaners and automatic cleaning machines.

[0299] "Device control means" refers to system functions for managing and coordinating the operation of the cleaning device, and this includes programs and interfaces for instructing the execution of the cleaning plan.

[0300] "Dynamic control function" refers to a function that adjusts the operation of the cleaning device according to the real-time situation in order to perform cleaning efficiently.

[0301] "Voice recognition technology" refers to the technology that converts a user's voice commands into a digital format that a machine can understand.

[0302] A "learning algorithm" refers to a programmatic method that analyzes trends and optimizes based on past cleaning data to improve the planning of future cleaning.

[0303] This invention is implemented as a smart cleaning system for streamlining the maintenance and management of living spaces. This system utilizes dynamic data provided by the user to generate and execute an optimized cleaning plan.

[0304] First, users input information about their living space through a dedicated mobile application. This information includes room layout, size, furniture placement, and the user's daily routine. This information is transmitted to a server via the internet and stored in a cloud database.

[0305] The server uses artificial intelligence algorithms to develop an optimal cleaning plan based on the received information. This process utilizes Google Cloud AI and AWS machine learning services to perform data analysis quickly and accurately. The generated cleaning plan takes into account the layout of the living space and includes dynamic routes for efficient cleaning.

[0306] Next, the server sends the cleaning plan to the cleaning device. The cleaning device receives instructions from the server via Wi-Fi and automatically starts cleaning while avoiding obstacles using the built-in LiDAR sensor and camera. This cleaning device has a dynamic control function that detects dirt in real time and focuses on cleaning areas with particularly heavy dirt.

[0307] The user can convey voice commands and text-based instructions to the server through a smart speaker or an app. Through voice recognition technology, the server understands the instructions from the user and proposes an optimal cleaning procedure. Also, after the cleaning is completed, clean data is sent to the server and used to improve the next cleaning plan. The server analyzes these data and continuously improves the efficiency of cleaning using machine learning algorithms.

[0308] For a specific example, when cleaning the living room at 3 pm on Friday every week, the user inputs a prompt sentence such as "Please start cleaning the living room at 3 pm on Friday" to give an instruction to the server. The server can then make a plan according to this instruction and let the cleaning device execute it.

[0309] This system enables efficient cleaning in a form that places less time and physical burden on the user.

[0310] The flow of the specific process in Example 1 will be described using FIG. 11.

[0311] Step 1:

[0312] The user opens a dedicated application and inputs residential information and life rhythm data. Specifically, the number of rooms, size, furniture arrangement, and daily activity time zones are input into the app. These data are sent to the server through the cloud. The data to be input is numerical information, location information, and text information of time zones. The server stores these as structured data in preparation for later processing. <00​​​

[0314] The server uses an AI algorithm to analyze the received residential information and lifestyle data, and then formulates an optimal cleaning plan. Specifically, it uses a generative AI model to calculate the optimal cleaning route and timing, and designs an efficient work sequence. The input is the residential layout and lifestyle patterns, and the output is a cleaning plan that includes the cleaning area, route, and time schedule.

[0315] Step 3:

[0316] The server transmits the formulated cleaning plan to the cleaning device. Specifically, instructions such as the cleaning start time and cleaning route are sent to the cleaning device (terminal) via Wi-Fi. The input instruction data is transmitted in a command format that the terminal can decipher, and the terminal starts its operation based on this.

[0317] Step 4:

[0318] The device cleans by scanning the environment in real time according to the received plan. Specifically, it uses LiDAR sensors and cameras to scan the living space, avoiding obstacles and detecting dirt. The input is scan data obtained from the sensors, and the device adjusts its cleaning route based on this data to perform efficient cleaning. The output is fed back to the server as cleaning progress and completion data.

[0319] Step 5:

[0320] Once cleaning is complete, the terminal sends the cleaning results to the server. Specifically, it sends data such as the degree of dirt detected during cleaning, the time taken for cleaning, and the power used. The server analyzes this data and uses it to improve the next cleaning plan. The input is the cleaning result data, and the output is the optimized cleaning parameters generated after analysis.

[0321] Step 6:

[0322] Users can provide voice or text instructions to the server via smart speakers or apps as needed. The server uses voice recognition technology to interpret user requests and perform optimal suggestions or immediate cleaning actions. The input is the user's voice command, which the server analyzes and outputs the necessary actions and suggestions.

[0323] (Application Example 1)

[0324] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0325] In today's busy lifestyle, efficient and effective cleaning of homes is crucial. However, conventional cleaning equipment and methods struggle to flexibly adapt to the layout of homes and the lifestyles of residents, leading to increased effort and time spent on cleaning. Furthermore, the lack of feedback mechanisms to utilize the results of cleaning for future improvements makes it difficult to create more efficient cleaning plans.

[0326] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0327] In this invention, the server includes information input means for inputting the configuration of the living area and lifestyle patterns; plan generation means for generating an optimal work plan corresponding to the living area based on the information input means; operation means for operating an automatic cleaning device that carries out the work plan generated by the plan generation means; and result feedback means for acquiring the cleaning results after the automatic cleaning device has been operated and reflecting them in the next work plan. This makes it possible to clean the residence automatically and efficiently in accordance with the user's lifestyle.

[0328] A "residential area" is a zone that includes spaces and rooms where residents live and carry out their activities.

[0329] "Lifestyle patterns" refer to the habitual patterns and time-based flow of daily activities of residents, and efficient work plans are developed based on these patterns.

[0330] "Information input means" refers to methods and technologies for collecting data on residents and housing information and transmitting it to a computer.

[0331] "Plan generation means" refers to methods and techniques for constructing an optimal work plan based on the input information.

[0332] An "automatic cleaning device" refers to equipment that performs cleaning tasks based on a set plan without requiring manual operation.

[0333] "Operating means" refers to methods or techniques for controlling an automatic cleaning device and causing it to perform specific actions.

[0334] "Results feedback methods" refer to methods and techniques for collecting the results of cleaning work and providing analysis and information to improve future work.

[0335] In this embodiment, the server provides an input means for receiving information on the configuration of the living area and lifestyle patterns. Users input living space information via a dedicated application using a smartphone or tablet. The server also functions as a plan generation means based on the input information in a cloud computing environment, generating an optimal work plan using an AI algorithm. This can utilize machine learning services provided by the cloud provider.

[0336] The automated cleaning device, acting as the terminal, receives instructions from a server via Wi-Fi or Bluetooth and performs specific cleaning actions using its control mechanisms. For example, the device automatically cleans a room, taking into account the time when residents are away during the day. At this time, the device uses sensors to identify dirt in real time and can perform focused cleaning as needed.

[0337] Once cleaning is complete, the terminal sends the cleaning results to the server, which then uses a results feedback mechanism to incorporate the findings into the next work plan. This includes analyzing the cleaning performance data and identifying areas for improvement.

[0338] As a concrete example, if a user enters into the app, "Please clean the living room for one hour starting at 2 PM while I'm out," the AI ​​model will generate a cleaning plan based on this prompt. After the cleaning is complete, the user receives a notification saying, "Cleaning complete. We will optimize the next plan."

[0339] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0340] Step 1:

[0341] Users input information about their living area (number of rooms, furniture arrangement) and lifestyle patterns (daily routine, time spent outside) using a dedicated app. This data is sent to and stored on a server. The entered information is used as base data necessary for subsequent plan generation.

[0342] Step 2:

[0343] Based on the residential area information and lifestyle pattern data received by the server, data analysis is performed using an AI model. In this process, the data acquired in step 1 is processed by an algorithm to generate a cleaning plan optimized for the residence. The output includes a proposal for specific cleaning dates and routes.

[0344] Step 3:

[0345] The server generates a cleaning plan and sends it to the automated cleaning device, which acts as the terminal. The terminal receives this plan via Wi-Fi or Bluetooth and, using its internal control system, begins operating according to the plan. Based on the distributed plan, the terminal calculates and executes an efficient movement path.

[0346] Step 4:

[0347] When the device performs cleaning, it uses sensors to detect dirt. Real-time identification of dirt and determination of priority cleaning areas are performed, and the cleaning intensity and path are dynamically adjusted. The output generates a history of highly efficient cleaning operations.

[0348] Step 5:

[0349] Once cleaning is complete, the terminal sends cleaning results and history data to the server. The server uses a results feedback mechanism to analyze this data and use it to plan future cleanings. This improves the accuracy and efficiency of cleaning plans.

[0350] Step 6:

[0351] Users receive notifications through the application regarding cleaning results and the next cleaning schedule. Feedback tailored to the user's lifestyle is provided, and they can modify the plan or add instructions as needed.

[0352] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0353] This invention combines a system for efficiently cleaning within a residence with a function that recognizes and reflects the user's emotions. The system formulates a cleaning plan based on information about the living space and residents' lifestyle data, and further uses an emotion engine to propose and execute cleaning that takes the user's emotions into consideration.

[0354] Program processing:

[0355] 1. Data entry and analysis

[0356] Users input information about their living space through a dedicated app. Their daily routines are also registered in the app.

[0357] The server receives this information and begins analyzing it to formulate the optimal cleaning plan.

[0358] 2. Implementation of emotion recognition function

[0359] The emotion engine analyzes the user's voice and video to identify their current emotional state. For example, it might determine if the user is tired or relaxed.

[0360] This emotional data will be a crucial element for the server to adjust its cleaning plan.

[0361] 3. Optimizing the cleaning plan

[0362] The server adjusts the cleaning process and timing based on emotional data. For example, if a user is feeling stressed, it may postpone cleaning or provide relaxation-focused advice.

[0363] 4. Control of the vacuum cleaner

[0364] The server sends the cleaning plan to the terminal (vacuum cleaner). The vacuum cleaner automatically starts cleaning according to the plan.

[0365] The vacuum cleaner uses sensors to detect dirt in real time and cleans those areas intensively.

[0366] 5. User Interaction

[0367] Users can send voice commands to the server via smart speakers or apps.

[0368] The server analyzes these instructions and uses its assistant function to suggest the optimal cleaning sequence and method to the user. For example, it might offer specific advice such as, "You seem tired, so I recommend a quick and effective cleaning."

[0369] This allows the system to provide a comfortable living environment by enabling flexible cleaning that adapts to the user's lifestyle and emotional state. For example, if the system determines that the user is feeling very tired after returning home from work, it could offer a cleaning plan that can be completed in a short time, or suggest a function to play relaxing music.

[0370] The following describes the processing flow.

[0371] Step 1:

[0372] The user launches a dedicated app and enters information about their living space (e.g., number of rooms, furniture arrangement) and their daily routine (e.g., wake-up time, return-home time). This data is then sent to the server.

[0373] Step 2:

[0374] The server analyzes the received residential information and lifestyle data, and uses an AI algorithm to formulate a cleaning plan, including the optimal timing for cleaning. This plan is later transmitted to the terminal.

[0375] Step 3:

[0376] The emotion engine analyzes the user's voice and video in real time to identify their current emotional state. For example, it can collect emotional data such as whether the user is tired or stressed.

[0377] Step 4:

[0378] The server adjusts the cleaning plan based on emotional data. For example, if the user is feeling fatigued, it will suggest shortening the cleaning time to reduce the burden of cleaning.

[0379] Step 5:

[0380] The server sends the adjusted cleaning plan to the terminal (cleaning machine). The terminal follows the instructions and automatically starts cleaning at the specified time.

[0381] Step 6:

[0382] The vacuum cleaner uses built-in sensors to detect dirt in real time, identify areas with heavy soiling, and focus its cleaning efforts on those areas.

[0383] Step 7:

[0384] Users can send additional instructions to the server via voice or app. The server analyzes these instructions and further adjusts the cleaning plan as needed.

[0385] Step 8:

[0386] Once cleaning is complete, the terminal reports the cleaning results and implementation data to the server. The server receives this data, records it for future cleaning planning, and optimizes the system based on the accumulated data to achieve more efficient cleaning.

[0387] (Example 2)

[0388] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0389] The present invention aims to provide a system that can respond to individual user needs and changing circumstances that cannot be addressed by conventional technologies, by offering an efficient and flexible cleaning plan for cleaning work in living environments, taking into account the user's emotional state and the characteristics of the living space. In particular, it aims to reduce the burden felt by the user and realize a comfortable living environment.

[0390] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0391] In this invention, the server includes data acquisition means for inputting data on the characteristics of the living space and lifestyle habits; planning means for formulating a dynamic cleaning plan that corresponds to the user's emotional state, utilizing an emotion recognition function based on the data acquisition means; and management means for executing the cleaning plan formulated by the planning means and controlling a cleaning device that includes adjustments based on emotions. This enables optimal cleaning according to the user's emotions and circumstances.

[0392] 1. "Characteristics of the living space" refers to physical and structural information within a residence, such as room size, furniture arrangement, and type of flooring.

[0393] 2. "Lifestyle data" refers to information including the user's daily behavior patterns, how they spend their time, their wake-up and bedtime, and when they are at home and when they are out.

[0394] 3. "Data acquisition means" refers to means used to collect data on the characteristics of living spaces and lifestyle habits from users, and includes smartphone applications and sensors.

[0395] 4. "Emotion recognition function" refers to technology that analyzes and identifies the emotional state from the user's voice and video, and is a function for understanding the user's current psychological and emotional state.

[0396] 5. A "dynamic cleaning plan" refers to a plan that determines the most appropriate cleaning content and timing at any given time, based on the user's emotional state and lifestyle.

[0397] 6. "Formulation methods" refer to processes and algorithms used to plan the content, procedures, and timing of cleaning based on collected data.

[0398] 7. "Control means" refers to a control device or system used to cause a cleaning device to perform a planned cleaning or to make adjustments as necessary.

[0399] 8. "Cleaning equipment" refers to machines or facilities that automatically clean within a user's residence, and specifically includes cleaning robots.

[0400] This system aims to efficiently clean the user's living space. The entire system operates primarily through the collaboration of three parties: the server, the terminal, and the user.

[0401] First, users input information about their living space and lifestyle data using a smartphone app. This includes room size, furniture arrangement, wake-up time, and bedtime. The app then sends the data obtained from the user to a server. The app can be implemented on a platform that runs on a common mobile operating system.

[0402] The server receives this data and uses advanced machine learning algorithms to formulate the optimal cleaning plan. By incorporating emotion recognition capabilities, it analyzes the user's current emotional state from their voice and video data. Specifically, it can utilize a software module that integrates an AI module for emotion analysis.

[0403] The server utilizes emotional data to develop a dynamic cleaning plan tailored to the user's state. For example, if the user is stressed, it can postpone cleaning or suggest a shorter cleaning plan. The server then transmits the developed cleaning plan to the cleaning device via Wi-Fi or Bluetooth.

[0404] The cleaning device, acting as a terminal, automatically starts cleaning based on the cleaning plan received from the server. The device is equipped with high-precision sensors that monitor the floor condition in real time and select the optimal cleaning method as needed. This allows, for example, to focus cleaning on areas with significant dirt.

[0405] Furthermore, users can send voice commands to the server via smart speakers or apps. In response to the user's commands, the server suggests the most suitable cleaning method. For example, if a user requests, "I want a quick and effective cleaning today," the server might respond, "I recommend a quick cleaning mode in the living room."

[0406] For example, if a user inputs "I'm tired," the system will automatically play relaxing music and provide a plan to do some light cleaning. An example of how the generating AI model would be used in this case would be a prompt message like this: "Generate a program that suggests the most suitable short cleaning when the user is in a fatigued state. The data to be used will include the layout of the living space, furniture placement information, and emotional data obtained by analyzing the voice."

[0407] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0408] Step 1:

[0409] Users input information about their living space and lifestyle habits using a smartphone app. This includes information such as room size, furniture arrangement, wake-up time, and bedtime. The data entered by the user is immediately sent to a cloud server. The input data is used as the basic data for the living space model.

[0410] Step 2:

[0411] The server receives living space information and lifestyle data sent by the user. Based on the received data, a machine learning algorithm is used to generate an initial cleaning plan. During this process, the data is cleansed and processed, and transformed into input for the algorithm to plan the statistically optimal cleaning sequence. As output, a rough cleaning schedule for each day of the week is formulated.

[0412] Step 3:

[0413] The emotion recognition engine on the server begins analyzing voice and video data from the user. The primary input is the voice the user sends to their smart speaker. The engine analyzes this voice data and identifies the user's emotional state, classifying it as "fatigue," "relaxation," or "stress." This results in the output of real-time emotional state data.

[0414] Step 4:

[0415] The server dynamically adjusts the initially generated cleaning plan based on emotional state data. It reflects the results of the emotional analysis in the cleaning schedule; for example, if the user is experiencing stress, it changes or reduces the cleaning date and time. This process also includes automatic checks and balance adjustments, generating a newly adjusted cleaning plan as output.

[0416] Step 5:

[0417] The server sends a new cleaning plan to the cleaning device. The cleaning device, acting as the terminal, receives this plan and acts as instructed. The cleaning device constantly monitors the environment with its sensors and performs cleaning according to the plan. Here, a sensor system that detects dirt and obstacles collects data in real time and determines priority cleaning areas.

[0418] Step 6:

[0419] Users can send voice commands directly to the server via smart speakers or apps. For example, they might say, "Please finish cleaning in under 30 minutes." This user input is interpreted by the server and processed as a prompt to determine the most efficient cleaning method. The server responds with a voice suggestion and outputs a quick cleaning option to the user.

[0420] (Application Example 2)

[0421] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0422] Conventional cleaning systems for homes, which do not take into account the user's emotional state, have the problem of not being able to perform flexible cleaning that adapts to their lifestyle. Furthermore, when cleaning is performed without considering when the user wants to relax or is tired, it is difficult to maintain a comfortable living environment.

[0423] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0424] In this invention, the server includes an input means for inputting information about the living space and residents' lifestyle data, a planning means for formulating an optimal cleaning plan, and an emotion recognition means for recognizing the user's emotional state and reflecting it in the plan. This makes it possible to flexibly adjust the timing and content of cleaning according to the user's emotional state, thereby maintaining a comfortable living environment.

[0425] "Residential space" refers to the physical area where an individual or family lives, and includes the indoor environment, including furniture and fixtures.

[0426] "Lifestyle data" refers to information about residents' daily routines, behavioral patterns, and activities.

[0427] "Input means" refers to a function or device for collecting information about living spaces and lifestyle data and providing it to the system.

[0428] "Planning method" refers to the function or process for formulating an optimal cleaning plan based on the collected information.

[0429] "Emotion recognition means" refers to technology that analyzes a user's voice and video data to identify the user's current emotional state.

[0430] A "cleaning machine" is a device designed to clean automatically, removing dirt from floors and furniture without human intervention.

[0431] "Control means" refers to the functions or devices that operate the cleaning machine in order to carry out the planned cleaning action.

[0432] "Relaxation suggestions" refer to providing advice and actions aimed at reducing stress and promoting relaxation, taking into account the user's emotional state.

[0433] The system for realizing this invention includes the following main elements: The user inputs information about their living space and lifestyle data using a smartphone app. This information is transmitted to a server via an input means. The server analyzes the collected data and uses a planning means to formulate a cleaning plan. This planning process incorporates an emotion recognition means that analyzes the user's voice and video data to recognize their emotional state, thereby reflecting the user's emotions in the cleaning plan.

[0434] The server transmits the formulated cleaning plan to the vacuum cleaner. The vacuum cleaner starts cleaning according to the plan via a control system, detects dirt in real time, and focuses cleaning as needed. Users can give direct instructions via voice or text through smart speakers or applications. The assistant function analyzes this and provides the optimal cleaning sequence and relaxation suggestions.

[0435] For example, if a user returns home after a long day of work and is deemed tired, a plan for quick and effective cleaning is offered, along with a suggestion to play relaxation music. Furthermore, by using a generative AI model and inputting prompts such as "Suggest the best cleaning plan based on my emotional state today," cleaning activities tailored to the user's needs are executed.

[0436] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0437] Step 1:

[0438] Users input information about their living space and lifestyle data using a smartphone app. This input data is then stored on the device. This input includes, for example, room layout, furniture arrangement, number of residents, and daily lifestyle patterns. The user's voice and facial expressions are also collected as video data.

[0439] Step 2:

[0440] The device transmits the collected input data to a server via the internet. The server analyzes the received data. This analysis uses a generative AI model to integrate the input lifestyle data with emotional judgments to evaluate the user's current needs and emotional state. In this process, the data is interpreted to determine the optimal cleaning method.

[0441] Step 3:

[0442] The server develops an optimal cleaning plan based on the evaluation results. Using emotion recognition, it creates different plans depending on whether the user wants to relax, clean quickly, or request a thorough cleaning. The output is an optimized cleaning schedule and specific cleaning tasks.

[0443] Step 4:

[0444] The server transmits the formulated cleaning plan to the vacuum cleaner. The vacuum cleaner acts based on the specified plan, using room sensors to detect dirt and focusing on removing it. It adapts to the plan in real time and adjusts its operation as needed.

[0445] Step 5:

[0446] Users can send instructions via voice or text through smart speakers or apps. The server analyzes these instructions using its assistant function and updates the cleaning plan as needed. It also outputs additional information, such as relaxation suggestions, and provides advice tailored to the user's emotions and needs.

[0447] Step 6:

[0448] As a final output, the user receives a summary of the cleaning performed and related suggestions. If the prompt "Suggest the best cleaning plan based on today's emotional state" is entered, the user interface will display the most suitable cleaning results and relaxation suggestions.

[0449] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0450] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0451] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0452] [Third Embodiment]

[0453] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0454] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0455] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0456] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0457] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0458] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0459] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0460] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0461] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0462] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0463] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0464] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0465] This invention is a system designed to streamline cleaning within homes for busy households. The system effectively performs cleaning tasks by formulating an optimal cleaning plan based on information about the living space and residents' lifestyle data, and by automatically controlling the cleaning machine. An embodiment of this system is described below.

[0466] Program processing:

[0467] 1. User registration and information entry

[0468] Users input information about their living space through a dedicated app. This includes the number of rooms and the arrangement of furniture.

[0469] Users set their own daily routines. This data is sent to and stored on the server.

[0470] 2. Developing an optimal cleaning plan

[0471] The server analyzes the received information. Using AI algorithms, it develops an optimal cleaning plan that takes into account the layout of the residence and the user's daily routine.

[0472] For example, you could set up a schedule to clean during times when people are away from home on weekdays.

[0473] 3. Control of the vacuum cleaner

[0474] The server sends the formulated plan to the cleaning machine. The cleaning machine (terminal) automatically starts cleaning based on the plan.

[0475] The vacuum cleaner uses sensors to detect dirt in real time and focuses on cleaning areas with heavy soiling.

[0476] 4. User Instructions and Assistant Functions

[0477] Users can give voice commands to the system using smart speakers or apps.

[0478] The server recognizes the instructions and uses its assistant function to suggest the optimal cleaning procedure to the user. For example, it might give instructions such as, "First clean the floor, then wipe the shelves."

[0479] 5. Data accumulation and plan improvement

[0480] Once the cleaning is complete, the results of the cleaning are sent to the server.

[0481] The server analyzes this data and uses it to plan future cleaning sessions. This allows for continuous improvement in cleaning efficiency.

[0482] The system described above allows users to significantly reduce the effort required for cleaning and perform cleaning efficiently. For example, by using the time when the house is normally empty, the vacuum cleaner can automatically clean, making it possible to keep the home clean at all times.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] Users input information about their living space and daily routine through a dedicated app. Specifically, they input details such as the number of rooms, furniture arrangement, and their usual wake-up and outing times.

[0486] Step 2:

[0487] The server receives input data from users and stores it in a database. This data is used to understand the layout of the residence and the user's lifestyle.

[0488] Step 3:

[0489] The server uses AI algorithms to create an optimal cleaning plan based on the living space and the residents' lifestyles. For example, it might schedule cleaning for times when the residents are away.

[0490] Step 4:

[0491] The server transmits the formulated cleaning plan to the cleaning machine, which acts as the terminal. The plan includes information such as the rooms to be cleaned, the start time, and the areas to focus on.

[0492] Step 5:

[0493] The device (vacuum cleaner) automatically starts cleaning based on the received plan. It uses built-in sensors to monitor the room's condition in real time, identify areas with heavy soiling, and clean those areas intensively.

[0494] Step 6:

[0495] Users can send real-time cleaning instructions to the server using smart speakers or apps as needed. For example, they might say, "Please clean the living room."

[0496] Step 7:

[0497] The server analyzes user instructions and uses its assistant function to transmit the optimal cleaning procedure to the terminal. It also provides the user with cleaning advice.

[0498] Step 8:

[0499] Once cleaning is complete, the device reports the cleaning results to the server. This report includes information such as the time taken for cleaning, the efficiency of the cleaning, and the location of any detected dirt.

[0500] Step 9:

[0501] The server records the received cleaning results in a database and uses them to improve future cleaning plans. This allows the system to continuously improve cleaning efficiency.

[0502] (Example 1)

[0503] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0504] In modern homes, efficiently and effectively cleaning living spaces is a time-consuming and physically demanding task for many people. Furthermore, conventional cleaning devices and systems rely on fixed cleaning plans, making them unable to adapt to real-time changes in conditions. The challenge lies in improving cleaning efficiency and providing flexible cleaning plans tailored to user needs.

[0505] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0506] In this invention, the server includes information acquisition means for inputting information about the living space and movement data of the occupants, planning processing means for formulating an optimal cleaning plan using an artificial intelligence algorithm, and device control means for transmitting the cleaning plan generated by the planning processing means to a cleaning device via communication technology and controlling the device. This makes it possible to dynamically and efficiently perform cleaning optimized for the user's living situation.

[0507] "Living space" refers to the physical space in which an individual or family conducts their daily life, and includes the environment within which rooms, furniture, and home appliances are located.

[0508] "Dynamic data" refers to information that reflects a user's daily routine and behavioral patterns, including data on daily activity time, movement, and location.

[0509] "Information acquisition means" refers to devices and interfaces used to input or collect data on a user's living space and movements, and specifically includes smartphone apps and sensors.

[0510] "Artificial intelligence algorithms" refer to mathematical and programmatic methods for analyzing data and automatically generating optimal cleaning plans.

[0511] "Planning and processing means" refers to a process or system function that creates an optimal cleaning plan based on acquired information.

[0512] "Communication technology" refers to the technology used to send and receive information between specific devices, and describes the mechanism for transferring data via a network environment.

[0513] "Cleaning equipment" refers to mechanical devices used to clean living spaces, and specifically includes robotic vacuum cleaners and automatic cleaning machines.

[0514] "Device control means" refers to system functions for managing and coordinating the operation of the cleaning device, and this includes programs and interfaces for instructing the execution of the cleaning plan.

[0515] "Dynamic control function" refers to a function that adjusts the operation of the cleaning device according to the real-time situation in order to perform cleaning efficiently.

[0516] "Voice recognition technology" refers to the technology that converts a user's voice commands into a digital format that a machine can understand.

[0517] A "learning algorithm" refers to a programmatic method that analyzes trends and optimizes based on past cleaning data to improve the planning of future cleaning.

[0518] This invention is implemented as a smart cleaning system for streamlining the maintenance and management of living spaces. This system utilizes dynamic data provided by the user to generate and execute an optimized cleaning plan.

[0519] First, users input information about their living space through a dedicated mobile application. This information includes room layout, size, furniture placement, and the user's daily routine. This information is transmitted to a server via the internet and stored in a cloud database.

[0520] The server uses artificial intelligence algorithms to develop an optimal cleaning plan based on the received information. This process utilizes Google Cloud AI and AWS machine learning services to perform data analysis quickly and accurately. The generated cleaning plan takes into account the layout of the living space and includes dynamic routes for efficient cleaning.

[0521] Next, the server sends the cleaning plan to the cleaning device. The cleaning device receives instructions from the server via Wi-Fi and automatically starts cleaning, using its built-in LiDAR sensors and cameras to avoid obstacles. This cleaning device has a dynamic control function that detects dirt in real time and focuses cleaning on areas that are particularly dirty.

[0522] Users can communicate voice commands or text instructions to the server via smart speakers or apps. Using voice recognition technology, the server understands user instructions and suggests the optimal cleaning procedure. After cleaning is complete, clean data is sent to the server to help improve future cleaning plans. The server analyzes this data and uses machine learning algorithms to continuously improve cleaning efficiency.

[0523] For example, if the living room is to be cleaned every Friday at 3 PM, the user can input a prompt message to the server instructing it to "start cleaning the living room at 3 PM on Friday." The server then creates a plan based on this instruction and has the cleaning device execute it.

[0524] This system enables efficient cleaning with minimal time and physical burden on the user.

[0525] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0526] Step 1:

[0527] Users open a dedicated application and input their living information and lifestyle data. Specifically, they enter the number and size of rooms, furniture arrangement, and daily activity times into the app. This data is sent to a server via the cloud. The input data includes numerical information, location information, and text information about time periods, which the server stores as structured data in preparation for subsequent processing.

[0528] Step 2:

[0529] The server uses an AI algorithm to analyze the received residential information and lifestyle data, and then formulates an optimal cleaning plan. Specifically, it uses a generative AI model to calculate the optimal cleaning route and timing, and designs an efficient work sequence. The input is the residential layout and lifestyle patterns, and the output is a cleaning plan that includes the cleaning area, route, and time schedule.

[0530] Step 3:

[0531] The server transmits the formulated cleaning plan to the cleaning device. Specifically, instructions such as the cleaning start time and cleaning route are sent to the cleaning device (terminal) via Wi-Fi. The input instruction data is transmitted in a command format that the terminal can decipher, and the terminal starts its operation based on this.

[0532] Step 4:

[0533] The device cleans by scanning the environment in real time according to the received plan. Specifically, it uses LiDAR sensors and cameras to scan the living space, avoiding obstacles and detecting dirt. The input is scan data obtained from the sensors, and the device adjusts its cleaning route based on this data to perform efficient cleaning. The output is fed back to the server as cleaning progress and completion data.

[0534] Step 5:

[0535] Once cleaning is complete, the terminal sends the cleaning results to the server. Specifically, it sends data such as the degree of dirt detected during cleaning, the time taken for cleaning, and the power used. The server analyzes this data and uses it to improve the next cleaning plan. The input is the cleaning result data, and the output is the optimized cleaning parameters generated after analysis.

[0536] Step 6:

[0537] Users can provide voice or text instructions to the server via smart speakers or apps as needed. The server uses voice recognition technology to interpret user requests and perform optimal suggestions or immediate cleaning actions. The input is the user's voice command, which the server analyzes and outputs the necessary actions and suggestions.

[0538] (Application Example 1)

[0539] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0540] In today's busy lifestyle, efficient and effective cleaning of homes is crucial. However, conventional cleaning equipment and methods struggle to flexibly adapt to the layout of homes and the lifestyles of residents, leading to increased effort and time spent on cleaning. Furthermore, the lack of feedback mechanisms to utilize the results of cleaning for future improvements makes it difficult to create more efficient cleaning plans.

[0541] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0542] In this invention, the server includes information input means for inputting the configuration of the living area and lifestyle patterns; plan generation means for generating an optimal work plan corresponding to the living area based on the information input means; operation means for operating an automatic cleaning device that carries out the work plan generated by the plan generation means; and result feedback means for acquiring the cleaning results after the automatic cleaning device has been operated and reflecting them in the next work plan. This makes it possible to clean the residence automatically and efficiently in accordance with the user's lifestyle.

[0543] A "residential area" is a zone that includes spaces and rooms where residents live and carry out their activities.

[0544] "Lifestyle patterns" refer to the habitual patterns and time-based flow of daily activities of residents, and efficient work plans are developed based on these patterns.

[0545] "Information input means" refers to methods and technologies for collecting data on residents and housing information and transmitting it to a computer.

[0546] "Plan generation means" refers to methods and techniques for constructing an optimal work plan based on the input information.

[0547] An "automatic cleaning device" refers to equipment that performs cleaning tasks based on a set plan without requiring manual operation.

[0548] "Operating means" refers to methods or techniques for controlling an automatic cleaning device and causing it to perform specific actions.

[0549] "Results feedback methods" refer to methods and techniques for collecting the results of cleaning work and providing analysis and information to improve future work.

[0550] In this embodiment, the server provides an input means for receiving information on the configuration of the living area and lifestyle patterns. Users input living space information via a dedicated application using a smartphone or tablet. The server also functions as a plan generation means based on the input information in a cloud computing environment, generating an optimal work plan using an AI algorithm. This can utilize machine learning services provided by the cloud provider.

[0551] The automated cleaning device, acting as the terminal, receives instructions from a server via Wi-Fi or Bluetooth and performs specific cleaning actions using its control mechanisms. For example, the device automatically cleans a room, taking into account the time when residents are away during the day. At this time, the device uses sensors to identify dirt in real time and can perform focused cleaning as needed.

[0552] Once cleaning is complete, the terminal sends the cleaning results to the server, which then uses a results feedback mechanism to incorporate the findings into the next work plan. This includes analyzing the cleaning performance data and identifying areas for improvement.

[0553] As a concrete example, if a user enters into the app, "Please clean the living room for one hour starting at 2 PM while I'm out," the AI ​​model will generate a cleaning plan based on this prompt. After the cleaning is complete, the user receives a notification saying, "Cleaning complete. We will optimize the next plan."

[0554] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0555] Step 1:

[0556] Users input information about their living area (number of rooms, furniture arrangement) and lifestyle patterns (daily routine, time spent outside) using a dedicated app. This data is sent to and stored on a server. The entered information is used as base data necessary for subsequent plan generation.

[0557] Step 2:

[0558] Based on the residential area information and lifestyle pattern data received by the server, data analysis is performed using an AI model. In this process, the data acquired in step 1 is processed by an algorithm to generate a cleaning plan optimized for the residence. The output includes a proposal for specific cleaning dates and routes.

[0559] Step 3:

[0560] The server generates a cleaning plan and sends it to the automated cleaning device, which acts as the terminal. The terminal receives this plan via Wi-Fi or Bluetooth and, using its internal control system, begins operating according to the plan. Based on the distributed plan, the terminal calculates and executes an efficient movement path.

[0561] Step 4:

[0562] When the device performs cleaning, it uses sensors to detect dirt. Real-time identification of dirt and determination of priority cleaning areas are performed, and the cleaning intensity and path are dynamically adjusted. The output generates a history of highly efficient cleaning operations.

[0563] Step 5:

[0564] Once cleaning is complete, the terminal sends cleaning results and history data to the server. The server uses a results feedback mechanism to analyze this data and use it to plan future cleanings. This improves the accuracy and efficiency of cleaning plans.

[0565] Step 6:

[0566] Users receive notifications through the application regarding cleaning results and the next cleaning schedule. Feedback tailored to the user's lifestyle is provided, and they can modify the plan or add instructions as needed.

[0567] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0568] This invention combines a system for efficiently cleaning within a residence with a function that recognizes and reflects the user's emotions. The system formulates a cleaning plan based on information about the living space and residents' lifestyle data, and further uses an emotion engine to propose and execute cleaning that takes the user's emotions into consideration.

[0569] Program processing:

[0570] 1. Data entry and analysis

[0571] Users input information about their living space through a dedicated app. Their daily routines are also registered in the app.

[0572] The server receives this information and begins analyzing it to formulate the optimal cleaning plan.

[0573] 2. Implementation of emotion recognition function

[0574] The emotion engine analyzes the user's voice and video to identify their current emotional state. For example, it might determine if the user is tired or relaxed.

[0575] This emotional data will be a crucial element for the server to adjust its cleaning plan.

[0576] 3. Optimizing the cleaning plan

[0577] The server adjusts the cleaning process and timing based on emotional data. For example, if a user is feeling stressed, it may postpone cleaning or provide relaxation-focused advice.

[0578] 4. Control of the vacuum cleaner

[0579] The server sends the cleaning plan to the terminal (vacuum cleaner). The vacuum cleaner automatically starts cleaning according to the plan.

[0580] The vacuum cleaner uses sensors to detect dirt in real time and cleans those areas intensively.

[0581] 5. User Interaction

[0582] Users can send voice commands to the server via smart speakers or apps.

[0583] The server analyzes these instructions and uses its assistant function to suggest the optimal cleaning sequence and method to the user. For example, it might offer specific advice such as, "You seem tired, so I recommend a quick and effective cleaning."

[0584] This allows the system to provide a comfortable living environment by enabling flexible cleaning that adapts to the user's lifestyle and emotional state. For example, if the system determines that the user is feeling very tired after returning home from work, it could offer a cleaning plan that can be completed in a short time, or suggest a function to play relaxing music.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The user launches a dedicated app and enters information about their living space (e.g., number of rooms, furniture arrangement) and their daily routine (e.g., wake-up time, return-home time). This data is then sent to the server.

[0588] Step 2:

[0589] The server analyzes the received residential information and lifestyle data, and uses an AI algorithm to formulate a cleaning plan, including the optimal timing for cleaning. This plan is later transmitted to the terminal.

[0590] Step 3:

[0591] The emotion engine analyzes the user's voice and video in real time to identify their current emotional state. For example, it can collect emotional data such as whether the user is tired or stressed.

[0592] Step 4:

[0593] The server adjusts the cleaning plan based on emotional data. For example, if the user is feeling fatigued, it will suggest shortening the cleaning time to reduce the burden of cleaning.

[0594] Step 5:

[0595] The server sends the adjusted cleaning plan to the terminal (cleaning machine). The terminal follows the instructions and automatically starts cleaning at the specified time.

[0596] Step 6:

[0597] The vacuum cleaner uses built-in sensors to detect dirt in real time, identify areas with heavy soiling, and focus its cleaning efforts on those areas.

[0598] Step 7:

[0599] Users can send additional instructions to the server via voice or app. The server analyzes these instructions and further adjusts the cleaning plan as needed.

[0600] Step 8:

[0601] Once cleaning is complete, the terminal reports the cleaning results and implementation data to the server. The server receives this data, records it for future cleaning planning, and optimizes the system based on the accumulated data to achieve more efficient cleaning.

[0602] (Example 2)

[0603] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0604] The present invention aims to provide a system that can respond to individual user needs and changing circumstances that cannot be addressed by conventional technologies, by offering an efficient and flexible cleaning plan for cleaning work in living environments, taking into account the user's emotional state and the characteristics of the living space. In particular, it aims to reduce the burden felt by the user and realize a comfortable living environment.

[0605] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0606] In this invention, the server includes data acquisition means for inputting data on the characteristics of the living space and lifestyle habits; planning means for formulating a dynamic cleaning plan that corresponds to the user's emotional state, utilizing an emotion recognition function based on the data acquisition means; and management means for executing the cleaning plan formulated by the planning means and controlling a cleaning device that includes adjustments based on emotions. This enables optimal cleaning according to the user's emotions and circumstances.

[0607] 1. "Characteristics of the living space" refers to physical and structural information within a residence, such as room size, furniture arrangement, and type of flooring.

[0608] 2. "Lifestyle data" refers to information including the user's daily behavior patterns, how they spend their time, their wake-up and bedtime, and when they are at home and when they are out.

[0609] 3. "Data acquisition means" refers to means used to collect data on the characteristics of living spaces and lifestyle habits from users, and includes smartphone applications and sensors.

[0610] 4. "Emotion recognition function" refers to technology that analyzes and identifies the emotional state from the user's voice and video, and is a function for understanding the user's current psychological and emotional state.

[0611] 5. A "dynamic cleaning plan" refers to a plan that determines the most appropriate cleaning content and timing at any given time, based on the user's emotional state and lifestyle.

[0612] 6. "Formulation methods" refer to processes and algorithms used to plan the content, procedures, and timing of cleaning based on collected data.

[0613] 7. "Control means" refers to a control device or system used to cause a cleaning device to perform a planned cleaning or to make adjustments as necessary.

[0614] 8. "Cleaning equipment" refers to machines or facilities that automatically clean within a user's residence, and specifically includes cleaning robots.

[0615] This system aims to efficiently clean the user's living space. The entire system operates primarily through the collaboration of three parties: the server, the terminal, and the user.

[0616] First, users input information about their living space and lifestyle data using a smartphone app. This includes room size, furniture arrangement, wake-up time, and bedtime. The app then sends the data obtained from the user to a server. The app can be implemented on a platform that runs on a common mobile operating system.

[0617] The server receives this data and uses advanced machine learning algorithms to formulate the optimal cleaning plan. By incorporating emotion recognition capabilities, it analyzes the user's current emotional state from their voice and video data. Specifically, it can utilize a software module that integrates an AI module for emotion analysis.

[0618] The server utilizes emotional data to develop a dynamic cleaning plan tailored to the user's state. For example, if the user is stressed, it can postpone cleaning or suggest a shorter cleaning plan. The server then transmits the developed cleaning plan to the cleaning device via Wi-Fi or Bluetooth.

[0619] The cleaning device, acting as a terminal, automatically starts cleaning based on the cleaning plan received from the server. The device is equipped with high-precision sensors that monitor the floor condition in real time and select the optimal cleaning method as needed. This allows, for example, to focus cleaning on areas with significant dirt.

[0620] Furthermore, users can send voice commands to the server via smart speakers or apps. In response to the user's commands, the server suggests the most suitable cleaning method. For example, if a user requests, "I want a quick and effective cleaning today," the server might respond, "I recommend a quick cleaning mode in the living room."

[0621] For example, if a user inputs "I'm tired," the system will automatically play relaxing music and provide a plan to do some light cleaning. An example of how the generating AI model would be used in this case would be a prompt message like this: "Generate a program that suggests the most suitable short cleaning when the user is in a fatigued state. The data to be used will include the layout of the living space, furniture placement information, and emotional data obtained by analyzing the voice."

[0622] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0623] Step 1:

[0624] Users input information about their living space and lifestyle habits using a smartphone app. This includes information such as room size, furniture arrangement, wake-up time, and bedtime. The data entered by the user is immediately sent to a cloud server. The input data is used as the basic data for the living space model.

[0625] Step 2:

[0626] The server receives living space information and lifestyle data sent by the user. Based on the received data, a machine learning algorithm is used to generate an initial cleaning plan. During this process, the data is cleansed and processed, and transformed into input for the algorithm to plan the statistically optimal cleaning sequence. As output, a rough cleaning schedule for each day of the week is formulated.

[0627] Step 3:

[0628] The emotion recognition engine on the server begins analyzing voice and video data from the user. The primary input is the voice the user sends to their smart speaker. The engine analyzes this voice data and identifies the user's emotional state, classifying it as "fatigue," "relaxation," or "stress." This results in the output of real-time emotional state data.

[0629] Step 4:

[0630] The server dynamically adjusts the initially generated cleaning plan based on emotional state data. It reflects the results of the emotional analysis in the cleaning schedule; for example, if the user is experiencing stress, it changes or reduces the cleaning date and time. This process also includes automatic checks and balance adjustments, generating a newly adjusted cleaning plan as output.

[0631] Step 5:

[0632] The server sends a new cleaning plan to the cleaning device. The cleaning device, acting as the terminal, receives this plan and acts as instructed. The cleaning device constantly monitors the environment with its sensors and performs cleaning according to the plan. Here, a sensor system that detects dirt and obstacles collects data in real time and determines priority cleaning areas.

[0633] Step 6:

[0634] Users can send voice commands directly to the server via smart speakers or apps. For example, they might say, "Please finish cleaning in under 30 minutes." This user input is interpreted by the server and processed as a prompt to determine the most efficient cleaning method. The server responds with a voice suggestion and outputs a quick cleaning option to the user.

[0635] (Application Example 2)

[0636] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0637] Conventional cleaning systems for homes, which do not take into account the user's emotional state, have the problem of not being able to perform flexible cleaning that adapts to their lifestyle. Furthermore, when cleaning is performed without considering when the user wants to relax or is tired, it is difficult to maintain a comfortable living environment.

[0638] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0639] In this invention, the server includes an input means for inputting information about the living space and residents' lifestyle data, a planning means for formulating an optimal cleaning plan, and an emotion recognition means for recognizing the user's emotional state and reflecting it in the plan. This makes it possible to flexibly adjust the timing and content of cleaning according to the user's emotional state, thereby maintaining a comfortable living environment.

[0640] "Residential space" refers to the physical area where an individual or family lives, and includes the indoor environment, including furniture and fixtures.

[0641] "Lifestyle data" refers to information about residents' daily routines, behavioral patterns, and activities.

[0642] "Input means" refers to a function or device for collecting information about living spaces and lifestyle data and providing it to the system.

[0643] "Planning method" refers to the function or process for formulating an optimal cleaning plan based on the collected information.

[0644] "Emotion recognition means" refers to technology that analyzes a user's voice and video data to identify the user's current emotional state.

[0645] A "cleaning machine" is a device designed to clean automatically, removing dirt from floors and furniture without human intervention.

[0646] "Control means" refers to the functions or devices that operate the cleaning machine in order to carry out the planned cleaning action.

[0647] "Relaxation suggestions" refer to providing advice and actions aimed at reducing stress and promoting relaxation, taking into account the user's emotional state.

[0648] The system for realizing this invention includes the following main elements: The user inputs information about their living space and lifestyle data using a smartphone app. This information is transmitted to a server via an input means. The server analyzes the collected data and uses a planning means to formulate a cleaning plan. This planning process incorporates an emotion recognition means that analyzes the user's voice and video data to recognize their emotional state, thereby reflecting the user's emotions in the cleaning plan.

[0649] The server transmits the formulated cleaning plan to the vacuum cleaner. The vacuum cleaner starts cleaning according to the plan via a control system, detects dirt in real time, and focuses cleaning as needed. Users can give direct instructions via voice or text through smart speakers or applications. The assistant function analyzes this and provides the optimal cleaning sequence and relaxation suggestions.

[0650] For example, if a user returns home after a long day of work and is deemed tired, a plan for quick and effective cleaning is offered, along with a suggestion to play relaxation music. Furthermore, by using a generative AI model and inputting prompts such as "Suggest the best cleaning plan based on my emotional state today," cleaning activities tailored to the user's needs are executed.

[0651] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0652] Step 1:

[0653] Users input information about their living space and lifestyle data using a smartphone app. This input data is then stored on the device. This input includes, for example, room layout, furniture arrangement, number of residents, and daily lifestyle patterns. The user's voice and facial expressions are also collected as video data.

[0654] Step 2:

[0655] The device transmits the collected input data to a server via the internet. The server analyzes the received data. This analysis uses a generative AI model to integrate the input lifestyle data with emotional judgments to evaluate the user's current needs and emotional state. In this process, the data is interpreted to determine the optimal cleaning method.

[0656] Step 3:

[0657] The server develops an optimal cleaning plan based on the evaluation results. Using emotion recognition, it creates different plans depending on whether the user wants to relax, clean quickly, or request a thorough cleaning. The output is an optimized cleaning schedule and specific cleaning tasks.

[0658] Step 4:

[0659] The server transmits the formulated cleaning plan to the vacuum cleaner. The vacuum cleaner acts based on the specified plan, using room sensors to detect dirt and focusing on removing it. It adapts to the plan in real time and adjusts its operation as needed.

[0660] Step 5:

[0661] Users can send instructions via voice or text through smart speakers or apps. The server analyzes these instructions using its assistant function and updates the cleaning plan as needed. It also outputs additional information, such as relaxation suggestions, and provides advice tailored to the user's emotions and needs.

[0662] Step 6:

[0663] As a final output, the user receives a summary of the cleaning performed and related suggestions. If the prompt "Suggest the best cleaning plan based on today's emotional state" is entered, the user interface will display the most suitable cleaning results and relaxation suggestions.

[0664] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0665] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0666] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0667] [Fourth Embodiment]

[0668] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0669] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0670] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0671] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0672] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0673] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0674] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0675] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0676] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0677] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0678] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0679] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0680] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0681] This invention is a system designed to streamline cleaning within homes for busy households. The system effectively performs cleaning tasks by formulating an optimal cleaning plan based on information about the living space and residents' lifestyle data, and by automatically controlling the cleaning machine. An embodiment of this system is described below.

[0682] Program processing:

[0683] 1. User registration and information entry

[0684] Users input information about their living space through a dedicated app. This includes the number of rooms and the arrangement of furniture.

[0685] Users set their own daily routines. This data is sent to and stored on the server.

[0686] 2. Developing an optimal cleaning plan

[0687] The server analyzes the received information. Using AI algorithms, it develops an optimal cleaning plan that takes into account the layout of the residence and the user's daily routine.

[0688] For example, you could set up a schedule to clean during times when people are away from home on weekdays.

[0689] 3. Control of the vacuum cleaner

[0690] The server sends the formulated plan to the cleaning machine. The cleaning machine (terminal) automatically starts cleaning based on the plan.

[0691] The vacuum cleaner uses sensors to detect dirt in real time and focuses on cleaning areas with heavy soiling.

[0692] 4. User Instructions and Assistant Functions

[0693] Users can give voice commands to the system using smart speakers or apps.

[0694] The server recognizes the instructions and uses its assistant function to suggest the optimal cleaning procedure to the user. For example, it might give instructions such as, "First clean the floor, then wipe the shelves."

[0695] 5. Data accumulation and plan improvement

[0696] Once the cleaning is complete, the results of the cleaning are sent to the server.

[0697] The server analyzes this data and uses it to plan future cleaning sessions. This allows for continuous improvement in cleaning efficiency.

[0698] The system described above allows users to significantly reduce the effort required for cleaning and perform cleaning efficiently. For example, by using the time when the house is normally empty, the vacuum cleaner can automatically clean, making it possible to keep the home clean at all times.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] Users input information about their living space and daily routine through a dedicated app. Specifically, they input details such as the number of rooms, furniture arrangement, and their usual wake-up and outing times.

[0702] Step 2:

[0703] The server receives input data from users and stores it in a database. This data is used to understand the layout of the residence and the user's lifestyle.

[0704] Step 3:

[0705] The server uses AI algorithms to create an optimal cleaning plan based on the living space and the residents' lifestyles. For example, it might schedule cleaning for times when the residents are away.

[0706] Step 4:

[0707] The server transmits the formulated cleaning plan to the cleaning machine, which acts as the terminal. The plan includes information such as the rooms to be cleaned, the start time, and the areas to focus on.

[0708] Step 5:

[0709] The device (vacuum cleaner) automatically starts cleaning based on the received plan. It uses built-in sensors to monitor the room's condition in real time, identify areas with heavy soiling, and clean those areas intensively.

[0710] Step 6:

[0711] Users can send real-time cleaning instructions to the server using smart speakers or apps as needed. For example, they might say, "Please clean the living room."

[0712] Step 7:

[0713] The server analyzes user instructions and uses its assistant function to transmit the optimal cleaning procedure to the terminal. It also provides the user with cleaning advice.

[0714] Step 8:

[0715] Once cleaning is complete, the device reports the cleaning results to the server. This report includes information such as the time taken for cleaning, the efficiency of the cleaning, and the location of any detected dirt.

[0716] Step 9:

[0717] The server records the received cleaning results in a database and uses them to improve future cleaning plans. This allows the system to continuously improve cleaning efficiency.

[0718] (Example 1)

[0719] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0720] In modern homes, efficiently and effectively cleaning living spaces is a time-consuming and physically demanding task for many people. Furthermore, conventional cleaning devices and systems rely on fixed cleaning plans, making them unable to adapt to real-time changes in conditions. The challenge lies in improving cleaning efficiency and providing flexible cleaning plans tailored to user needs.

[0721] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0722] In this invention, the server includes information acquisition means for inputting information about the living space and movement data of the occupants, planning processing means for formulating an optimal cleaning plan using an artificial intelligence algorithm, and device control means for transmitting the cleaning plan generated by the planning processing means to a cleaning device via communication technology and controlling the device. This makes it possible to dynamically and efficiently perform cleaning optimized for the user's living situation.

[0723] "Living space" refers to the physical space in which an individual or family conducts their daily life, and includes the environment within which rooms, furniture, and home appliances are located.

[0724] "Dynamic data" refers to information that reflects a user's daily routine and behavioral patterns, including data on daily activity time, movement, and location.

[0725] "Information acquisition means" refers to devices and interfaces used to input or collect data on a user's living space and movements, and specifically includes smartphone apps and sensors.

[0726] "Artificial intelligence algorithms" refer to mathematical and programmatic methods for analyzing data and automatically generating optimal cleaning plans.

[0727] "Planning and processing means" refers to a process or system function that creates an optimal cleaning plan based on acquired information.

[0728] "Communication technology" refers to the technology used to send and receive information between specific devices, and describes the mechanism for transferring data via a network environment.

[0729] "Cleaning equipment" refers to mechanical devices used to clean living spaces, and specifically includes robotic vacuum cleaners and automatic cleaning machines.

[0730] "Device control means" refers to system functions for managing and coordinating the operation of the cleaning device, and this includes programs and interfaces for instructing the execution of the cleaning plan.

[0731] "Dynamic control function" refers to a function that adjusts the operation of the cleaning device according to the real-time situation in order to perform cleaning efficiently.

[0732] "Voice recognition technology" refers to the technology that converts a user's voice commands into a digital format that a machine can understand.

[0733] A "learning algorithm" refers to a programmatic method that analyzes trends and optimizes based on past cleaning data to improve the planning of future cleaning.

[0734] This invention is implemented as a smart cleaning system for streamlining the maintenance and management of living spaces. This system utilizes dynamic data provided by the user to generate and execute an optimized cleaning plan.

[0735] First, users input information about their living space through a dedicated mobile application. This information includes room layout, size, furniture placement, and the user's daily routine. This information is transmitted to a server via the internet and stored in a cloud database.

[0736] The server uses artificial intelligence algorithms to develop an optimal cleaning plan based on the received information. This process utilizes Google Cloud AI and AWS machine learning services to perform data analysis quickly and accurately. The generated cleaning plan takes into account the layout of the living space and includes dynamic routes for efficient cleaning.

[0737] Next, the server sends the cleaning plan to the cleaning device. The cleaning device receives instructions from the server via Wi-Fi and automatically starts cleaning, using its built-in LiDAR sensors and cameras to avoid obstacles. This cleaning device has a dynamic control function that detects dirt in real time and focuses cleaning on areas that are particularly dirty.

[0738] Users can communicate voice commands or text instructions to the server via smart speakers or apps. Using voice recognition technology, the server understands user instructions and suggests the optimal cleaning procedure. After cleaning is complete, clean data is sent to the server to help improve future cleaning plans. The server analyzes this data and uses machine learning algorithms to continuously improve cleaning efficiency.

[0739] For example, if the living room is to be cleaned every Friday at 3 PM, the user can input a prompt message to the server instructing it to "start cleaning the living room at 3 PM on Friday." The server then creates a plan based on this instruction and has the cleaning device execute it.

[0740] This system enables efficient cleaning with minimal time and physical burden on the user.

[0741] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0742] Step 1:

[0743] Users open a dedicated application and input their living information and lifestyle data. Specifically, they enter the number and size of rooms, furniture arrangement, and daily activity times into the app. This data is sent to a server via the cloud. The input data includes numerical information, location information, and text information about time periods, which the server stores as structured data in preparation for subsequent processing.

[0744] Step 2:

[0745] The server uses an AI algorithm to analyze the received residential information and lifestyle data, and then formulates an optimal cleaning plan. Specifically, it uses a generative AI model to calculate the optimal cleaning route and timing, and designs an efficient work sequence. The input is the residential layout and lifestyle patterns, and the output is a cleaning plan that includes the cleaning area, route, and time schedule.

[0746] Step 3:

[0747] The server transmits the formulated cleaning plan to the cleaning device. Specifically, instructions such as the cleaning start time and cleaning route are sent to the cleaning device (terminal) via Wi-Fi. The input instruction data is transmitted in a command format that the terminal can decipher, and the terminal starts its operation based on this.

[0748] Step 4:

[0749] The device cleans by scanning the environment in real time according to the received plan. Specifically, it uses LiDAR sensors and cameras to scan the living space, avoiding obstacles and detecting dirt. The input is scan data obtained from the sensors, and the device adjusts its cleaning route based on this data to perform efficient cleaning. The output is fed back to the server as cleaning progress and completion data.

[0750] Step 5:

[0751] Once cleaning is complete, the terminal sends the cleaning results to the server. Specifically, it sends data such as the degree of dirt detected during cleaning, the time taken for cleaning, and the power used. The server analyzes this data and uses it to improve the next cleaning plan. The input is the cleaning result data, and the output is the optimized cleaning parameters generated after analysis.

[0752] Step 6:

[0753] Users can provide voice or text instructions to the server via smart speakers or apps as needed. The server uses voice recognition technology to interpret user requests and perform optimal suggestions or immediate cleaning actions. The input is the user's voice command, which the server analyzes and outputs the necessary actions and suggestions.

[0754] (Application Example 1)

[0755] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] In today's busy lifestyle, efficient and effective cleaning of homes is crucial. However, conventional cleaning equipment and methods struggle to flexibly adapt to the layout of homes and the lifestyles of residents, leading to increased effort and time spent on cleaning. Furthermore, the lack of feedback mechanisms to utilize the results of cleaning for future improvements makes it difficult to create more efficient cleaning plans.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0758] In this invention, the server includes information input means for inputting the configuration of the living area and lifestyle patterns; plan generation means for generating an optimal work plan corresponding to the living area based on the information input means; operation means for operating an automatic cleaning device that carries out the work plan generated by the plan generation means; and result feedback means for acquiring the cleaning results after the automatic cleaning device has been operated and reflecting them in the next work plan. This makes it possible to clean the residence automatically and efficiently in accordance with the user's lifestyle.

[0759] A "residential area" is a zone that includes spaces and rooms where residents live and carry out their activities.

[0760] "Lifestyle patterns" refer to the habitual patterns and time-based flow of daily activities of residents, and efficient work plans are developed based on these patterns.

[0761] "Information input means" refers to methods and technologies for collecting data on residents and housing information and transmitting it to a computer.

[0762] "Plan generation means" refers to methods and techniques for constructing an optimal work plan based on the input information.

[0763] An "automatic cleaning device" refers to equipment that performs cleaning tasks based on a set plan without requiring manual operation.

[0764] "Operating means" refers to methods or techniques for controlling an automatic cleaning device and causing it to perform specific actions.

[0765] "Results feedback methods" refer to methods and techniques for collecting the results of cleaning work and providing analysis and information to improve future work.

[0766] In this embodiment, the server provides an input means for receiving information on the configuration of the living area and lifestyle patterns. Users input living space information via a dedicated application using a smartphone or tablet. The server also functions as a plan generation means based on the input information in a cloud computing environment, generating an optimal work plan using an AI algorithm. This can utilize machine learning services provided by the cloud provider.

[0767] The automated cleaning device, acting as the terminal, receives instructions from a server via Wi-Fi or Bluetooth and performs specific cleaning actions using its control mechanisms. For example, the device automatically cleans a room, taking into account the time when residents are away during the day. At this time, the device uses sensors to identify dirt in real time and can perform focused cleaning as needed.

[0768] Once cleaning is complete, the terminal sends the cleaning results to the server, which then uses a results feedback mechanism to incorporate the findings into the next work plan. This includes analyzing the cleaning performance data and identifying areas for improvement.

[0769] As a concrete example, if a user enters into the app, "Please clean the living room for one hour starting at 2 PM while I'm out," the AI ​​model will generate a cleaning plan based on this prompt. After the cleaning is complete, the user receives a notification saying, "Cleaning complete. We will optimize the next plan."

[0770] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0771] Step 1:

[0772] Users input information about their living area (number of rooms, furniture arrangement) and lifestyle patterns (daily routine, time spent outside) using a dedicated app. This data is sent to and stored on a server. The entered information is used as base data necessary for subsequent plan generation.

[0773] Step 2:

[0774] Based on the residential area information and lifestyle pattern data received by the server, data analysis is performed using an AI model. In this process, the data acquired in step 1 is processed by an algorithm to generate a cleaning plan optimized for the residence. The output includes a proposal for specific cleaning dates and routes.

[0775] Step 3:

[0776] The server generates a cleaning plan and sends it to the automated cleaning device, which acts as the terminal. The terminal receives this plan via Wi-Fi or Bluetooth and, using its internal control system, begins operating according to the plan. Based on the distributed plan, the terminal calculates and executes an efficient movement path.

[0777] Step 4:

[0778] When the device performs cleaning, it uses sensors to detect dirt. Real-time identification of dirt and determination of priority cleaning areas are performed, and the cleaning intensity and path are dynamically adjusted. The output generates a history of highly efficient cleaning operations.

[0779] Step 5:

[0780] Once cleaning is complete, the terminal sends cleaning results and history data to the server. The server uses a results feedback mechanism to analyze this data and use it to plan future cleanings. This improves the accuracy and efficiency of cleaning plans.

[0781] Step 6:

[0782] Users receive notifications through the application regarding cleaning results and the next cleaning schedule. Feedback tailored to the user's lifestyle is provided, and they can modify the plan or add instructions as needed.

[0783] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0784] This invention combines a system for efficiently cleaning within a residence with a function that recognizes and reflects the user's emotions. The system formulates a cleaning plan based on information about the living space and residents' lifestyle data, and further uses an emotion engine to propose and execute cleaning that takes the user's emotions into consideration.

[0785] Program processing:

[0786] 1. Data entry and analysis

[0787] Users input information about their living space through a dedicated app. Their daily routines are also registered in the app.

[0788] The server receives this information and begins analyzing it to formulate the optimal cleaning plan.

[0789] 2. Implementation of emotion recognition function

[0790] The emotion engine analyzes the user's voice and video to identify their current emotional state. For example, it might determine if the user is tired or relaxed.

[0791] This emotional data will be a crucial element for the server to adjust its cleaning plan.

[0792] 3. Optimizing the cleaning plan

[0793] The server adjusts the cleaning process and timing based on emotional data. For example, if a user is feeling stressed, it may postpone cleaning or provide relaxation-focused advice.

[0794] 4. Control of the vacuum cleaner

[0795] The server sends the cleaning plan to the terminal (vacuum cleaner). The vacuum cleaner automatically starts cleaning according to the plan.

[0796] The vacuum cleaner uses sensors to detect dirt in real time and cleans those areas intensively.

[0797] 5. User Interaction

[0798] Users can send voice commands to the server via smart speakers or apps.

[0799] The server analyzes these instructions and uses its assistant function to suggest the optimal cleaning sequence and method to the user. For example, it might offer specific advice such as, "You seem tired, so I recommend a quick and effective cleaning."

[0800] This allows the system to provide a comfortable living environment by enabling flexible cleaning that adapts to the user's lifestyle and emotional state. For example, if the system determines that the user is feeling very tired after returning home from work, it could offer a cleaning plan that can be completed in a short time, or suggest a function to play relaxing music.

[0801] The following describes the processing flow.

[0802] Step 1:

[0803] The user launches a dedicated app and enters information about their living space (e.g., number of rooms, furniture arrangement) and their daily routine (e.g., wake-up time, return-home time). This data is then sent to the server.

[0804] Step 2:

[0805] The server analyzes the received residential information and lifestyle data, and uses an AI algorithm to formulate a cleaning plan, including the optimal timing for cleaning. This plan is later transmitted to the terminal.

[0806] Step 3:

[0807] The emotion engine analyzes the user's voice and video in real time to identify their current emotional state. For example, it can collect emotional data such as whether the user is tired or stressed.

[0808] Step 4:

[0809] The server adjusts the cleaning plan based on emotional data. For example, if the user is feeling fatigued, it will suggest shortening the cleaning time to reduce the burden of cleaning.

[0810] Step 5:

[0811] The server sends the adjusted cleaning plan to the terminal (cleaning machine). The terminal follows the instructions and automatically starts cleaning at the specified time.

[0812] Step 6:

[0813] The vacuum cleaner uses built-in sensors to detect dirt in real time, identify areas with heavy soiling, and focus its cleaning efforts on those areas.

[0814] Step 7:

[0815] Users can send additional instructions to the server via voice or app. The server analyzes these instructions and further adjusts the cleaning plan as needed.

[0816] Step 8:

[0817] Once cleaning is complete, the terminal reports the cleaning results and implementation data to the server. The server receives this data, records it for future cleaning planning, and optimizes the system based on the accumulated data to achieve more efficient cleaning.

[0818] (Example 2)

[0819] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0820] The present invention aims to provide a system that can respond to individual user needs and changing circumstances that cannot be addressed by conventional technologies, by offering an efficient and flexible cleaning plan for cleaning work in living environments, taking into account the user's emotional state and the characteristics of the living space. In particular, it aims to reduce the burden felt by the user and realize a comfortable living environment.

[0821] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0822] In this invention, the server includes data acquisition means for inputting data on the characteristics of the living space and lifestyle habits; planning means for formulating a dynamic cleaning plan that corresponds to the user's emotional state, utilizing an emotion recognition function based on the data acquisition means; and management means for executing the cleaning plan formulated by the planning means and controlling a cleaning device that includes adjustments based on emotions. This enables optimal cleaning according to the user's emotions and circumstances.

[0823] 1. "Characteristics of the living space" refers to physical and structural information within a residence, such as room size, furniture arrangement, and type of flooring.

[0824] 2. "Lifestyle data" refers to information including the user's daily behavior patterns, how they spend their time, their wake-up and bedtime, and when they are at home and when they are out.

[0825] 3. "Data acquisition means" refers to means used to collect data on the characteristics of living spaces and lifestyle habits from users, and includes smartphone applications and sensors.

[0826] 4. "Emotion recognition function" refers to technology that analyzes and identifies the emotional state from the user's voice and video, and is a function for understanding the user's current psychological and emotional state.

[0827] 5. A "dynamic cleaning plan" refers to a plan that determines the most appropriate cleaning content and timing at any given time, based on the user's emotional state and lifestyle.

[0828] 6. "Formulation methods" refer to processes and algorithms used to plan the content, procedures, and timing of cleaning based on collected data.

[0829] 7. "Control means" refers to a control device or system used to cause a cleaning device to perform a planned cleaning or to make adjustments as necessary.

[0830] 8. "Cleaning equipment" refers to machines or facilities that automatically clean within a user's residence, and specifically includes cleaning robots.

[0831] This system aims to efficiently clean the user's living space. The entire system operates primarily through the collaboration of three parties: the server, the terminal, and the user.

[0832] First, users input information about their living space and lifestyle data using a smartphone app. This includes room size, furniture arrangement, wake-up time, and bedtime. The app then sends the data obtained from the user to a server. The app can be implemented on a platform that runs on a common mobile operating system.

[0833] The server receives this data and uses advanced machine learning algorithms to formulate the optimal cleaning plan. By incorporating emotion recognition capabilities, it analyzes the user's current emotional state from their voice and video data. Specifically, it can utilize a software module that integrates an AI module for emotion analysis.

[0834] The server utilizes emotional data to develop a dynamic cleaning plan tailored to the user's state. For example, if the user is stressed, it can postpone cleaning or suggest a shorter cleaning plan. The server then transmits the developed cleaning plan to the cleaning device via Wi-Fi or Bluetooth.

[0835] The cleaning device, acting as a terminal, automatically starts cleaning based on the cleaning plan received from the server. The device is equipped with high-precision sensors that monitor the floor condition in real time and select the optimal cleaning method as needed. This allows, for example, to focus cleaning on areas with significant dirt.

[0836] Furthermore, users can send voice commands to the server via smart speakers or apps. In response to the user's commands, the server suggests the most suitable cleaning method. For example, if a user requests, "I want a quick and effective cleaning today," the server might respond, "I recommend a quick cleaning mode in the living room."

[0837] For example, if a user inputs "I'm tired," the system will automatically play relaxing music and provide a plan to do some light cleaning. An example of how the generating AI model would be used in this case would be a prompt message like this: "Generate a program that suggests the most suitable short cleaning when the user is in a fatigued state. The data to be used will include the layout of the living space, furniture placement information, and emotional data obtained by analyzing the voice."

[0838] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0839] Step 1:

[0840] Users input information about their living space and lifestyle habits using a smartphone app. This includes information such as room size, furniture arrangement, wake-up time, and bedtime. The data entered by the user is immediately sent to a cloud server. The input data is used as the basic data for the living space model.

[0841] Step 2:

[0842] The server receives living space information and lifestyle data sent by the user. Based on the received data, a machine learning algorithm is used to generate an initial cleaning plan. During this process, the data is cleansed and processed, and transformed into input for the algorithm to plan the statistically optimal cleaning sequence. As output, a rough cleaning schedule for each day of the week is formulated.

[0843] Step 3:

[0844] The emotion recognition engine on the server begins analyzing voice and video data from the user. The primary input is the voice the user sends to their smart speaker. The engine analyzes this voice data and identifies the user's emotional state, classifying it as "fatigue," "relaxation," or "stress." This results in the output of real-time emotional state data.

[0845] Step 4:

[0846] The server dynamically adjusts the initially generated cleaning plan based on emotional state data. It reflects the results of the emotional analysis in the cleaning schedule; for example, if the user is experiencing stress, it changes or reduces the cleaning date and time. This process also includes automatic checks and balance adjustments, generating a newly adjusted cleaning plan as output.

[0847] Step 5:

[0848] The server sends a new cleaning plan to the cleaning device. The cleaning device, acting as the terminal, receives this plan and acts as instructed. The cleaning device constantly monitors the environment with its sensors and performs cleaning according to the plan. Here, a sensor system that detects dirt and obstacles collects data in real time and determines priority cleaning areas.

[0849] Step 6:

[0850] Users can send voice commands directly to the server via smart speakers or apps. For example, they might say, "Please finish cleaning in under 30 minutes." This user input is interpreted by the server and processed as a prompt to determine the most efficient cleaning method. The server responds with a voice suggestion and outputs a quick cleaning option to the user.

[0851] (Application Example 2)

[0852] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0853] Conventional cleaning systems for homes, which do not take into account the user's emotional state, have the problem of not being able to perform flexible cleaning that adapts to their lifestyle. Furthermore, when cleaning is performed without considering when the user wants to relax or is tired, it is difficult to maintain a comfortable living environment.

[0854] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0855] In this invention, the server includes an input means for inputting information about the living space and residents' lifestyle data, a planning means for formulating an optimal cleaning plan, and an emotion recognition means for recognizing the user's emotional state and reflecting it in the plan. This makes it possible to flexibly adjust the timing and content of cleaning according to the user's emotional state, thereby maintaining a comfortable living environment.

[0856] "Residential space" refers to the physical area where an individual or family lives, and includes the indoor environment, including furniture and fixtures.

[0857] "Lifestyle data" refers to information about residents' daily routines, behavioral patterns, and activities.

[0858] "Input means" refers to a function or device for collecting information about living spaces and lifestyle data and providing it to the system.

[0859] "Planning method" refers to the function or process for formulating an optimal cleaning plan based on the collected information.

[0860] "Emotion recognition means" refers to technology that analyzes a user's voice and video data to identify the user's current emotional state.

[0861] A "cleaning machine" is a device designed to clean automatically, removing dirt from floors and furniture without human intervention.

[0862] "Control means" refers to the functions or devices that operate the cleaning machine in order to carry out the planned cleaning action.

[0863] "Relaxation suggestions" refer to providing advice and actions aimed at reducing stress and promoting relaxation, taking into account the user's emotional state.

[0864] The system for realizing this invention includes the following main elements: The user inputs information about their living space and lifestyle data using a smartphone app. This information is transmitted to a server via an input means. The server analyzes the collected data and uses a planning means to formulate a cleaning plan. This planning process incorporates an emotion recognition means that analyzes the user's voice and video data to recognize their emotional state, thereby reflecting the user's emotions in the cleaning plan.

[0865] The server transmits the formulated cleaning plan to the vacuum cleaner. The vacuum cleaner starts cleaning according to the plan via a control system, detects dirt in real time, and focuses cleaning as needed. Users can give direct instructions via voice or text through smart speakers or applications. The assistant function analyzes this and provides the optimal cleaning sequence and relaxation suggestions.

[0866] For example, if a user returns home after a long day of work and is deemed tired, a plan for quick and effective cleaning is offered, along with a suggestion to play relaxation music. Furthermore, by using a generative AI model and inputting prompts such as "Suggest the best cleaning plan based on my emotional state today," cleaning activities tailored to the user's needs are executed.

[0867] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0868] Step 1:

[0869] Users input information about their living space and lifestyle data using a smartphone app. This input data is then stored on the device. This input includes, for example, room layout, furniture arrangement, number of residents, and daily lifestyle patterns. The user's voice and facial expressions are also collected as video data.

[0870] Step 2:

[0871] The device transmits the collected input data to a server via the internet. The server analyzes the received data. This analysis uses a generative AI model to integrate the input lifestyle data with emotional judgments to evaluate the user's current needs and emotional state. In this process, the data is interpreted to determine the optimal cleaning method.

[0872] Step 3:

[0873] The server develops an optimal cleaning plan based on the evaluation results. Using emotion recognition, it creates different plans depending on whether the user wants to relax, clean quickly, or request a thorough cleaning. The output is an optimized cleaning schedule and specific cleaning tasks.

[0874] Step 4:

[0875] The server transmits the formulated cleaning plan to the vacuum cleaner. The vacuum cleaner acts based on the specified plan, using room sensors to detect dirt and focusing on removing it. It adapts to the plan in real time and adjusts its operation as needed.

[0876] Step 5:

[0877] Users can send instructions via voice or text through smart speakers or apps. The server analyzes these instructions using its assistant function and updates the cleaning plan as needed. It also outputs additional information, such as relaxation suggestions, and provides advice tailored to the user's emotions and needs.

[0878] Step 6:

[0879] As a final output, the user receives a summary of the cleaning performed and related suggestions. If the prompt "Suggest the best cleaning plan based on today's emotional state" is entered, the user interface will display the most suitable cleaning results and relaxation suggestions.

[0880] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0881] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0882] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0883] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0884] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0885] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0886] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0887] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0888] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0889] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0890] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0891] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0892] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0893] 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.

[0894] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0895] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0896] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0897] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0898] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0899] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0900] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0901] The following is further disclosed regarding the embodiments described above.

[0902] (Claim 1)

[0903] An input method for entering information about living spaces and residents' lifestyle data,

[0904] A planning means for formulating an optimal cleaning plan based on the input means,

[0905] Control means for controlling a cleaning machine that executes the cleaning plan formulated by the aforementioned planning means,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, wherein the cleaning machine has a function to detect dirt in real time and to focus on cleaning areas with a lot of dirt.

[0909] (Claim 3)

[0910] The system according to claim 1, comprising an assistant function that receives voice or text instructions from the user and suggests the optimal cleaning sequence and method.

[0911] "Example 1"

[0912] (Claim 1)

[0913] Information acquisition means for inputting information on living spaces and movement data of residents,

[0914] Based on the aforementioned information acquisition means, a planning and processing means for formulating an optimal cleaning plan using an artificial intelligence algorithm,

[0915] A device control means transmits the cleaning plan generated by the planning processing means to a cleaning device via communication technology and controls the device,

[0916] The cleaning device has a built-in detection sensor that enables dynamic control to optimize the cleaning area,

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The system according to claim 1, comprising an input means for receiving voice or text instructions from a user using voice recognition technology, and an interface for providing an optimal cleaning procedure.

[0920] (Claim 3)

[0921] The system according to claim 1, comprising a learning algorithm that accumulates data obtained after cleaning and continuously analyzes it for the purpose of formulating the next cleaning plan.

[0922] "Application Example 1"

[0923] (Claim 1)

[0924] Information input means for inputting the configuration of the living area and lifestyle patterns,

[0925] A plan generation means that generates an optimal work plan corresponding to the living area based on the information input means,

[0926] An operating means for operating an automatic cleaning device that carries out the work plan generated by the plan generation means,

[0927] After the automatic cleaning device has been operated, a results feedback means is provided to acquire the cleaning results and reflect them in the next work plan.

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, wherein the automatic cleaning device has the ability to identify dirt in real time and to concentrate cleaning on areas with high levels of contamination.

[0931] (Claim 3)

[0932] The system according to claim 1, comprising an auxiliary function that receives voice or written commands from the user and suggests the best work sequence or method.

[0933] "Example 2 of combining an emotion engine"

[0934] (Claim 1)

[0935] A data acquisition method for inputting data on the characteristics of living spaces and lifestyle habits,

[0936] Based on the aforementioned data acquisition means, a planning means is provided that utilizes an emotion recognition function to formulate a dynamic cleaning plan that corresponds to the user's emotional state.

[0937] A management means for executing a cleaning plan formulated by the aforementioned formulation means and controlling a cleaning device including emotion-based adjustments,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, wherein the cleaning device has a function to detect contamination using a sensor and perform focused cleaning.

[0941] (Claim 3)

[0942] The system according to claim 1, comprising an assistance function that receives voice input from the user and provides cleaning suggestions based on the user's emotional state.

[0943] "Application example 2 when combining with an emotional engine"

[0944] (Claim 1)

[0945] An input method for entering information about living spaces and residents' lifestyle data,

[0946] A planning means for formulating an optimal cleaning plan based on the input means,

[0947] An emotion recognition method that recognizes the user's emotional state and reflects it in the cleaning plan,

[0948] Control means for controlling a cleaning machine that executes the cleaning plan formulated by the aforementioned planning means,

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, wherein the cleaning machine has a function to detect dirt in real time and focus on cleaning areas with a lot of dirt, and adjusts the timing and content of cleaning based on the user's feelings.

[0952] (Claim 3)

[0953] The system according to claim 1, comprising an assistant function that receives voice or text instructions from the user, suggests the optimal cleaning sequence and method, and includes relaxation suggestions according to the user's emotional state. [Explanation of Symbols]

[0954] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An input method for entering information about living spaces and residents' lifestyle data, A planning means for formulating an optimal cleaning plan based on the input means, Control means for controlling a cleaning machine that executes the cleaning plan formulated by the aforementioned planning means, A system that includes this.

2. The system according to claim 1, wherein the cleaning machine has a function to detect dirt in real time and to focus on cleaning areas with a lot of dirt.

3. The system according to claim 1, comprising an assistant function that receives voice or text instructions from the user and suggests the optimal cleaning sequence and method.

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