system

Abstract

We provide the system. [Solution] An information processing device for recording the schedule information of each member of the household, An optimization processing means for optimally distributing household tasks based on the aforementioned information processing means, A notification means for notifying each member of the household work schedule generated by the optimization processing means, An update means that receives feedback from members and updates the information processing means and the optimization processing means, A voice command receiving device that receives voice commands and adjusts the schedule for household tasks, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a dual-income family, the division of housework is often unequal, and a difference in burden among family members is likely to occur. In addition, there is a problem that the planned management of household work becomes complicated and it is difficult to make flexible adjustments according to the schedules of each family member. As a result, a sense of inequality within the family and a decrease in the efficiency of housework are caused, which affects the quality of life of family members. Therefore, there is a need for a system that can efficiently and fairly distribute household work and flexibly adjust it according to the schedules of each family member.

Means for Solving the Problems

[0005] This invention provides a system comprising information processing means for recording the schedule information of each member of a household, and optimization processing means for optimally distributing household tasks based on that information. Furthermore, by providing notification means for notifying each member of the generated household task schedule, and update means for receiving feedback from each member and updating the information processing means and optimization processing means, the system achieves efficient and fair distribution of household tasks. In addition, by including voice instruction receiving means for receiving voice instructions and adjusting the household task schedule, flexible adjustments according to each member's schedule are possible. This can eliminate feelings of inequality within the household and improve the quality of life for the members.

[0006] "Information processing means" refers to a device or system for recording the schedule information of each member of a household and updating it as needed.

[0007] An "optimization processing means" is an algorithm or device that distributes household tasks in the most efficient and fair way, based on data obtained from an information processing means.

[0008] "Notification means" refers to a device or system that electronically or audibly communicates the generated household work schedule to each member.

[0009] "Update means" refers to a device or function for receiving feedback from members, updating information processing means and optimization processing means based on that feedback, and adjusting the schedule.

[0010] A "voice instruction receiving means" is a device or system that receives instructions input by voice and adjusts the schedule of household tasks based on these instructions. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, a tagged communication I / F (Interface) is an interface that includes a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is a system that enables the efficient division and management of household chores in dual-income households. The system aims to centrally manage the schedule information of each household member and optimally schedule household tasks based on this information. The main processes constituting the system's program and specific examples are shown below.

[0033] The server stores data such as the daily schedules, preferences, and task progress of all members in a database, centralizing the information. The server also has an optimization algorithm implemented that uses this data to optimally distribute household chores, calculating task allocation according to individual circumstances. For example, it takes into account the work and school schedules of all family members to calculate who should perform household chores at what time.

[0034] Once a schedule is generated, the server sends the household chore schedule to each member's device via notification methods. This notification can be sent via email, smartphone app notifications, or voice through a smart speaker. The schedule received by the device is visualized for the user, allowing them to view their weekly chore plan, for example, in a dashboard format.

[0035] Users can provide feedback when they complete their daily tasks. For example, if they complete a large amount of household chores on a given day, they can input feedback into their device indicating that they would like a lighter schedule the following day. The device sends this feedback to the server, which then updates the user's profile and scheduling parameters based on the information.

[0036] Furthermore, the voice command receiving mechanism helps users adjust their schedules using voice commands. For example, a user can give a voice command to a smart speaker saying, "I want to cancel Sunday afternoon cleaning," and that information is immediately sent to the server and reflected in the schedule.

[0037] In this way, the system of the present invention efficiently distributes household chores and flexibly adapts to the lifestyle of each member, thereby reducing the burden of housework in dual-income households and improving their quality of life.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users input their own and their family's schedules and preferences for household chores using the application. For example, they can input things like their strengths and weaknesses in household chores and their preferred time slots.

[0041] Step 2:

[0042] The terminal receives information entered by the user and sends it to the server as organized data. This allows the server to integrate the necessary information.

[0043] Step 3:

[0044] The server uses the information received from the terminal to create a profile of each member of the household and saves it in a database. This profile details various attributes of the user.

[0045] Step 4:

[0046] The server uses an optimization algorithm to efficiently distribute household tasks based on profiles. The algorithm considers each member's schedule and preferences to calculate the optimal task assignment.

[0047] Step 5:

[0048] The server generates an optimized schedule and sends reminders to each member's device via a notification system. For example, it might notify them of specific tasks such as "Wash the dishes at 9am on Saturday."

[0049] Step 6:

[0050] Users input their daily task completion status and feedback into their devices. This includes reporting task completion and suggesting improvements for future tasks.

[0051] Step 7:

[0052] The device collects user feedback and sends it to the server. The server updates the profile and schedule based on the received feedback, which is then reflected in the next scheduling.

[0053] Step 8:

[0054] The user gives voice commands to the smart speaker as needed. For example, they might say, "Change tomorrow's cleaning to the weekend." The smart speaker recognizes this and sends the information to the server.

[0055] Step 9:

[0056] The server analyzes the voice instructions and adjusts the schedule for the corresponding household tasks. A new schedule is generated and communicated to each member again via the notification system.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In modern dual-income households, the burden of housework is a major challenge. Each member has a different schedule, making it extremely difficult to efficiently divide household chores amidst busy lives. As a result, coordination within the household becomes complicated, stress increases, and the quality of life can decline. It is necessary to solve these problems and improve the efficiency of household management and the satisfaction of its inhabitants.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes: information aggregation means for recording the schedule information of each member of the household; data analysis means for executing an optimization algorithm that efficiently allocates household tasks based on the information aggregation means; information distribution means for transmitting the household schedule generated by the data analysis means to each member using various communication methods; data update means for collecting feedback from members and updating the information aggregation means and the optimization algorithm; and voice input processing means for receiving voice instructions and changing the household schedule. This makes it possible to generate and adjust household schedules that flexibly adapt to the lifestyle of each member.

[0062] "Information aggregation means" refers to a method of comprehensively collecting and organizing planned information provided by each member of the household.

[0063] An "optimization algorithm" is an algorithm that includes equations and formulas for efficiently distributing household chores based on collected information.

[0064] "Data analysis means" refers to methods for performing the necessary analysis to execute optimization algorithms and effectively allocate tasks within the household.

[0065] "Information distribution means" refers to the means of communicating the generated household schedule to each member using various communication methods.

[0066] A "data update method" is a means of receiving feedback from members and updating the information aggregation and optimization algorithms based on that information.

[0067] The "voice input processing means" is a means for receiving voice instructions from members and appropriately adjusting the schedule of household tasks based on that information.

[0068] This invention is a system for streamlining the division of household chores in dual-income households. This system comprehensively utilizes information aggregation means, data analysis means, information distribution means, data update means, and voice input processing means.

[0069] The server uses information aggregation tools to collect data such as the schedules of each household member, their preferences regarding individual household tasks, and their progress, and manages this data centrally in a database. At this stage, electronic devices such as smartphones and tablets, mainly used within the home, are employed, and information is usually entered through dedicated application software.

[0070] Next, the server executes an optimization algorithm based on the collected data through data analysis tools. This algorithm analyzes the entire household schedule and calculates the time each member can spend most efficiently completing household tasks. This utilizes optimization techniques and machine learning models, with generative AI models being a concrete example.

[0071] Subsequently, the server transmits the calculated household chore schedule to each member's terminal using an information distribution method. This process utilizes a variety of communication methods, including email notifications, smartphone app push notifications, and even voice notifications using home audio output devices.

[0072] In the feedback process, users input feedback from their devices upon completing daily tasks and send it to the server via a data update mechanism. This feedback allows the server to update individual profiles and scheduling parameters, which are then reflected in future schedules.

[0073] Furthermore, using voice input processing, users can easily change their schedules via voice commands. Home smart speakers equipped with voice recognition technology are used, and the user's voice instructions are transmitted to the server in real time, allowing for immediate schedule adjustments.

[0074] As a concrete example, consider a scenario where user A works Monday through Friday, and user B has Wednesdays off. Taking these conditions into account, the server will suggest the most rational arrangement for each user to handle which household chore and at what time.

[0075] Example of a prompt:

[0076] "Enter the schedules of each member of a dual-income household and generate an optimal household chore schedule for each person."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server first collects data such as schedules, preferences, and current task status from each member of the household through information aggregation means. This collected data is entered via a smartphone app or web portal. The entered data is stored in the server's database and prepared for subsequent analysis.

[0080] Step 2:

[0081] The server analyzes the collected data using data analysis tools to execute optimization algorithms. This analysis combines each member's schedule with the overall household tasks to generate an optimal household schedule. Specifically, it uses a generative AI model to calculate efficient task assignments.

[0082] Step 3:

[0083] The server sends the optimal household chore schedule, derived from the analysis results, to each member's terminal using an information distribution method. The input here is the generated schedule information, and the output is email notification, smartphone app push notification, or voice notification. The terminal notifies the user of the schedule, and the user understands the tasks.

[0084] Step 4:

[0085] Users enter feedback into their terminal upon completing daily tasks. This feedback is sent to the server via a data update mechanism. The server uses this feedback to adjust and update user profiles and scheduling parameters in the database, reflecting these changes in future schedule generation.

[0086] Step 5:

[0087] Users transmit voice commands to the server using home smart speakers or similar devices. These voice commands are analyzed through a voice input processing system, and the home work schedule is dynamically modified. The schedule is immediately recalculated based on the input voice commands, and the updated schedule is again notified to each device.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In dual-income households, there is a need to efficiently distribute household chores and manage them flexibly according to the lifestyles and schedules of each family member. Furthermore, it is necessary to improve the efficiency of household chores and the quality of life by utilizing in-home support devices. Conventional systems have challenges in achieving optimal chore distribution and real-time task management.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes information processing means for recording the schedule information of each member of the household, task management means for assigning tasks to a home support device, and progress confirmation means for monitoring the work progress of the home support device. This enables efficient division of household chores adapted to the schedules of family members and task management utilizing a home support device.

[0093] "Information processing means" refers to devices or systems that have the function of recording and centrally managing the schedule information of each member of a household.

[0094] An "optimization processing means" is a device or system that has the function of performing calculations and algorithms to efficiently distribute household tasks based on data obtained by an information processing means.

[0095] "Notification means" refers to a device or system for communicating the homework schedule generated by the optimization processing means to each member, and the notification is made via an electronic device or an audio output device.

[0096] An "update means" is a device or system that has the function of receiving feedback from its members and updating and adjusting the information processing means and optimization processing means based on that feedback.

[0097] A "voice command receiving means" is a device or system that has the function of receiving voice commands and adjusting the schedule of household tasks.

[0098] A "task management device" is a device or system that has the function of assigning household tasks to be performed to a home support device.

[0099] A "progress monitoring device" is a device or system that has the function of monitoring the progress of work performed by a home support device and obtaining timely feedback.

[0100] The system for implementing this invention consists of software and hardware for efficiently dividing and managing household chores. The server has a database that manages the schedules of each member of the household and performs information processing. Specifically, the server stores the schedule data of family members on a cloud server and retrieves schedule information using Google® Calendar or Apple Calendar API.

[0101] On the server, optimization algorithms implemented in Python or other programming languages ​​calculate the optimal distribution of household tasks based on each member's schedule. Based on these results, tasks are assigned to home assistance devices, such as robotic vacuum cleaners and smart home appliances.

[0102] The devices are smartphones and tablets used within the home, and they provide a dashboard that allows users to visually check the progress of tasks. The devices receive updates and notifications from the server through backend services such as Firebase, and accept voice commands through voice recognition devices such as Alexa and Google Assistant.

[0103] Users can send feedback using a smartphone app, and the system can automatically reduce their workload for the next day if they complete a large number of tasks. Additionally, they can add new tasks to the home assistance device simply by saying a voice command such as, "Help me cook tonight."

[0104] A concrete example of a prompt would be, "Please tell me how to optimize and manage the tasks of a household assistance robot based on my household schedule." By inputting such a prompt into the generating AI model, the system's response can be obtained.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server retrieves schedule data for each household member from a cloud database. Input includes schedule information via Google Calendar and Apple Calendar APIs, and output is aggregated schedule data. This schedule data is used to prepare for the efficient distribution of household tasks.

[0108] Step 2:

[0109] The server calculates the optimal task allocation for household chores using an optimization algorithm implemented in Python, based on the acquired schedule data. Inputs include each member's schedule and performance information for household support devices, and output is an optimized task schedule. The server then prepares this result for transmission to the household support devices.

[0110] Step 3:

[0111] The server notifies each household member's device of the calculated task schedule. The input is the optimized task schedule, and the output is notification information displayed via an app on the user's smartphone or tablet. The user can then proceed with household tasks based on this information.

[0112] Step 4:

[0113] Users submit feedback on completed tasks using a smartphone app. Inputs include task completion status and desired changes, while output is feedback data sent to the server. This data is stored in a database to help optimize future task schedules.

[0114] Step 5:

[0115] The terminal receives voice commands through a voice recognition device and adjusts the schedule in real time. Inputs include voice commands such as "Help me cook tonight," and output is updated schedule information reflected on the server. This information is immediately applied to the operation of the home assistance device.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention combines a system that enables efficient division and management of household chores with an emotion engine that recognizes the user's emotions. This system uses emotion data to adjust the assignment and scheduling of household tasks, and by taking into account the atmosphere in the home and the user's mental state, it enables more personalized household management.

[0118] The server manages profiles of each member, including their schedule information and emotional data. These profiles include emotional states obtained from voice input and sensor data, in addition to regular schedule information. The server uses this data to refine its optimization algorithm, distributing household tasks efficiently and with emotional considerations in mind.

[0119] The emotion engine analyzes the emotions of household members from voice data and device inputs within the home and sends the results to the server. For example, if it detects that someone is "feeling stressed," the server can use the emotion engine's information to temporarily reschedule the household chores of that member.

[0120] Once a schedule is generated, household chore schedules are sent to each member via notification. When the device delivers a notification to the user, the content and timing of the notification are flexibly adjusted based on the results of the emotion engine's analysis. For example, notifications may be sent in a calm tone or at times when the user has more free time.

[0121] Users can input their emotions and feedback into their device, which is then sent to the server. The server analyzes the received feedback along with the emotional data and updates the profile and schedule. This results in a more personalized schedule, optimized for each member's life and emotions.

[0122] In this way, the system of the present invention not only efficiently manages the division of household chores, but also improves the quality of life by taking into account the emotional state of the members, thereby facilitating interpersonal relationships within the family.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] Users input information into the device that reflects each member of the household's schedule, preferences for household chores, and even their emotions. For example, a user might input a request such as, "I'm busy with work on Mondays, so I'd like to reduce the time I spend on housework."

[0126] Step 2:

[0127] The terminal organizes the entered schedule information and sentiment data and sends it to the server. This data is recorded as a profile for each member.

[0128] Step 3:

[0129] The server activates the emotion engine and analyzes the emotional state of each member in real time. It extracts emotions from voice data and sensor data and reflects the results in a profile.

[0130] Step 4:

[0131] The server uses an optimization algorithm to schedule household tasks, taking emotional data into account. It adjusts task priorities and workloads according to emotional states, generating a schedule that does not burden members.

[0132] Step 5:

[0133] The server generates a schedule and sends it to each member's terminal via a notification system. The notification includes recommended tasks and reminders based on sentiment analysis results.

[0134] Step 6:

[0135] Users input feedback on completed tasks and their current emotional state via their devices. This feedback is used as foundational data for future schedule optimization.

[0136] Step 7:

[0137] The device collects user feedback and sends it to the server along with sentiment data. The server uses this information to continuously update profiles and schedules.

[0138] Step 8:

[0139] The user gives a voice command to the smart speaker. For example, the user might say, "I want to switch laundry duties with someone this week," and the voice command receiving device recognizes this and sends it to the server.

[0140] Step 9:

[0141] The server readjusts the schedule based on voice commands and emotion data. The adjusted schedule is then communicated to the members again through notification channels.

[0142] Through the above process, the distribution of household chores that best suits the feelings and circumstances of each member will be achieved.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] In household chore division and scheduling, tasks are often assigned without considering the emotional state of each family member, which can lead to stress, dissatisfaction, and a deterioration of the family atmosphere. This problem needs to be addressed. Furthermore, there is a need to achieve flexible task redistribution and system adaptation that takes individual emotions into account.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes information processing means for recording the schedule information of each member of the household; optimization processing means for optimally distributing household tasks based on the information processing means and analyzing emotional data using a generative artificial intelligence model; and emotion analysis means for adjusting the assignment of household tasks based on the emotional data analyzed by the emotion engine. This makes it possible to schedule household chores while taking into account the emotional state of each member, thereby promoting harmony within the household.

[0148] "Information processing means" refers to a device or system that has the function of recording and managing the schedule information of each member of a household.

[0149] An "optimization processing means" is a device or system that uses a generative artificial intelligence model based on collected information to analyze emotional data and has the function of efficiently distributing household tasks.

[0150] "Emotional analysis means" refers to a device or system that has the function of adjusting household chore assignments based on emotional data analyzed by an emotional engine.

[0151] "Notification means" refers to a device or system for communicating an optimized household work schedule to each member, which is done via an electronic device or an audio output device.

[0152] "Update means" refers to a device or system that has the function of receiving feedback from members, updating information processing means and optimization processing means, and generating prompt statements.

[0153] A "voice instruction receiving means" is a device or system that has the function of receiving voice instructions and adjusting the schedule of household tasks based on those instructions.

[0154] This invention provides a system for efficiently distributing household chores within a family while taking into account the emotional state of each family member. This system involves multiple hardware and software components that acquire and analyze household data and generate an optimized schedule.

[0155] A server acts as the central point, using information processing tools to record the schedule information of each member. This information is collected using data from calendar applications and sensors. Based on this information, a generative AI model operates as an optimization processing tool and performs data analysis. Specifically, it analyzes voice data acquired by a voice instruction receiving tool using an emotion engine, and uses the results to optimally assign household tasks.

[0156] The device functions as a notification system, informing the user of household chore schedules. Based on data obtained from emotion analysis, it is possible to adjust the content and timing of notifications to take the user's emotions into consideration. For example, if the user is feeling stressed, notifications will be delivered in a softer voice tone.

[0157] Users can utilize update mechanisms to input their emotions and feedback into the device. The server then re-analyzes the feedback and updates the system's schedule and prompts. This enables more personalized household management, improving harmony and efficiency within the home.

[0158] For example, when the server receives a sentiment analysis result indicating that "Person A is tired," it could dynamically change the household chore schedule to reduce Person A's burden. As a result, the user would receive a schedule notification for the day with a prompt message such as, "Today, focus on activities that allow you to relax."

[0159] An example of a prompt message would be, "Use sentiment analysis data regarding household schedule optimization to generate a notification message that takes emotions into consideration when reallocating tasks."

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server collects schedule information and emotional data from each member of the household and uses information processing tools to create profiles. Inputs include schedule data from calendar applications and emotional data acquired from voice input devices and sensors. These data are combined to produce profiles for each member. These profiles include not only regular schedule information but also a record of their emotional state.

[0163] Step 2:

[0164] The emotion engine analyzes the audio data received from the device to identify the emotional state of each member. The input is raw audio data, which is then analyzed by a generating AI model to output emotional state labels. For example, specific emotional states such as "high stress" or "relaxed" may be output.

[0165] Step 3:

[0166] The server uses the emotion analysis results and the members' schedule information to distribute household tasks as an optimization process. The inputs are the emotion analysis results and existing schedule information. Using a generative AI model, the task distribution is appropriately recalculated and the adjusted schedule is output for each member. For example, a member experiencing stress will be given a schedule that reduces their burden.

[0167] Step 4:

[0168] The terminal notifies each member based on the new schedule sent from the server. The input is the newly optimized schedule data. The notification method generates a prompt message and outputs a notification in a soft voice tone and at an appropriate time. Specifically, the notification will say something like, "We have made adjustments to reduce your workload."

[0169] Step 5:

[0170] Users input their thoughts and opinions on received notifications and schedules into their terminals, and the server receives this as an update mechanism to adjust the entire system. The input is user feedback, and based on this, new prompt messages are generated or schedules are modified. For example, if a user inputs "The notification was late," the system will be improved to send notifications earlier next time.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] The division and management of household chores within a family are often carried out mechanically without sufficient consideration of the schedules and feelings of each member, which can lead to mental burden and dissatisfaction. Furthermore, there is the challenge of dynamically optimizing schedules using feedback. This invention aims to solve these problems and promote smooth task division within the family and the maintenance of the mental health of each member.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes data processing means for recording the schedule information of each member of the household, optimization processing means for optimally distributing household tasks based on the data processing means, and emotion analysis means for analyzing the emotional state of the members. This enables flexible scheduling of household chores that takes into account the schedules and emotions of the members.

[0176] "Data processing means" refers to a device or function for recording and managing the schedule information of each member of a household.

[0177] "Optimization processing means" refers to a device or function for efficiently allocating household tasks based on information obtained by data processing means.

[0178] "Communication means" refers to a device or function for notifying each member of the household work plan generated by the optimization processing means.

[0179] "Correction means" refers to a device or function for receiving feedback from members and updating data processing means and optimization processing means.

[0180] "Voice instruction receiving means" refers to a device or function for receiving voice instructions and adjusting household chore plans.

[0181] "Emotional analysis means" refers to a device or function that analyzes the voice and sensor information of a member to recognize their emotional state.

[0182] "Adjustment means" refers to a device or function for dynamically adjusting the household work plan based on the results of the emotion analysis means.

[0183] This system is designed to efficiently and individually manage household chores based on the schedules and emotions of each family member. The server uses the Google Cloud Natural Language API to perform emotion analysis. It acquires the emotional state of each family member in real time based on voice input and sensor information, and generates an optimal chore schedule based on the information acquired through data processing.

[0184] The emotional data of each member, obtained through emotion analysis, is analyzed on the server using an optimization algorithm based on Java® or Python, and the schedule for household tasks is readjusted. The schedule is flexibly notified to each member via a computing device and an audio output device. This notification is given at the optimal timing and tone, taking into account the emotional state.

[0185] The terminal receives feedback from the user and sends this data back to the server to update the schedule. This process allows the system to sequentially provide household chore assignments optimized for the lifestyles and mental health of each member.

[0186] For example, when a family is relaxing on a holiday, a housekeeping robot might suggest a schedule, saying, "Please relax and enjoy your time. Leave today's cleaning to me." This kind of intervention can reduce the mental burden on family members and improve harmony within the household.

[0187] An example of a prompt for a generative AI model is: "Describe the actions of a robot assistant that recognizes your emotional state when you are relaxed at home and adjusts your household chore schedule accordingly."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server records the schedule information of each member. Daily schedule information is provided as input from each member. Based on this data, the server updates each member's profile and saves it in the database. This profile forms the basis for schedule generation.

[0191] Step 2:

[0192] The server collects emotional data from each member through voice input and sensor information. It receives data from smart microphones and other environmental sensors as input and analyzes each member's emotional state using emotion analysis tools. This data is converted into emotional states such as "stress" or "relaxed" by emotion analysis software.

[0193] Step 3:

[0194] The server combines emotional state data and scheduled information, and runs an optimization algorithm to generate a highly efficient household chore schedule. The inputs used are the results of emotional analysis and scheduled information, and the algorithm calculates the ideal distribution of household tasks. The calculation results are output as a new household chore schedule.

[0195] Step 4:

[0196] The server notifies each member of the generated schedule via a computing device and an audio output device. During schedule notification, the timing and tone of the notification are optimized based on sentiment analysis results. The content received by the user includes the specific tasks scheduled for the day.

[0197] Step 5:

[0198] Users enter feedback about the schedule they receive into their terminal. This feedback is sent to the server as material for improving the system. The feedback is added to the database as new data points and used in the optimization algorithm for future updates.

[0199] Step 6:

[0200] The server reanalyzes the feedback and emotional state received from the user and updates the homework schedule. Based on the feedback, data processing and optimization processing means are dynamically adjusted to generate a more personalized schedule.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] This invention is a system that enables the efficient division and management of household chores in dual-income households. The system aims to centrally manage the schedule information of each household member and optimally schedule household tasks based on this information. The main processes constituting the system's program and specific examples are shown below.

[0218] The server stores data such as the daily schedules, preferences, and task progress of all members in a database, centralizing the information. The server also has an optimization algorithm implemented that uses this data to optimally distribute household chores, calculating task allocation according to individual circumstances. For example, it takes into account the work and school schedules of all family members to calculate who should perform household chores at what time.

[0219] Once a schedule is generated, the server sends the household chore schedule to each member's device via notification methods. This notification can be sent via email, smartphone app notifications, or voice through a smart speaker. The schedule received by the device is visualized for the user, allowing them to view their weekly chore plan, for example, in a dashboard format.

[0220] Users can provide feedback when they complete their daily tasks. For example, if they complete a large amount of household chores on a given day, they can input feedback into their device indicating that they would like a lighter schedule the following day. The device sends this feedback to the server, which then updates the user's profile and scheduling parameters based on the information.

[0221] Furthermore, the voice command receiving mechanism helps users adjust their schedules using voice commands. For example, a user can give a voice command to a smart speaker saying, "I want to cancel Sunday afternoon cleaning," and that information is immediately sent to the server and reflected in the schedule.

[0222] In this way, the system of the present invention efficiently distributes household chores and flexibly adapts to the lifestyle of each member, thereby reducing the burden of housework in dual-income households and improving their quality of life.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] Users input their own and their family's schedules and preferences for household chores using the application. For example, they can input things like their strengths and weaknesses in household chores and their preferred time slots.

[0226] Step 2:

[0227] The terminal receives information entered by the user and sends it to the server as organized data. This allows the server to integrate the necessary information.

[0228] Step 3:

[0229] The server uses the information received from the terminal to create a profile of each member of the household and saves it in a database. This profile details various attributes of the user.

[0230] Step 4:

[0231] The server uses an optimization algorithm to efficiently distribute household tasks based on profiles. The algorithm considers each member's schedule and preferences to calculate the optimal task assignment.

[0232] Step 5:

[0233] The server generates an optimized schedule and sends reminders to each member's device via a notification system. For example, it might notify them of specific tasks such as "Wash the dishes at 9am on Saturday."

[0234] Step 6:

[0235] Users input their daily task completion status and feedback into their devices. This includes reporting task completion and suggesting improvements for future tasks.

[0236] Step 7:

[0237] The device collects user feedback and sends it to the server. The server updates the profile and schedule based on the received feedback, which is then reflected in the next scheduling.

[0238] Step 8:

[0239] The user gives voice commands to the smart speaker as needed. For example, they might say, "Change tomorrow's cleaning to the weekend." The smart speaker recognizes this and sends the information to the server.

[0240] Step 9:

[0241] The server analyzes the voice instructions and adjusts the schedule for the corresponding household tasks. A new schedule is generated and communicated to each member again via the notification system.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] In modern dual-income households, the burden of housework is a major challenge. Each member has a different schedule, making it extremely difficult to efficiently divide household chores amidst busy lives. As a result, coordination within the household becomes complicated, stress increases, and the quality of life can decline. It is necessary to solve these problems and improve the efficiency of household management and the satisfaction of its inhabitants.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] In this invention, the server includes: information aggregation means for recording the schedule information of each member of the household; data analysis means for executing an optimization algorithm that efficiently allocates household tasks based on the information aggregation means; information distribution means for transmitting the household schedule generated by the data analysis means to each member using various communication methods; data update means for collecting feedback from members and updating the information aggregation means and the optimization algorithm; and voice input processing means for receiving voice instructions and changing the household schedule. This makes it possible to generate and adjust household schedules that flexibly adapt to the lifestyle of each member.

[0247] "Information aggregation means" refers to a method of comprehensively collecting and organizing planned information provided by each member of the household.

[0248] An "optimization algorithm" is an algorithm that includes equations and formulas for efficiently distributing household chores based on collected information.

[0249] "Data analysis means" refers to methods for performing the necessary analysis to execute optimization algorithms and effectively allocate tasks within the household.

[0250] "Information distribution means" refers to the means of communicating the generated household schedule to each member using various communication methods.

[0251] A "data update method" is a means of receiving feedback from members and updating the information aggregation and optimization algorithms based on that information.

[0252] The "voice input processing means" is a means for receiving voice instructions from members and appropriately adjusting the schedule of household tasks based on that information.

[0253] This invention is a system for streamlining the division of household chores in dual-income households. This system comprehensively utilizes information aggregation means, data analysis means, information distribution means, data update means, and voice input processing means.

[0254] The server uses information aggregation tools to collect data such as the schedules of each household member, their preferences regarding individual household tasks, and their progress, and manages this data centrally in a database. At this stage, electronic devices such as smartphones and tablets, mainly used within the home, are employed, and information is usually entered through dedicated application software.

[0255] Next, the server executes an optimization algorithm based on the collected data through data analysis tools. This algorithm analyzes the entire household schedule and calculates the time each member can spend most efficiently completing household tasks. This utilizes optimization techniques and machine learning models, with generative AI models being a concrete example.

[0256] Subsequently, the server transmits the calculated household chore schedule to each member's terminal using an information distribution method. This process utilizes a variety of communication methods, including email notifications, smartphone app push notifications, and even voice notifications using home audio output devices.

[0257] In the feedback process, users input feedback from their devices upon completing daily tasks and send it to the server via a data update mechanism. This feedback allows the server to update individual profiles and scheduling parameters, which are then reflected in future schedules.

[0258] Furthermore, using voice input processing, users can easily change their schedules via voice commands. Home smart speakers equipped with voice recognition technology are used, and the user's voice instructions are transmitted to the server in real time, allowing for immediate schedule adjustments.

[0259] As a concrete example, consider a scenario where user A works Monday through Friday, and user B has Wednesdays off. Taking these conditions into account, the server will suggest the most rational arrangement for each user to handle which household chore and at what time.

[0260] Example of a prompt:

[0261] "Enter the schedules of each member of a dual-income household and generate an optimal household chore schedule for each person."

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The server first collects data such as schedules, preferences, and current task status from each member of the household through information aggregation means. This collected data is entered via a smartphone app or web portal. The entered data is stored in the server's database and prepared for subsequent analysis.

[0265] Step 2:

[0266] The server analyzes the collected data using data analysis tools to execute optimization algorithms. This analysis combines each member's schedule with the overall household tasks to generate an optimal household schedule. Specifically, it uses a generative AI model to calculate efficient task assignments.

[0267] Step 3:

[0268] The server sends the optimal household chore schedule, derived from the analysis results, to each member's terminal using an information distribution method. The input here is the generated schedule information, and the output is email notification, smartphone app push notification, or voice notification. The terminal notifies the user of the schedule, and the user understands the tasks.

[0269] Step 4:

[0270] Users enter feedback into their terminal upon completing daily tasks. This feedback is sent to the server via a data update mechanism. The server uses this feedback to adjust and update user profiles and scheduling parameters in the database, reflecting these changes in future schedule generation.

[0271] Step 5:

[0272] Users transmit voice commands to the server using home smart speakers or similar devices. These voice commands are analyzed through a voice input processing system, and the home work schedule is dynamically modified. The schedule is immediately recalculated based on the input voice commands, and the updated schedule is again notified to each device.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] In dual-income households, there is a need to efficiently distribute household chores and manage them flexibly according to the lifestyles and schedules of each family member. Furthermore, it is necessary to improve the efficiency of household chores and the quality of life by utilizing in-home support devices. Conventional systems have challenges in achieving optimal chore distribution and real-time task management.

[0276] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0277] In this invention, the server includes information processing means for recording the schedule information of each member in the household, task management means for assigning tasks to the in-house support device, and progress confirmation means for monitoring the work progress of the in-house support device. Thereby, efficient housework sharing adapted to the schedules of family members and task management using the in-house support device become possible.

[0278] The "information processing means" is a device or system having a function for recording and centrally managing the schedule information of each member in the household.

[0279] The "optimization processing means" is a device or system having a function for executing calculations and algorithms for efficiently distributing household work based on the data obtained by the information processing means.

[0280] The "notification means" is a device or system for conveying the schedule of household work generated by the optimization processing means to each member, and performs notifications through an electronic device or an acoustic output device.

[0281] The "update means" is a device or system having a function for receiving feedback from the members and updating and adjusting the information processing means and the optimization processing means based thereon.

[0282] The "voice instruction reception means" is a device or system having a function for receiving voice commands and adjusting the schedule of household work.

[0283] The "task management means" is a device or system having a function for assigning housework tasks to be executed to the in-house support device.

[0284] The "progress confirmation means" is a device or system having a function for monitoring the progress status of the work of the in-house support device and obtaining timely feedback.

[0285] The system for implementing this invention is composed of software and hardware for efficiently sharing and managing housework. The server has a database for managing the schedules of each household member and performs information processing. Specifically, the server accumulates the schedule data of family members on the cloud server and obtains schedule information using Google Calendar or Apple Calendar API.

[0286] The server calculates the optimal task allocation of housework based on the schedules of each member using an optimization algorithm implemented in Python or other programming languages. Based on this result, tasks are assigned to in-home support devices such as robotic vacuum cleaners and smart home appliances.

[0287] The terminal is a smartphone or tablet used within the home and provides a dashboard where users can visually confirm the progress of tasks. The terminal receives update information and notifications from the server through a backend service such as Firebase and accepts voice instructions through a voice recognition device such as Alexa or Google Assistant.

[0288] Users can send feedback using the smartphone app and can also automatically reduce the next day's schedule when a large number of tasks are completed. In addition, a new task can be added to the in-home support device simply by speaking a voice command such as "Help with cooking tonight."

[0289] An example of a specific prompt sentence is "Please teach me a method to optimize and manage the tasks of a housework support robot based on the household schedule." By inputting such a prompt into the generative AI model, a response from the system can be obtained.

[0290] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0291] Step 1:

[0292] The server retrieves schedule data for each household member from a cloud database. Input includes schedule information via Google Calendar and Apple Calendar APIs, and output is aggregated schedule data. This schedule data is used to prepare for the efficient distribution of household tasks.

[0293] Step 2:

[0294] The server calculates the optimal task allocation for household chores using an optimization algorithm implemented in Python, based on the acquired schedule data. Inputs include each member's schedule and performance information for household support devices, and output is an optimized task schedule. The server then prepares this result for transmission to the household support devices.

[0295] Step 3:

[0296] The server notifies each household member's device of the calculated task schedule. The input is the optimized task schedule, and the output is notification information displayed via an app on the user's smartphone or tablet. The user can then proceed with household tasks based on this information.

[0297] Step 4:

[0298] Users submit feedback on completed tasks using a smartphone app. Inputs include task completion status and desired changes, while output is feedback data sent to the server. This data is stored in a database to help optimize future task schedules.

[0299] Step 5:

[0300] The terminal receives voice commands through a voice recognition device and adjusts the schedule in real time. Inputs include voice commands such as "Help me cook tonight," and output is updated schedule information reflected on the server. This information is immediately applied to the operation of the home assistance device.

[0301] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0302] This invention combines a system that enables efficient division and management of household chores with an emotion engine that recognizes the user's emotions. This system uses emotion data to adjust the assignment and scheduling of household tasks, and by taking into account the atmosphere in the home and the user's mental state, it enables more personalized household management.

[0303] The server manages profiles of each member, including their schedule information and emotional data. These profiles include emotional states obtained from voice input and sensor data, in addition to regular schedule information. The server uses this data to refine its optimization algorithm, distributing household tasks efficiently and with emotional considerations in mind.

[0304] The emotion engine analyzes the emotions of household members from voice data and device inputs within the home and sends the results to the server. For example, if it detects that someone is "feeling stressed," the server can use the emotion engine's information to temporarily reschedule the household chores of that member.

[0305] Once a schedule is generated, household chore schedules are sent to each member via notification. When the device delivers a notification to the user, the content and timing of the notification are flexibly adjusted based on the results of the emotion engine's analysis. For example, notifications may be sent in a calm tone or at times when the user has more free time.

[0306] The user can input their own feelings and feedback into the terminal, which is then sent to the server. The server analyzes the received feedback together with the emotional data and updates the profile and schedule. As a result, the schedule becomes more personalized and is provided in a form optimized for the life and feelings of each member.

[0307] In this way, the system of the present invention can not only efficiently manage the sharing of household chores, but also smooth out the interpersonal relationships within the family and improve the quality of life by taking into account the emotional states of the members.

[0308] The processing flow will be described below.

[0309] Step 1:

[0310] The user inputs into the terminal the schedule information, household chore preferences, and information reflecting emotions of each member within the family. For example, the user inputs a desire such as "I'm busy with work on Monday, so I want to reduce my housework time."

[0311] Step 2:

[0312] The terminal organizes the input schedule information and emotional data and sends it to the server. This data is recorded as a profile for each member.

[0313] Step 3:

[0314] The server activates the emotion engine and analyzes the emotional states of each member in real time. It extracts emotions from voice data and sensor data and reflects the results in the profile.

[0315] Step 4:

[0316] The server uses an optimization algorithm to schedule household tasks, taking emotional data into account. It adjusts task priorities and workloads according to emotional states, generating a schedule that does not burden members.

[0317] Step 5:

[0318] The server generates a schedule and sends it to each member's terminal via a notification system. The notification includes recommended tasks and reminders based on sentiment analysis results.

[0319] Step 6:

[0320] Users input feedback on completed tasks and their current emotional state via their devices. This feedback is used as foundational data for future schedule optimization.

[0321] Step 7:

[0322] The device collects user feedback and sends it to the server along with sentiment data. The server uses this information to continuously update profiles and schedules.

[0323] Step 8:

[0324] The user gives a voice command to the smart speaker. For example, the user might say, "I want to switch laundry duties with someone this week," and the voice command receiving device recognizes this and sends it to the server.

[0325] Step 9:

[0326] The server readjusts the schedule based on voice commands and emotion data. The adjusted schedule is then communicated to the members again through notification channels.

[0327] Through the above process, the distribution of household chores that best suits the feelings and circumstances of each member will be achieved.

[0328] (Example 2)

[0329] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0330] In household chore division and scheduling, tasks are often assigned without considering the emotional state of each family member, which can lead to stress, dissatisfaction, and a deterioration of the family atmosphere. This problem needs to be addressed. Furthermore, there is a need to achieve flexible task redistribution and system adaptation that takes individual emotions into account.

[0331] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0332] In this invention, the server includes information processing means for recording the schedule information of each member of the household; optimization processing means for optimally distributing household tasks based on the information processing means and analyzing emotional data using a generative artificial intelligence model; and emotion analysis means for adjusting the assignment of household tasks based on the emotional data analyzed by the emotion engine. This makes it possible to schedule household chores while taking into account the emotional state of each member, thereby promoting harmony within the household.

[0333] "Information processing means" refers to a device or system that has the function of recording and managing the schedule information of each member of a household.

[0334] An "optimization processing means" is a device or system that uses a generative artificial intelligence model based on collected information to analyze emotional data and has the function of efficiently distributing household tasks.

[0335] "Emotional analysis means" refers to a device or system that has the function of adjusting household chore assignments based on emotional data analyzed by an emotional engine.

[0336] "Notification means" refers to a device or system for communicating an optimized household work schedule to each member, which is done via an electronic device or an audio output device.

[0337] "Update means" refers to a device or system that has the function of receiving feedback from members, updating information processing means and optimization processing means, and generating prompt statements.

[0338] A "voice instruction receiving means" is a device or system that has the function of receiving voice instructions and adjusting the schedule of household tasks based on those instructions.

[0339] This invention provides a system for efficiently distributing household chores within a family while taking into account the emotional state of each family member. This system involves multiple hardware and software components that acquire and analyze household data and generate an optimized schedule.

[0340] A server acts as the central point, using information processing tools to record the schedule information of each member. This information is collected using data from calendar applications and sensors. Based on this information, a generative AI model operates as an optimization processing tool and performs data analysis. Specifically, it analyzes voice data acquired by a voice instruction receiving tool using an emotion engine, and uses the results to optimally assign household tasks.

[0341] The device functions as a notification system, informing the user of household chore schedules. Based on data obtained from emotion analysis, it is possible to adjust the content and timing of notifications to take the user's emotions into consideration. For example, if the user is feeling stressed, notifications will be delivered in a softer voice tone.

[0342] Users can utilize update mechanisms to input their emotions and feedback into the device. The server then re-analyzes the feedback and updates the system's schedule and prompts. This enables more personalized household management, improving harmony and efficiency within the home.

[0343] For example, when the server receives a sentiment analysis result indicating that "Person A is tired," it could dynamically change the household chore schedule to reduce Person A's burden. As a result, the user would receive a schedule notification for the day with a prompt message such as, "Today, focus on activities that allow you to relax."

[0344] An example of a prompt message would be, "Use sentiment analysis data regarding household schedule optimization to generate a notification message that takes emotions into consideration when reallocating tasks."

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The server collects schedule information and emotional data from each member of the household and uses information processing tools to create profiles. Inputs include schedule data from calendar applications and emotional data acquired from voice input devices and sensors. These data are combined to produce profiles for each member. These profiles include not only regular schedule information but also a record of their emotional state.

[0348] Step 2:

[0349] The emotion engine analyzes the audio data received from the device to identify the emotional state of each member. The input is raw audio data, which is then analyzed by a generating AI model to output emotional state labels. For example, specific emotional states such as "high stress" or "relaxed" may be output.

[0350] Step 3:

[0351] The server uses the emotion analysis results and the members' schedule information to distribute household tasks as an optimization process. The inputs are the emotion analysis results and existing schedule information. Using a generative AI model, the task distribution is appropriately recalculated and the adjusted schedule is output for each member. For example, a member experiencing stress will be given a schedule that reduces their burden.

[0352] Step 4:

[0353] The terminal notifies each member based on the new schedule sent from the server. The input is the newly optimized schedule data. The notification method generates a prompt message and outputs a notification in a soft voice tone and at an appropriate time. Specifically, the notification will say something like, "We have made adjustments to reduce your workload."

[0354] Step 5:

[0355] Users input their thoughts and opinions on received notifications and schedules into their terminals, and the server receives this as an update mechanism to adjust the entire system. The input is user feedback, and based on this, new prompt messages are generated or schedules are modified. For example, if a user inputs "The notification was late," the system will be improved to send notifications earlier next time.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0358] The division and management of household chores within a family are often carried out mechanically without sufficient consideration of the schedules and feelings of each member, which can lead to mental burden and dissatisfaction. Furthermore, there is the challenge of dynamically optimizing schedules using feedback. This invention aims to solve these problems and promote smooth task division within the family and the maintenance of the mental health of each member.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0360] In this invention, the server includes data processing means for recording the schedule information of each member of the household, optimization processing means for optimally distributing household tasks based on the data processing means, and emotion analysis means for analyzing the emotional state of the members. This enables flexible scheduling of household chores that takes into account the schedules and emotions of the members.

[0361] "Data processing means" refers to a device or function for recording and managing the schedule information of each member of a household.

[0362] "Optimization processing means" refers to a device or function for efficiently allocating household tasks based on information obtained by data processing means.

[0363] "Communication means" refers to a device or function for notifying each member of the household work plan generated by the optimization processing means.

[0364] "Correction means" refers to a device or function for receiving feedback from members and updating data processing means and optimization processing means.

[0365] "Voice instruction receiving means" refers to a device or function for receiving voice instructions and adjusting household chore plans.

[0366] "Emotional analysis means" refers to a device or function that analyzes the voice and sensor information of a member to recognize their emotional state.

[0367] "Adjustment means" refers to a device or function for dynamically adjusting the household work plan based on the results of the emotion analysis means.

[0368] This system is designed to efficiently and individually manage household chores based on the schedules and emotions of each family member. The server uses the Google Cloud Natural Language API to perform emotion analysis. It acquires the emotional state of each family member in real time based on voice input and sensor information, and generates an optimal chore schedule based on the information acquired through data processing.

[0369] The emotional data of each member, obtained through emotion analysis, is analyzed on the server using an optimization algorithm based on Java or Python, and the schedule for household tasks is readjusted. The schedule is flexibly notified to each member via a computing device and an audio output device. This notification is given at the optimal timing and tone, taking into account the emotional state.

[0370] The terminal receives feedback from the user and sends this data back to the server to update the schedule. This process allows the system to sequentially provide household chore assignments optimized for the lifestyles and mental health of each member.

[0371] For example, when a family is relaxing on a holiday, a housekeeping robot might suggest a schedule, saying, "Please relax and enjoy your time. Leave today's cleaning to me." This kind of intervention can reduce the mental burden on family members and improve harmony within the household.

[0372] An example of a prompt for a generative AI model is: "Describe the actions of a robot assistant that recognizes your emotional state when you are relaxed at home and adjusts your household chore schedule accordingly."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The server records the schedule information of each member. Daily schedule information is provided as input from each member. Based on this data, the server updates each member's profile and saves it in the database. This profile forms the basis for schedule generation.

[0376] Step 2:

[0377] The server collects emotional data from each member through voice input and sensor information. It receives data from smart microphones and other environmental sensors as input and analyzes each member's emotional state using emotion analysis tools. This data is converted into emotional states such as "stress" or "relaxed" by emotion analysis software.

[0378] Step 3:

[0379] The server combines emotional state data and scheduled information, and runs an optimization algorithm to generate a highly efficient household chore schedule. The inputs used are the results of emotional analysis and scheduled information, and the algorithm calculates the ideal distribution of household tasks. The calculation results are output as a new household chore schedule.

[0380] Step 4:

[0381] The server notifies each member of the generated schedule via a computing device and an audio output device. During schedule notification, the timing and tone of the notification are optimized based on sentiment analysis results. The content received by the user includes the specific tasks scheduled for the day.

[0382] Step 5:

[0383] Users enter feedback about the schedule they receive into their terminal. This feedback is sent to the server as material for improving the system. The feedback is added to the database as new data points and used in the optimization algorithm for future updates.

[0384] Step 6:

[0385] The server reanalyzes the feedback and emotional state received from the user and updates the homework schedule. Based on the feedback, data processing and optimization processing means are dynamically adjusted to generate a more personalized schedule.

[0386] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0387] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0388] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0392] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0393] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0394] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0395] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0396] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0397] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0398] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0399] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0400] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0401] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0402] This invention is a system that enables the efficient division and management of household chores in dual-income households. The system aims to centrally manage the schedule information of each household member and optimally schedule household tasks based on this information. The main processes constituting the system's program and specific examples are shown below.

[0403] The server stores data such as the daily schedules, preferences, and task progress of all members in a database, centralizing the information. The server also has an optimization algorithm implemented that uses this data to optimally distribute household chores, calculating task allocation according to individual circumstances. For example, it takes into account the work and school schedules of all family members to calculate who should perform household chores at what time.

[0404] Once a schedule is generated, the server sends the household chore schedule to each member's device via notification methods. This notification can be sent via email, smartphone app notifications, or voice through a smart speaker. The schedule received by the device is visualized for the user, allowing them to view their weekly chore plan, for example, in a dashboard format.

[0405] Users can provide feedback when they complete their daily tasks. For example, if they complete a large amount of household chores on a given day, they can input feedback into their device indicating that they would like a lighter schedule the following day. The device sends this feedback to the server, which then updates the user's profile and scheduling parameters based on the information.

[0406] Furthermore, the voice command receiving mechanism helps users adjust their schedules using voice commands. For example, a user can give a voice command to a smart speaker saying, "I want to cancel Sunday afternoon cleaning," and that information is immediately sent to the server and reflected in the schedule.

[0407] In this way, the system of the present invention efficiently distributes household chores and flexibly adapts to the lifestyle of each member, thereby reducing the burden of housework in dual-income households and improving their quality of life.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] Users input their own and their family's schedules and preferences for household chores using the application. For example, they can input things like their strengths and weaknesses in household chores and their preferred time slots.

[0411] Step 2:

[0412] The terminal receives information entered by the user and sends it to the server as organized data. This allows the server to integrate the necessary information.

[0413] Step 3:

[0414] The server uses the information received from the terminal to create a profile of each member of the household and saves it in a database. This profile details various attributes of the user.

[0415] Step 4:

[0416] The server uses an optimization algorithm to efficiently distribute household tasks based on profiles. The algorithm considers each member's schedule and preferences to calculate the optimal task assignment.

[0417] Step 5:

[0418] The server generates an optimized schedule and sends reminders to each member's device via a notification system. For example, it might notify them of specific tasks such as "Wash the dishes at 9am on Saturday."

[0419] Step 6:

[0420] Users input their daily task completion status and feedback into their devices. This includes reporting task completion and suggesting improvements for future tasks.

[0421] Step 7:

[0422] The device collects user feedback and sends it to the server. The server updates the profile and schedule based on the received feedback, which is then reflected in the next scheduling.

[0423] Step 8:

[0424] The user gives voice commands to the smart speaker as needed. For example, they might say, "Change tomorrow's cleaning to the weekend." The smart speaker recognizes this and sends the information to the server.

[0425] Step 9:

[0426] The server analyzes the voice instructions and adjusts the schedule for the corresponding household tasks. A new schedule is generated and communicated to each member again via the notification system.

[0427] (Example 1)

[0428] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0429] In modern dual-income households, the burden of housework is a major challenge. Each member has a different schedule, making it extremely difficult to efficiently divide household chores amidst busy lives. As a result, coordination within the household becomes complicated, stress increases, and the quality of life can decline. It is necessary to solve these problems and improve the efficiency of household management and the satisfaction of its inhabitants.

[0430] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0431] In this invention, the server includes: information aggregation means for recording the schedule information of each member of the household; data analysis means for executing an optimization algorithm that efficiently allocates household tasks based on the information aggregation means; information distribution means for transmitting the household schedule generated by the data analysis means to each member using various communication methods; data update means for collecting feedback from members and updating the information aggregation means and the optimization algorithm; and voice input processing means for receiving voice instructions and changing the household schedule. This makes it possible to generate and adjust household schedules that flexibly adapt to the lifestyle of each member.

[0432] "Information aggregation means" refers to a method of comprehensively collecting and organizing planned information provided by each member of the household.

[0433] An "optimization algorithm" is an algorithm that includes equations and formulas for efficiently distributing household chores based on collected information.

[0434] "Data analysis means" refers to methods for performing the necessary analysis to execute optimization algorithms and effectively allocate tasks within the household.

[0435] "Information distribution means" refers to the means of communicating the generated household schedule to each member using various communication methods.

[0436] A "data update method" is a means of receiving feedback from members and updating the information aggregation and optimization algorithms based on that information.

[0437] The "voice input processing means" is a means for receiving voice instructions from members and appropriately adjusting the schedule of household tasks based on that information.

[0438] This invention is a system for streamlining the division of household chores in dual-income households. This system comprehensively utilizes information aggregation means, data analysis means, information distribution means, data update means, and voice input processing means.

[0439] The server uses information aggregation tools to collect data such as the schedules of each household member, their preferences regarding individual household tasks, and their progress, and manages this data centrally in a database. At this stage, electronic devices such as smartphones and tablets, mainly used within the home, are employed, and information is usually entered through dedicated application software.

[0440] Next, the server executes an optimization algorithm based on the collected data through data analysis tools. This algorithm analyzes the entire household schedule and calculates the time each member can spend most efficiently completing household tasks. This utilizes optimization techniques and machine learning models, with generative AI models being a concrete example.

[0441] Subsequently, the server transmits the calculated household chore schedule to each member's terminal using an information distribution method. This process utilizes a variety of communication methods, including email notifications, smartphone app push notifications, and even voice notifications using home audio output devices.

[0442] In the feedback process, users input feedback from their devices upon completing daily tasks and send it to the server via a data update mechanism. This feedback allows the server to update individual profiles and scheduling parameters, which are then reflected in future schedules.

[0443] Furthermore, using voice input processing, users can easily change their schedules via voice commands. Home smart speakers equipped with voice recognition technology are used, and the user's voice instructions are transmitted to the server in real time, allowing for immediate schedule adjustments.

[0444] As a concrete example, consider a scenario where user A works Monday through Friday, and user B has Wednesdays off. Taking these conditions into account, the server will suggest the most rational arrangement for each user to handle which household chore and at what time.

[0445] Example of a prompt:

[0446] "Enter the schedules of each member of a dual-income household and generate an optimal household chore schedule for each person."

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The server first collects data such as schedules, preferences, and current task status from each member of the household through information aggregation means. This collected data is entered via a smartphone app or web portal. The entered data is stored in the server's database and prepared for subsequent analysis.

[0450] Step 2:

[0451] The server analyzes the collected data using data analysis tools to execute optimization algorithms. This analysis combines each member's schedule with the overall household tasks to generate an optimal household schedule. Specifically, it uses a generative AI model to calculate efficient task assignments.

[0452] Step 3:

[0453] The server sends the optimal household chore schedule, derived from the analysis results, to each member's terminal using an information distribution method. The input here is the generated schedule information, and the output is email notification, smartphone app push notification, or voice notification. The terminal notifies the user of the schedule, and the user understands the tasks.

[0454] Step 4:

[0455] Users enter feedback into their terminal upon completing daily tasks. This feedback is sent to the server via a data update mechanism. The server uses this feedback to adjust and update user profiles and scheduling parameters in the database, reflecting these changes in future schedule generation.

[0456] Step 5:

[0457] Users transmit voice commands to the server using home smart speakers or similar devices. These voice commands are analyzed through a voice input processing system, and the home work schedule is dynamically modified. The schedule is immediately recalculated based on the input voice commands, and the updated schedule is again notified to each device.

[0458] (Application Example 1)

[0459] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0460] In dual-income households, there is a need to efficiently distribute household chores and manage them flexibly according to the lifestyles and schedules of each family member. Furthermore, it is necessary to improve the efficiency of household chores and the quality of life by utilizing in-home support devices. Conventional systems have challenges in achieving optimal chore distribution and real-time task management.

[0461] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0462] In this invention, the server includes information processing means for recording the schedule information of each member of the household, task management means for assigning tasks to a home support device, and progress confirmation means for monitoring the work progress of the home support device. This enables efficient division of household chores adapted to the schedules of family members and task management utilizing a home support device.

[0463] "Information processing means" refers to devices or systems that have the function of recording and centrally managing the schedule information of each member of a household.

[0464] An "optimization processing means" is a device or system that has the function of performing calculations and algorithms to efficiently distribute household tasks based on data obtained by an information processing means.

[0465] "Notification means" refers to a device or system for communicating the homework schedule generated by the optimization processing means to each member, and the notification is made via an electronic device or an audio output device.

[0466] An "update means" is a device or system that has the function of receiving feedback from its members and updating and adjusting the information processing means and optimization processing means based on that feedback.

[0467] A "voice command receiving means" is a device or system that has the function of receiving voice commands and adjusting the schedule of household tasks.

[0468] A "task management device" is a device or system that has the function of assigning household tasks to be performed to a home support device.

[0469] A "progress monitoring device" is a device or system that has the function of monitoring the progress of work performed by a home support device and obtaining timely feedback.

[0470] The system for implementing this invention consists of software and hardware for efficiently dividing and managing household chores. The server has a database that manages the schedules of each member of the household and performs information processing. Specifically, the server stores the schedule data of family members on a cloud server and retrieves schedule information using Google Calendar or Apple Calendar APIs.

[0471] On the server, optimization algorithms implemented in Python or other programming languages ​​calculate the optimal distribution of household tasks based on each member's schedule. Based on these results, tasks are assigned to home assistance devices, such as robotic vacuum cleaners and smart home appliances.

[0472] The devices are smartphones and tablets used within the home, and they provide a dashboard that allows users to visually check the progress of tasks. The devices receive updates and notifications from the server through backend services such as Firebase, and accept voice commands through voice recognition devices such as Alexa and Google Assistant.

[0473] Users can send feedback using a smartphone app, and the system can automatically reduce their workload for the next day if they complete a large number of tasks. Additionally, they can add new tasks to the home assistance device simply by saying a voice command such as, "Help me cook tonight."

[0474] A concrete example of a prompt would be, "Please tell me how to optimize and manage the tasks of a household assistance robot based on my household schedule." By inputting such a prompt into the generating AI model, the system's response can be obtained.

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The server retrieves schedule data for each household member from a cloud database. Input includes schedule information via Google Calendar and Apple Calendar APIs, and output is aggregated schedule data. This schedule data is used to prepare for the efficient distribution of household tasks.

[0478] Step 2:

[0479] The server calculates the optimal task allocation for household chores using an optimization algorithm implemented in Python, based on the acquired schedule data. Inputs include each member's schedule and performance information for household support devices, and output is an optimized task schedule. The server then prepares this result for transmission to the household support devices.

[0480] Step 3:

[0481] The server notifies each household member's device of the calculated task schedule. The input is the optimized task schedule, and the output is notification information displayed via an app on the user's smartphone or tablet. The user can then proceed with household tasks based on this information.

[0482] Step 4:

[0483] Users submit feedback on completed tasks using a smartphone app. Inputs include task completion status and desired changes, while output is feedback data sent to the server. This data is stored in a database to help optimize future task schedules.

[0484] Step 5:

[0485] The terminal receives voice commands through a voice recognition device and adjusts the schedule in real time. Inputs include voice commands such as "Help me cook tonight," and output is updated schedule information reflected on the server. This information is immediately applied to the operation of the home assistance device.

[0486] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0487] This invention combines a system that enables efficient division and management of household chores with an emotion engine that recognizes the user's emotions. This system uses emotion data to adjust the assignment and scheduling of household tasks, and by taking into account the atmosphere in the home and the user's mental state, it enables more personalized household management.

[0488] The server manages profiles of each member, including their schedule information and emotional data. These profiles include emotional states obtained from voice input and sensor data, in addition to regular schedule information. The server uses this data to refine its optimization algorithm, distributing household tasks efficiently and with emotional considerations in mind.

[0489] The emotion engine analyzes the emotions of household members from voice data and device inputs within the home and sends the results to the server. For example, if it detects that someone is "feeling stressed," the server can use the emotion engine's information to temporarily reschedule the household chores of that member.

[0490] Once a schedule is generated, household chore schedules are sent to each member via notification. When the device delivers a notification to the user, the content and timing of the notification are flexibly adjusted based on the results of the emotion engine's analysis. For example, notifications may be sent in a calm tone or at times when the user has more free time.

[0491] Users can input their emotions and feedback into their device, which is then sent to the server. The server analyzes the received feedback along with the emotional data and updates the profile and schedule. This results in a more personalized schedule, optimized for each member's life and emotions.

[0492] In this way, the system of the present invention not only efficiently manages the division of household chores, but also improves the quality of life by taking into account the emotional state of the members, thereby facilitating interpersonal relationships within the family.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] Users input information into the device that reflects each member of the household's schedule, preferences for household chores, and even their emotions. For example, a user might input a request such as, "I'm busy with work on Mondays, so I'd like to reduce the time I spend on housework."

[0496] Step 2:

[0497] The terminal organizes the entered schedule information and sentiment data and sends it to the server. This data is recorded as a profile for each member.

[0498] Step 3:

[0499] The server activates the emotion engine and analyzes the emotional state of each member in real time. It extracts emotions from voice data and sensor data and reflects the results in a profile.

[0500] Step 4:

[0501] The server uses an optimization algorithm to schedule household tasks, taking emotional data into account. It adjusts task priorities and workloads according to emotional states, generating a schedule that does not burden members.

[0502] Step 5:

[0503] The server generates a schedule and sends it to each member's terminal via a notification system. The notification includes recommended tasks and reminders based on sentiment analysis results.

[0504] Step 6:

[0505] Users input feedback on completed tasks and their current emotional state via their devices. This feedback is used as foundational data for future schedule optimization.

[0506] Step 7:

[0507] The device collects user feedback and sends it to the server along with sentiment data. The server uses this information to continuously update profiles and schedules.

[0508] Step 8:

[0509] The user gives a voice command to the smart speaker. For example, the user might say, "I want to switch laundry duties with someone this week," and the voice command receiving device recognizes this and sends it to the server.

[0510] Step 9:

[0511] The server readjusts the schedule based on voice commands and emotion data. The adjusted schedule is then communicated to the members again through notification channels.

[0512] Through the above process, the distribution of household chores that best suits the feelings and circumstances of each member will be achieved.

[0513] (Example 2)

[0514] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] In household chore division and scheduling, tasks are often assigned without considering the emotional state of each family member, which can lead to stress, dissatisfaction, and a deterioration of the family atmosphere. This problem needs to be addressed. Furthermore, there is a need to achieve flexible task redistribution and system adaptation that takes individual emotions into account.

[0516] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0517] In this invention, the server includes information processing means for recording the schedule information of each member of the household; optimization processing means for optimally distributing household tasks based on the information processing means and analyzing emotional data using a generative artificial intelligence model; and emotion analysis means for adjusting the assignment of household tasks based on the emotional data analyzed by the emotion engine. This makes it possible to schedule household chores while taking into account the emotional state of each member, thereby promoting harmony within the household.

[0518] "Information processing means" refers to a device or system that has the function of recording and managing the schedule information of each member of a household.

[0519] An "optimization processing means" is a device or system that uses a generative artificial intelligence model based on collected information to analyze emotional data and has the function of efficiently distributing household tasks.

[0520] "Emotional analysis means" refers to a device or system that has the function of adjusting household chore assignments based on emotional data analyzed by an emotional engine.

[0521] "Notification means" refers to a device or system for communicating an optimized household work schedule to each member, which is done via an electronic device or an audio output device.

[0522] "Update means" refers to a device or system that has the function of receiving feedback from members, updating information processing means and optimization processing means, and generating prompt statements.

[0523] A "voice instruction receiving means" is a device or system that has the function of receiving voice instructions and adjusting the schedule of household tasks based on those instructions.

[0524] This invention provides a system for efficiently distributing household chores within a family while taking into account the emotional state of each family member. This system involves multiple hardware and software components that acquire and analyze household data and generate an optimized schedule.

[0525] A server acts as the central point, using information processing tools to record the schedule information of each member. This information is collected using data from calendar applications and sensors. Based on this information, a generative AI model operates as an optimization processing tool and performs data analysis. Specifically, it analyzes voice data acquired by a voice instruction receiving tool using an emotion engine, and uses the results to optimally assign household tasks.

[0526] The device functions as a notification system, informing the user of household chore schedules. Based on data obtained from emotion analysis, it is possible to adjust the content and timing of notifications to take the user's emotions into consideration. For example, if the user is feeling stressed, notifications will be delivered in a softer voice tone.

[0527] Users can utilize update mechanisms to input their emotions and feedback into the device. The server then re-analyzes the feedback and updates the system's schedule and prompts. This enables more personalized household management, improving harmony and efficiency within the home.

[0528] For example, when the server receives a sentiment analysis result indicating that "Person A is tired," it could dynamically change the household chore schedule to reduce Person A's burden. As a result, the user would receive a schedule notification for the day with a prompt message such as, "Today, focus on activities that allow you to relax."

[0529] An example of a prompt message would be, "Use sentiment analysis data regarding household schedule optimization to generate a notification message that takes emotions into consideration when reallocating tasks."

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The server collects schedule information and emotional data from each member of the household and uses information processing tools to create profiles. Inputs include schedule data from calendar applications and emotional data acquired from voice input devices and sensors. These data are combined to produce profiles for each member. These profiles include not only regular schedule information but also a record of their emotional state.

[0533] Step 2:

[0534] The emotion engine analyzes the audio data received from the device to identify the emotional state of each member. The input is raw audio data, which is then analyzed by a generating AI model to output emotional state labels. For example, specific emotional states such as "high stress" or "relaxed" may be output.

[0535] Step 3:

[0536] The server uses the emotion analysis results and the members' schedule information to distribute household tasks as an optimization process. The inputs are the emotion analysis results and existing schedule information. Using a generative AI model, the task distribution is appropriately recalculated and the adjusted schedule is output for each member. For example, a member experiencing stress will be given a schedule that reduces their burden.

[0537] Step 4:

[0538] The terminal notifies each member based on the new schedule sent from the server. The input is the newly optimized schedule data. The notification method generates a prompt message and outputs a notification in a soft voice tone and at an appropriate time. Specifically, the notification will say something like, "We have made adjustments to reduce your workload."

[0539] Step 5:

[0540] Users input their thoughts and opinions on received notifications and schedules into their terminals, and the server receives this as an update mechanism to adjust the entire system. The input is user feedback, and based on this, new prompt messages are generated or schedules are modified. For example, if a user inputs "The notification was late," the system will be improved to send notifications earlier next time.

[0541] (Application Example 2)

[0542] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] The division and management of household chores within a family are often carried out mechanically without sufficient consideration of the schedules and feelings of each member, which can lead to mental burden and dissatisfaction. Furthermore, there is the challenge of dynamically optimizing schedules using feedback. This invention aims to solve these problems and promote smooth task division within the family and the maintenance of the mental health of each member.

[0544] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0545] In this invention, the server includes data processing means for recording the schedule information of each member of the household, optimization processing means for optimally distributing household tasks based on the data processing means, and emotion analysis means for analyzing the emotional state of the members. This enables flexible scheduling of household chores that takes into account the schedules and emotions of the members.

[0546] "Data processing means" refers to a device or function for recording and managing the schedule information of each member of a household.

[0547] "Optimization processing means" refers to a device or function for efficiently allocating household tasks based on information obtained by data processing means.

[0548] "Communication means" refers to a device or function for notifying each member of the household work plan generated by the optimization processing means.

[0549] "Correction means" refers to a device or function for receiving feedback from members and updating data processing means and optimization processing means.

[0550] "Voice instruction receiving means" refers to a device or function for receiving voice instructions and adjusting household chore plans.

[0551] "Emotional analysis means" refers to a device or function that analyzes the voice and sensor information of a member to recognize their emotional state.

[0552] "Adjustment means" refers to a device or function for dynamically adjusting the household work plan based on the results of the emotion analysis means.

[0553] This system is designed to efficiently and individually manage household chores based on the schedules and emotions of each family member. The server uses the Google Cloud Natural Language API to perform emotion analysis. It acquires the emotional state of each family member in real time based on voice input and sensor information, and generates an optimal chore schedule based on the information acquired through data processing.

[0554] The emotional data of each member, obtained through emotion analysis, is analyzed on the server using an optimization algorithm based on Java or Python, and the schedule for household tasks is readjusted. The schedule is flexibly notified to each member via a computing device and an audio output device. This notification is given at the optimal timing and tone, taking into account the emotional state.

[0555] The terminal receives feedback from the user and sends this data back to the server to update the schedule. This process allows the system to sequentially provide household chore assignments optimized for the lifestyles and mental health of each member.

[0556] For example, when a family is relaxing on a holiday, a housekeeping robot might suggest a schedule, saying, "Please relax and enjoy your time. Leave today's cleaning to me." This kind of intervention can reduce the mental burden on family members and improve harmony within the household.

[0557] An example of a prompt for a generative AI model is: "Describe the actions of a robot assistant that recognizes your emotional state when you are relaxed at home and adjusts your household chore schedule accordingly."

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The server records the schedule information of each member. Daily schedule information is provided as input from each member. Based on this data, the server updates each member's profile and saves it in the database. This profile forms the basis for schedule generation.

[0561] Step 2:

[0562] The server collects emotional data from each member through voice input and sensor information. It receives data from smart microphones and other environmental sensors as input and analyzes each member's emotional state using emotion analysis tools. This data is converted into emotional states such as "stress" or "relaxed" by emotion analysis software.

[0563] Step 3:

[0564] The server combines emotional state data and scheduled information, and runs an optimization algorithm to generate a highly efficient household chore schedule. The inputs used are the results of emotional analysis and scheduled information, and the algorithm calculates the ideal distribution of household tasks. The calculation results are output as a new household chore schedule.

[0565] Step 4:

[0566] The server notifies each member of the generated schedule via a computing device and an audio output device. During schedule notification, the timing and tone of the notification are optimized based on sentiment analysis results. The content received by the user includes the specific tasks scheduled for the day.

[0567] Step 5:

[0568] Users enter feedback about the schedule they receive into their terminal. This feedback is sent to the server as material for improving the system. The feedback is added to the database as new data points and used in the optimization algorithm for future updates.

[0569] Step 6:

[0570] The server reanalyzes the feedback and emotional state received from the user and updates the homework schedule. Based on the feedback, data processing and optimization processing means are dynamically adjusted to generate a more personalized schedule.

[0571] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0572] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0573] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0577] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0578] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0579] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0580] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0581] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0582] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0583] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0584] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0585] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0586] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0587] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0588] This invention is a system that enables the efficient division and management of household chores in dual-income households. The system aims to centrally manage the schedule information of each household member and optimally schedule household tasks based on this information. The main processes constituting the system's program and specific examples are shown below.

[0589] The server stores data such as the daily schedules, preferences, and task progress of all members in a database, centralizing the information. The server also has an optimization algorithm implemented that uses this data to optimally distribute household chores, calculating task allocation according to individual circumstances. For example, it takes into account the work and school schedules of all family members to calculate who should perform household chores at what time.

[0590] Once a schedule is generated, the server sends the household chore schedule to each member's device via notification methods. This notification can be sent via email, smartphone app notifications, or voice through a smart speaker. The schedule received by the device is visualized for the user, allowing them to view their weekly chore plan, for example, in a dashboard format.

[0591] Users can provide feedback when they complete their daily tasks. For example, if they complete a large amount of household chores on a given day, they can input feedback into their device indicating that they would like a lighter schedule the following day. The device sends this feedback to the server, which then updates the user's profile and scheduling parameters based on the information.

[0592] Furthermore, the voice command receiving mechanism helps users adjust their schedules using voice commands. For example, a user can give a voice command to a smart speaker saying, "I want to cancel Sunday afternoon cleaning," and that information is immediately sent to the server and reflected in the schedule.

[0593] In this way, the system of the present invention efficiently distributes household chores and flexibly adapts to the lifestyle of each member, thereby reducing the burden of housework in dual-income households and improving their quality of life.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] Users input their own and their family's schedules and preferences for household chores using the application. For example, they can input things like their strengths and weaknesses in household chores and their preferred time slots.

[0597] Step 2:

[0598] The terminal receives information entered by the user and sends it to the server as organized data. This allows the server to integrate the necessary information.

[0599] Step 3:

[0600] The server uses the information received from the terminal to create a profile of each member of the household and saves it in a database. This profile details various attributes of the user.

[0601] Step 4:

[0602] The server uses an optimization algorithm to efficiently distribute household tasks based on profiles. The algorithm considers each member's schedule and preferences to calculate the optimal task assignment.

[0603] Step 5:

[0604] The server generates an optimized schedule and sends reminders to each member's device via a notification system. For example, it might notify them of specific tasks such as "Wash the dishes at 9am on Saturday."

[0605] Step 6:

[0606] Users input their daily task completion status and feedback into their devices. This includes reporting task completion and suggesting improvements for future tasks.

[0607] Step 7:

[0608] The device collects user feedback and sends it to the server. The server updates the profile and schedule based on the received feedback, which is then reflected in the next scheduling.

[0609] Step 8:

[0610] The user gives voice commands to the smart speaker as needed. For example, they might say, "Change tomorrow's cleaning to the weekend." The smart speaker recognizes this and sends the information to the server.

[0611] Step 9:

[0612] The server analyzes the voice instructions and adjusts the schedule for the corresponding household tasks. A new schedule is generated and communicated to each member again via the notification system.

[0613] (Example 1)

[0614] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0615] In modern dual-income households, the burden of housework is a major challenge. Each member has a different schedule, making it extremely difficult to efficiently divide household chores amidst busy lives. As a result, coordination within the household becomes complicated, stress increases, and the quality of life can decline. It is necessary to solve these problems and improve the efficiency of household management and the satisfaction of its inhabitants.

[0616] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0617] In this invention, the server includes: information aggregation means for recording the schedule information of each member of the household; data analysis means for executing an optimization algorithm that efficiently allocates household tasks based on the information aggregation means; information distribution means for transmitting the household schedule generated by the data analysis means to each member using various communication methods; data update means for collecting feedback from members and updating the information aggregation means and the optimization algorithm; and voice input processing means for receiving voice instructions and changing the household schedule. This makes it possible to generate and adjust household schedules that flexibly adapt to the lifestyle of each member.

[0618] "Information aggregation means" refers to a method of comprehensively collecting and organizing planned information provided by each member of the household.

[0619] An "optimization algorithm" is an algorithm that includes equations and formulas for efficiently distributing household chores based on collected information.

[0620] "Data analysis means" refers to methods for performing the necessary analysis to execute optimization algorithms and effectively allocate tasks within the household.

[0621] "Information distribution means" refers to the means of communicating the generated household schedule to each member using various communication methods.

[0622] A "data update method" is a means of receiving feedback from members and updating the information aggregation and optimization algorithms based on that information.

[0623] The "voice input processing means" is a means for receiving voice instructions from members and appropriately adjusting the schedule of household tasks based on that information.

[0624] This invention is a system for streamlining the division of household chores in dual-income households. This system comprehensively utilizes information aggregation means, data analysis means, information distribution means, data update means, and voice input processing means.

[0625] The server uses information aggregation tools to collect data such as the schedules of each household member, their preferences regarding individual household tasks, and their progress, and manages this data centrally in a database. At this stage, electronic devices such as smartphones and tablets, mainly used within the home, are employed, and information is usually entered through dedicated application software.

[0626] Next, the server executes an optimization algorithm based on the collected data through data analysis tools. This algorithm analyzes the entire household schedule and calculates the time each member can spend most efficiently completing household tasks. This utilizes optimization techniques and machine learning models, with generative AI models being a concrete example.

[0627] Subsequently, the server transmits the calculated household chore schedule to each member's terminal using an information distribution method. This process utilizes a variety of communication methods, including email notifications, smartphone app push notifications, and even voice notifications using home audio output devices.

[0628] In the feedback process, users input feedback from their devices upon completing daily tasks and send it to the server via a data update mechanism. This feedback allows the server to update individual profiles and scheduling parameters, which are then reflected in future schedules.

[0629] Furthermore, using voice input processing, users can easily change their schedules via voice commands. Home smart speakers equipped with voice recognition technology are used, and the user's voice instructions are transmitted to the server in real time, allowing for immediate schedule adjustments.

[0630] As a concrete example, consider a scenario where user A works Monday through Friday, and user B has Wednesdays off. Taking these conditions into account, the server will suggest the most rational arrangement for each user to handle which household chore and at what time.

[0631] Example of a prompt:

[0632] "Enter the schedules of each member of a dual-income household and generate an optimal household chore schedule for each person."

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] The server first collects data such as schedules, preferences, and current task status from each member of the household through information aggregation means. This collected data is entered via a smartphone app or web portal. The entered data is stored in the server's database and prepared for subsequent analysis.

[0636] Step 2:

[0637] The server analyzes the collected data using data analysis tools to execute optimization algorithms. This analysis combines each member's schedule with the overall household tasks to generate an optimal household schedule. Specifically, it uses a generative AI model to calculate efficient task assignments.

[0638] Step 3:

[0639] The server sends the optimal household chore schedule, derived from the analysis results, to each member's terminal using an information distribution method. The input here is the generated schedule information, and the output is email notification, smartphone app push notification, or voice notification. The terminal notifies the user of the schedule, and the user understands the tasks.

[0640] Step 4:

[0641] Users enter feedback into their terminal upon completing daily tasks. This feedback is sent to the server via a data update mechanism. The server uses this feedback to adjust and update user profiles and scheduling parameters in the database, reflecting these changes in future schedule generation.

[0642] Step 5:

[0643] Users transmit voice commands to the server using home smart speakers or similar devices. These voice commands are analyzed through a voice input processing system, and the home work schedule is dynamically modified. The schedule is immediately recalculated based on the input voice commands, and the updated schedule is again notified to each device.

[0644] (Application Example 1)

[0645] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] In dual-income households, there is a need to efficiently distribute household chores and manage them flexibly according to the lifestyles and schedules of each family member. Furthermore, it is necessary to improve the efficiency of household chores and the quality of life by utilizing in-home support devices. Conventional systems have challenges in achieving optimal chore distribution and real-time task management.

[0647] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0648] In this invention, the server includes information processing means for recording the schedule information of each member of the household, task management means for assigning tasks to a home support device, and progress confirmation means for monitoring the work progress of the home support device. This enables efficient division of household chores adapted to the schedules of family members and task management utilizing a home support device.

[0649] "Information processing means" refers to devices or systems that have the function of recording and centrally managing the schedule information of each member of a household.

[0650] An "optimization processing means" is a device or system that has the function of performing calculations and algorithms to efficiently distribute household tasks based on data obtained by an information processing means.

[0651] "Notification means" refers to a device or system for communicating the homework schedule generated by the optimization processing means to each member, and the notification is made via an electronic device or an audio output device.

[0652] An "update means" is a device or system that has the function of receiving feedback from its members and updating and adjusting the information processing means and optimization processing means based on that feedback.

[0653] A "voice command receiving means" is a device or system that has the function of receiving voice commands and adjusting the schedule of household tasks.

[0654] A "task management device" is a device or system that has the function of assigning household tasks to be performed to a home support device.

[0655] A "progress monitoring device" is a device or system that has the function of monitoring the progress of work performed by a home support device and obtaining timely feedback.

[0656] The system for implementing this invention consists of software and hardware for efficiently dividing and managing household chores. The server has a database that manages the schedules of each member of the household and performs information processing. Specifically, the server stores the schedule data of family members on a cloud server and retrieves schedule information using Google Calendar or Apple Calendar APIs.

[0657] On the server, optimization algorithms implemented in Python or other programming languages ​​calculate the optimal distribution of household tasks based on each member's schedule. Based on these results, tasks are assigned to home assistance devices, such as robotic vacuum cleaners and smart home appliances.

[0658] The devices are smartphones and tablets used within the home, and they provide a dashboard that allows users to visually check the progress of tasks. The devices receive updates and notifications from the server through backend services such as Firebase, and accept voice commands through voice recognition devices such as Alexa and Google Assistant.

[0659] Users can send feedback using a smartphone app, and the system can automatically reduce their workload for the next day if they complete a large number of tasks. Additionally, they can add new tasks to the home assistance device simply by saying a voice command such as, "Help me cook tonight."

[0660] A concrete example of a prompt would be, "Please tell me how to optimize and manage the tasks of a household assistance robot based on my household schedule." By inputting such a prompt into the generating AI model, the system's response can be obtained.

[0661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0662] Step 1:

[0663] The server retrieves schedule data for each household member from a cloud database. Input includes schedule information via Google Calendar and Apple Calendar APIs, and output is aggregated schedule data. This schedule data is used to prepare for the efficient distribution of household tasks.

[0664] Step 2:

[0665] The server calculates the optimal task allocation for household chores using an optimization algorithm implemented in Python, based on the acquired schedule data. Inputs include each member's schedule and performance information for household support devices, and output is an optimized task schedule. The server then prepares this result for transmission to the household support devices.

[0666] Step 3:

[0667] The server notifies each household member's device of the calculated task schedule. The input is the optimized task schedule, and the output is notification information displayed via an app on the user's smartphone or tablet. The user can then proceed with household tasks based on this information.

[0668] Step 4:

[0669] Users submit feedback on completed tasks using a smartphone app. Inputs include task completion status and desired changes, while output is feedback data sent to the server. This data is stored in a database to help optimize future task schedules.

[0670] Step 5:

[0671] The terminal receives voice commands through a voice recognition device and adjusts the schedule in real time. Inputs include voice commands such as "Help me cook tonight," and output is updated schedule information reflected on the server. This information is immediately applied to the operation of the home assistance device.

[0672] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0673] This invention combines a system that enables efficient division and management of household chores with an emotion engine that recognizes the user's emotions. This system uses emotion data to adjust the assignment and scheduling of household tasks, and by taking into account the atmosphere in the home and the user's mental state, it enables more personalized household management.

[0674] The server manages profiles of each member, including their schedule information and emotional data. These profiles include emotional states obtained from voice input and sensor data, in addition to regular schedule information. The server uses this data to refine its optimization algorithm, distributing household tasks efficiently and with emotional considerations in mind.

[0675] The emotion engine analyzes the emotions of household members from voice data and device inputs within the home and sends the results to the server. For example, if it detects that someone is "feeling stressed," the server can use the emotion engine's information to temporarily reschedule the household chores of that member.

[0676] Once a schedule is generated, household chore schedules are sent to each member via notification. When the device delivers a notification to the user, the content and timing of the notification are flexibly adjusted based on the results of the emotion engine's analysis. For example, notifications may be sent in a calm tone or at times when the user has more free time.

[0677] Users can input their emotions and feedback into their device, which is then sent to the server. The server analyzes the received feedback along with the emotional data and updates the profile and schedule. This results in a more personalized schedule, optimized for each member's life and emotions.

[0678] In this way, the system of the present invention not only efficiently manages the division of household chores, but also improves the quality of life by taking into account the emotional state of the members, thereby facilitating interpersonal relationships within the family.

[0679] The following describes the processing flow.

[0680] Step 1:

[0681] Users input information into the device that reflects each member of the household's schedule, preferences for household chores, and even their emotions. For example, a user might input a request such as, "I'm busy with work on Mondays, so I'd like to reduce the time I spend on housework."

[0682] Step 2:

[0683] The terminal organizes the entered schedule information and sentiment data and sends it to the server. This data is recorded as a profile for each member.

[0684] Step 3:

[0685] The server activates the emotion engine and analyzes the emotional state of each member in real time. It extracts emotions from voice data and sensor data and reflects the results in a profile.

[0686] Step 4:

[0687] The server uses an optimization algorithm to schedule household tasks, taking emotional data into account. It adjusts task priorities and workloads according to emotional states, generating a schedule that does not burden members.

[0688] Step 5:

[0689] The server generates a schedule and sends it to each member's terminal via a notification system. The notification includes recommended tasks and reminders based on sentiment analysis results.

[0690] Step 6:

[0691] Users input feedback on completed tasks and their current emotional state via their devices. This feedback is used as foundational data for future schedule optimization.

[0692] Step 7:

[0693] The device collects user feedback and sends it to the server along with sentiment data. The server uses this information to continuously update profiles and schedules.

[0694] Step 8:

[0695] The user gives a voice command to the smart speaker. For example, the user might say, "I want to switch laundry duties with someone this week," and the voice command receiving device recognizes this and sends it to the server.

[0696] Step 9:

[0697] The server readjusts the schedule based on voice commands and emotion data. The adjusted schedule is then communicated to the members again through notification channels.

[0698] Through the above process, the distribution of household chores that best suits the feelings and circumstances of each member will be achieved.

[0699] (Example 2)

[0700] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] In household chore division and scheduling, tasks are often assigned without considering the emotional state of each family member, which can lead to stress, dissatisfaction, and a deterioration of the family atmosphere. This problem needs to be addressed. Furthermore, there is a need to achieve flexible task redistribution and system adaptation that takes individual emotions into account.

[0702] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0703] In this invention, the server includes information processing means for recording the schedule information of each member of the household; optimization processing means for optimally distributing household tasks based on the information processing means and analyzing emotional data using a generative artificial intelligence model; and emotion analysis means for adjusting the assignment of household tasks based on the emotional data analyzed by the emotion engine. This makes it possible to schedule household chores while taking into account the emotional state of each member, thereby promoting harmony within the household.

[0704] "Information processing means" refers to a device or system that has the function of recording and managing the schedule information of each member of a household.

[0705] An "optimization processing means" is a device or system that uses a generative artificial intelligence model based on collected information to analyze emotional data and has the function of efficiently distributing household tasks.

[0706] "Emotional analysis means" refers to a device or system that has the function of adjusting household chore assignments based on emotional data analyzed by an emotional engine.

[0707] "Notification means" refers to a device or system for communicating an optimized household work schedule to each member, which is done via an electronic device or an audio output device.

[0708] "Update means" refers to a device or system that has the function of receiving feedback from members, updating information processing means and optimization processing means, and generating prompt statements.

[0709] A "voice instruction receiving means" is a device or system that has the function of receiving voice instructions and adjusting the schedule of household tasks based on those instructions.

[0710] This invention provides a system for efficiently distributing household chores within a family while taking into account the emotional state of each family member. This system involves multiple hardware and software components that acquire and analyze household data and generate an optimized schedule.

[0711] A server acts as the central point, using information processing tools to record the schedule information of each member. This information is collected using data from calendar applications and sensors. Based on this information, a generative AI model operates as an optimization processing tool and performs data analysis. Specifically, it analyzes voice data acquired by a voice instruction receiving tool using an emotion engine, and uses the results to optimally assign household tasks.

[0712] The device functions as a notification system, informing the user of household chore schedules. Based on data obtained from emotion analysis, it is possible to adjust the content and timing of notifications to take the user's emotions into consideration. For example, if the user is feeling stressed, notifications will be delivered in a softer voice tone.

[0713] Users can utilize update mechanisms to input their emotions and feedback into the device. The server then re-analyzes the feedback and updates the system's schedule and prompts. This enables more personalized household management, improving harmony and efficiency within the home.

[0714] For example, when the server receives a sentiment analysis result indicating that "Person A is tired," it could dynamically change the household chore schedule to reduce Person A's burden. As a result, the user would receive a schedule notification for the day with a prompt message such as, "Today, focus on activities that allow you to relax."

[0715] An example of a prompt message would be, "Use sentiment analysis data regarding household schedule optimization to generate a notification message that takes emotions into consideration when reallocating tasks."

[0716] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0717] Step 1:

[0718] The server collects schedule information and emotional data from each member of the household and uses information processing tools to create profiles. Inputs include schedule data from calendar applications and emotional data acquired from voice input devices and sensors. These data are combined to produce profiles for each member. These profiles include not only regular schedule information but also a record of their emotional state.

[0719] Step 2:

[0720] The emotion engine analyzes the audio data received from the device to identify the emotional state of each member. The input is raw audio data, which is then analyzed by a generating AI model to output emotional state labels. For example, specific emotional states such as "high stress" or "relaxed" may be output.

[0721] Step 3:

[0722] The server uses the emotion analysis results and the members' schedule information to distribute household tasks as an optimization process. The inputs are the emotion analysis results and existing schedule information. Using a generative AI model, the task distribution is appropriately recalculated and the adjusted schedule is output for each member. For example, a member experiencing stress will be given a schedule that reduces their burden.

[0723] Step 4:

[0724] The terminal notifies each member based on the new schedule sent from the server. The input is the newly optimized schedule data. The notification method generates a prompt message and outputs a notification in a soft voice tone and at an appropriate time. Specifically, the notification will say something like, "We have made adjustments to reduce your workload."

[0725] Step 5:

[0726] Users input their thoughts and opinions on received notifications and schedules into their terminals, and the server receives this as an update mechanism to adjust the entire system. The input is user feedback, and based on this, new prompt messages are generated or schedules are modified. For example, if a user inputs "The notification was late," the system will be improved to send notifications earlier next time.

[0727] (Application Example 2)

[0728] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0729] The division and management of household chores within a family are often carried out mechanically without sufficient consideration of the schedules and feelings of each member, which can lead to mental burden and dissatisfaction. Furthermore, there is the challenge of dynamically optimizing schedules using feedback. This invention aims to solve these problems and promote smooth task division within the family and the maintenance of the mental health of each member.

[0730] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0731] In this invention, the server includes data processing means for recording the schedule information of each member of the household, optimization processing means for optimally distributing household tasks based on the data processing means, and emotion analysis means for analyzing the emotional state of the members. This enables flexible scheduling of household chores that takes into account the schedules and emotions of the members.

[0732] "Data processing means" refers to a device or function for recording and managing the schedule information of each member of a household.

[0733] "Optimization processing means" refers to a device or function for efficiently allocating household tasks based on information obtained by data processing means.

[0734] "Communication means" refers to a device or function for notifying each member of the household work plan generated by the optimization processing means.

[0735] "Correction means" refers to a device or function for receiving feedback from members and updating data processing means and optimization processing means.

[0736] "Voice instruction receiving means" refers to a device or function for receiving voice instructions and adjusting household chore plans.

[0737] "Emotional analysis means" refers to a device or function that analyzes the voice and sensor information of a member to recognize their emotional state.

[0738] "Adjustment means" refers to a device or function for dynamically adjusting the household work plan based on the results of the emotion analysis means.

[0739] This system is designed to efficiently and individually manage household chores based on the schedules and emotions of each family member. The server uses the Google Cloud Natural Language API to perform emotion analysis. It acquires the emotional state of each family member in real time based on voice input and sensor information, and generates an optimal chore schedule based on the information acquired through data processing.

[0740] The emotional data of each member, obtained through emotion analysis, is analyzed on the server using an optimization algorithm based on Java or Python, and the schedule for household tasks is readjusted. The schedule is flexibly notified to each member via a computing device and an audio output device. This notification is given at the optimal timing and tone, taking into account the emotional state.

[0741] The terminal receives feedback from the user and sends this data back to the server to update the schedule. This process allows the system to sequentially provide household chore assignments optimized for the lifestyles and mental health of each member.

[0742] For example, when a family is relaxing on a holiday, a housekeeping robot might suggest a schedule, saying, "Please relax and enjoy your time. Leave today's cleaning to me." This kind of intervention can reduce the mental burden on family members and improve harmony within the household.

[0743] An example of a prompt for a generative AI model is: "Describe the actions of a robot assistant that recognizes your emotional state when you are relaxed at home and adjusts your household chore schedule accordingly."

[0744] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0745] Step 1:

[0746] The server records the schedule information of each member. Daily schedule information is provided as input from each member. Based on this data, the server updates each member's profile and saves it in the database. This profile forms the basis for schedule generation.

[0747] Step 2:

[0748] The server collects emotional data from each member through voice input and sensor information. It receives data from smart microphones and other environmental sensors as input and analyzes each member's emotional state using emotion analysis tools. This data is converted into emotional states such as "stress" or "relaxed" by emotion analysis software.

[0749] Step 3:

[0750] The server combines emotional state data and scheduled information, and runs an optimization algorithm to generate a highly efficient household chore schedule. The inputs used are the results of emotional analysis and scheduled information, and the algorithm calculates the ideal distribution of household tasks. The calculation results are output as a new household chore schedule.

[0751] Step 4:

[0752] The server notifies each member of the generated schedule via a computing device and an audio output device. During schedule notification, the timing and tone of the notification are optimized based on sentiment analysis results. The content received by the user includes the specific tasks scheduled for the day.

[0753] Step 5:

[0754] Users enter feedback about the schedule they receive into their terminal. This feedback is sent to the server as material for improving the system. The feedback is added to the database as new data points and used in the optimization algorithm for future updates.

[0755] Step 6:

[0756] The server reanalyzes the feedback and emotional state received from the user and updates the homework schedule. Based on the feedback, data processing and optimization processing means are dynamically adjusted to generate a more personalized schedule.

[0757] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0758] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0759] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0760] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0761] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0762] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0763] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0764] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0765] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0766] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0767] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0768] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0769] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0770] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0771] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0772] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0773] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0774] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0775] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0776] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0777] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0778] The following is further disclosed regarding the embodiments described above.

[0779] (Claim 1)

[0780] An information processing device for recording the schedule information of each member of the household,

[0781] An optimization processing means for optimally distributing household tasks based on the aforementioned information processing means,

[0782] A notification means for notifying each member of the household work schedule generated by the optimization processing means,

[0783] An update means that receives feedback from members and updates the information processing means and the optimization processing means,

[0784] A voice command receiving device that receives voice commands and adjusts the schedule for household tasks,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, further comprising a function for adjusting the schedule of household tasks in real time based on voice instructions received by a voice instruction receiving means.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein the notification means has a function of transmitting a notification to a member via an electronic device and an audio output device.

[0790] "Example 1"

[0791] (Claim 1)

[0792] An information aggregation means for recording the schedule information of each member of the household,

[0793] A data analysis means that executes an optimization algorithm for efficiently allocating household tasks based on the aforementioned information aggregation means,

[0794] Information distribution means that transmits the household chore schedule generated by the data analysis means to each member using various communication methods,

[0795] A data update means that collects feedback from members and updates the information aggregation means and the optimization algorithm,

[0796] A voice input processing means that receives voice instructions and changes the schedule of household tasks,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, further comprising a function to adaptively change the schedule of household tasks in real time based on voice instructions received by a voice input processing means.

[0800] (Claim 3)

[0801] The system according to claim 1, wherein the information distribution means has a function to provide notifications to members via electronic terminals and audio output devices.

[0802] "Application Example 1"

[0803] (Claim 1)

[0804] An information processing device for recording the schedule information of each member of the household,

[0805] An optimization processing means for optimally distributing household tasks based on the aforementioned information processing means,

[0806] A notification means for notifying each member of the household work schedule generated by the optimization processing means,

[0807] An update means that receives feedback from members and updates the information processing means and the optimization processing means,

[0808] A voice command receiving device that receives voice commands and adjusts the schedule for household tasks,

[0809] A task management means for assigning tasks to in-home support devices,

[0810] A progress confirmation means for monitoring the work progress of the aforementioned in-home support device,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, further comprising a function for adjusting the schedule of household tasks in real time based on voice instructions received by a voice instruction receiving means.

[0814] (Claim 3)

[0815] The system according to claim 1, wherein the notification means has the function of transmitting a notification to a member via an electronic device and an acoustic output device.

[0816] "Example 2 of combining an emotion engine"

[0817] (Claim 1)

[0818] An information processing device for recording the schedule information of each member of the household,

[0819] An optimization processing means that optimally distributes household tasks based on the aforementioned information processing means and analyzes emotional data using a generative artificial intelligence model,

[0820] An emotion analysis means that adjusts the assignment of household tasks based on emotion data analyzed by an emotion engine,

[0821] A notification means for notifying each member of the household work schedule generated by the optimization processing means,

[0822] An update means that receives feedback from members, updates the information processing means and the optimization processing means, and generates a prompt statement,

[0823] A voice command receiving device that receives voice commands and adjusts the schedule for household tasks,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, which adjusts the schedule of household tasks in real time based on voice instructions received by a voice instruction receiving means and uses data from an emotion analysis means.

[0827] (Claim 3)

[0828] The system according to claim 1, wherein the notification means transmits a notification to a member via an electronic device and an audio output device, and flexibly adjusts it based on the emotion analysis means.

[0829] "Application example 2 when combining with an emotional engine"

[0830] (Claim 1)

[0831] A data processing means for recording the schedule information of each member of the household,

[0832] An optimization processing means for optimally distributing household tasks based on the aforementioned data processing means,

[0833] A communication means for notifying each member of the household work plan generated by the optimization processing means,

[0834] A modification means that receives feedback from members and updates the data processing means and the optimization processing means,

[0835] A voice instruction receiving device that receives voice instructions and adjusts household chore plans,

[0836] An emotional analysis tool for analyzing the emotional state of the members,

[0837] An adjustment means for adjusting the household work plan based on the results of the emotion analysis means,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising a function for adjusting household chore plans in real time based on information received by a voice instruction receiving means and an emotion analysis means.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the communication means has the function of transmitting a notification to a member via a computing device and an audio output device. [Explanation of Symbols]

[0843] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An information processing device for recording the schedule information of each member of the household, An optimization processing means for optimally distributing household tasks based on the aforementioned information processing means, A notification means for notifying each member of the household work schedule generated by the optimization processing means, An update means that receives feedback from members and updates the information processing means and the optimization processing means, A voice command receiving device that receives voice commands and adjusts the schedule for household tasks, A system that includes this.

2. The system according to claim 1, further comprising a function for adjusting the schedule of household tasks in real time based on voice instructions received by a voice instruction receiving means.

3. The system according to claim 1, wherein the notification means has a function of transmitting a notification to a member via an electronic device and an audio output device.

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