System

Abstract

A system is provided.SOLUTION: This system is provided with a means for allowing a user to set a household work schedule, a means for allowing a server to analyze household work items and start time received from the user, and to generate the optimal schedule of the household work by a generation AI, and a means for allowing the server to transmit a command for instructing washing, drying and folding work to a land machine based on the generated schedule. A system comprising: means for sending commands to a vacuum cleaner to begin and end cleaning; means for a land machine to automatically perform washing, drying, and folding operations in accordance with the commands from a server; means for the vacuum cleaner to automatically perform cleaning in accordance with the commands from the server; means for the land machine and the vacuum cleaner to notify the server of the completion of housework; and means for the server to notify a user of the completion of housework.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] This is a draft of the "problem the invention aims to solve" and the "means for solving the problem."

[0005] Currently, housework such as laundry and cleaning requires a lot of time and effort, making it difficult to complete, especially during busy morning hours. Furthermore, the time required for housework increases when the series of tasks from hanging out laundry to folding it is included. For this reason, there is a demand for fully automated systems that can reduce the time required for housework and improve the quality of daily life. [Means for solving the problem]

[0006] In order to solve the above problems, the present invention provides the following means.

[0007] The system includes a means for a user to set a housework schedule, a means for a server to analyze housework items and start time data received from the user and generate an optimal housework schedule using a generation AI, a means for the server to send commands to a land machine to instruct it to do the washing, drying, and folding tasks based on the generated schedule, a means for the server to send commands to a vacuum cleaner to instruct it to start and finish cleaning based on the generated schedule, a means for the land machine to automatically perform the washing, drying, and folding tasks in accordance with commands from the server, a means for the vacuum cleaner to automatically perform cleaning in accordance with commands from the server, a means for the land machine and vacuum cleaner to notify the server that the housework has been completed, and a means for the server to notify the user that the housework has been completed.

[0008] Understood. Below are definitions of important terms contained in the patent claims.

[0009] A "user" is an individual or group of people who use the system and set up a household schedule.

[0010] A "housework schedule" is a schedule including housework items set by the user and the dates and times when they are to be performed.

[0011] A "server" is a central computer that receives and analyzes data from users and controls the operation of the entire system.

[0012] "Generative AI" is an artificial intelligence that is built into a server and analyzes data received from users to generate an optimal housework schedule.

[0013] A "landmachine" is a household appliance that automatically washes, dries, and folds laundry.

[0014] The "vacuum cleaner" is a robot vacuum cleaner that automatically cleans the inside of the house.

[0015] A "chore item" is a specific household task, such as washing, drying, folding, or cleaning, that a user configures in the system.

[0016] The "start time data" is data that indicates the start time of a household chore set by the user.

[0017] "Generating a schedule" means that the generation AI determines the optimal order and time for performing housework based on the user's housework items and start time data.

[0018] "Sending a command" means sending a control signal to the land machine or vacuum cleaner to instruct it to perform a particular household task based on a schedule generated by the server.

[0019] "Performing an action" means that the land machine or vacuum cleaner follows commands from the server to start and complete washing, drying, folding or cleaning tasks.

[0020] A "completion notification" is a notification sent to the server when the land machine and the vacuum cleaner complete a household chore.

[0021] "Notifying" means that the server notifies the user's application of the completion of the housework. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] Understood. Below is a draft of the "Description of the Invention".

[0044] This invention is a system that performs housework such as laundry and cleaning fully automatically, in which a server, a land machine, and a robot vacuum cleaner work together to automatically perform housework based on a housework schedule set by a user. The following describes an embodiment of the present invention in detail.

[0045] Program processing explanation

[0046] 1. The user sets the household chores

[0047] Using a dedicated application, users can input the chores to be done that day and their start times. For example, they can set a chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00."

[0048] plaintext

[0049] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[0050] 2. The server receives and analyzes the data

[0051] The server receives data on chore items and start times sent from the application, and uses AI to analyze the user's chore behavior patterns based on the received data to generate an optimal chore schedule.

[0052] plaintext

[0053] Server: Analyzes the household chore items and start time data received from the user and generates an optimal household chore schedule using generation AI.

[0054] 3. The server issues a command

[0055] Based on the generated schedule, the server issues housework commands to the land machine and the robot vacuum cleaner. For example, it sends a command to the land machine to "start laundry at 7:30" and a command to the robot vacuum cleaner to "start cleaning at 9:00."

[0056] plaintext

[0057] Server: Based on the generated schedule, it sends commands to the Land Machine to "start laundry at 7:30" and to the Robot Vacuum Cleaner to "start cleaning at 9:00."

[0058] 4. Your device will do the housework for you

[0059] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0060] plaintext

[0061] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0062] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0063] 5. Completion notification

[0064] When the housework is completed, the land machine and the robot vacuum cleaner each send a completion notification to the server, which then receives the notification and sends it to the user's application.

[0065] plaintext

[0066] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0067] Server: Receives the notification that the housework has been completed and sends a notification to the user's application saying "The housework has been completed."

[0068] User: Receives a notification through the application that says "Laundry and cleaning completed" and confirms that all household chores are completed.

[0069] Specific examples

[0070] For example, suppose a user wakes up at 7:00 a.m. and before leaving the house sets the settings to "Start laundry with the Land Machine at 7:30 a.m." and "Start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., then drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, they will receive a notification from the application that the housework has been completed, freeing up their busy daily routine from housework.

[0071] As described above, the embodiment of the present invention can provide a system that enables users to make effective use of their busy time and complete laundry and cleaning fully automatically.

[0072] The processing flow will be explained below.

[0073] Understood. Below, we will explain the program's processing steps based on the scope of the patent claims.

[0074] Step 1:

[0075] The user launches a dedicated application and inputs the household chores to be done that day and their start times. For example, they can set "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the household chores and start times are sent from the application to the server.

[0076] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and after the settings are complete, sends the information to the server.

[0077] Step 2:

[0078] The server analyzes the household chore items and start time data received from the application and passes the data to the generation AI module, which then analyzes the user's household chore behavior patterns based on the received data and generates an optimal household chore schedule.

[0079] Server: The household chore items and start time data received from the user are passed to the generation AI, which then generates an optimal household chore schedule.

[0080] Step 3:

[0081] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner.

[0082] Server: Based on the generated schedule, it sends commands to the Land Machine to "start laundry at 7:30" and to the Robot Vacuum Cleaner to "start cleaning at 9:00."

[0083] Step 4:

[0084] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0085] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0086] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0087] Step 5:

[0088] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[0089] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0090] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0091] Step 6:

[0092] The user receives a notification through the application that the laundry and cleaning has been completed, confirming that all the household chores have been completed. In this way, the user can efficiently complete household chores even when they are not at home.

[0093] User: Receives a notification through the application that says "Laundry and cleaning completed" and confirms that all household chores are completed.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] In today's busy living environment, users are required to perform housework efficiently without spending time and effort. However, conventional manual or semi-automatic household appliances require users to perform detailed settings and operations, preventing full automation. Furthermore, schedule management and checking the progress of housework are burdensome for users. Therefore, there is a demand for a system that reduces the burden on users and fully automates housework.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes a means for a user to set a housework schedule, a means for the server to analyze the housework information and start time data received from the user and generate an optimal housework schedule using a generative AI model, a means for the server to send commands to a washing machine to instruct it to wash, dry, and fold based on the generated schedule, and a means for the server to send commands to a vacuum cleaner to instruct it to start and finish cleaning based on the generated schedule. This eliminates the need for the user to manually manage the start times of housework, making it possible to fully automate and efficiently perform housework. Furthermore, the server manages the progress of housework and notifies the user when it is completed, allowing the user to always be aware of the status of the housework.

[0099] "User" refers to an individual or user who sets a household schedule and uses the system through the interface.

[0100] A "housework schedule" is a timetable that includes housework items such as laundry and cleaning and the start times for performing them.

[0101] The "server" is a central control device that receives instructions from the user, manages and analyzes the schedule of household items, and sends execution commands to the corresponding devices.

[0102] "Housework information" is a general term for housework items set by the user and data related to them.

[0103] "Start time data" is information including the time when each household chore item is scheduled to be performed.

[0104] The "generative AI model" is an artificial intelligence model that generates an optimal housework schedule based on received housework information and start time data.

[0105] A "washing machine" is a household appliance that automatically washes, dries, and folds clothes.

[0106] A "vacuum cleaner" is a device that automatically cleans a designated area and automatically returns to its charging station once cleaning is complete.

[0107] A "command" refers to a specific instruction for operation sent from the server to a washing machine or vacuum cleaner.

[0108] "Laundry" is a household chore in which clothes are washed with water and detergent.

[0109] "Drying" is a household chore of drying clothes after washing.

[0110] "Folding" refers to the process of sorting and folding clothes after they have been dried.

[0111] "Sweeping" is the act of vacuuming up dirt and dust to clean floors and other surfaces.

[0112] "Automatically executing" means that the equipment operates without human intervention based on a pre-set schedule or received commands.

[0113] The "completion notification" is information that notifies the user that the housework has been completed from the appliance to the server, and from the server to the user.

[0114] This invention is a system that performs housework such as laundry and cleaning fully automatically, in which a server, a washing machine, and a vacuum cleaner work together to automatically perform housework based on a housework schedule set by a user. Specific embodiments for carrying out the invention are described below.

[0115] System configuration

[0116] The system consists of four main components: an application that allows users to set housework schedules, a server, a washing machine, and a vacuum cleaner. The server analyzes the housework information and start time data sent by the user and generates an optimal housework schedule using a generative AI model. The server then sends commands to the washing machine and vacuum cleaner to perform the housework, and notifies the user when the housework is completed.

[0117] Operation flow

[0118] 1. Using a dedicated application, the user inputs the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00."

[0119] 2. Once the user completes the setup, the housework information and start time data are sent to the server. The server then uses the received data to analyze the user's housework behavior patterns using a generative AI model and generates an optimal housework schedule.

[0120] 3. Based on the generated schedule, the server issues commands to the washing machine and vacuum cleaner to perform household chores, such as "start laundry at 7:30" and "start cleaning at 9:00."

[0121] 4. The washing machine and vacuum cleaner receive commands from the server and automatically perform household chores at the specified time. The washing machine washes, dries, and folds laundry in succession, while the vacuum cleaner cleans designated areas in sequence. Each appliance performs its own task automatically.

[0122] 5. When the chore is completed, the washing machine and vacuum cleaner each send a completion notification to the server. The server receives this and sends a completion notification to the user's application. The user can confirm that the chore is complete by receiving a notification that "choice is completed."

[0123] Specific examples

[0124] For example, suppose a user wakes up at 7:00 a.m. and sets the schedules "Start laundry at 7:30" and "Start cleaning at 9:00" before leaving the house. After the user leaves the house, the server sends commands to the washing machine and vacuum cleaner based on these schedules. The washing machine starts washing at 7:30, continues drying at 8:30, and folds at 9:30. The vacuum cleaner starts cleaning at 9:00, and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, freeing up their busy daily routine from housework.

[0125] As described above, the embodiment of the present invention can provide a system that allows users to make effective use of their busy time and complete laundry and cleaning fully automatically.

[0126] Prompt Sentence Examples

[0127] An example of a specific prompt sentence for a generative AI model is shown below.

[0128] plaintext

[0129] User chores and schedules:

[0130] Start washing at 7:30

[0131] Cleaning begins at 9am

[0132] Based on this prompt, the generative AI model generates an optimal housework schedule.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] The user sets the household chore schedule. The user launches a dedicated application and enters the household chores, such as laundry and cleaning, and their start times on the screen. Next, the user presses the Set button to confirm the information. This operation formats the entered data and saves it within the application as household chore schedule data. Specifically, the user sets a schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00" and sends it to the server.

[0136] Step 2:

[0137] The server receives the housework schedule data sent by the user. The server first checks the data format and verifies that the housework items and their start times have been entered correctly. The input in this step is the user's housework schedule data, and the output is the housework information stored in the server's internal data storage. Specifically, the data is converted into a data format such as JSON and stored in an internal database.

[0138] Step 3:

[0139] The server optimizes the schedule using the generative AI model. The server inputs the housework information saved earlier as prompts to the generative AI model to optimize the schedule. The generative AI model analyzes the input prompts and generates an optimal schedule plan. In this process, the input is housework information and start time data, and the output is an optimized housework schedule. As a specific example of operation, a schedule such as "taking into account the user's housework behavior patterns, start washing at 7:30, then dry at 8:30, and start cleaning at 9:00" is generated.

[0140] Step 4:

[0141] The server issues execution commands to the washing machine and vacuum cleaner based on the optimized schedule. Based on the generated schedule, the server generates specific operating instructions for each device and sends them as commands. The input is the optimized housework schedule, and the output is specific operation commands. For example, commands include "Tell the washing machine to start washing at 7:30, start drying at 8:30, and start folding at 9:30" and "Tell the vacuum cleaner to start cleaning at 9:00."

[0142] Step 5:

[0143] The terminal devices, the washing machine and vacuum cleaner, perform housework based on commands received from the server. Each terminal device automatically starts operating at the specified time and performs each process as programmed. The input is the execution command sent from the server, and the output is the actual housework execution status. The washing machine starts washing at the specified time, and when washing is finished, it starts drying according to the next command, and then folds the laundry. The vacuum cleaner starts cleaning at the specified time, cleaning the specified areas in sequence.

[0144] Step 6:

[0145] The washing machine and vacuum cleaner, which are the terminals, notify the server that they have completed their chores. The completion notification includes various sensor and status information, and the server receives this to confirm that the chores have been completed. The input is the completion notification, and the output is an update to the server's internal status and a notification to the user. The server receives the completion notification and sends a notification to the user's application that "choices have been completed." The user receives a notification that chores have been completed through the application, and can confirm that the chores have been completed.

[0146] (Application example 1)

[0147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] Conventional household automation systems were effective in automating tasks such as laundry and cleaning, but they were unable to manage security while the user was away from home. Furthermore, they lacked the functionality to check the safety of the home or detect abnormalities while the user was away, leaving users unable to feel safe while they were out. Furthermore, to check the security of the home, users had to manually check camera footage and check door locks, which was time-consuming and could lead to security oversights.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0150] In this invention, the server comprises: means for a user to set a housework schedule; means for the server to analyze the housework items and start time data received from the user and generate an optimal housework schedule using a generation AI; means for the server to send commands to the laundry machine to perform washing, drying, and folding tasks based on the generated schedule; means for the server to send commands to the vacuum cleaner to start and finish cleaning based on the generated schedule; means for the laundry machine to automatically perform washing, drying, and folding tasks according to commands from the server; means for the vacuum cleaner to automatically perform cleaning according to commands from the server; and means for the laundry machine and vacuum cleaner to notify the server that housework has been completed. The system includes a means for the server to notify the user that housework has been completed, a means for the user to set a security check schedule, a means for the server to analyze the security check items and start time data received and generate an optimal security check schedule using a generation AI, a means for the server to send commands to the robot device to check door locks and take camera footage based on the generated security schedule, a means for the robot device to automatically check door locks and take camera footage in accordance with the commands from the server, a means for the robot device to notify the server that the security check has been completed, and a means for the server to notify the user that the security check has been completed. This allows the user to not only have housework done automatically even when they are out, but also to perform integrated security management when they are away.

[0151] A "user" is a person who operates the system and schedules housework and security checks.

[0152] The "housework schedule" refers to a schedule for daily housework such as laundry and cleaning set by the user.

[0153] "Server" is a central computer system for receiving and analyzing chore item and start time data.

[0154] "Generative AI" is an artificial intelligence system that generates optimal schedules based on data received from users.

[0155] A "land machine" is a fully automatic washing machine that washes, dries, and folds laundry.

[0156] A "vacuum cleaner" is a robot that automatically cleans the inside of a house.

[0157] A "housework item" is a specific housework item (e.g., laundry, cleaning) set by the user.

[0158] "Start time data" refers to information about the time when housework or security checks should begin.

[0159] A "schedule" is a plan for housekeeping or security checks to be performed based on a specified time.

[0160] A "command" is an instruction to execute sent from a server to a land machine, vacuum cleaner, or robotic device.

[0161] "Means for automatic execution" refers to a function that enables the device to automatically perform a set task in accordance with a command from the server.

[0162] A "robot device" is a robot that performs security checks on a home (e.g., checking door locks, capturing camera footage).

[0163] A "security check schedule" is a schedule for checking the safety of a home set by a user.

[0164] "Completion notification" refers to a notification from the system to report that housework or security checks have been completed.

[0165] This invention is a system in which users set schedules for housework and security checks, and the server analyzes them, uses AI to generate optimal schedules, and issues instructions to each device. This system not only performs laundry and cleaning fully automatically, but also manages home security when the user is away.

[0166] System configuration

[0167] The system consists of the following main components:

[0168] 1. User's operating device (smartphone application)

[0169] 2. Central Server

[0170] 3. Land Machine (fully automatic washing machine)

[0171] 4. Vacuum cleaner (robot vacuum cleaner)

[0172] 5. Security Check Robot (Robot Device)

[0173] Program processing explanation

[0174] 1. How users set schedules

[0175] Using a dedicated smartphone application, users can input the household chores and security check items and their start times. For example, they can set a schedule such as "Start laundry at 7:30," "Start cleaning at 9:00," "Check door locks at 7:30," and "Take camera footage at 9:00."

[0176] 2. How the server analyzes the data and generates the schedule

[0177] The server receives schedule data sent by the user. Based on the received data, the server uses generation AI to analyze the user's behavioral patterns and generate an optimal schedule.

[0178] 3. A means for the server to send commands to each device

[0179] Based on the generated schedule, the server sends execution commands to each device. For example, it can instruct the laundry machine to "start washing at 7:30," the vacuum cleaner to "start cleaning at 9:00," and the security check robot to "check the door locks at 7:30" and "take camera footage at 9:00."

[0180] 4. A means for each device to automatically execute tasks according to commands

[0181] Each device automatically performs designated tasks according to commands from the server: the land machine automatically washes, dries, and folds laundry, the vacuum cleaner cleans designated areas, and the security check robot checks door locks and captures camera footage.

[0182] 5. How the device notifies the server of completion and the server notifies the user

[0183] When each device completes a task, it sends a completion notification to the server, which then receives it and sends a "task completed" notification to the user's smartphone app.

[0184] Hardware and software used

[0185] Sensor-equipped door lock device

[0186] Robot with camera at the feet

[0187] Fully automatic washing machine

[0188] Robot vacuum cleaner

[0189] Smartphone application

[0190] Central Server

[0191] Generative AI model (AI analysis model)

[0192] Specific examples

[0193] For example, if a user wakes up at 7:00 a.m. and before going to work, they can set the following schedules: "Start laundry at 7:30 a.m.", "Start cleaning at 9:00 a.m.", "Check door locks at 7:30 a.m.", and "Take camera footage at 9:00 a.m." Based on the set schedule, the land machine will start washing at 7:30 a.m., followed by drying and folding. The vacuum cleaner will start cleaning at 9:00 a.m. and clean the house. The security check robot will check door locks at 7:30 a.m. and take camera footage at 9:00 a.m. Once all tasks are completed, the server will notify the user, allowing them to go out with peace of mind.

[0194] Prompt Sentence Examples

[0195] The AI ​​model can generate an optimal schedule by inputting the following prompts:

[0196] plaintext

[0197] Username: Taro Tanaka

[0198] Security Schedule Items:

[0199] 1. Door lock check - 7:30

[0200] 2. Camera Shooting - 9:00

[0201] Household chore schedule items:

[0202] 1. Laundry - 7:30

[0203] 2. Cleaning - 9am

[0204] The above is the details of the "Mode for Carrying Out the Invention."

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

[0206] Step 1:

[0207] Users use a dedicated smartphone application to set schedules for housework and security checks. The application has input fields for "housework schedule" and "security schedule," and users input each item and start time. For example, they can set "start laundry at 7:30," "start cleaning at 9," "check door locks at 7:30," and "take camera footage at 9." The input data is formatted and sent to the server in JSON format or similar.

[0208] Step 2:

[0209] The server receives schedule data sent by the user. The received data includes each household chore and security check item and their start time. The server analyzes this data and generates prompts to input into the generative AI model. For example, it generates the following prompts:

[0210] plaintext

[0211] Username: Taro Tanaka

[0212] Security Schedule Items:

[0213] 1. Door lock check - 7:30

[0214] 2. Camera Shooting - 9:00

[0215] Household chore schedule items:

[0216] 1. Laundry - 7:30

[0217] 2. Cleaning - 9am

[0218] Step 3:

[0219] The server inputs prompts into the generative AI model to generate an optimal schedule. In this process, the generative AI model determines the priority of each task based on the input data and adjusts overlapping work times. Because the AI ​​model has learned from the user's past behavioral data, it outputs an optimized schedule. The output result is as follows:

[0220] plaintext

[0221] Optimal Schedule:

[0222] 1. 7:30 - Check door lock, start washing

[0223] 2. 9:00 AM - Cleaning begins, camera footage is taken

[0224] Step 4:

[0225] The server issues commands to each device based on the generated optimal schedule. Specifically, it sends commands to the land machine to "start laundry at 7:30," to the vacuum cleaner to "start cleaning at 9:00," and to the security check robot to "check door locks at 7:30" and "take camera footage at 9:00." Each command is sent via software to the API of the corresponding device.

[0226] Step 5:

[0227] The land machine, vacuum cleaner, and security check robot receive commands from the server and automatically carry out the set tasks. The land machine starts washing at 7:30, followed by drying and folding. The vacuum cleaner cleans the designated area at 9:00 and returns to the home station after cleaning is complete. The security check robot checks the door locks at 7:30 and takes camera footage at 9:00.

[0228] Step 6:

[0229] When each device completes a task, it sends a completion notification to the server. For example, a laundry machine notifies the server that "laundry is complete," a vacuum cleaner notifies the server that "cleaning is complete," and a security check robot also notifies the server that "door lock check is complete" or "camera video recording is complete."

[0230] Step 7:

[0231] The server receives the completion notification and sends a "Work Completed" notification to the user's smartphone application, allowing the user to check in real time that all housework and security checks have been completed.

[0232] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0233] Understood. Below is a draft of the "Description of the Invention" for an invention that combines an emotion engine that recognizes user emotions.

[0234] This invention is a system that performs housework such as laundry and cleaning fully automatically by combining an emotion engine that recognizes the user's emotions, and generates and adjusts an optimal housework schedule taking the user's emotions into consideration. The following describes in detail an embodiment of the present invention.

[0235] Program processing explanation

[0236] 1. The user sets the household chores

[0237] The user uses a dedicated application to input the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the application sends the household chore items and start time data to the server.

[0238] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[0239] 2. The server receives and analyzes the data

[0240] The server receives the chore items and start time data sent from the application. The server also simultaneously collects the user's emotional data using an emotion engine. The emotion engine uses a generation AI to generate an optimal chore schedule based on the chore items and emotional data set by the user.

[0241] Server: The household chore items and start time data received from the user are passed to the generation AI, which then takes into account the emotion data from the emotion engine to generate an optimal household chore schedule.

[0242] 3. The server issues a command

[0243] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner. The server also adjusts the schedule in real time according to the user's emotional state.

[0244] Server: Based on the generated schedule, it sends commands to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It adjusts the schedule based on the user's emotional state.

[0245] 4. Your device will do the housework for you

[0246] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0247] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0248] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0249] 5. Completion notification

[0250] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[0251] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0252] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0253] 6. User Notices

[0254] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[0255] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[0256] Specific examples

[0257] For example, suppose a user wakes up at 7:00 a.m. and, before leaving the house, sets the settings to "start laundry with the Land Machine at 7:30 a.m." and "start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." The emotion engine collects the user's emotional data, and the generation AI generates an optimal housework schedule taking into account the user's emotional state. After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., followed by drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, and they can also receive feedback based on their emotional state.

[0258] As described above, the embodiment of the present invention provides a system that not only enables a user to effectively utilize their busy time and complete laundry and cleaning fully automatically, but also optimizes the housework schedule by taking into account the user's emotional state.

[0259] The processing flow will be explained below.

[0260] Understood. Below is a detailed step-by-step explanation of the processing flow of the invention that combines the emotion engine.

[0261] Step 1:

[0262] The user launches a dedicated application and inputs the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the application sends the household chore items and start times to the server.

[0263] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and after the settings are complete, sends the information to the server.

[0264] Step 2:

[0265] The server receives the chore items and start time data from the application and passes it to the generation AI module for analysis. At the same time, it collects the user's emotional data using the emotion engine. The generation AI module analyzes the user's chore behavior patterns based on the received chore items, start time data, and emotional data, and generates an optimal chore schedule.

[0266] Server: The household chore items and start time data received from the user are passed to the generation AI, and emotion data from the emotion engine is added. The generation AI then generates an optimal household chore schedule.

[0267] Step 3:

[0268] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner. The server also adjusts the schedule in real time based on the user's emotional state.

[0269] Server: Based on the generated schedule, it sends commands to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It adjusts the schedule based on the user's emotional state.

[0270] Step 4:

[0271] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0272] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0273] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0274] Step 5:

[0275] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[0276] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0277] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0278] Step 6:

[0279] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[0280] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[0281] Example 2

[0282] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0283] Conventional household automation systems set and execute household schedules without considering the user's emotional state, which has not sufficiently reduced the user's mental burden. In addition, because household schedules are not changed or adjusted in real time, it is difficult to respond flexibly to the user's stress level.

[0284] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to set a housework schedule, a means for analyzing the housework items and start time data received from the user and collecting emotion data, and a means for generating an optimal housework schedule using a generative AI model. This enables the generation of an optimal housework schedule that takes into account the user's emotional state and the schedule adjustment in real time.

[0285] The "means for the user to set the housework schedule" is a function that allows the user to input and set housework items and their start times using a dedicated application.

[0286] "Means for analyzing the household chore item and start time data received by the server from the user" is a function in which the server receives the household chore item and start time data sent by the user and analyzes the data.

[0287] The "means for collecting emotional data" is a function for collecting the user's emotional state in real time using an emotion engine, a sensor, or the like.

[0288] "Means for generating optimal housework schedules using a generative AI model" is a function that uses a generative AI model based on collected housework item data and emotion data to generate optimal housework schedules.

[0289] "Means for sending commands to the laundry machine to instruct it to perform washing, drying and folding operations" is a function that sends commands to the laundry machine to instruct it to start washing, drying and folding operations according to the schedule generated by the server.

[0290] The "means for sending commands to the vacuum cleaner to instruct it to start and finish cleaning" is a function for sending commands to the vacuum cleaner to instruct it to start and finish cleaning according to the schedule generated by the server.

[0291] "Means for the land machine to automatically perform the washing, drying and folding tasks in accordance with commands from the server" is a function for the land machine to automatically perform the washing, drying and folding tasks in accordance with instructions from the server.

[0292] "Means for the vacuum cleaner to automatically perform cleaning in accordance with commands from the server" is a function for the vacuum cleaner to automatically perform cleaning in accordance with instructions from the server.

[0293] The "means for notifying the server that the land machine and the vacuum cleaner have completed the housework" is a function for the land machine and the vacuum cleaner to notify the server of the completion information after completing the set housework.

[0294] The "means for the server to notify the user that the housework has been completed" is a function in which the server sends a notification of the completion of the housework to the user's application, thereby informing the user that the housework has been completed.

[0295] The "means for adjusting the schedule based on the user's emotional data" is a function for adjusting the housework schedule in real time based on the user's emotional data and making necessary changes.

[0296] This invention is a system that performs housework such as laundry and cleaning fully automatically by combining an emotion engine that recognizes the user's emotions, and generates and adjusts an optimal housework schedule taking the user's emotions into consideration. The following describes in detail an embodiment of the present invention.

[0297] 1. The user sets the household chore items

[0298] The user uses a dedicated application to input the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "start laundry at 7:30" and "start cleaning at 9:00." Once the settings are complete, the household chore items and start time data are sent from the application to the server. Specifically, the system has an interface that can be operated intuitively using a smartphone or tablet.

[0299] User: Uses a dedicated application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[0300] 2. The server receives and analyzes the data

[0301] The server receives the chore items and start time data sent from the application. Next, the server collects the user's emotional data using an emotion engine. This allows the server to generate a chore schedule that takes the user's emotional state into account. The emotion data is collected using multiple technologies, including facial expression recognition and voice analysis.

[0302] Server: Analyzes the household chore items and start time data received from the user, takes into account the emotion data from the emotion engine, and generates an optimal household chore schedule using a generative AI model.

[0303] 3. The server issues a command

[0304] Based on the schedule generated by the generative AI model, the server issues housework execution commands to the land machine and robot vacuum cleaner. For example, it sends a command to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It also has a function to adjust the schedule in real time according to the user's emotional state.

[0305] Server: Based on the generated schedule, it sends commands to the land machine such as "start laundry at 7:30" and to the robot vacuum cleaner such as "start cleaning at 9:00", and adjusts the schedule based on the user's emotional state.

[0306] 4. Your device will do the housework for you

[0307] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0308] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0309] Terminal (robot vacuum cleaner): Cleaning starts at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0310] 5. Completion notification

[0311] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends a "housework completed" notification to the user's application.

[0312] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0313] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0314] 6. User Notices

[0315] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[0316] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[0317] Specific examples

[0318] For example, suppose a user wakes up at 7:00 a.m. and, before leaving the house, sets the settings to "start laundry with the Land Machine at 7:30 a.m." and "start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." The emotion engine collects the user's emotional data, and the generation AI generates an optimal housework schedule taking into account the user's emotional state. After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., followed by drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, and they can also receive feedback based on their emotional state.

[0319] Examples of prompts for generative AI models

[0320] Here are some examples of prompts to input to a generative AI model:

[0321] Prompt: Generate an optimal schedule based on the user's specified household schedule, taking into account emotional data. For example, please set the household chore "Laundry" to start at 7:30 and "Cleaning" to start at 9:00. The user's emotional state is relaxed.

[0322] As described above, the embodiment of the present invention allows users to efficiently utilize their busy time and complete laundry and cleaning tasks fully automatically. Furthermore, a system can be realized that monitors the user's emotional state using an emotion engine and provides an optimal housework schedule.

[0323] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0324] Explanation of program processing steps

[0325] Step 1: User sets chore items

[0326] Input: The user launches a dedicated application on their smartphone or tablet and enters the household chores and their start times.

[0327] Specific operation: The user uses the application to set a household schedule, for example, "laundry starts at 7:30" or "cleaning starts at 9:00." The user can operate it intuitively through the user interface.

[0328] Output: The set household chore items and start time data are generated.

[0329] Data processing: The application formats the input data and prepares it for sending to the server.

[0330] User: Uses the dedicated app to set the household chore items and start time, then presses the send button. The output data is sent to the server.

[0331] Step 2: The server receives and analyzes the data

[0332] Input: Chore item and start time data submitted by the user.

[0333] Specific operation: The server analyzes the received data to understand the contents and schedule of housework. It also uses an emotion engine to collect the user's emotion data in real time. If necessary, it uses facial expression recognition and voice data analysis.

[0334] Output: Analyzed household chore items, start time data, and emotion data.

[0335] Data processing: Analyze the received data and combine it with emotion data.

[0336] Server: Analyzes data received from users, collects emotional data from the emotion engine, and prepares it for input into the generative AI model. The output data is passed to the generative AI model.

[0337] Step 3: Generate a schedule using a generative AI model

[0338] Input: Parsed chore items, start time data, and emotion data.

[0339] Specific operation: The generative AI model generates an optimal housework schedule based on the user's emotional state and housework data. It uses prompt sentences to generate a schedule that takes emotional data into account.

[0340] Output: Optimized housework schedule.

[0341] Data processing: The generative AI model generates a schedule based on the prompt text.

[0342] Server: Uses generative AI models to create optimal housework schedules, which are then sent to land machines and robot vacuum cleaners.

[0343] Step 4: Server issues command

[0344] Input: Optimized housework schedule.

[0345] Specific operation: Based on the optimized schedule, the server generates and sends commands to the land machine to start washing, drying, and folding, and to the robot vacuum cleaner to start and finish cleaning.

[0346] Output: Execution commands to the Land Machine and the robot vacuum cleaner.

[0347] Data processing: Converts schedule data into command format and sends it to each device.

[0348] Server: Based on the generated schedule, it sends execution commands to the land machine and the robot vacuum cleaner. The output commands are used by each device to perform the specified household chores.

[0349] Step 5: Your device does the chore

[0350] Input: The execution command sent by the server.

[0351] Specific operation: The land machine starts washing at 7:30, then drying at 8:30 and folding at 9:30. The robot vacuum cleaner starts cleaning at 9:00, cleaning designated areas one by one, and returns to the home station after completion.

[0352] Output: Completion status of the performed chore.

[0353] Data processing: Monitors the execution status and generates completion information.

[0354] Terminals (land machines and robot vacuum cleaners): Follow commands from the server and automatically perform the designated housework. The output completion information is sent to the server.

[0355] Step 6: Completion Notification

[0356] Input: Notification of housework completion from device.

[0357] Specific operation: When the Land Machine and the robot vacuum cleaner complete the chore, they send a notification to the server, which then sends a "choice completed" notification to the user's application.

[0358] Output: Completion notification to the user.

[0359] Data processing: Analyzes the completion information from the terminal and generates a message to notify the user.

[0360] Server: Based on the received completion notification, it sends a completion notification to the user's application. The output notification notifies the user that the housework has been completed.

[0361] Step 7: User Notification

[0362] Input: Notification of housework completion from the server.

[0363] Specific behavior: The user receives a notification through the application that says "Laundry and cleaning are done," confirming that all household chores have been completed. In addition, the user can receive feedback based on emotion data.

[0364] Output: Feedback of chore completion and emotional state.

[0365] Data processing: Analyzes the emotion data and generates feedback data to provide to the user.

[0366] User: Receives a notification through the application that the laundry and cleaning is complete and sees feedback based on emotional data.

[0367] Through the above processing steps, the system of the present invention can generate an optimal housework schedule taking into consideration the user's emotional state, and can perform housework and adjust the schedule in real time.

[0368] (Application example 2)

[0369] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0370] In factory work, it is necessary to optimize work schedules by taking into account the emotional state of each employee. However, conventional systems face challenges in that it is difficult to collect and analyze emotional data in real time and automatically adjust work schedules based on the results. In addition, there are limited means to efficiently manage work progress and completion notifications, which can lead to a decline in overall productivity and employee satisfaction.

[0371] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0372] In this invention, the server includes a means for allowing the user to set a work schedule, a means for the server to analyze the work items and start time data received from the user and generate an optimal work schedule using a generation AI, and a means for an emotion analysis engine to collect user emotion data and for the server to analyze this emotion data and adjust the schedule in real time. This makes it possible to generate an optimal work schedule taking into account the user's emotional state and adjust it in real time.

[0373] The "means for the user to set a work schedule" is an interface for the user to input schedule information such as the type of work to be performed and the start time.

[0374] "Generative AI" is an artificial intelligence technology that generates a schedule for optimally executing a specific task based on input data.

[0375] A "server" is a central control device that receives and analyzes data via a network and sends necessary instructions to related devices.

[0376] An "emotion analysis engine" is software that collects and analyzes a user's emotional state and adjusts schedules based on the results.

[0377] A "robot" is an automated mechanical device that performs designated tasks automatically.

[0378] A "machine" is a device that operates in response to instructions from a server to perform various tasks.

[0379] The "means for sending commands" is a mechanism for sending specific work instructions to robots and machinery based on the schedule generated by the server.

[0380] "Means for adjusting schedules in real time" refers to technology that instantly updates and optimizes work schedules based on data collected by a sentiment analysis engine.

[0381] The "means for notifying the server that a task has been completed" is a system in which a robot or machine reports the completion status to a server when the robot or machine completes a task as instructed.

[0382] "Means for notifying the user of task completion" refers to a mechanism by which the server confirms task completion and notifies the user of that information.

[0383] The present invention is a system for optimizing work schedules within a factory and making real-time adjustments taking into account the emotional state of employees. Hereinafter, an embodiment of the present invention will be described in detail.

[0384] System Overview

[0385] The system of the present invention is composed of a server, a sentiment analysis engine, a generative AI model, a user interface, a robot, and a machine device. The server controls the data exchange with each of these elements and manages the overall work schedule.

[0386] Hardware and software used

[0387] Hardware: Factory robots (e.g., KUKA and FANUC products), machinery.

[0388] Software: Python scripts, sentiment analysis engines (e.g., Emotion Analysis API), generative AI models.

[0389] Data processing and calculation

[0390] User Interface

[0391] Users set up work schedules using a dedicated application, entering specific work items and their start times, such as "assembly work begins at 8:00" or "inspection work begins at 10:00." Once the settings are complete, the work items and start times are sent to the server.

[0392] Server - receives and analyzes data

[0393] The server analyzes the work items and start time data received from the user. The server also collects the user's emotional data using a sentiment analysis engine. The user's emotional data (e.g., happy, neutral, stressed, tired) is obtained from the sentiment analysis engine. Based on this, the server uses a generative AI model to generate an optimal schedule for the work.

[0394] Server - Issue commands

[0395] The server issues work execution commands to robots and machines based on the generated schedule. For example, it sends commands such as "Start assembly work at 8:00" to robots and "Start inspection work at 10:00" to machines. It also adjusts the schedule in real time based on the user's emotional state.

[0396] Terminals - Work execution and notifications

[0397] Robots and machines automatically perform tasks at designated times according to commands from the server. For example, a robot may "start assembly work at 8:00" and a machine may "start inspection work at 10:00." When a task is completed, the robot and machine each send a work completion notification to the server.

[0398] Server - User Notification

[0399] When the server detects that the work is complete, it notifies the user's application that "assembly and inspection work is complete." Furthermore, it can also grasp the user's stress and satisfaction level based on emotional data obtained by the sentiment analysis engine.

[0400] Specific examples

[0401] For example, suppose Employee A is scheduled to start assembly work at 8:00 a.m. and perform inspection work at 10:00 a.m. The sentiment analysis engine collects Employee A's emotional data, and the generative AI model generates an optimal work schedule taking into account that emotional state. Based on this schedule, the server sends work execution commands to the robots that perform assembly work and the machinery that performs inspection work. The robots start assembly work at 8:00 a.m., and the machinery starts inspection work at 10:00 a.m. When the work is completed, each device sends a completion notification to the server, and the server sends a completion notification to the user, as well as providing feedback based on the emotional data.

[0402] Employee: Worker A

[0403] Emotional state: stressed

[0404] Generated schedule:

[0405] Inspection Task

[0406] Packing Task

[0407] Robot: robot_1

[0408] Working command:

[0409] http: / / robot-scheduler.example.com / command?robot_id=robot_1&task=Inspection Task

[0410] http: / / robot-scheduler.example.com / command?robot_id=robot_1&task=Packing Task

[0411] As can be seen, embodiments of the present invention enable optimization of work schedules within a factory and adjustments of work in real time taking into account the emotional state of employees.

[0412] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0413] Step 1:

[0414] Users use the application to schedule work.

[0415] Input: The work item (e.g., assembly work, inspection work) to be performed by the user and its start time.

[0416] Data processing: Schedule information is converted into a unique format and prepared for transmission to the server.

[0417] Output: Work item and start time data is sent to the server.

[0418] Step 2:

[0419] The server analyzes the work item and start time data received from the user and collects user emotion data using a sentiment analysis engine.

[0420] Input: Work item and start time data received from users. Sentiment data from the sentiment analysis engine.

[0421] Data processing: Analyze task item and start time data and collect emotion data.

[0422] Output: Parsed work item data and emotion data are passed to the generative AI model.

[0423] Step 3:

[0424] The server uses the generated AI model to generate an optimal work schedule that takes into account the user's emotional data.

[0425] Input: Sentiment data from the sentiment analysis engine, parsed work item data.

[0426] Data calculation: A generative AI model calculates the optimal work schedule based on this data.

[0427] Output: The optimized work schedule is provided to the server.

[0428] Step 4:

[0429] The server sends work execution commands to the robots and mechanical devices based on the generated schedule.

[0430] Input: Optimized work schedule.

[0431] Data calculation: Generates specific execution commands for each task.

[0432] Output: Specific work execution commands sent to robots and machinery.

[0433] Step 5:

[0434] Robots and machinery automatically perform tasks according to task execution commands from the server.

[0435] Input: Work execution command from the server.

[0436] Specific operation: The robot starts assembly work at the specified time, and the machine starts inspection work at the specified time.

[0437] Output: Data to send to the server to notify the progress and completion of work.

[0438] Step 6:

[0439] Robots and mechanical devices send work completion notifications to the server.

[0440] Input: Data on the work completed.

[0441] Data processing: Convert the work completion notification into a unique format and send it to the server.

[0442] Output: Notification data is provided to the server that the work has been completed.

[0443] Step 7:

[0444] The server notifies the user of the work status and completion notification.

[0445] Input: Work completion notification data from robots and machinery.

[0446] Data processing: Converting work completion notifications into user-friendly messages.

[0447] Output: Data to send task completion notifications to the user's application and provide feedback based on sentiment data.

[0448] The above are the processing steps of the system program that realizes the application example.

[0449] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0450] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0451] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0452] [Second embodiment]

[0453] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0454] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0456] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0457] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

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

[0459] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0460] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0463] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0464] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0465] Understood. Below is a draft of the "Description of the Invention".

[0466] This invention is a system that performs housework such as laundry and cleaning fully automatically, in which a server, a land machine, and a robot vacuum cleaner work together to automatically perform housework based on a housework schedule set by a user. The following describes an embodiment of the present invention in detail.

[0467] Program processing explanation

[0468] 1. The user sets the household chores

[0469] Using a dedicated application, users can input the chores to be done that day and their start times. For example, they can set a chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00."

[0470] plaintext

[0471] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[0472] 2. The server receives and analyzes the data

[0473] The server receives data on chore items and start times sent from the application, and uses AI to analyze the user's chore behavior patterns based on the received data to generate an optimal chore schedule.

[0474] plaintext

[0475] Server: Analyzes the household chore items and start time data received from the user and generates an optimal household chore schedule using generation AI.

[0476] 3. The server issues a command

[0477] Based on the generated schedule, the server issues housework commands to the land machine and the robot vacuum cleaner. For example, it sends a command to the land machine to "start laundry at 7:30" and a command to the robot vacuum cleaner to "start cleaning at 9:00."

[0478] plaintext

[0479] Server: Based on the generated schedule, it sends commands to the Land Machine to "start laundry at 7:30" and to the Robot Vacuum Cleaner to "start cleaning at 9:00."

[0480] 4. Your device will do the housework for you

[0481] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0482] plaintext

[0483] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0484] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0485] 5. Completion notification

[0486] When the housework is completed, the land machine and the robot vacuum cleaner each send a completion notification to the server, which then receives the notification and sends it to the user's application.

[0487] plaintext

[0488] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0489] Server: Receives the notification that the housework has been completed and sends a notification to the user's application saying "The housework has been completed."

[0490] User: Receives a notification through the application that says "Laundry and cleaning completed" and confirms that all household chores are completed.

[0491] Specific examples

[0492] For example, suppose a user wakes up at 7:00 a.m. and before leaving the house sets the settings to "Start laundry with the Land Machine at 7:30 a.m." and "Start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., then drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, they will receive a notification from the application that the housework has been completed, freeing up their busy daily routine from housework.

[0493] As described above, the embodiment of the present invention can provide a system that enables users to make effective use of their busy time and complete laundry and cleaning fully automatically.

[0494] The processing flow will be explained below.

[0495] Understood. Below, we will explain the program's processing steps based on the scope of the patent claims.

[0496] Step 1:

[0497] The user launches a dedicated application and inputs the household chores to be done that day and their start times. For example, they can set "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the household chores and start times are sent from the application to the server.

[0498] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and after the settings are complete, sends the information to the server.

[0499] Step 2:

[0500] The server analyzes the household chore items and start time data received from the application and passes the data to the generation AI module, which then analyzes the user's household chore behavior patterns based on the received data and generates an optimal household chore schedule.

[0501] Server: The household chore items and start time data received from the user are passed to the generation AI, which then generates an optimal household chore schedule.

[0502] Step 3:

[0503] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner.

[0504] Server: Based on the generated schedule, it sends commands to the Land Machine to "start laundry at 7:30" and to the Robot Vacuum Cleaner to "start cleaning at 9:00."

[0505] Step 4:

[0506] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0507] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0508] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0509] Step 5:

[0510] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[0511] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0512] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0513] Step 6:

[0514] The user receives a notification through the application that the laundry and cleaning has been completed, confirming that all the household chores have been completed. In this way, the user can efficiently complete household chores even when they are not at home.

[0515] User: Receives a notification through the application that says "Laundry and cleaning completed" and confirms that all household chores are completed.

[0516] Example 1

[0517] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0518] In today's busy living environment, users are required to perform housework efficiently without spending time and effort. However, conventional manual or semi-automatic household appliances require users to perform detailed settings and operations, preventing full automation. Furthermore, schedule management and checking the progress of housework are burdensome for users. Therefore, there is a demand for a system that reduces the burden on users and fully automates housework.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0520] In this invention, the server includes a means for a user to set a housework schedule, a means for the server to analyze the housework information and start time data received from the user and generate an optimal housework schedule using a generative AI model, a means for the server to send commands to a washing machine to instruct it to wash, dry, and fold based on the generated schedule, and a means for the server to send commands to a vacuum cleaner to instruct it to start and finish cleaning based on the generated schedule. This eliminates the need for the user to manually manage the start times of housework, making it possible to fully automate and efficiently perform housework. Furthermore, the server manages the progress of housework and notifies the user when it is completed, allowing the user to always be aware of the status of the housework.

[0521] "User" refers to an individual or user who sets a household schedule and uses the system through the interface.

[0522] A "housework schedule" is a timetable that includes housework items such as laundry and cleaning and the start times for performing them.

[0523] The "server" is a central control device that receives instructions from the user, manages and analyzes the schedule of household items, and sends execution commands to the corresponding devices.

[0524] "Housework information" is a general term for housework items set by the user and data related to them.

[0525] "Start time data" is information including the time when each household chore item is scheduled to be performed.

[0526] The "generative AI model" is an artificial intelligence model that generates an optimal housework schedule based on received housework information and start time data.

[0527] A "washing machine" is a household appliance that automatically washes, dries, and folds clothes.

[0528] A "vacuum cleaner" is a device that automatically cleans a designated area and automatically returns to its charging station once cleaning is complete.

[0529] A "command" refers to a specific instruction for operation sent from the server to a washing machine or vacuum cleaner.

[0530] "Laundry" is a household chore in which clothes are washed with water and detergent.

[0531] "Drying" is a household chore of drying clothes after washing.

[0532] "Folding" refers to the process of sorting and folding clothes after they have been dried.

[0533] "Sweeping" is the act of vacuuming up dirt and dust to clean floors and other surfaces.

[0534] "Automatically executing" means that the equipment operates without human intervention based on a pre-set schedule or received commands.

[0535] The "completion notification" is information that notifies the user that the housework has been completed from the appliance to the server, and from the server to the user.

[0536] This invention is a system that performs housework such as laundry and cleaning fully automatically, in which a server, a washing machine, and a vacuum cleaner work together to automatically perform housework based on a housework schedule set by a user. Specific embodiments for carrying out the invention are described below.

[0537] System configuration

[0538] The system consists of four main components: an application that allows users to set housework schedules, a server, a washing machine, and a vacuum cleaner. The server analyzes the housework information and start time data sent by the user and generates an optimal housework schedule using a generative AI model. The server then sends commands to the washing machine and vacuum cleaner to perform the housework, and notifies the user when the housework is completed.

[0539] Operation flow

[0540] 1. Using a dedicated application, the user inputs the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00."

[0541] 2. Once the user completes the setup, the housework information and start time data are sent to the server. The server then uses the received data to analyze the user's housework behavior patterns using a generative AI model and generates an optimal housework schedule.

[0542] 3. Based on the generated schedule, the server issues commands to the washing machine and vacuum cleaner to perform household chores, such as "start laundry at 7:30" and "start cleaning at 9:00."

[0543] 4. The washing machine and vacuum cleaner receive commands from the server and automatically perform household chores at the specified time. The washing machine washes, dries, and folds laundry in succession, while the vacuum cleaner cleans designated areas in sequence. Each appliance performs its own task automatically.

[0544] 5. When the chore is completed, the washing machine and vacuum cleaner each send a completion notification to the server. The server receives this and sends a completion notification to the user's application. The user can confirm that the chore is complete by receiving a notification that "choice is completed."

[0545] Specific examples

[0546] For example, suppose a user wakes up at 7:00 a.m. and sets the schedules "Start laundry at 7:30" and "Start cleaning at 9:00" before leaving the house. After the user leaves the house, the server sends commands to the washing machine and vacuum cleaner based on these schedules. The washing machine starts washing at 7:30, continues drying at 8:30, and folds at 9:30. The vacuum cleaner starts cleaning at 9:00, and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, freeing up their busy daily routine from housework.

[0547] As described above, the embodiment of the present invention can provide a system that allows users to make effective use of their busy time and complete laundry and cleaning fully automatically.

[0548] Prompt Sentence Examples

[0549] An example of a specific prompt sentence for a generative AI model is shown below.

[0550] plaintext

[0551] User chores and schedules:

[0552] Start washing at 7:30

[0553] Cleaning begins at 9am

[0554] Based on this prompt, the generative AI model generates an optimal housework schedule.

[0555] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0556] Step 1:

[0557] The user sets the household chore schedule. The user launches a dedicated application and enters the household chores, such as laundry and cleaning, and their start times on the screen. Next, the user presses the Set button to confirm the information. This operation formats the entered data and saves it within the application as household chore schedule data. Specifically, the user sets a schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00" and sends it to the server.

[0558] Step 2:

[0559] The server receives the housework schedule data sent by the user. The server first checks the data format and verifies that the housework items and their start times have been entered correctly. The input in this step is the user's housework schedule data, and the output is the housework information stored in the server's internal data storage. Specifically, the data is converted into a data format such as JSON and stored in an internal database.

[0560] Step 3:

[0561] The server optimizes the schedule using the generative AI model. The server inputs the housework information saved earlier as prompts to the generative AI model to optimize the schedule. The generative AI model analyzes the input prompts and generates an optimal schedule plan. In this process, the input is housework information and start time data, and the output is an optimized housework schedule. As a specific example of operation, a schedule such as "taking into account the user's housework behavior patterns, start washing at 7:30, then dry at 8:30, and start cleaning at 9:00" is generated.

[0562] Step 4:

[0563] The server issues execution commands to the washing machine and vacuum cleaner based on the optimized schedule. Based on the generated schedule, the server generates specific operating instructions for each device and sends them as commands. The input is the optimized housework schedule, and the output is specific operation commands. For example, commands include "Tell the washing machine to start washing at 7:30, start drying at 8:30, and start folding at 9:30" and "Tell the vacuum cleaner to start cleaning at 9:00."

[0564] Step 5:

[0565] The terminal devices, the washing machine and vacuum cleaner, perform housework based on commands received from the server. Each terminal device automatically starts operating at the specified time and performs each process as programmed. The input is the execution command sent from the server, and the output is the actual housework execution status. The washing machine starts washing at the specified time, and when washing is finished, it starts drying according to the next command, and then folds the laundry. The vacuum cleaner starts cleaning at the specified time, cleaning the specified areas in sequence.

[0566] Step 6:

[0567] The washing machine and vacuum cleaner, which are the terminals, notify the server that they have completed their chores. The completion notification includes various sensor and status information, and the server receives this to confirm that the chores have been completed. The input is the completion notification, and the output is an update to the server's internal status and a notification to the user. The server receives the completion notification and sends a notification to the user's application that "choices have been completed." The user receives a notification that chores have been completed through the application, and can confirm that the chores have been completed.

[0568] (Application example 1)

[0569] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0570] Conventional household automation systems were effective in automating tasks such as laundry and cleaning, but they were unable to manage security while the user was away from home. Furthermore, they lacked the functionality to check the safety of the home or detect abnormalities while the user was away, leaving users unable to feel safe while they were out. Furthermore, to check the security of the home, users had to manually check camera footage and check door locks, which was time-consuming and could lead to security oversights.

[0571] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0572] In this invention, the server comprises: means for a user to set a housework schedule; means for the server to analyze the housework items and start time data received from the user and generate an optimal housework schedule using a generation AI; means for the server to send commands to the laundry machine to perform washing, drying, and folding tasks based on the generated schedule; means for the server to send commands to the vacuum cleaner to start and finish cleaning based on the generated schedule; means for the laundry machine to automatically perform washing, drying, and folding tasks according to commands from the server; means for the vacuum cleaner to automatically perform cleaning according to commands from the server; and means for the laundry machine and vacuum cleaner to notify the server that housework has been completed. The system includes a means for the server to notify the user that housework has been completed, a means for the user to set a security check schedule, a means for the server to analyze the security check items and start time data received and generate an optimal security check schedule using a generation AI, a means for the server to send commands to the robot device to check door locks and take camera footage based on the generated security schedule, a means for the robot device to automatically check door locks and take camera footage in accordance with the commands from the server, a means for the robot device to notify the server that the security check has been completed, and a means for the server to notify the user that the security check has been completed. This allows the user to not only have housework done automatically even when they are out, but also to perform integrated security management when they are away.

[0573] A "user" is a person who operates the system and schedules housework and security checks.

[0574] The "housework schedule" refers to a schedule for daily housework such as laundry and cleaning set by the user.

[0575] "Server" is a central computer system for receiving and analyzing chore item and start time data.

[0576] "Generative AI" is an artificial intelligence system that generates optimal schedules based on data received from users.

[0577] A "land machine" is a fully automatic washing machine that washes, dries, and folds laundry.

[0578] A "vacuum cleaner" is a robot that automatically cleans the inside of a house.

[0579] A "housework item" is a specific housework item (e.g., laundry, cleaning) set by the user.

[0580] "Start time data" refers to information about the time when housework or security checks should begin.

[0581] A "schedule" is a plan for housekeeping or security checks to be performed based on a specified time.

[0582] A "command" is an instruction to execute sent from a server to a land machine, vacuum cleaner, or robotic device.

[0583] "Means for automatic execution" refers to a function that enables the device to automatically perform a set task in accordance with a command from the server.

[0584] A "robot device" is a robot that performs security checks on a home (e.g., checking door locks, capturing camera footage).

[0585] A "security check schedule" is a schedule for checking the safety of a home set by a user.

[0586] "Completion notification" refers to a notification from the system to report that housework or security checks have been completed.

[0587] This invention is a system in which users set schedules for housework and security checks, and the server analyzes them, uses AI to generate optimal schedules, and issues instructions to each device. This system not only performs laundry and cleaning fully automatically, but also manages home security when the user is away.

[0588] System configuration

[0589] The system consists of the following main components:

[0590] 1. User's operating device (smartphone application)

[0591] 2. Central Server

[0592] 3. Land Machine (fully automatic washing machine)

[0593] 4. Vacuum cleaner (robot vacuum cleaner)

[0594] 5. Security Check Robot (Robot Device)

[0595] Program processing explanation

[0596] 1. How users set schedules

[0597] Using a dedicated smartphone application, users can input the household chores and security check items and their start times. For example, they can set a schedule such as "Start laundry at 7:30," "Start cleaning at 9:00," "Check door locks at 7:30," and "Take camera footage at 9:00."

[0598] 2. How the server analyzes the data and generates the schedule

[0599] The server receives schedule data sent by the user. Based on the received data, the server uses generation AI to analyze the user's behavioral patterns and generate an optimal schedule.

[0600] 3. A means for the server to send commands to each device

[0601] Based on the generated schedule, the server sends execution commands to each device. For example, it can instruct the laundry machine to "start washing at 7:30," the vacuum cleaner to "start cleaning at 9:00," and the security check robot to "check the door locks at 7:30" and "take camera footage at 9:00."

[0602] 4. A means for each device to automatically execute tasks according to commands

[0603] Each device automatically performs designated tasks according to commands from the server: the land machine automatically washes, dries, and folds laundry, the vacuum cleaner cleans designated areas, and the security check robot checks door locks and captures camera footage.

[0604] 5. How the device notifies the server of completion and the server notifies the user

[0605] When each device completes a task, it sends a completion notification to the server, which then receives it and sends a "task completed" notification to the user's smartphone app.

[0606] Hardware and software used

[0607] Sensor-equipped door lock device

[0608] Robot with camera at the feet

[0609] Fully automatic washing machine

[0610] Robot vacuum cleaner

[0611] Smartphone application

[0612] Central Server

[0613] Generative AI model (AI analysis model)

[0614] Specific examples

[0615] For example, if a user wakes up at 7:00 a.m. and before going to work, they can set the following schedules: "Start laundry at 7:30 a.m.", "Start cleaning at 9:00 a.m.", "Check door locks at 7:30 a.m.", and "Take camera footage at 9:00 a.m." Based on the set schedule, the land machine will start washing at 7:30 a.m., followed by drying and folding. The vacuum cleaner will start cleaning at 9:00 a.m. and clean the house. The security check robot will check door locks at 7:30 a.m. and take camera footage at 9:00 a.m. Once all tasks are completed, the server will notify the user, allowing them to go out with peace of mind.

[0616] Prompt Sentence Examples

[0617] The AI ​​model can generate an optimal schedule by inputting the following prompts:

[0618] plaintext

[0619] Username: Taro Tanaka

[0620] Security Schedule Items:

[0621] 1. Door lock check - 7:30

[0622] 2. Camera Shooting - 9:00

[0623] Household chore schedule items:

[0624] 1. Laundry - 7:30

[0625] 2. Cleaning - 9am

[0626] The above is the details of the "Mode for Carrying Out the Invention."

[0627] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0628] Step 1:

[0629] Users use a dedicated smartphone application to set schedules for housework and security checks. The application has input fields for "housework schedule" and "security schedule," and users input each item and start time. For example, they can set "start laundry at 7:30," "start cleaning at 9," "check door locks at 7:30," and "take camera footage at 9." The input data is formatted and sent to the server in JSON format or similar.

[0630] Step 2:

[0631] The server receives schedule data sent by the user. The received data includes each household chore and security check item and their start time. The server analyzes this data and generates prompts to input into the generative AI model. For example, it generates the following prompts:

[0632] plaintext

[0633] Username: Taro Tanaka

[0634] Security Schedule Items:

[0635] 1. Door lock check - 7:30

[0636] 2. Camera Shooting - 9:00

[0637] Household chore schedule items:

[0638] 1. Laundry - 7:30

[0639] 2. Cleaning - 9am

[0640] Step 3:

[0641] The server inputs prompts into the generative AI model to generate an optimal schedule. In this process, the generative AI model determines the priority of each task based on the input data and adjusts overlapping work times. Because the AI ​​model has learned from the user's past behavioral data, it outputs an optimized schedule. The output result is as follows:

[0642] plaintext

[0643] Optimal Schedule:

[0644] 1. 7:30 - Check door lock, start washing

[0645] 2. 9:00 AM - Cleaning begins, camera footage is taken

[0646] Step 4:

[0647] The server issues commands to each device based on the generated optimal schedule. Specifically, it sends commands to the land machine to "start laundry at 7:30," to the vacuum cleaner to "start cleaning at 9:00," and to the security check robot to "check door locks at 7:30" and "take camera footage at 9:00." Each command is sent via software to the API of the corresponding device.

[0648] Step 5:

[0649] The land machine, vacuum cleaner, and security check robot receive commands from the server and automatically carry out the set tasks. The land machine starts washing at 7:30, followed by drying and folding. The vacuum cleaner cleans the designated area at 9:00 and returns to the home station after cleaning is complete. The security check robot checks the door locks at 7:30 and takes camera footage at 9:00.

[0650] Step 6:

[0651] When each device completes a task, it sends a completion notification to the server. For example, a laundry machine notifies the server that "laundry is complete," a vacuum cleaner notifies the server that "cleaning is complete," and a security check robot also notifies the server that "door lock check is complete" or "camera video recording is complete."

[0652] Step 7:

[0653] The server receives the completion notification and sends a "Work Completed" notification to the user's smartphone application, allowing the user to check in real time that all housework and security checks have been completed.

[0654] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0655] Understood. Below is a draft of the "Description of the Invention" for an invention that combines an emotion engine that recognizes user emotions.

[0656] This invention is a system that performs housework such as laundry and cleaning fully automatically by combining an emotion engine that recognizes the user's emotions, and generates and adjusts an optimal housework schedule taking the user's emotions into consideration. The following describes in detail an embodiment of the present invention.

[0657] Program processing explanation

[0658] 1. The user sets the household chores

[0659] The user uses a dedicated application to input the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the application sends the household chore items and start time data to the server.

[0660] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[0661] 2. The server receives and analyzes the data

[0662] The server receives the chore items and start time data sent from the application. The server also simultaneously collects the user's emotional data using an emotion engine. The emotion engine uses a generation AI to generate an optimal chore schedule based on the chore items and emotional data set by the user.

[0663] Server: The household chore items and start time data received from the user are passed to the generation AI, which then takes into account the emotion data from the emotion engine to generate an optimal household chore schedule.

[0664] 3. The server issues a command

[0665] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner. The server also adjusts the schedule in real time according to the user's emotional state.

[0666] Server: Based on the generated schedule, it sends commands to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It adjusts the schedule based on the user's emotional state.

[0667] 4. Your device will do the housework for you

[0668] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0669] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0670] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0671] 5. Completion notification

[0672] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[0673] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0674] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0675] 6. User Notices

[0676] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[0677] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[0678] Specific examples

[0679] For example, suppose a user wakes up at 7:00 a.m. and, before leaving the house, sets the settings to "start laundry with the Land Machine at 7:30 a.m." and "start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." The emotion engine collects the user's emotional data, and the generation AI generates an optimal housework schedule taking into account the user's emotional state. After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., followed by drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, and they can also receive feedback based on their emotional state.

[0680] As described above, the embodiment of the present invention provides a system that not only enables a user to effectively utilize their busy time and complete laundry and cleaning fully automatically, but also optimizes the housework schedule by taking into account the user's emotional state.

[0681] The processing flow will be explained below.

[0682] Understood. Below is a detailed step-by-step explanation of the processing flow of the invention that combines the emotion engine.

[0683] Step 1:

[0684] The user launches a dedicated application and inputs the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the application sends the household chore items and start times to the server.

[0685] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and after the settings are complete, sends the information to the server.

[0686] Step 2:

[0687] The server receives the chore items and start time data from the application and passes it to the generation AI module for analysis. At the same time, it collects the user's emotional data using the emotion engine. The generation AI module analyzes the user's chore behavior patterns based on the received chore items, start time data, and emotional data, and generates an optimal chore schedule.

[0688] Server: The household chore items and start time data received from the user are passed to the generation AI, and emotion data from the emotion engine is added. The generation AI then generates an optimal household chore schedule.

[0689] Step 3:

[0690] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner. The server also adjusts the schedule in real time based on the user's emotional state.

[0691] Server: Based on the generated schedule, it sends commands to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It adjusts the schedule based on the user's emotional state.

[0692] Step 4:

[0693] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0694] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0695] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0696] Step 5:

[0697] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[0698] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0699] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0700] Step 6:

[0701] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[0702] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[0703] Example 2

[0704] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0705] Conventional household automation systems set and execute household schedules without considering the user's emotional state, which has not sufficiently reduced the user's mental burden. In addition, because household schedules are not changed or adjusted in real time, it is difficult to respond flexibly to the user's stress level.

[0706] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to set a housework schedule, a means for analyzing the housework items and start time data received from the user and collecting emotion data, and a means for generating an optimal housework schedule using a generative AI model. This enables the generation of an optimal housework schedule that takes into account the user's emotional state and the schedule adjustment in real time.

[0707] The "means for the user to set the housework schedule" is a function that allows the user to input and set housework items and their start times using a dedicated application.

[0708] "Means for analyzing the household chore item and start time data received by the server from the user" is a function in which the server receives the household chore item and start time data sent by the user and analyzes the data.

[0709] The "means for collecting emotional data" is a function for collecting the user's emotional state in real time using an emotion engine, a sensor, or the like.

[0710] "Means for generating optimal housework schedules using a generative AI model" is a function that uses a generative AI model based on collected housework item data and emotion data to generate optimal housework schedules.

[0711] "Means for sending commands to the laundry machine to instruct it to perform washing, drying and folding operations" is a function that sends commands to the laundry machine to instruct it to start washing, drying and folding operations according to the schedule generated by the server.

[0712] The "means for sending commands to the vacuum cleaner to instruct it to start and finish cleaning" is a function for sending commands to the vacuum cleaner to instruct it to start and finish cleaning according to the schedule generated by the server.

[0713] "Means for the land machine to automatically perform the washing, drying and folding tasks in accordance with commands from the server" is a function for the land machine to automatically perform the washing, drying and folding tasks in accordance with instructions from the server.

[0714] "Means for the vacuum cleaner to automatically perform cleaning in accordance with commands from the server" is a function for the vacuum cleaner to automatically perform cleaning in accordance with instructions from the server.

[0715] The "means for notifying the server that the land machine and the vacuum cleaner have completed the housework" is a function for the land machine and the vacuum cleaner to notify the server of the completion information after completing the set housework.

[0716] The "means for the server to notify the user that the housework has been completed" is a function in which the server sends a notification of the completion of the housework to the user's application, thereby informing the user that the housework has been completed.

[0717] The "means for adjusting the schedule based on the user's emotional data" is a function for adjusting the housework schedule in real time based on the user's emotional data and making necessary changes.

[0718] This invention is a system that performs housework such as laundry and cleaning fully automatically by combining an emotion engine that recognizes the user's emotions, and generates and adjusts an optimal housework schedule taking the user's emotions into consideration. The following describes in detail an embodiment of the present invention.

[0719] 1. The user sets the household chore items

[0720] The user uses a dedicated application to input the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "start laundry at 7:30" and "start cleaning at 9:00." Once the settings are complete, the household chore items and start time data are sent from the application to the server. Specifically, the system has an interface that can be operated intuitively using a smartphone or tablet.

[0721] User: Uses a dedicated application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[0722] 2. The server receives and analyzes the data

[0723] The server receives the chore items and start time data sent from the application. Next, the server collects the user's emotional data using an emotion engine. This allows the server to generate a chore schedule that takes the user's emotional state into account. The emotion data is collected using multiple technologies, including facial expression recognition and voice analysis.

[0724] Server: Analyzes the household chore items and start time data received from the user, takes into account the emotion data from the emotion engine, and generates an optimal household chore schedule using a generative AI model.

[0725] 3. The server issues a command

[0726] Based on the schedule generated by the generative AI model, the server issues housework execution commands to the land machine and robot vacuum cleaner. For example, it sends a command to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It also has a function to adjust the schedule in real time according to the user's emotional state.

[0727] Server: Based on the generated schedule, it sends commands to the land machine such as "start laundry at 7:30" and to the robot vacuum cleaner such as "start cleaning at 9:00", and adjusts the schedule based on the user's emotional state.

[0728] 4. Your device will do the housework for you

[0729] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0730] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0731] Terminal (robot vacuum cleaner): Cleaning starts at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0732] 5. Completion notification

[0733] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends a "housework completed" notification to the user's application.

[0734] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0735] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0736] 6. User Notices

[0737] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[0738] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[0739] Specific examples

[0740] For example, suppose a user wakes up at 7:00 a.m. and, before leaving the house, sets the settings to "start laundry with the Land Machine at 7:30 a.m." and "start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." The emotion engine collects the user's emotional data, and the generation AI generates an optimal housework schedule taking into account the user's emotional state. After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., followed by drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, and they can also receive feedback based on their emotional state.

[0741] Examples of prompts for generative AI models

[0742] Here are some examples of prompts to input to a generative AI model:

[0743] Prompt: Generate an optimal schedule based on the user's specified household schedule, taking into account emotional data. For example, please set the household chore "Laundry" to start at 7:30 and "Cleaning" to start at 9:00. The user's emotional state is relaxed.

[0744] As described above, the embodiment of the present invention allows users to efficiently utilize their busy time and complete laundry and cleaning tasks fully automatically. Furthermore, a system can be realized that monitors the user's emotional state using an emotion engine and provides an optimal housework schedule.

[0745] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0746] Explanation of program processing steps

[0747] Step 1: User sets chore items

[0748] Input: The user launches a dedicated application on their smartphone or tablet and enters the household chores and their start times.

[0749] Specific operation: The user uses the application to set a household schedule, for example, "laundry starts at 7:30" or "cleaning starts at 9:00." The user can operate it intuitively through the user interface.

[0750] Output: The set household chore items and start time data are generated.

[0751] Data processing: The application formats the input data and prepares it for sending to the server.

[0752] User: Uses the dedicated app to set the household chore items and start time, then presses the send button. The output data is sent to the server.

[0753] Step 2: The server receives and analyzes the data

[0754] Input: Chore item and start time data submitted by the user.

[0755] Specific operation: The server analyzes the received data to understand the contents and schedule of housework. It also uses an emotion engine to collect the user's emotion data in real time. If necessary, it uses facial expression recognition and voice data analysis.

[0756] Output: Analyzed household chore items, start time data, and emotion data.

[0757] Data processing: Analyze the received data and combine it with emotion data.

[0758] Server: Analyzes data received from users, collects emotional data from the emotion engine, and prepares it for input into the generative AI model. The output data is passed to the generative AI model.

[0759] Step 3: Generate a schedule using a generative AI model

[0760] Input: Parsed chore items, start time data, and emotion data.

[0761] Specific operation: The generative AI model generates an optimal housework schedule based on the user's emotional state and housework data. It uses prompt sentences to generate a schedule that takes emotional data into account.

[0762] Output: Optimized housework schedule.

[0763] Data processing: The generative AI model generates a schedule based on the prompt text.

[0764] Server: Uses generative AI models to create optimal housework schedules, which are then sent to land machines and robot vacuum cleaners.

[0765] Step 4: Server issues command

[0766] Input: Optimized housework schedule.

[0767] Specific operation: Based on the optimized schedule, the server generates and sends commands to the land machine to start washing, drying, and folding, and to the robot vacuum cleaner to start and finish cleaning.

[0768] Output: Execution commands to the Land Machine and the robot vacuum cleaner.

[0769] Data processing: Converts schedule data into command format and sends it to each device.

[0770] Server: Based on the generated schedule, it sends execution commands to the land machine and the robot vacuum cleaner. The output commands are used by each device to perform the specified household chores.

[0771] Step 5: Your device does the chore

[0772] Input: The execution command sent by the server.

[0773] Specific operation: The land machine starts washing at 7:30, then drying at 8:30 and folding at 9:30. The robot vacuum cleaner starts cleaning at 9:00, cleaning designated areas one by one, and returns to the home station after completion.

[0774] Output: Completion status of the performed chore.

[0775] Data processing: Monitors the execution status and generates completion information.

[0776] Terminals (land machines and robot vacuum cleaners): Follow commands from the server and automatically perform the designated housework. The output completion information is sent to the server.

[0777] Step 6: Completion Notification

[0778] Input: Notification of housework completion from device.

[0779] Specific operation: When the Land Machine and the robot vacuum cleaner complete the chore, they send a notification to the server, which then sends a "choice completed" notification to the user's application.

[0780] Output: Completion notification to the user.

[0781] Data processing: Analyzes the completion information from the terminal and generates a message to notify the user.

[0782] Server: Based on the received completion notification, it sends a completion notification to the user's application. The output notification notifies the user that the housework has been completed.

[0783] Step 7: User Notification

[0784] Input: Notification of housework completion from the server.

[0785] Specific behavior: The user receives a notification through the application that says "Laundry and cleaning are done," confirming that all household chores have been completed. In addition, the user can receive feedback based on emotion data.

[0786] Output: Feedback of chore completion and emotional state.

[0787] Data processing: Analyzes the emotion data and generates feedback data to provide to the user.

[0788] User: Receives a notification through the application that the laundry and cleaning is complete and sees feedback based on emotional data.

[0789] Through the above processing steps, the system of the present invention can generate an optimal housework schedule taking into consideration the user's emotional state, and can perform housework and adjust the schedule in real time.

[0790] (Application example 2)

[0791] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0792] In factory work, it is necessary to optimize work schedules by taking into account the emotional state of each employee. However, conventional systems face challenges in that it is difficult to collect and analyze emotional data in real time and automatically adjust work schedules based on the results. In addition, there are limited means to efficiently manage work progress and completion notifications, which can lead to a decline in overall productivity and employee satisfaction.

[0793] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0794] In this invention, the server includes a means for allowing the user to set a work schedule, a means for the server to analyze the work items and start time data received from the user and generate an optimal work schedule using a generation AI, and a means for an emotion analysis engine to collect user emotion data and for the server to analyze this emotion data and adjust the schedule in real time. This makes it possible to generate an optimal work schedule taking into account the user's emotional state and adjust it in real time.

[0795] The "means for the user to set a work schedule" is an interface for the user to input schedule information such as the type of work to be performed and the start time.

[0796] "Generative AI" is an artificial intelligence technology that generates a schedule for optimally executing a specific task based on input data.

[0797] A "server" is a central control device that receives and analyzes data via a network and sends necessary instructions to related devices.

[0798] An "emotion analysis engine" is software that collects and analyzes a user's emotional state and adjusts schedules based on the results.

[0799] A "robot" is an automated mechanical device that performs designated tasks automatically.

[0800] A "machine" is a device that operates in response to instructions from a server to perform various tasks.

[0801] The "means for sending commands" is a mechanism for sending specific work instructions to robots and machinery based on the schedule generated by the server.

[0802] "Means for adjusting schedules in real time" refers to technology that instantly updates and optimizes work schedules based on data collected by a sentiment analysis engine.

[0803] The "means for notifying the server that a task has been completed" is a system in which a robot or machine reports the completion status to a server when the robot or machine completes a task as instructed.

[0804] "Means for notifying the user of task completion" refers to a mechanism by which the server confirms task completion and notifies the user of that information.

[0805] The present invention is a system for optimizing work schedules within a factory and making real-time adjustments taking into account the emotional state of employees. Hereinafter, an embodiment of the present invention will be described in detail.

[0806] System Overview

[0807] The system of the present invention is composed of a server, a sentiment analysis engine, a generative AI model, a user interface, a robot, and a machine device. The server controls the data exchange with each of these elements and manages the overall work schedule.

[0808] Hardware and software used

[0809] Hardware: Factory robots (e.g., KUKA and FANUC products), machinery.

[0810] Software: Python scripts, sentiment analysis engines (e.g., Emotion Analysis API), generative AI models.

[0811] Data processing and calculation

[0812] User Interface

[0813] Users set up work schedules using a dedicated application, entering specific work items and their start times, such as "assembly work begins at 8:00" or "inspection work begins at 10:00." Once the settings are complete, the work items and start times are sent to the server.

[0814] Server - receives and analyzes data

[0815] The server analyzes the work items and start time data received from the user. The server also collects the user's emotional data using a sentiment analysis engine. The user's emotional data (e.g., happy, neutral, stressed, tired) is obtained from the sentiment analysis engine. Based on this, the server uses a generative AI model to generate an optimal schedule for the work.

[0816] Server - Issue commands

[0817] The server issues work execution commands to robots and machines based on the generated schedule. For example, it sends commands such as "Start assembly work at 8:00" to robots and "Start inspection work at 10:00" to machines. It also adjusts the schedule in real time based on the user's emotional state.

[0818] Terminals - Work execution and notifications

[0819] Robots and machines automatically perform tasks at designated times according to commands from the server. For example, a robot may "start assembly work at 8:00" and a machine may "start inspection work at 10:00." When a task is completed, the robot and machine each send a work completion notification to the server.

[0820] Server - User Notification

[0821] When the server detects that the work is complete, it notifies the user's application that "assembly and inspection work is complete." Furthermore, it can also grasp the user's stress and satisfaction level based on emotional data obtained by the sentiment analysis engine.

[0822] Specific examples

[0823] For example, suppose Employee A is scheduled to start assembly work at 8:00 a.m. and perform inspection work at 10:00 a.m. The sentiment analysis engine collects Employee A's emotional data, and the generative AI model generates an optimal work schedule taking into account that emotional state. Based on this schedule, the server sends work execution commands to the robots that perform assembly work and the machinery that performs inspection work. The robots start assembly work at 8:00 a.m., and the machinery starts inspection work at 10:00 a.m. When the work is completed, each device sends a completion notification to the server, and the server sends a completion notification to the user, as well as providing feedback based on the emotional data.

[0824] Employee: Worker A

[0825] Emotional state: stressed

[0826] Generated schedule:

[0827] Inspection Task

[0828] Packing Task

[0829] Robot: robot_1

[0830] Working command:

[0831] http: / / robot-scheduler.example.com / command?robot_id=robot_1&task=Inspection Task

[0832] http: / / robot-scheduler.example.com / command?robot_id=robot_1&task=Packing Task

[0833] As can be seen, embodiments of the present invention enable optimization of work schedules within a factory and adjustments of work in real time taking into account the emotional state of employees.

[0834] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0835] Step 1:

[0836] Users use the application to schedule work.

[0837] Input: The work item (e.g., assembly work, inspection work) to be performed by the user and its start time.

[0838] Data processing: Schedule information is converted into a unique format and prepared for transmission to the server.

[0839] Output: Work item and start time data is sent to the server.

[0840] Step 2:

[0841] The server analyzes the work item and start time data received from the user and collects user emotion data using a sentiment analysis engine.

[0842] Input: Work item and start time data received from users. Sentiment data from the sentiment analysis engine.

[0843] Data processing: Analyze task item and start time data and collect emotion data.

[0844] Output: Parsed work item data and emotion data are passed to the generative AI model.

[0845] Step 3:

[0846] The server uses the generated AI model to generate an optimal work schedule that takes into account the user's emotional data.

[0847] Input: Sentiment data from the sentiment analysis engine, parsed work item data.

[0848] Data calculation: A generative AI model calculates the optimal work schedule based on this data.

[0849] Output: The optimized work schedule is provided to the server.

[0850] Step 4:

[0851] The server sends work execution commands to the robots and mechanical devices based on the generated schedule.

[0852] Input: Optimized work schedule.

[0853] Data calculation: Generates specific execution commands for each task.

[0854] Output: Specific work execution commands sent to robots and machinery.

[0855] Step 5:

[0856] Robots and machinery automatically perform tasks according to task execution commands from the server.

[0857] Input: Work execution command from the server.

[0858] Specific operation: The robot starts assembly work at the specified time, and the machine starts inspection work at the specified time.

[0859] Output: Data to send to the server to notify the progress and completion of work.

[0860] Step 6:

[0861] Robots and mechanical devices send work completion notifications to the server.

[0862] Input: Data on the work completed.

[0863] Data processing: Convert the work completion notification into a unique format and send it to the server.

[0864] Output: Notification data is provided to the server that the work has been completed.

[0865] Step 7:

[0866] The server notifies the user of the work status and completion notification.

[0867] Input: Work completion notification data from robots and machinery.

[0868] Data processing: Converting work completion notifications into user-friendly messages.

[0869] Output: Data to send task completion notifications to the user's application and provide feedback based on sentiment data.

[0870] The above are the processing steps of the system program that realizes the application example.

[0871] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0872] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0873] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0874] [Third embodiment]

[0875] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0876] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0877] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0878] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0879] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0880] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0881] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0882] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0883] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0884] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0885] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0886] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0887] Understood. Below is a draft of the "Description of the Invention".

[0888] This invention is a system that performs housework such as laundry and cleaning fully automatically, in which a server, a land machine, and a robot vacuum cleaner work together to automatically perform housework based on a housework schedule set by a user. The following describes an embodiment of the present invention in detail.

[0889] Program processing explanation

[0890] 1. The user sets the household chores

[0891] Using a dedicated application, users can input the chores to be done that day and their start times. For example, they can set a chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00."

[0892] plaintext

[0893] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[0894] 2. The server receives and analyzes the data

[0895] The server receives data on chore items and start times sent from the application, and uses AI to analyze the user's chore behavior patterns based on the received data to generate an optimal chore schedule.

[0896] plaintext

[0897] Server: Analyzes the household chore items and start time data received from the user and generates an optimal household chore schedule using generation AI.

[0898] 3. The server issues a command

[0899] Based on the generated schedule, the server issues housework commands to the land machine and the robot vacuum cleaner. For example, it sends a command to the land machine to "start laundry at 7:30" and a command to the robot vacuum cleaner to "start cleaning at 9:00."

[0900] plaintext

[0901] Server: Based on the generated schedule, it sends commands to the Land Machine to "start laundry at 7:30" and to the Robot Vacuum Cleaner to "start cleaning at 9:00."

[0902] 4. Your device will do the housework for you

[0903] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0904] plaintext

[0905] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0906] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0907] 5. Completion notification

[0908] When the housework is completed, the land machine and the robot vacuum cleaner each send a completion notification to the server, which then receives the notification and sends it to the user's application.

[0909] plaintext

[0910] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0911] Server: Receives the notification that the housework has been completed and sends a notification to the user's application saying "The housework has been completed."

[0912] User: Receives a notification through the application that says "Laundry and cleaning completed" and confirms that all household chores are completed.

[0913] Specific examples

[0914] For example, suppose a user wakes up at 7:00 a.m. and before leaving the house sets the settings to "Start laundry with the Land Machine at 7:30 a.m." and "Start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., then drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, they will receive a notification from the application that the housework has been completed, freeing up their busy daily routine from housework.

[0915] As described above, the embodiment of the present invention can provide a system that enables users to make effective use of their busy time and complete laundry and cleaning fully automatically.

[0916] The processing flow will be explained below.

[0917] Understood. Below, we will explain the program's processing steps based on the scope of the patent claims.

[0918] Step 1:

[0919] The user launches a dedicated application and inputs the household chores to be done that day and their start times. For example, they can set "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the household chores and start times are sent from the application to the server.

[0920] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and after the settings are complete, sends the information to the server.

[0921] Step 2:

[0922] The server analyzes the household chore items and start time data received from the application and passes the data to the generation AI module, which then analyzes the user's household chore behavior patterns based on the received data and generates an optimal household chore schedule.

[0923] Server: The household chore items and start time data received from the user are passed to the generation AI, which then generates an optimal household chore schedule.

[0924] Step 3:

[0925] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner.

[0926] Server: Based on the generated schedule, it sends commands to the Land Machine to "start laundry at 7:30" and to the Robot Vacuum Cleaner to "start cleaning at 9:00."

[0927] Step 4:

[0928] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[0929] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[0930] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[0931] Step 5:

[0932] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[0933] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[0934] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[0935] Step 6:

[0936] The user receives a notification through the application that the laundry and cleaning has been completed, confirming that all the household chores have been completed. In this way, the user can efficiently complete household chores even when they are not at home.

[0937] User: Receives a notification through the application that says "Laundry and cleaning completed" and confirms that all household chores are completed.

[0938] Example 1

[0939] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0940] In today's busy living environment, users are required to perform housework efficiently without spending time and effort. However, conventional manual or semi-automatic household appliances require users to perform detailed settings and operations, preventing full automation. Furthermore, schedule management and checking the progress of housework are burdensome for users. Therefore, there is a demand for a system that reduces the burden on users and fully automates housework.

[0941] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0942] In this invention, the server includes a means for a user to set a housework schedule, a means for the server to analyze the housework information and start time data received from the user and generate an optimal housework schedule using a generative AI model, a means for the server to send commands to a washing machine to instruct it to wash, dry, and fold based on the generated schedule, and a means for the server to send commands to a vacuum cleaner to instruct it to start and finish cleaning based on the generated schedule. This eliminates the need for the user to manually manage the start times of housework, making it possible to fully automate and efficiently perform housework. Furthermore, the server manages the progress of housework and notifies the user when it is completed, allowing the user to always be aware of the status of the housework.

[0943] "User" refers to an individual or user who sets a household schedule and uses the system through the interface.

[0944] A "housework schedule" is a timetable that includes housework items such as laundry and cleaning and the start times for performing them.

[0945] The "server" is a central control device that receives instructions from the user, manages and analyzes the schedule of household items, and sends execution commands to the corresponding devices.

[0946] "Housework information" is a general term for housework items set by the user and data related to them.

[0947] "Start time data" is information including the time when each household chore item is scheduled to be performed.

[0948] The "generative AI model" is an artificial intelligence model that generates an optimal housework schedule based on received housework information and start time data.

[0949] A "washing machine" is a household appliance that automatically washes, dries, and folds clothes.

[0950] A "vacuum cleaner" is a device that automatically cleans a designated area and automatically returns to its charging station once cleaning is complete.

[0951] A "command" refers to a specific instruction for operation sent from the server to a washing machine or vacuum cleaner.

[0952] "Laundry" is a household chore in which clothes are washed with water and detergent.

[0953] "Drying" is a household chore of drying clothes after washing.

[0954] "Folding" refers to the process of sorting and folding clothes after they have been dried.

[0955] "Sweeping" is the act of vacuuming up dirt and dust to clean floors and other surfaces.

[0956] "Automatically executing" means that the equipment operates without human intervention based on a pre-set schedule or received commands.

[0957] The "completion notification" is information that notifies the user that the housework has been completed from the appliance to the server, and from the server to the user.

[0958] This invention is a system that performs housework such as laundry and cleaning fully automatically, in which a server, a washing machine, and a vacuum cleaner work together to automatically perform housework based on a housework schedule set by a user. Specific embodiments for carrying out the invention are described below.

[0959] System configuration

[0960] The system consists of four main components: an application that allows users to set housework schedules, a server, a washing machine, and a vacuum cleaner. The server analyzes the housework information and start time data sent by the user and generates an optimal housework schedule using a generative AI model. The server then sends commands to the washing machine and vacuum cleaner to perform the housework, and notifies the user when the housework is completed.

[0961] Operation flow

[0962] 1. Using a dedicated application, the user inputs the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00."

[0963] 2. Once the user completes the setup, the housework information and start time data are sent to the server. The server then uses the received data to analyze the user's housework behavior patterns using a generative AI model and generates an optimal housework schedule.

[0964] 3. Based on the generated schedule, the server issues commands to the washing machine and vacuum cleaner to perform household chores, such as "start laundry at 7:30" and "start cleaning at 9:00."

[0965] 4. The washing machine and vacuum cleaner receive commands from the server and automatically perform household chores at the specified time. The washing machine washes, dries, and folds laundry in succession, while the vacuum cleaner cleans designated areas in sequence. Each appliance performs its own task automatically.

[0966] 5. When the chore is completed, the washing machine and vacuum cleaner each send a completion notification to the server. The server receives this and sends a completion notification to the user's application. The user can confirm that the chore is complete by receiving a notification that "choice is completed."

[0967] Specific examples

[0968] For example, suppose a user wakes up at 7:00 a.m. and sets the schedules "Start laundry at 7:30" and "Start cleaning at 9:00" before leaving the house. After the user leaves the house, the server sends commands to the washing machine and vacuum cleaner based on these schedules. The washing machine starts washing at 7:30, continues drying at 8:30, and folds at 9:30. The vacuum cleaner starts cleaning at 9:00, and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, freeing up their busy daily routine from housework.

[0969] As described above, the embodiment of the present invention can provide a system that allows users to make effective use of their busy time and complete laundry and cleaning fully automatically.

[0970] Prompt Sentence Examples

[0971] An example of a specific prompt sentence for a generative AI model is shown below.

[0972] plaintext

[0973] User chores and schedules:

[0974] Start washing at 7:30

[0975] Cleaning begins at 9am

[0976] Based on this prompt, the generative AI model generates an optimal housework schedule.

[0977] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0978] Step 1:

[0979] The user sets the household chore schedule. The user launches a dedicated application and enters the household chores, such as laundry and cleaning, and their start times on the screen. Next, the user presses the Set button to confirm the information. This operation formats the entered data and saves it within the application as household chore schedule data. Specifically, the user sets a schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00" and sends it to the server.

[0980] Step 2:

[0981] The server receives the housework schedule data sent by the user. The server first checks the data format and verifies that the housework items and their start times have been entered correctly. The input in this step is the user's housework schedule data, and the output is the housework information stored in the server's internal data storage. Specifically, the data is converted into a data format such as JSON and stored in an internal database.

[0982] Step 3:

[0983] The server optimizes the schedule using the generative AI model. The server inputs the housework information saved earlier as prompts to the generative AI model to optimize the schedule. The generative AI model analyzes the input prompts and generates an optimal schedule plan. In this process, the input is housework information and start time data, and the output is an optimized housework schedule. As a specific example of operation, a schedule such as "taking into account the user's housework behavior patterns, start washing at 7:30, then dry at 8:30, and start cleaning at 9:00" is generated.

[0984] Step 4:

[0985] The server issues execution commands to the washing machine and vacuum cleaner based on the optimized schedule. Based on the generated schedule, the server generates specific operating instructions for each device and sends them as commands. The input is the optimized housework schedule, and the output is specific operation commands. For example, commands include "Tell the washing machine to start washing at 7:30, start drying at 8:30, and start folding at 9:30" and "Tell the vacuum cleaner to start cleaning at 9:00."

[0986] Step 5:

[0987] The terminal devices, the washing machine and vacuum cleaner, perform housework based on commands received from the server. Each terminal device automatically starts operating at the specified time and performs each process as programmed. The input is the execution command sent from the server, and the output is the actual housework execution status. The washing machine starts washing at the specified time, and when washing is finished, it starts drying according to the next command, and then folds the laundry. The vacuum cleaner starts cleaning at the specified time, cleaning the specified areas in sequence.

[0988] Step 6:

[0989] The washing machine and vacuum cleaner, which are the terminals, notify the server that they have completed their chores. The completion notification includes various sensor and status information, and the server receives this to confirm that the chores have been completed. The input is the completion notification, and the output is an update to the server's internal status and a notification to the user. The server receives the completion notification and sends a notification to the user's application that "choices have been completed." The user receives a notification that chores have been completed through the application, and can confirm that the chores have been completed.

[0990] (Application example 1)

[0991] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0992] Conventional household automation systems were effective in automating tasks such as laundry and cleaning, but they were unable to manage security while the user was away from home. Furthermore, they lacked the functionality to check the safety of the home or detect abnormalities while the user was away, leaving users unable to feel safe while they were out. Furthermore, to check the security of the home, users had to manually check camera footage and check door locks, which was time-consuming and could lead to security oversights.

[0993] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0994] In this invention, the server comprises: means for a user to set a housework schedule; means for the server to analyze the housework items and start time data received from the user and generate an optimal housework schedule using a generation AI; means for the server to send commands to the laundry machine to perform washing, drying, and folding tasks based on the generated schedule; means for the server to send commands to the vacuum cleaner to start and finish cleaning based on the generated schedule; means for the laundry machine to automatically perform washing, drying, and folding tasks according to commands from the server; means for the vacuum cleaner to automatically perform cleaning according to commands from the server; and means for the laundry machine and vacuum cleaner to notify the server that housework has been completed. The system includes a means for the server to notify the user that housework has been completed, a means for the user to set a security check schedule, a means for the server to analyze the security check items and start time data received and generate an optimal security check schedule using a generation AI, a means for the server to send commands to the robot device to check door locks and take camera footage based on the generated security schedule, a means for the robot device to automatically check door locks and take camera footage in accordance with the commands from the server, a means for the robot device to notify the server that the security check has been completed, and a means for the server to notify the user that the security check has been completed. This allows the user to not only have housework done automatically even when they are out, but also to perform integrated security management when they are away.

[0995] A "user" is a person who operates the system and schedules housework and security checks.

[0996] The "housework schedule" refers to a schedule for daily housework such as laundry and cleaning set by the user.

[0997] "Server" is a central computer system for receiving and analyzing chore item and start time data.

[0998] "Generative AI" is an artificial intelligence system that generates optimal schedules based on data received from users.

[0999] A "land machine" is a fully automatic washing machine that washes, dries, and folds laundry.

[1000] A "vacuum cleaner" is a robot that automatically cleans the inside of a house.

[1001] A "housework item" is a specific housework item (e.g., laundry, cleaning) set by the user.

[1002] "Start time data" refers to information about the time when housework or security checks should begin.

[1003] A "schedule" is a plan for housekeeping or security checks to be performed based on a specified time.

[1004] A "command" is an instruction to execute sent from a server to a land machine, vacuum cleaner, or robotic device.

[1005] "Means for automatic execution" refers to a function that enables the device to automatically perform a set task in accordance with a command from the server.

[1006] A "robot device" is a robot that performs security checks on a home (e.g., checking door locks, capturing camera footage).

[1007] A "security check schedule" is a schedule for checking the safety of a home set by a user.

[1008] "Completion notification" refers to a notification from the system to report that housework or security checks have been completed.

[1009] This invention is a system in which users set schedules for housework and security checks, and the server analyzes them, uses AI to generate optimal schedules, and issues instructions to each device. This system not only performs laundry and cleaning fully automatically, but also manages home security when the user is away.

[1010] System configuration

[1011] The system consists of the following main components:

[1012] 1. User's operating device (smartphone application)

[1013] 2. Central Server

[1014] 3. Land Machine (fully automatic washing machine)

[1015] 4. Vacuum cleaner (robot vacuum cleaner)

[1016] 5. Security Check Robot (Robot Device)

[1017] Program processing explanation

[1018] 1. How users set schedules

[1019] Using a dedicated smartphone application, users can input the household chores and security check items and their start times. For example, they can set a schedule such as "Start laundry at 7:30," "Start cleaning at 9:00," "Check door locks at 7:30," and "Take camera footage at 9:00."

[1020] 2. How the server analyzes the data and generates the schedule

[1021] The server receives schedule data sent by the user. Based on the received data, the server uses generation AI to analyze the user's behavioral patterns and generate an optimal schedule.

[1022] 3. A means for the server to send commands to each device

[1023] Based on the generated schedule, the server sends execution commands to each device. For example, it can instruct the laundry machine to "start washing at 7:30," the vacuum cleaner to "start cleaning at 9:00," and the security check robot to "check the door locks at 7:30" and "take camera footage at 9:00."

[1024] 4. A means for each device to automatically execute tasks according to commands

[1025] Each device automatically performs designated tasks according to commands from the server: the land machine automatically washes, dries, and folds laundry, the vacuum cleaner cleans designated areas, and the security check robot checks door locks and captures camera footage.

[1026] 5. How the device notifies the server of completion and the server notifies the user

[1027] When each device completes a task, it sends a completion notification to the server, which then receives it and sends a "task completed" notification to the user's smartphone app.

[1028] Hardware and software used

[1029] Sensor-equipped door lock device

[1030] Robot with camera at the feet

[1031] Fully automatic washing machine

[1032] Robot vacuum cleaner

[1033] Smartphone application

[1034] Central Server

[1035] Generative AI model (AI analysis model)

[1036] Specific examples

[1037] For example, if a user wakes up at 7:00 a.m. and before going to work, they can set the following schedules: "Start laundry at 7:30 a.m.", "Start cleaning at 9:00 a.m.", "Check door locks at 7:30 a.m.", and "Take camera footage at 9:00 a.m." Based on the set schedule, the land machine will start washing at 7:30 a.m., followed by drying and folding. The vacuum cleaner will start cleaning at 9:00 a.m. and clean the house. The security check robot will check door locks at 7:30 a.m. and take camera footage at 9:00 a.m. Once all tasks are completed, the server will notify the user, allowing them to go out with peace of mind.

[1038] Prompt Sentence Examples

[1039] The AI ​​model can generate an optimal schedule by inputting the following prompts:

[1040] plaintext

[1041] Username: Taro Tanaka

[1042] Security Schedule Items:

[1043] 1. Door lock check - 7:30

[1044] 2. Camera Shooting - 9:00

[1045] Household chore schedule items:

[1046] 1. Laundry - 7:30

[1047] 2. Cleaning - 9am

[1048] The above is the details of the "Mode for Carrying Out the Invention."

[1049] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1050] Step 1:

[1051] Users use a dedicated smartphone application to set schedules for housework and security checks. The application has input fields for "housework schedule" and "security schedule," and users input each item and start time. For example, they can set "start laundry at 7:30," "start cleaning at 9," "check door locks at 7:30," and "take camera footage at 9." The input data is formatted and sent to the server in JSON format or similar.

[1052] Step 2:

[1053] The server receives schedule data sent by the user. The received data includes each household chore and security check item and their start time. The server analyzes this data and generates prompts to input into the generative AI model. For example, it generates the following prompts:

[1054] plaintext

[1055] Username: Taro Tanaka

[1056] Security Schedule Items:

[1057] 1. Door lock check - 7:30

[1058] 2. Camera Shooting - 9:00

[1059] Household chore schedule items:

[1060] 1. Laundry - 7:30

[1061] 2. Cleaning - 9am

[1062] Step 3:

[1063] The server inputs prompts into the generative AI model to generate an optimal schedule. In this process, the generative AI model determines the priority of each task based on the input data and adjusts overlapping work times. Because the AI ​​model has learned from the user's past behavioral data, it outputs an optimized schedule. The output result is as follows:

[1064] plaintext

[1065] Optimal Schedule:

[1066] 1. 7:30 - Check door lock, start washing

[1067] 2. 9:00 AM - Cleaning begins, camera footage is taken

[1068] Step 4:

[1069] The server issues commands to each device based on the generated optimal schedule. Specifically, it sends commands to the land machine to "start laundry at 7:30," to the vacuum cleaner to "start cleaning at 9:00," and to the security check robot to "check door locks at 7:30" and "take camera footage at 9:00." Each command is sent via software to the API of the corresponding device.

[1070] Step 5:

[1071] The land machine, vacuum cleaner, and security check robot receive commands from the server and automatically carry out the set tasks. The land machine starts washing at 7:30, followed by drying and folding. The vacuum cleaner cleans the designated area at 9:00 and returns to the home station after cleaning is complete. The security check robot checks the door locks at 7:30 and takes camera footage at 9:00.

[1072] Step 6:

[1073] When each device completes a task, it sends a completion notification to the server. For example, a laundry machine notifies the server that "laundry is complete," a vacuum cleaner notifies the server that "cleaning is complete," and a security check robot also notifies the server that "door lock check is complete" or "camera video recording is complete."

[1074] Step 7:

[1075] The server receives the completion notification and sends a "Work Completed" notification to the user's smartphone application, allowing the user to check in real time that all housework and security checks have been completed.

[1076] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1077] Understood. Below is a draft of the "Description of the Invention" for an invention that combines an emotion engine that recognizes user emotions.

[1078] This invention is a system that performs housework such as laundry and cleaning fully automatically by combining an emotion engine that recognizes the user's emotions, and generates and adjusts an optimal housework schedule taking the user's emotions into consideration. The following describes in detail an embodiment of the present invention.

[1079] Program processing explanation

[1080] 1. The user sets the household chores

[1081] The user uses a dedicated application to input the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the application sends the household chore items and start time data to the server.

[1082] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[1083] 2. The server receives and analyzes the data

[1084] The server receives the chore items and start time data sent from the application. The server also simultaneously collects the user's emotional data using an emotion engine. The emotion engine uses a generation AI to generate an optimal chore schedule based on the chore items and emotional data set by the user.

[1085] Server: The household chore items and start time data received from the user are passed to the generation AI, which then takes into account the emotion data from the emotion engine to generate an optimal household chore schedule.

[1086] 3. The server issues a command

[1087] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner. The server also adjusts the schedule in real time according to the user's emotional state.

[1088] Server: Based on the generated schedule, it sends commands to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It adjusts the schedule based on the user's emotional state.

[1089] 4. Your device will do the housework for you

[1090] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[1091] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[1092] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[1093] 5. Completion notification

[1094] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[1095] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[1096] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[1097] 6. User Notices

[1098] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[1099] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[1100] Specific examples

[1101] For example, suppose a user wakes up at 7:00 a.m. and, before leaving the house, sets the settings to "start laundry with the Land Machine at 7:30 a.m." and "start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." The emotion engine collects the user's emotional data, and the generation AI generates an optimal housework schedule taking into account the user's emotional state. After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., followed by drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, and they can also receive feedback based on their emotional state.

[1102] As described above, the embodiment of the present invention provides a system that not only enables a user to effectively utilize their busy time and complete laundry and cleaning fully automatically, but also optimizes the housework schedule by taking into account the user's emotional state.

[1103] The processing flow will be explained below.

[1104] Understood. Below is a detailed step-by-step explanation of the processing flow of the invention that combines the emotion engine.

[1105] Step 1:

[1106] The user launches a dedicated application and inputs the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the application sends the household chore items and start times to the server.

[1107] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and after the settings are complete, sends the information to the server.

[1108] Step 2:

[1109] The server receives the chore items and start time data from the application and passes it to the generation AI module for analysis. At the same time, it collects the user's emotional data using the emotion engine. The generation AI module analyzes the user's chore behavior patterns based on the received chore items, start time data, and emotional data, and generates an optimal chore schedule.

[1110] Server: The household chore items and start time data received from the user are passed to the generation AI, and emotion data from the emotion engine is added. The generation AI then generates an optimal household chore schedule.

[1111] Step 3:

[1112] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner. The server also adjusts the schedule in real time based on the user's emotional state.

[1113] Server: Based on the generated schedule, it sends commands to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It adjusts the schedule based on the user's emotional state.

[1114] Step 4:

[1115] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[1116] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[1117] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[1118] Step 5:

[1119] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[1120] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[1121] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[1122] Step 6:

[1123] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[1124] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[1125] Example 2

[1126] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1127] Conventional household automation systems set and execute household schedules without considering the user's emotional state, which has not sufficiently reduced the user's mental burden. In addition, because household schedules are not changed or adjusted in real time, it is difficult to respond flexibly to the user's stress level.

[1128] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to set a housework schedule, a means for analyzing the housework items and start time data received from the user and collecting emotion data, and a means for generating an optimal housework schedule using a generative AI model. This enables the generation of an optimal housework schedule that takes into account the user's emotional state and the schedule adjustment in real time.

[1129] The "means for the user to set the housework schedule" is a function that allows the user to input and set housework items and their start times using a dedicated application.

[1130] "Means for analyzing the household chore item and start time data received by the server from the user" is a function in which the server receives the household chore item and start time data sent by the user and analyzes the data.

[1131] The "means for collecting emotional data" is a function for collecting the user's emotional state in real time using an emotion engine, a sensor, or the like.

[1132] "Means for generating optimal housework schedules using a generative AI model" is a function that uses a generative AI model based on collected housework item data and emotion data to generate optimal housework schedules.

[1133] "Means for sending commands to the laundry machine to instruct it to perform washing, drying and folding operations" is a function that sends commands to the laundry machine to instruct it to start washing, drying and folding operations according to the schedule generated by the server.

[1134] The "means for sending commands to the vacuum cleaner to instruct it to start and finish cleaning" is a function for sending commands to the vacuum cleaner to instruct it to start and finish cleaning according to the schedule generated by the server.

[1135] "Means for the land machine to automatically perform the washing, drying and folding tasks in accordance with commands from the server" is a function for the land machine to automatically perform the washing, drying and folding tasks in accordance with instructions from the server.

[1136] "Means for the vacuum cleaner to automatically perform cleaning in accordance with commands from the server" is a function for the vacuum cleaner to automatically perform cleaning in accordance with instructions from the server.

[1137] The "means for notifying the server that the land machine and the vacuum cleaner have completed the housework" is a function for the land machine and the vacuum cleaner to notify the server of the completion information after completing the set housework.

[1138] The "means for the server to notify the user that the housework has been completed" is a function in which the server sends a notification of the completion of the housework to the user's application, thereby informing the user that the housework has been completed.

[1139] The "means for adjusting the schedule based on the user's emotional data" is a function for adjusting the housework schedule in real time based on the user's emotional data and making necessary changes.

[1140] This invention is a system that performs housework such as laundry and cleaning fully automatically by combining an emotion engine that recognizes the user's emotions, and generates and adjusts an optimal housework schedule taking the user's emotions into consideration. The following describes in detail an embodiment of the present invention.

[1141] 1. The user sets the household chore items

[1142] The user uses a dedicated application to input the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "start laundry at 7:30" and "start cleaning at 9:00." Once the settings are complete, the household chore items and start time data are sent from the application to the server. Specifically, the system has an interface that can be operated intuitively using a smartphone or tablet.

[1143] User: Uses a dedicated application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[1144] 2. The server receives and analyzes the data

[1145] The server receives the chore items and start time data sent from the application. Next, the server collects the user's emotional data using an emotion engine. This allows the server to generate a chore schedule that takes the user's emotional state into account. The emotion data is collected using multiple technologies, including facial expression recognition and voice analysis.

[1146] Server: Analyzes the household chore items and start time data received from the user, takes into account the emotion data from the emotion engine, and generates an optimal household chore schedule using a generative AI model.

[1147] 3. The server issues a command

[1148] Based on the schedule generated by the generative AI model, the server issues housework execution commands to the land machine and robot vacuum cleaner. For example, it sends a command to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It also has a function to adjust the schedule in real time according to the user's emotional state.

[1149] Server: Based on the generated schedule, it sends commands to the land machine such as "start laundry at 7:30" and to the robot vacuum cleaner such as "start cleaning at 9:00", and adjusts the schedule based on the user's emotional state.

[1150] 4. Your device will do the housework for you

[1151] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[1152] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[1153] Terminal (robot vacuum cleaner): Cleaning starts at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[1154] 5. Completion notification

[1155] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends a "housework completed" notification to the user's application.

[1156] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[1157] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[1158] 6. User Notices

[1159] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[1160] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[1161] Specific examples

[1162] For example, suppose a user wakes up at 7:00 a.m. and, before leaving the house, sets the settings to "start laundry with the Land Machine at 7:30 a.m." and "start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." The emotion engine collects the user's emotional data, and the generation AI generates an optimal housework schedule taking into account the user's emotional state. After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., followed by drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, and they can also receive feedback based on their emotional state.

[1163] Examples of prompts for generative AI models

[1164] Here are some examples of prompts to input to a generative AI model:

[1165] Prompt: Generate an optimal schedule based on the user's specified household schedule, taking into account emotional data. For example, please set the household chore "Laundry" to start at 7:30 and "Cleaning" to start at 9:00. The user's emotional state is relaxed.

[1166] As described above, the embodiment of the present invention allows users to efficiently utilize their busy time and complete laundry and cleaning tasks fully automatically. Furthermore, a system can be realized that monitors the user's emotional state using an emotion engine and provides an optimal housework schedule.

[1167] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1168] Explanation of program processing steps

[1169] Step 1: User sets chore items

[1170] Input: The user launches a dedicated application on their smartphone or tablet and enters the household chores and their start times.

[1171] Specific operation: The user uses the application to set a household schedule, for example, "laundry starts at 7:30" or "cleaning starts at 9:00." The user can operate it intuitively through the user interface.

[1172] Output: The set household chore items and start time data are generated.

[1173] Data processing: The application formats the input data and prepares it for sending to the server.

[1174] User: Uses the dedicated app to set the household chore items and start time, then presses the send button. The output data is sent to the server.

[1175] Step 2: The server receives and analyzes the data

[1176] Input: Chore item and start time data submitted by the user.

[1177] Specific operation: The server analyzes the received data to understand the contents and schedule of housework. It also uses an emotion engine to collect the user's emotion data in real time. If necessary, it uses facial expression recognition and voice data analysis.

[1178] Output: Analyzed household chore items, start time data, and emotion data.

[1179] Data processing: Analyze the received data and combine it with emotion data.

[1180] Server: Analyzes data received from users, collects emotional data from the emotion engine, and prepares it for input into the generative AI model. The output data is passed to the generative AI model.

[1181] Step 3: Generate a schedule using a generative AI model

[1182] Input: Parsed chore items, start time data, and emotion data.

[1183] Specific operation: The generative AI model generates an optimal housework schedule based on the user's emotional state and housework data. It uses prompt sentences to generate a schedule that takes emotional data into account.

[1184] Output: Optimized housework schedule.

[1185] Data processing: The generative AI model generates a schedule based on the prompt text.

[1186] Server: Uses generative AI models to create optimal housework schedules, which are then sent to land machines and robot vacuum cleaners.

[1187] Step 4: Server issues command

[1188] Input: Optimized housework schedule.

[1189] Specific operation: Based on the optimized schedule, the server generates and sends commands to the land machine to start washing, drying, and folding, and to the robot vacuum cleaner to start and finish cleaning.

[1190] Output: Execution commands to the Land Machine and the robot vacuum cleaner.

[1191] Data processing: Converts schedule data into command format and sends it to each device.

[1192] Server: Based on the generated schedule, it sends execution commands to the land machine and the robot vacuum cleaner. The output commands are used by each device to perform the specified household chores.

[1193] Step 5: Your device does the chore

[1194] Input: The execution command sent by the server.

[1195] Specific operation: The land machine starts washing at 7:30, then drying at 8:30 and folding at 9:30. The robot vacuum cleaner starts cleaning at 9:00, cleaning designated areas one by one, and returns to the home station after completion.

[1196] Output: Completion status of the performed chore.

[1197] Data processing: Monitors the execution status and generates completion information.

[1198] Terminals (land machines and robot vacuum cleaners): Follow commands from the server and automatically perform the designated housework. The output completion information is sent to the server.

[1199] Step 6: Completion Notification

[1200] Input: Notification of housework completion from device.

[1201] Specific operation: When the Land Machine and the robot vacuum cleaner complete the chore, they send a notification to the server, which then sends a "choice completed" notification to the user's application.

[1202] Output: Completion notification to the user.

[1203] Data processing: Analyzes the completion information from the terminal and generates a message to notify the user.

[1204] Server: Based on the received completion notification, it sends a completion notification to the user's application. The output notification notifies the user that the housework has been completed.

[1205] Step 7: User Notification

[1206] Input: Notification of housework completion from the server.

[1207] Specific behavior: The user receives a notification through the application that says "Laundry and cleaning are done," confirming that all household chores have been completed. In addition, the user can receive feedback based on emotion data.

[1208] Output: Feedback of chore completion and emotional state.

[1209] Data processing: Analyzes the emotion data and generates feedback data to provide to the user.

[1210] User: Receives a notification through the application that the laundry and cleaning is complete and sees feedback based on emotional data.

[1211] Through the above processing steps, the system of the present invention can generate an optimal housework schedule taking into consideration the user's emotional state, and can perform housework and adjust the schedule in real time.

[1212] (Application example 2)

[1213] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1214] In factory work, it is necessary to optimize work schedules by taking into account the emotional state of each employee. However, conventional systems face challenges in that it is difficult to collect and analyze emotional data in real time and automatically adjust work schedules based on the results. In addition, there are limited means to efficiently manage work progress and completion notifications, which can lead to a decline in overall productivity and employee satisfaction.

[1215] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1216] In this invention, the server includes a means for allowing the user to set a work schedule, a means for the server to analyze the work items and start time data received from the user and generate an optimal work schedule using a generation AI, and a means for an emotion analysis engine to collect user emotion data and for the server to analyze this emotion data and adjust the schedule in real time. This makes it possible to generate an optimal work schedule taking into account the user's emotional state and adjust it in real time.

[1217] The "means for the user to set a work schedule" is an interface for the user to input schedule information such as the type of work to be performed and the start time.

[1218] "Generative AI" is an artificial intelligence technology that generates a schedule for optimally executing a specific task based on input data.

[1219] A "server" is a central control device that receives and analyzes data via a network and sends necessary instructions to related devices.

[1220] An "emotion analysis engine" is software that collects and analyzes a user's emotional state and adjusts schedules based on the results.

[1221] A "robot" is an automated mechanical device that performs designated tasks automatically.

[1222] A "machine" is a device that operates in response to instructions from a server to perform various tasks.

[1223] The "means for sending commands" is a mechanism for sending specific work instructions to robots and machinery based on the schedule generated by the server.

[1224] "Means for adjusting schedules in real time" refers to technology that instantly updates and optimizes work schedules based on data collected by a sentiment analysis engine.

[1225] The "means for notifying the server that a task has been completed" is a system in which a robot or machine reports the completion status to a server when the robot or machine completes a task as instructed.

[1226] "Means for notifying the user of task completion" refers to a mechanism by which the server confirms task completion and notifies the user of that information.

[1227] The present invention is a system for optimizing work schedules within a factory and making real-time adjustments taking into account the emotional state of employees. Hereinafter, an embodiment of the present invention will be described in detail.

[1228] System Overview

[1229] The system of the present invention is composed of a server, a sentiment analysis engine, a generative AI model, a user interface, a robot, and a machine device. The server controls the data exchange with each of these elements and manages the overall work schedule.

[1230] Hardware and software used

[1231] Hardware: Factory robots (e.g., KUKA and FANUC products), machinery.

[1232] Software: Python scripts, sentiment analysis engines (e.g., Emotion Analysis API), generative AI models.

[1233] Data processing and calculation

[1234] User Interface

[1235] Users set up work schedules using a dedicated application, entering specific work items and their start times, such as "assembly work begins at 8:00" or "inspection work begins at 10:00." Once the settings are complete, the work items and start times are sent to the server.

[1236] Server - receives and analyzes data

[1237] The server analyzes the work items and start time data received from the user. The server also collects the user's emotional data using a sentiment analysis engine. The user's emotional data (e.g., happy, neutral, stressed, tired) is obtained from the sentiment analysis engine. Based on this, the server uses a generative AI model to generate an optimal schedule for the work.

[1238] Server - Issue commands

[1239] The server issues work execution commands to robots and machines based on the generated schedule. For example, it sends commands such as "Start assembly work at 8:00" to robots and "Start inspection work at 10:00" to machines. It also adjusts the schedule in real time based on the user's emotional state.

[1240] Terminals - Work execution and notifications

[1241] Robots and machines automatically perform tasks at designated times according to commands from the server. For example, a robot may "start assembly work at 8:00" and a machine may "start inspection work at 10:00." When a task is completed, the robot and machine each send a work completion notification to the server.

[1242] Server - User Notification

[1243] When the server detects that the work is complete, it notifies the user's application that "assembly and inspection work is complete." Furthermore, it can also grasp the user's stress and satisfaction level based on emotional data obtained by the sentiment analysis engine.

[1244] Specific examples

[1245] For example, suppose Employee A is scheduled to start assembly work at 8:00 a.m. and perform inspection work at 10:00 a.m. The sentiment analysis engine collects Employee A's emotional data, and the generative AI model generates an optimal work schedule taking into account that emotional state. Based on this schedule, the server sends work execution commands to the robots that perform assembly work and the machinery that performs inspection work. The robots start assembly work at 8:00 a.m., and the machinery starts inspection work at 10:00 a.m. When the work is completed, each device sends a completion notification to the server, and the server sends a completion notification to the user, as well as providing feedback based on the emotional data.

[1246] Employee: Worker A

[1247] Emotional state: stressed

[1248] Generated schedule:

[1249] Inspection Task

[1250] Packing Task

[1251] Robot: robot_1

[1252] Working command:

[1253] http: / / robot-scheduler.example.com / command?robot_id=robot_1&task=Inspection Task

[1254] http: / / robot-scheduler.example.com / command?robot_id=robot_1&task=Packing Task

[1255] As can be seen, embodiments of the present invention enable optimization of work schedules within a factory and adjustments of work in real time taking into account the emotional state of employees.

[1256] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1257] Step 1:

[1258] Users use the application to schedule work.

[1259] Input: The work item (e.g., assembly work, inspection work) to be performed by the user and its start time.

[1260] Data processing: Schedule information is converted into a unique format and prepared for transmission to the server.

[1261] Output: Work item and start time data is sent to the server.

[1262] Step 2:

[1263] The server analyzes the work item and start time data received from the user and collects user emotion data using a sentiment analysis engine.

[1264] Input: Work item and start time data received from users. Sentiment data from the sentiment analysis engine.

[1265] Data processing: Analyze task item and start time data and collect emotion data.

[1266] Output: Parsed work item data and emotion data are passed to the generative AI model.

[1267] Step 3:

[1268] The server uses the generated AI model to generate an optimal work schedule that takes into account the user's emotional data.

[1269] Input: Sentiment data from the sentiment analysis engine, parsed work item data.

[1270] Data calculation: A generative AI model calculates the optimal work schedule based on this data.

[1271] Output: The optimized work schedule is provided to the server.

[1272] Step 4:

[1273] The server sends work execution commands to the robots and mechanical devices based on the generated schedule.

[1274] Input: Optimized work schedule.

[1275] Data calculation: Generates specific execution commands for each task.

[1276] Output: Specific work execution commands sent to robots and machinery.

[1277] Step 5:

[1278] Robots and machinery automatically perform tasks according to task execution commands from the server.

[1279] Input: Work execution command from the server.

[1280] Specific operation: The robot starts assembly work at the specified time, and the machine starts inspection work at the specified time.

[1281] Output: Data to send to the server to notify the progress and completion of work.

[1282] Step 6:

[1283] Robots and mechanical devices send work completion notifications to the server.

[1284] Input: Data on the work completed.

[1285] Data processing: Convert the work completion notification into a unique format and send it to the server.

[1286] Output: Notification data is provided to the server that the work has been completed.

[1287] Step 7:

[1288] The server notifies the user of the work status and completion notification.

[1289] Input: Work completion notification data from robots and machinery.

[1290] Data processing: Converting work completion notifications into user-friendly messages.

[1291] Output: Data to send task completion notifications to the user's application and provide feedback based on sentiment data.

[1292] The above are the processing steps of the system program that realizes the application example.

[1293] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1294] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1295] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1296] [Fourth embodiment]

[1297] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1298] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1299] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1300] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1301] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1302] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1303] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1304] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1305] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1306] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1307] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1308] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1309] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1310] Understood. Below is a draft of the "Description of the Invention".

[1311] This invention is a system that performs housework such as laundry and cleaning fully automatically, in which a server, a land machine, and a robot vacuum cleaner work together to automatically perform housework based on a housework schedule set by a user. The following describes an embodiment of the present invention in detail.

[1312] Program processing explanation

[1313] 1. The user sets the household chores

[1314] Using a dedicated application, users can input the chores to be done that day and their start times. For example, they can set a chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00."

[1315] plaintext

[1316] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[1317] 2. The server receives and analyzes the data

[1318] The server receives data on chore items and start times sent from the application, and uses AI to analyze the user's chore behavior patterns based on the received data to generate an optimal chore schedule.

[1319] plaintext

[1320] Server: Analyzes the household chore items and start time data received from the user and generates an optimal household chore schedule using generation AI.

[1321] 3. The server issues a command

[1322] Based on the generated schedule, the server issues housework commands to the land machine and the robot vacuum cleaner. For example, it sends a command to the land machine to "start laundry at 7:30" and a command to the robot vacuum cleaner to "start cleaning at 9:00."

[1323] plaintext

[1324] Server: Based on the generated schedule, it sends commands to the Land Machine to "start laundry at 7:30" and to the Robot Vacuum Cleaner to "start cleaning at 9:00."

[1325] 4. Your device will do the housework for you

[1326] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[1327] plaintext

[1328] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[1329] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[1330] 5. Completion notification

[1331] When the housework is completed, the land machine and the robot vacuum cleaner each send a completion notification to the server, which then receives the notification and sends it to the user's application.

[1332] plaintext

[1333] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[1334] Server: Receives the notification that the housework has been completed and sends a notification to the user's application saying "The housework has been completed."

[1335] User: Receives a notification through the application that says "Laundry and cleaning completed" and confirms that all household chores are completed.

[1336] Specific examples

[1337] For example, suppose a user wakes up at 7:00 a.m. and before leaving the house sets the settings to "Start laundry with the Land Machine at 7:30 a.m." and "Start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., then drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, they will receive a notification from the application that the housework has been completed, freeing up their busy daily routine from housework.

[1338] As described above, the embodiment of the present invention can provide a system that enables users to make effective use of their busy time and complete laundry and cleaning fully automatically.

[1339] The processing flow will be explained below.

[1340] Understood. Below, we will explain the program's processing steps based on the scope of the patent claims.

[1341] Step 1:

[1342] The user launches a dedicated application and inputs the household chores to be done that day and their start times. For example, they can set "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the household chores and start times are sent from the application to the server.

[1343] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and after the settings are complete, sends the information to the server.

[1344] Step 2:

[1345] The server analyzes the household chore items and start time data received from the application and passes the data to the generation AI module, which then analyzes the user's household chore behavior patterns based on the received data and generates an optimal household chore schedule.

[1346] Server: The household chore items and start time data received from the user are passed to the generation AI, which then generates an optimal household chore schedule.

[1347] Step 3:

[1348] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner.

[1349] Server: Based on the generated schedule, it sends commands to the Land Machine to "start laundry at 7:30" and to the Robot Vacuum Cleaner to "start cleaning at 9:00."

[1350] Step 4:

[1351] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[1352] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[1353] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[1354] Step 5:

[1355] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[1356] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[1357] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[1358] Step 6:

[1359] The user receives a notification through the application that the laundry and cleaning has been completed, confirming that all the household chores have been completed. In this way, the user can efficiently complete household chores even when they are not at home.

[1360] User: Receives a notification through the application that says "Laundry and cleaning completed" and confirms that all household chores are completed.

[1361] Example 1

[1362] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1363] In today's busy living environment, users are required to perform housework efficiently without spending time and effort. However, conventional manual or semi-automatic household appliances require users to perform detailed settings and operations, preventing full automation. Furthermore, schedule management and checking the progress of housework are burdensome for users. Therefore, there is a demand for a system that reduces the burden on users and fully automates housework.

[1364] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1365] In this invention, the server includes a means for a user to set a housework schedule, a means for the server to analyze the housework information and start time data received from the user and generate an optimal housework schedule using a generative AI model, a means for the server to send commands to a washing machine to instruct it to wash, dry, and fold based on the generated schedule, and a means for the server to send commands to a vacuum cleaner to instruct it to start and finish cleaning based on the generated schedule. This eliminates the need for the user to manually manage the start times of housework, making it possible to fully automate and efficiently perform housework. Furthermore, the server manages the progress of housework and notifies the user when it is completed, allowing the user to always be aware of the status of the housework.

[1366] "User" refers to an individual or user who sets a household schedule and uses the system through the interface.

[1367] A "housework schedule" is a timetable that includes housework items such as laundry and cleaning and the start times for performing them.

[1368] The "server" is a central control device that receives instructions from the user, manages and analyzes the schedule of household items, and sends execution commands to the corresponding devices.

[1369] "Housework information" is a general term for housework items set by the user and data related to them.

[1370] "Start time data" is information including the time when each household chore item is scheduled to be performed.

[1371] The "generative AI model" is an artificial intelligence model that generates an optimal housework schedule based on received housework information and start time data.

[1372] A "washing machine" is a household appliance that automatically washes, dries, and folds clothes.

[1373] A "vacuum cleaner" is a device that automatically cleans a designated area and automatically returns to its charging station once cleaning is complete.

[1374] A "command" refers to a specific instruction for operation sent from the server to a washing machine or vacuum cleaner.

[1375] "Laundry" is a household chore in which clothes are washed with water and detergent.

[1376] "Drying" is a household chore of drying clothes after washing.

[1377] "Folding" refers to the process of sorting and folding clothes after they have been dried.

[1378] "Sweeping" is the act of vacuuming up dirt and dust to clean floors and other surfaces.

[1379] "Automatically executing" means that the equipment operates without human intervention based on a pre-set schedule or received commands.

[1380] The "completion notification" is information that notifies the user that the housework has been completed from the appliance to the server, and from the server to the user.

[1381] This invention is a system that performs housework such as laundry and cleaning fully automatically, in which a server, a washing machine, and a vacuum cleaner work together to automatically perform housework based on a housework schedule set by a user. Specific embodiments for carrying out the invention are described below.

[1382] System configuration

[1383] The system consists of four main components: an application that allows users to set housework schedules, a server, a washing machine, and a vacuum cleaner. The server analyzes the housework information and start time data sent by the user and generates an optimal housework schedule using a generative AI model. The server then sends commands to the washing machine and vacuum cleaner to perform the housework, and notifies the user when the housework is completed.

[1384] Operation flow

[1385] 1. Using a dedicated application, the user inputs the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00."

[1386] 2. Once the user completes the setup, the housework information and start time data are sent to the server. The server then uses the received data to analyze the user's housework behavior patterns using a generative AI model and generates an optimal housework schedule.

[1387] 3. Based on the generated schedule, the server issues commands to the washing machine and vacuum cleaner to perform household chores, such as "start laundry at 7:30" and "start cleaning at 9:00."

[1388] 4. The washing machine and vacuum cleaner receive commands from the server and automatically perform household chores at the specified time. The washing machine washes, dries, and folds laundry in succession, while the vacuum cleaner cleans designated areas in sequence. Each appliance performs its own task automatically.

[1389] 5. When the chore is completed, the washing machine and vacuum cleaner each send a completion notification to the server. The server receives this and sends a completion notification to the user's application. The user can confirm that the chore is complete by receiving a notification that "choice is completed."

[1390] Specific examples

[1391] For example, suppose a user wakes up at 7:00 a.m. and sets the schedules "Start laundry at 7:30" and "Start cleaning at 9:00" before leaving the house. After the user leaves the house, the server sends commands to the washing machine and vacuum cleaner based on these schedules. The washing machine starts washing at 7:30, continues drying at 8:30, and folds at 9:30. The vacuum cleaner starts cleaning at 9:00, and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, freeing up their busy daily routine from housework.

[1392] As described above, the embodiment of the present invention can provide a system that allows users to make effective use of their busy time and complete laundry and cleaning fully automatically.

[1393] Prompt Sentence Examples

[1394] An example of a specific prompt sentence for a generative AI model is shown below.

[1395] plaintext

[1396] User chores and schedules:

[1397] Start washing at 7:30

[1398] Cleaning begins at 9am

[1399] Based on this prompt, the generative AI model generates an optimal housework schedule.

[1400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1401] Step 1:

[1402] The user sets the household chore schedule. The user launches a dedicated application and enters the household chores, such as laundry and cleaning, and their start times on the screen. Next, the user presses the Set button to confirm the information. This operation formats the entered data and saves it within the application as household chore schedule data. Specifically, the user sets a schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00" and sends it to the server.

[1403] Step 2:

[1404] The server receives the housework schedule data sent by the user. The server first checks the data format and verifies that the housework items and their start times have been entered correctly. The input in this step is the user's housework schedule data, and the output is the housework information stored in the server's internal data storage. Specifically, the data is converted into a data format such as JSON and stored in an internal database.

[1405] Step 3:

[1406] The server optimizes the schedule using the generative AI model. The server inputs the housework information saved earlier as prompts to the generative AI model to optimize the schedule. The generative AI model analyzes the input prompts and generates an optimal schedule plan. In this process, the input is housework information and start time data, and the output is an optimized housework schedule. As a specific example of operation, a schedule such as "taking into account the user's housework behavior patterns, start washing at 7:30, then dry at 8:30, and start cleaning at 9:00" is generated.

[1407] Step 4:

[1408] The server issues execution commands to the washing machine and vacuum cleaner based on the optimized schedule. Based on the generated schedule, the server generates specific operating instructions for each device and sends them as commands. The input is the optimized housework schedule, and the output is specific operation commands. For example, commands include "Tell the washing machine to start washing at 7:30, start drying at 8:30, and start folding at 9:30" and "Tell the vacuum cleaner to start cleaning at 9:00."

[1409] Step 5:

[1410] The terminal devices, the washing machine and vacuum cleaner, perform housework based on commands received from the server. Each terminal device automatically starts operating at the specified time and performs each process as programmed. The input is the execution command sent from the server, and the output is the actual housework execution status. The washing machine starts washing at the specified time, and when washing is finished, it starts drying according to the next command, and then folds the laundry. The vacuum cleaner starts cleaning at the specified time, cleaning the specified areas in sequence.

[1411] Step 6:

[1412] The washing machine and vacuum cleaner, which are the terminals, notify the server that they have completed their chores. The completion notification includes various sensor and status information, and the server receives this to confirm that the chores have been completed. The input is the completion notification, and the output is an update to the server's internal status and a notification to the user. The server receives the completion notification and sends a notification to the user's application that "choices have been completed." The user receives a notification that chores have been completed through the application, and can confirm that the chores have been completed.

[1413] (Application example 1)

[1414] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1415] Conventional household automation systems were effective in automating tasks such as laundry and cleaning, but they were unable to manage security while the user was away from home. Furthermore, they lacked the functionality to check the safety of the home or detect abnormalities while the user was away, leaving users unable to feel safe while they were out. Furthermore, to check the security of the home, users had to manually check camera footage and check door locks, which was time-consuming and could lead to security oversights.

[1416] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1417] In this invention, the server comprises: means for a user to set a housework schedule; means for the server to analyze the housework items and start time data received from the user and generate an optimal housework schedule using a generation AI; means for the server to send commands to the laundry machine to perform washing, drying, and folding tasks based on the generated schedule; means for the server to send commands to the vacuum cleaner to start and finish cleaning based on the generated schedule; means for the laundry machine to automatically perform washing, drying, and folding tasks according to commands from the server; means for the vacuum cleaner to automatically perform cleaning according to commands from the server; and means for the laundry machine and vacuum cleaner to notify the server that housework has been completed. The system includes a means for the server to notify the user that housework has been completed, a means for the user to set a security check schedule, a means for the server to analyze the security check items and start time data received and generate an optimal security check schedule using a generation AI, a means for the server to send commands to the robot device to check door locks and take camera footage based on the generated security schedule, a means for the robot device to automatically check door locks and take camera footage in accordance with the commands from the server, a means for the robot device to notify the server that the security check has been completed, and a means for the server to notify the user that the security check has been completed. This allows the user to not only have housework done automatically even when they are out, but also to perform integrated security management when they are away.

[1418] A "user" is a person who operates the system and schedules housework and security checks.

[1419] The "housework schedule" refers to a schedule for daily housework such as laundry and cleaning set by the user.

[1420] "Server" is a central computer system for receiving and analyzing chore item and start time data.

[1421] "Generative AI" is an artificial intelligence system that generates optimal schedules based on data received from users.

[1422] A "land machine" is a fully automatic washing machine that washes, dries, and folds laundry.

[1423] A "vacuum cleaner" is a robot that automatically cleans the inside of a house.

[1424] A "housework item" is a specific housework item (e.g., laundry, cleaning) set by the user.

[1425] "Start time data" refers to information about the time when housework or security checks should begin.

[1426] A "schedule" is a plan for housekeeping or security checks to be performed based on a specified time.

[1427] A "command" is an instruction to execute sent from a server to a land machine, vacuum cleaner, or robotic device.

[1428] "Means for automatic execution" refers to a function that enables the device to automatically perform a set task in accordance with a command from the server.

[1429] A "robot device" is a robot that performs security checks on a home (e.g., checking door locks, capturing camera footage).

[1430] A "security check schedule" is a schedule for checking the safety of a home set by a user.

[1431] "Completion notification" refers to a notification from the system to report that housework or security checks have been completed.

[1432] This invention is a system in which users set schedules for housework and security checks, and the server analyzes them, uses AI to generate optimal schedules, and issues instructions to each device. This system not only performs laundry and cleaning fully automatically, but also manages home security when the user is away.

[1433] System configuration

[1434] The system consists of the following main components:

[1435] 1. User's operating device (smartphone application)

[1436] 2. Central Server

[1437] 3. Land Machine (fully automatic washing machine)

[1438] 4. Vacuum cleaner (robot vacuum cleaner)

[1439] 5. Security Check Robot (Robot Device)

[1440] Program processing explanation

[1441] 1. How users set schedules

[1442] Using a dedicated smartphone application, users can input the household chores and security check items and their start times. For example, they can set a schedule such as "Start laundry at 7:30," "Start cleaning at 9:00," "Check door locks at 7:30," and "Take camera footage at 9:00."

[1443] 2. How the server analyzes the data and generates the schedule

[1444] The server receives schedule data sent by the user. Based on the received data, the server uses generation AI to analyze the user's behavioral patterns and generate an optimal schedule.

[1445] 3. A means for the server to send commands to each device

[1446] Based on the generated schedule, the server sends execution commands to each device. For example, it can instruct the laundry machine to "start washing at 7:30," the vacuum cleaner to "start cleaning at 9:00," and the security check robot to "check the door locks at 7:30" and "take camera footage at 9:00."

[1447] 4. A means for each device to automatically execute tasks according to commands

[1448] Each device automatically performs designated tasks according to commands from the server: the land machine automatically washes, dries, and folds laundry, the vacuum cleaner cleans designated areas, and the security check robot checks door locks and captures camera footage.

[1449] 5. How the device notifies the server of completion and the server notifies the user

[1450] When each device completes a task, it sends a completion notification to the server, which then receives it and sends a "task completed" notification to the user's smartphone app.

[1451] Hardware and software used

[1452] Sensor-equipped door lock device

[1453] Robot with camera at the feet

[1454] Fully automatic washing machine

[1455] Robot vacuum cleaner

[1456] Smartphone application

[1457] Central Server

[1458] Generative AI model (AI analysis model)

[1459] Specific examples

[1460] For example, if a user wakes up at 7:00 a.m. and before going to work, they can set the following schedules: "Start laundry at 7:30 a.m.", "Start cleaning at 9:00 a.m.", "Check door locks at 7:30 a.m.", and "Take camera footage at 9:00 a.m." Based on the set schedule, the land machine will start washing at 7:30 a.m., followed by drying and folding. The vacuum cleaner will start cleaning at 9:00 a.m. and clean the house. The security check robot will check door locks at 7:30 a.m. and take camera footage at 9:00 a.m. Once all tasks are completed, the server will notify the user, allowing them to go out with peace of mind.

[1461] Prompt Sentence Examples

[1462] The AI ​​model can generate an optimal schedule by inputting the following prompts:

[1463] plaintext

[1464] Username: Taro Tanaka

[1465] Security Schedule Items:

[1466] 1. Door lock check - 7:30

[1467] 2. Camera Shooting - 9:00

[1468] Household chore schedule items:

[1469] 1. Laundry - 7:30

[1470] 2. Cleaning - 9am

[1471] The above is the details of the "Mode for Carrying Out the Invention."

[1472] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1473] Step 1:

[1474] Users use a dedicated smartphone application to set schedules for housework and security checks. The application has input fields for "housework schedule" and "security schedule," and users input each item and start time. For example, they can set "start laundry at 7:30," "start cleaning at 9," "check door locks at 7:30," and "take camera footage at 9." The input data is formatted and sent to the server in JSON format or similar.

[1475] Step 2:

[1476] The server receives schedule data sent by the user. The received data includes each household chore and security check item and their start time. The server analyzes this data and generates prompts to input into the generative AI model. For example, it generates the following prompts:

[1477] plaintext

[1478] Username: Taro Tanaka

[1479] Security Schedule Items:

[1480] 1. Door lock check - 7:30

[1481] 2. Camera Shooting - 9:00

[1482] Household chore schedule items:

[1483] 1. Laundry - 7:30

[1484] 2. Cleaning - 9am

[1485] Step 3:

[1486] The server inputs prompts into the generative AI model to generate an optimal schedule. In this process, the generative AI model determines the priority of each task based on the input data and adjusts overlapping work times. Because the AI ​​model has learned from the user's past behavioral data, it outputs an optimized schedule. The output result is as follows:

[1487] plaintext

[1488] Optimal Schedule:

[1489] 1. 7:30 - Check door lock, start washing

[1490] 2. 9:00 AM - Cleaning begins, camera footage is taken

[1491] Step 4:

[1492] The server issues commands to each device based on the generated optimal schedule. Specifically, it sends commands to the land machine to "start laundry at 7:30," to the vacuum cleaner to "start cleaning at 9:00," and to the security check robot to "check door locks at 7:30" and "take camera footage at 9:00." Each command is sent via software to the API of the corresponding device.

[1493] Step 5:

[1494] The land machine, vacuum cleaner, and security check robot receive commands from the server and automatically carry out the set tasks. The land machine starts washing at 7:30, followed by drying and folding. The vacuum cleaner cleans the designated area at 9:00 and returns to the home station after cleaning is complete. The security check robot checks the door locks at 7:30 and takes camera footage at 9:00.

[1495] Step 6:

[1496] When each device completes a task, it sends a completion notification to the server. For example, a laundry machine notifies the server that "laundry is complete," a vacuum cleaner notifies the server that "cleaning is complete," and a security check robot also notifies the server that "door lock check is complete" or "camera video recording is complete."

[1497] Step 7:

[1498] The server receives the completion notification and sends a "Work Completed" notification to the user's smartphone application, allowing the user to check in real time that all housework and security checks have been completed.

[1499] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1500] Understood. Below is a draft of the "Description of the Invention" for an invention that combines an emotion engine that recognizes user emotions.

[1501] This invention is a system that performs housework such as laundry and cleaning fully automatically by combining an emotion engine that recognizes the user's emotions, and generates and adjusts an optimal housework schedule taking the user's emotions into consideration. The following describes in detail an embodiment of the present invention.

[1502] Program processing explanation

[1503] 1. The user sets the household chores

[1504] The user uses a dedicated application to input the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the application sends the household chore items and start time data to the server.

[1505] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[1506] 2. The server receives and analyzes the data

[1507] The server receives the chore items and start time data sent from the application. The server also simultaneously collects the user's emotional data using an emotion engine. The emotion engine uses a generation AI to generate an optimal chore schedule based on the chore items and emotional data set by the user.

[1508] Server: The household chore items and start time data received from the user are passed to the generation AI, which then takes into account the emotion data from the emotion engine to generate an optimal household chore schedule.

[1509] 3. The server issues a command

[1510] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner. The server also adjusts the schedule in real time according to the user's emotional state.

[1511] Server: Based on the generated schedule, it sends commands to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It adjusts the schedule based on the user's emotional state.

[1512] 4. Your device will do the housework for you

[1513] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[1514] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[1515] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[1516] 5. Completion notification

[1517] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[1518] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[1519] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[1520] 6. User Notices

[1521] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[1522] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[1523] Specific examples

[1524] For example, suppose a user wakes up at 7:00 a.m. and, before leaving the house, sets the settings to "start laundry with the Land Machine at 7:30 a.m." and "start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." The emotion engine collects the user's emotional data, and the generation AI generates an optimal housework schedule taking into account the user's emotional state. After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., followed by drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, and they can also receive feedback based on their emotional state.

[1525] As described above, the embodiment of the present invention provides a system that not only enables a user to effectively utilize their busy time and complete laundry and cleaning fully automatically, but also optimizes the housework schedule by taking into account the user's emotional state.

[1526] The processing flow will be explained below.

[1527] Understood. Below is a detailed step-by-step explanation of the processing flow of the invention that combines the emotion engine.

[1528] Step 1:

[1529] The user launches a dedicated application and inputs the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "Start laundry at 7:30" and "Start cleaning at 9:00." Once the settings are complete, the application sends the household chore items and start times to the server.

[1530] User: Uses the application to set household chores (laundry, cleaning, etc.) and their start times, and after the settings are complete, sends the information to the server.

[1531] Step 2:

[1532] The server receives the chore items and start time data from the application and passes it to the generation AI module for analysis. At the same time, it collects the user's emotional data using the emotion engine. The generation AI module analyzes the user's chore behavior patterns based on the received chore items, start time data, and emotional data, and generates an optimal chore schedule.

[1533] Server: The household chore items and start time data received from the user are passed to the generation AI, and emotion data from the emotion engine is added. The generation AI then generates an optimal household chore schedule.

[1534] Step 3:

[1535] Based on the generated schedule, the server issues housework execution commands to the land machine and the robot vacuum cleaner. Specifically, it sends commands such as "Start laundry at 7:30" to the land machine and "Start cleaning at 9:00" to the robot vacuum cleaner. The server also adjusts the schedule in real time based on the user's emotional state.

[1536] Server: Based on the generated schedule, it sends commands to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It adjusts the schedule based on the user's emotional state.

[1537] Step 4:

[1538] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[1539] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[1540] Terminal (robot vacuum cleaner): Starts cleaning at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[1541] Step 5:

[1542] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends it to the user's application.

[1543] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[1544] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[1545] Step 6:

[1546] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[1547] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[1548] Example 2

[1549] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1550] Conventional household automation systems set and execute household schedules without considering the user's emotional state, which has not sufficiently reduced the user's mental burden. In addition, because household schedules are not changed or adjusted in real time, it is difficult to respond flexibly to the user's stress level.

[1551] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the user to set a housework schedule, a means for analyzing the housework items and start time data received from the user and collecting emotion data, and a means for generating an optimal housework schedule using a generative AI model. This enables the generation of an optimal housework schedule that takes into account the user's emotional state and the schedule adjustment in real time.

[1552] The "means for the user to set the housework schedule" is a function that allows the user to input and set housework items and their start times using a dedicated application.

[1553] "Means for analyzing the household chore item and start time data received by the server from the user" is a function in which the server receives the household chore item and start time data sent by the user and analyzes the data.

[1554] The "means for collecting emotional data" is a function for collecting the user's emotional state in real time using an emotion engine, a sensor, or the like.

[1555] "Means for generating optimal housework schedules using a generative AI model" is a function that uses a generative AI model based on collected housework item data and emotion data to generate optimal housework schedules.

[1556] "Means for sending commands to the laundry machine to instruct it to perform washing, drying and folding operations" is a function that sends commands to the laundry machine to instruct it to start washing, drying and folding operations according to the schedule generated by the server.

[1557] The "means for sending commands to the vacuum cleaner to instruct it to start and finish cleaning" is a function for sending commands to the vacuum cleaner to instruct it to start and finish cleaning according to the schedule generated by the server.

[1558] "Means for the land machine to automatically perform the washing, drying and folding tasks in accordance with commands from the server" is a function for the land machine to automatically perform the washing, drying and folding tasks in accordance with instructions from the server.

[1559] "Means for the vacuum cleaner to automatically perform cleaning in accordance with commands from the server" is a function for the vacuum cleaner to automatically perform cleaning in accordance with instructions from the server.

[1560] The "means for notifying the server that the land machine and the vacuum cleaner have completed the housework" is a function for the land machine and the vacuum cleaner to notify the server of the completion information after completing the set housework.

[1561] The "means for the server to notify the user that the housework has been completed" is a function in which the server sends a notification of the completion of the housework to the user's application, thereby informing the user that the housework has been completed.

[1562] The "means for adjusting the schedule based on the user's emotional data" is a function for adjusting the housework schedule in real time based on the user's emotional data and making necessary changes.

[1563] This invention is a system that performs housework such as laundry and cleaning fully automatically by combining an emotion engine that recognizes the user's emotions, and generates and adjusts an optimal housework schedule taking the user's emotions into consideration. The following describes in detail an embodiment of the present invention.

[1564] 1. The user sets the household chore items

[1565] The user uses a dedicated application to input the household chores to be done that day and their start times. For example, they can set a household chore schedule such as "start laundry at 7:30" and "start cleaning at 9:00." Once the settings are complete, the household chore items and start time data are sent from the application to the server. Specifically, the system has an interface that can be operated intuitively using a smartphone or tablet.

[1566] User: Uses a dedicated application to set household chores (laundry, cleaning, etc.) and their start times, and then sends the information to the server after the settings are complete.

[1567] 2. The server receives and analyzes the data

[1568] The server receives the chore items and start time data sent from the application. Next, the server collects the user's emotional data using an emotion engine. This allows the server to generate a chore schedule that takes the user's emotional state into account. The emotion data is collected using multiple technologies, including facial expression recognition and voice analysis.

[1569] Server: Analyzes the household chore items and start time data received from the user, takes into account the emotion data from the emotion engine, and generates an optimal household chore schedule using a generative AI model.

[1570] 3. The server issues a command

[1571] Based on the schedule generated by the generative AI model, the server issues housework execution commands to the land machine and robot vacuum cleaner. For example, it sends a command to the land machine such as "Start laundry at 7:30" and to the robot vacuum cleaner such as "Start cleaning at 9:00." It also has a function to adjust the schedule in real time according to the user's emotional state.

[1572] Server: Based on the generated schedule, it sends commands to the land machine such as "start laundry at 7:30" and to the robot vacuum cleaner such as "start cleaning at 9:00", and adjusts the schedule based on the user's emotional state.

[1573] 4. Your device will do the housework for you

[1574] The Land Machine and robot vacuum cleaner receive commands from the server and automatically perform household chores at the specified times. The Land Machine will "start washing at 7:30," and once the washing is done, "start drying at 8:30," and once the drying is done, "fold the laundry at 9:30." The robot vacuum cleaner will "start cleaning at 9:00," cleaning the specified areas in sequence.

[1575] Terminal (land machine): Following commands from the server, it starts washing at 7:30, and when washing is finished, it starts drying at 8:30. After drying is finished, it folds the laundry at 9:30.

[1576] Terminal (robot vacuum cleaner): Cleaning starts at 9:00 and cleans the designated areas one by one. Once cleaning is complete, it automatically returns to the home station.

[1577] 5. Completion notification

[1578] When the housework is completed, the land machine and the robot vacuum cleaner each send a housework completion notification to the server, which then receives the notification and sends a "housework completed" notification to the user's application.

[1579] Terminals (land machines and robot vacuum cleaners): Notify the server that the housework has been completed.

[1580] Server: Receives notification that housework has been completed and sends a "housework completed" notification to the user's application.

[1581] 6. User Notices

[1582] The user receives a notification through the application that the laundry and cleaning are complete, confirming that all housework has been completed. Furthermore, the emotion engine can grasp the user's stress and satisfaction levels based on the emotional data obtained.

[1583] User: Receives a notification through the application saying "Laundry and cleaning completed" to confirm that all household chores are completed, and receives feedback based on emotional data.

[1584] Specific examples

[1585] For example, suppose a user wakes up at 7:00 a.m. and, before leaving the house, sets the settings to "start laundry with the Land Machine at 7:30 a.m." and "start cleaning with the Robot Vacuum Cleaner at 9:00 a.m." The emotion engine collects the user's emotional data, and the generation AI generates an optimal housework schedule taking into account the user's emotional state. After the user leaves the house, the server sends commands to the Land Machine and the Robot Vacuum Cleaner based on this schedule. The Land Machine starts washing at 7:30 a.m., followed by drying at 8:30 a.m. and folding at 9:30 a.m. The Robot Vacuum Cleaner starts cleaning at 9:00 a.m., and all housework is completed before the user returns home. When the user returns home, the application notifies them that the housework has been completed, and they can also receive feedback based on their emotional state.

[1586] Examples of prompts for generative AI models

[1587] Here are some examples of prompts to input to a generative AI model:

[1588] Prompt: Generate an optimal schedule based on the user's specified household schedule, taking into account emotional data. For example, please set the household chore "Laundry" to start at 7:30 and "Cleaning" to start at 9:00. The user's emotional state is relaxed.

[1589] As described above, the embodiment of the present invention allows users to efficiently utilize their busy time and complete laundry and cleaning tasks fully automatically. Furthermore, a system can be realized that monitors the user's emotional state using an emotion engine and provides an optimal housework schedule.

[1590] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1591] Explanation of program processing steps

[1592] Step 1: User sets chore items

[1593] Input: The user launches a dedicated application on their smartphone or tablet and enters the household chores and their start times.

[1594] Specific operation: The user uses the application to set a household schedule, for example, "laundry starts at 7:30" or "cleaning starts at 9:00." The user can operate it intuitively through the user interface.

[1595] Output: The set household chore items and start time data are generated.

[1596] Data processing: The application formats the input data and prepares it for sending to the server.

[1597] User: Uses the dedicated app to set the household chore items and start time, then presses the send button. The output data is sent to the server.

[1598] Step 2: The server receives and analyzes the data

[1599] Input: Chore item and start time data submitted by the user.

[1600] Specific operation: The server analyzes the received data to understand the contents and schedule of housework. It also uses an emotion engine to collect the user's emotion data in real time. If necessary, it uses facial expression recognition and voice data analysis.

[1601] Output: Analyzed household chore items, start time data, and emotion data.

[1602] Data processing: Analyze the received data and combine it with emotion data.

[1603] Server: Analyzes data received from users, collects emotional data from the emotion engine, and prepares it for input into the generative AI model. The output data is passed to the generative AI model.

[1604] Step 3: Generate a schedule using a generative AI model

[1605] Input: Parsed chore items, start time data, and emotion data.

[1606] Specific operation: The generative AI model generates an optimal housework schedule based on the user's emotional state and housework data. It uses prompt sentences to generate a schedule that takes emotional data into account.

[1607] Output: Optimized housework schedule.

[1608] Data processing: The generative AI model generates a schedule based on the prompt text.

[1609] Server: Uses generative AI models to create optimal housework schedules, which are then sent to land machines and robot vacuum cleaners.

[1610] Step 4: Server issues command

[1611] Input: Optimized housework schedule.

[1612] Specific operation: Based on the optimized schedule, the server generates and sends commands to the land machine to start washing, drying, and folding, and to the robot vacuum cleaner to start and finish cleaning.

[1613] Output: Execution commands to the Land Machine and the robot vacuum cleaner.

[1614] Data processing: Converts schedule data into command format and sends it to each device.

[1615] Server: Based on the generated schedule, it sends execution commands to the land machine and the robot vacuum cleaner. The output commands are used by each device to perform the specified household chores.

[1616] Step 5: Your device does the chore

[1617] Input: The execution command sent by the server.

[1618] Specific operation: The land machine starts washing at 7:30, then drying at 8:30 and folding at 9:30. The robot vacuum cleaner starts cleaning at 9:00, cleaning designated areas one by one, and returns to the home station after completion.

[1619] Output: Completion status of the performed chore.

[1620] Data processing: Monitors the execution status and generates completion information.

[1621] Terminals (land machines and robot vacuum cleaners): Follow commands from the server and automatically perform the designated housework. The output completion information is sent to the server.

[1622] Step 6: Completion Notification

[1623] Input: Notification of housework completion from device.

[1624] Specific operation: When the Land Machine and the robot vacuum cleaner complete the chore, they send a notification to the server, which then sends a "choice completed" notification to the user's application.

[1625] Output: Completion notification to the user.

[1626] Data processing: Analyzes the completion information from the terminal and generates a message to notify the user.

[1627] Server: Based on the received completion notification, it sends a completion notification to the user's application. The output notification notifies the user that the housework has been completed.

[1628] Step 7: User Notification

[1629] Input: Notification of housework completion from the server.

[1630] Specific behavior: The user receives a notification through the application that says "Laundry and cleaning are done," confirming that all household chores have been completed. In addition, the user can receive feedback based on emotion data.

[1631] Output: Feedback of chore completion and emotional state.

[1632] Data processing: Analyzes the emotion data and generates feedback data to provide to the user.

[1633] User: Receives a notification through the application that the laundry and cleaning is complete and sees feedback based on emotional data.

[1634] Through the above processing steps, the system of the present invention can generate an optimal housework schedule taking into consideration the user's emotional state, and can perform housework and adjust the schedule in real time.

[1635] (Application example 2)

[1636] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1637] In factory work, it is necessary to optimize work schedules by taking into account the emotional state of each employee. However, conventional systems face challenges in that it is difficult to collect and analyze emotional data in real time and automatically adjust work schedules based on the results. In addition, there are limited means to efficiently manage work progress and completion notifications, which can lead to a decline in overall productivity and employee satisfaction.

[1638] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1639] In this invention, the server includes a means for allowing the user to set a work schedule, a means for the server to analyze the work items and start time data received from the user and generate an optimal work schedule using a generation AI, and a means for an emotion analysis engine to collect user emotion data and for the server to analyze this emotion data and adjust the schedule in real time. This makes it possible to generate an optimal work schedule taking into account the user's emotional state and adjust it in real time.

[1640] The "means for the user to set a work schedule" is an interface for the user to input schedule information such as the type of work to be performed and the start time.

[1641] "Generative AI" is an artificial intelligence technology that generates a schedule for optimally executing a specific task based on input data.

[1642] A "server" is a central control device that receives and analyzes data via a network and sends necessary instructions to related devices.

[1643] An "emotion analysis engine" is software that collects and analyzes a user's emotional state and adjusts schedules based on the results.

[1644] A "robot" is an automated mechanical device that performs designated tasks automatically.

[1645] A "machine" is a device that operates in response to instructions from a server to perform various tasks.

[1646] The "means for sending commands" is a mechanism for sending specific work instructions to robots and machinery based on the schedule generated by the server.

[1647] "Means for adjusting schedules in real time" refers to technology that instantly updates and optimizes work schedules based on data collected by a sentiment analysis engine.

[1648] The "means for notifying the server that a task has been completed" is a system in which a robot or machine reports the completion status to a server when the robot or machine completes a task as instructed.

[1649] "Means for notifying the user of task completion" refers to a mechanism by which the server confirms task completion and notifies the user of that information.

[1650] The present invention is a system for optimizing work schedules within a factory and making real-time adjustments taking into account the emotional state of employees. Hereinafter, an embodiment of the present invention will be described in detail.

[1651] System Overview

[1652] The system of the present invention is composed of a server, a sentiment analysis engine, a generative AI model, a user interface, a robot, and a machine device. The server controls the data exchange with each of these elements and manages the overall work schedule.

[1653] Hardware and software used

[1654] Hardware: Factory robots (e.g., KUKA and FANUC products), machinery.

[1655] Software: Python scripts, sentiment analysis engines (e.g., Emotion Analysis API), generative AI models.

[1656] Data processing and calculation

[1657] User Interface

[1658] Users set up work schedules using a dedicated application, entering specific work items and their start times, such as "assembly work begins at 8:00" or "inspection work begins at 10:00." Once the settings are complete, the work items and start times are sent to the server.

[1659] Server - receives and analyzes data

[1660] The server analyzes the work items and start time data received from the user. The server also collects the user's emotional data using a sentiment analysis engine. The user's emotional data (e.g., happy, neutral, stressed, tired) is obtained from the sentiment analysis engine. Based on this, the server uses a generative AI model to generate an optimal schedule for the work.

[1661] Server - Issue commands

[1662] The server issues work execution commands to robots and machines based on the generated schedule. For example, it sends commands such as "Start assembly work at 8:00" to robots and "Start inspection work at 10:00" to machines. It also adjusts the schedule in real time based on the user's emotional state.

[1663] Terminals - Work execution and notifications

[1664] Robots and machines automatically perform tasks at designated times according to commands from the server. For example, a robot may "start assembly work at 8:00" and a machine may "start inspection work at 10:00." When a task is completed, the robot and machine each send a work completion notification to the server.

[1665] Server - User Notification

[1666] When the server detects that the work is complete, it notifies the user's application that "assembly and inspection work is complete." Furthermore, it can also grasp the user's stress and satisfaction level based on emotional data obtained by the sentiment analysis engine.

[1667] Specific examples

[1668] For example, suppose Employee A is scheduled to start assembly work at 8:00 a.m. and perform inspection work at 10:00 a.m. The sentiment analysis engine collects Employee A's emotional data, and the generative AI model generates an optimal work schedule taking into account that emotional state. Based on this schedule, the server sends work execution commands to the robots that perform assembly work and the machinery that performs inspection work. The robots start assembly work at 8:00 a.m., and the machinery starts inspection work at 10:00 a.m. When the work is completed, each device sends a completion notification to the server, and the server sends a completion notification to the user, as well as providing feedback based on the emotional data.

[1669] Employee: Worker A

[1670] Emotional state: stressed

[1671] Generated schedule:

[1672] Inspection Task

[1673] Packing Task

[1674] Robot: robot_1

[1675] Working command:

[1676] http: / / robot-scheduler.example.com / command?robot_id=robot_1&task=Inspection Task

[1677] http: / / robot-scheduler.example.com / command?robot_id=robot_1&task=Packing Task

[1678] As can be seen, embodiments of the present invention enable optimization of work schedules within a factory and adjustments of work in real time taking into account the emotional state of employees.

[1679] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1680] Step 1:

[1681] Users use the application to schedule work.

[1682] Input: The work item (e.g., assembly work, inspection work) to be performed by the user and its start time.

[1683] Data processing: Schedule information is converted into a unique format and prepared for transmission to the server.

[1684] Output: Work item and start time data is sent to the server.

[1685] Step 2:

[1686] The server analyzes the work item and start time data received from the user and collects user emotion data using a sentiment analysis engine.

[1687] Input: Work item and start time data received from users. Sentiment data from the sentiment analysis engine.

[1688] Data processing: Analyze task item and start time data and collect emotion data.

[1689] Output: Parsed work item data and emotion data are passed to the generative AI model.

[1690] Step 3:

[1691] The server uses the generated AI model to generate an optimal work schedule that takes into account the user's emotional data.

[1692] Input: Sentiment data from the sentiment analysis engine, parsed work item data.

[1693] Data calculation: A generative AI model calculates the optimal work schedule based on this data.

[1694] Output: The optimized work schedule is provided to the server.

[1695] Step 4:

[1696] The server sends work execution commands to the robots and mechanical devices based on the generated schedule.

[1697] Input: Optimized work schedule.

[1698] Data calculation: Generates specific execution commands for each task.

[1699] Output: Specific work execution commands sent to robots and machinery.

[1700] Step 5:

[1701] Robots and machinery automatically perform tasks according to task execution commands from the server.

[1702] Input: Work execution command from the server.

[1703] Specific operation: The robot starts assembly work at the specified time, and the machine starts inspection work at the specified time.

[1704] Output: Data to send to the server to notify the progress and completion of work.

[1705] Step 6:

[1706] Robots and mechanical devices send work completion notifications to the server.

[1707] Input: Data on the work completed.

[1708] Data processing: Convert the work completion notification into a unique format and send it to the server.

[1709] Output: Notification data is provided to the server that the work has been completed.

[1710] Step 7:

[1711] The server notifies the ...

Claims

1. A means for a user to set a housework schedule; A means for the server to analyze the housework items and start time data received from the user and generate an optimal housework schedule using a generation AI; A means for the server to send commands to the laundry machine to instruct it to perform washing, drying, and folding tasks based on the generated schedule; A means for transmitting commands to the vacuum cleaner to start and end cleaning based on the schedule generated by the server; means for the land machine to automatically perform the washing, drying and folding operations in accordance with commands from the server; a means for the vacuum cleaner to automatically perform cleaning in accordance with commands from the server; means for notifying the server that the land machine and the vacuum cleaner have completed the chore; The server needs a way to notify the user when the chore is complete. Including system.

2. 2. The system according to claim 1, further comprising means for adjusting the timing of the start and end of each operation when the laundry machine performs washing, drying and folding operations.

3. 2. The system of claim 1, further comprising means for the cleaner to sequentially clean a plurality of designated areas and automatically return to the home station after cleaning is completed.

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