system

Abstract

The system according to this embodiment aims to efficiently distribute household chores within the home and perform them at the optimal time. [Solution] The system according to the embodiment comprises a learning unit, a suggestion unit, a calculation unit, and a maintenance suggestion unit. The learning unit learns the daily schedules of all family members. The suggestion unit proposes efficient distribution and start timing of household tasks based on the schedule learned by the learning unit. The calculation unit calculates task time based on the home appliances, cooking utensils, and cleaning tools owned. The maintenance suggestion unit proposes optimal usage methods and maintenance schedules based on the task time calculated by the calculation unit.

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 the conventional technology, it is difficult to perform optimal housework sharing and scheduling based on the schedules of each individual within a household, and there is a problem of a large burden of housework.

[0005] The system according to the embodiment aims to efficiently share housework tasks within a household and perform them at an optimal timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a suggestion unit, a calculation unit, and a maintenance suggestion unit. The learning unit learns the daily schedules of all family members. The suggestion unit proposes efficient distribution and start times for household tasks based on the schedule learned by the learning unit. The calculation unit calculates task times based on the home appliances, cooking utensils, and cleaning tools owned. The maintenance suggestion unit proposes optimal usage methods and maintenance schedules based on the task times calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently distribute household chores within the home and perform them at the optimal time. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The household chore management system according to an embodiment of the present invention is a system for understanding each individual's schedule within the household and, based on that, for optimal distribution and scheduling of household chores. This household chore management system is provided through a smartphone / tablet app and a personal computer website, and a generating AI learns the daily schedules of all family members and proposes efficient distribution of household chore tasks and start times. For example, the household chore management system's generating AI understands all household chore tasks and daily schedules in a conversational format and proposes distribution and adjustments. The household chore management system also calculates the time required for cooking and cleaning tasks based on owned home appliances, cooking utensils, and cleaning tools, and proposes optimal usage methods and maintenance schedules. Furthermore, the household chore management system links with an e-commerce site to propose home appliances and tools suited to each household, aiming to generate revenue by leading to purchases. It stimulates purchasing intent by proposing efficient household chore methods using the latest home appliances. The household chore management system provides menu suggestions, checks ingredient inventory, and allows online purchase of necessary ingredients. The household chore management system also allows family members to share schedules and chore divisions through the app. As an option, the system also offers suggestions for recommended bedtimes and wake-up times, exercise time and methods, optimal route planning using AI-generated data during travel, and suggestions for modes of transportation (car, bicycle, public transport, etc.). This allows for the optimization of daily tasks such as commuting, going to school, and shopping. The household management system efficiently manages all household tasks and significantly reduces the burden of housework through the use of the latest appliances and tools. Furthermore, by optimizing daily travel such as commuting, going to school, and shopping, it enables a more efficient and comfortable life. It also allows for more time for family communication and relaxation.

[0029] The household management system according to this embodiment comprises a learning unit, a suggestion unit, a calculation unit, and a maintenance suggestion unit. The learning unit learns the daily schedules of all family members. The learning unit analyzes the schedules of all family members using, for example, generative AI, and understands each individual's schedule. The learning unit can, for example, collect calendar information of all family members and learn their schedules. The learning unit can also analyze the social media activities of all family members and learn their schedules. The suggestion unit proposes efficient distribution and start timing of household tasks based on the schedule learned by the learning unit. The suggestion unit optimizes the distribution of household tasks using, for example, generative AI. The suggestion unit can, for example, consider the schedules of all family members and propose start timings for household tasks. The suggestion unit can also determine the priority of household tasks and propose efficient distribution. The calculation unit calculates task time based on owned home appliances, cooking utensils, and cleaning tools. The calculation unit calculates task time based on, for example, performance data of home appliances. The calculation unit can, for example, analyze the usage history of cooking utensils and calculate task time. Furthermore, the calculation unit can also calculate task time considering the geographical location information of home appliances and cooking utensils. The maintenance proposal unit proposes optimal usage methods and maintenance schedules based on the task time calculated by the calculation unit. For example, the maintenance proposal unit proposes a maintenance schedule based on the frequency of use of home appliances. For example, the maintenance proposal unit can propose a maintenance schedule considering the performance data of cooking utensils. In addition, the maintenance proposal unit can propose a maintenance schedule by referring to relevant literature on home appliances and cooking utensils. As a result, the household management system according to the embodiment can efficiently manage the daily schedules of all family members and optimize the distribution and start timing of household tasks.

[0030] The learning unit learns the daily schedules of all family members. For example, it uses generative AI to analyze the schedules of all family members and understand each individual's plans. Specifically, the generative AI collects calendar information from all family members and analyzes each individual's plans in detail. This calendar information includes work appointments, school events, and personal appointments, and the unit learns each individual's schedule based on this information. The learning unit can also learn schedules by analyzing the social media activities of all family members. Social media activities include posts, event participation information, and location information, and by analyzing this data, it understands each individual's behavioral patterns and plans. Furthermore, the learning unit can accumulate past schedule data from all family members and learn long-term trends and patterns. For example, it can analyze behavioral patterns on specific days of the week or time slots to help predict future schedules. As a result, the learning unit can gain a detailed understanding of the schedules of all family members and provide a foundation for efficient household management.

[0031] The suggestion unit proposes efficient division and start times for household tasks based on schedules learned by the learning unit. For example, the suggestion unit optimizes the division of household tasks using generative AI. Specifically, the generative AI considers the schedules of all family members and distributes household tasks in a way that minimizes each individual's free time and burden. For example, members who have time to do chores in the evenings on weekdays are assigned to prepare dinner and clean, while members who have time on weekends are assigned to do laundry and shopping. The suggestion unit can also determine the priority of household tasks and propose efficient division. For example, it prioritizes assigning urgent and important tasks to ensure that household chores are completed efficiently. Furthermore, the suggestion unit can collect feedback from all family members and continuously improve its suggestions. For example, it evaluates satisfaction with the division of household tasks and the difficulty of execution, and reflects this in future suggestions. As a result, the suggestion unit can propose efficient division and start times for household tasks based on the schedules of all family members, thereby reducing the burden of household chores.

[0032] The calculation unit calculates task times based on the home appliances, cooking utensils, and cleaning tools owned. For example, the calculation unit calculates task times based on the performance data of home appliances. Specifically, it collects performance data for appliances such as washing machines, dishwashers, and vacuum cleaners and accurately calculates the time required for each task. For example, it calculates the time required for washing clothes and washing dishes based on the washing cycle time of a washing machine or the washing time of a dishwasher. The calculation unit can also analyze the usage history of cooking utensils and calculate task times. For example, it analyzes the usage history of ovens and microwave ovens to predict the time required for cooking. Furthermore, the calculation unit can also calculate task times considering the geographical location information of home appliances and cooking utensils. For example, it considers the location of the vacuum cleaner charging station or the storage location of cooking utensils and calculates the total task time, including travel time from the start to the end of the task. As a result, the calculation unit can accurately calculate task times based on the home appliances, cooking utensils, and cleaning tools owned, supporting efficient household management.

[0033] The Maintenance Proposal Department proposes optimal usage methods and maintenance schedules based on task times calculated by the Calculation Department. For example, the Maintenance Proposal Department proposes maintenance schedules based on the frequency of use of home appliances. Specifically, it analyzes the frequency of use of appliances such as washing machines, dishwashers, and vacuum cleaners and proposes appropriate maintenance timings. For example, it notifies users of the timing for cleaning washing machine filters or internal cleaning of dishwashers. The Maintenance Proposal Department can also propose maintenance schedules considering the performance data of cooking appliances. For example, it proposes the timing for internal cleaning of ovens or replacement of microwave filters. Furthermore, the Maintenance Proposal Department can propose maintenance schedules by referring to relevant literature on home appliances and cooking appliances. For example, it proposes optimal maintenance methods and schedules based on manufacturer instruction manuals and expert advice. In this way, the Maintenance Proposal Department can propose optimal usage methods and maintenance schedules for home appliances and cooking appliances, supporting increased efficiency in household chores and extending the lifespan of equipment.

[0034] The suggestion department can propose home appliances and tools in conjunction with the e-commerce site, leading to purchases. For example, the suggestion department can use generative AI to suggest home appliances and tools. For example, the suggestion department can suggest the most suitable home appliances and tools based on family schedules and household tasks. Furthermore, the suggestion department can also suggest home appliances and tools based on data from the e-commerce site. This allows for increased purchasing intent and monetization through suggestions for home appliances and tools. Some or all of the above-mentioned processes in the suggestion department may be performed using generative AI, or not. For example, the suggestion department can input data from the e-commerce site into the generative AI and have the generative AI execute suggestions for home appliances and tools.

[0035] The suggestion unit can suggest menus, check ingredient inventory, and enable online purchase of necessary ingredients. For example, the suggestion unit can use generative AI to suggest menus. For example, the suggestion unit can suggest menus considering family preferences and nutritional balance. The suggestion unit can also check ingredient inventory based on sensors in the refrigerator or manual input. Furthermore, the suggestion unit can be linked to e-commerce sites to enable online purchase of necessary ingredients. This makes meal preparation more efficient through menu suggestions and online ingredient purchases. Some or all of the above processes in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input sensor data from the refrigerator into the generative AI and have the generative AI perform ingredient inventory checks.

[0036] The suggestion unit can make suggestions regarding recommended bedtime and wake-up times, exercise time and methods, etc. For example, the suggestion unit can use a generative AI to suggest recommended bedtime and wake-up times. For example, the suggestion unit can suggest bedtime and wake-up times based on the family's lifestyle rhythm and health guidelines. The suggestion unit can also make suggestions regarding exercise time and methods. For example, the suggestion unit can suggest exercise time and methods considering the family's fitness level and health guidelines. In this way, it can support the health of the family by suggesting healthy lifestyle habits. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or without using a generative AI. For example, the suggestion unit can input family lifestyle rhythm data into a generative AI and have the generative AI make suggestions regarding recommended bedtime and wake-up times.

[0037] The proposal unit can generate optimal route plans using a generation AI during travel and propose modes of transportation. For example, the proposal unit generates optimal route plans using a generation AI. For example, the proposal unit can propose optimal routes based on traffic information and geographical information. The proposal unit can also propose modes of transportation. For example, the proposal unit can propose modes of transportation such as public transport, private cars, bicycles, and walking. This can optimize daily travel such as commuting, going to school, and shopping. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input traffic information data into a generation AI and have the generation AI generate optimal route plans.

[0038] The household management system according to this embodiment includes a shared unit, which shares schedules and responsibilities among family members via an app. The shared unit can share schedules and responsibilities among family members via an app. The shared unit can share schedules, for example, using a smartphone or tablet app. The shared unit can, for example, centrally manage and update the schedules of all family members in real time. The shared unit also has a function to notify changes or additions to the schedule. This facilitates communication among family members by sharing schedules and responsibilities. Some or all of the above-described processes in the shared unit may be performed using, for example, a generation AI, or without a generation AI. For example, the shared unit can input schedule data for all family members into a generation AI and have the generation AI perform schedule sharing.

[0039] The learning unit can analyze a family's past schedule history and select the optimal learning algorithm. For example, the learning unit can use generative AI to analyze past schedule history. For example, the learning unit can collect past schedule data of the family and identify frequently performed tasks. The learning unit can also select a learning algorithm that takes seasonal variations into account. Furthermore, the learning unit can select the optimal learning algorithm for specific days of the week or time slots. This allows for the selection of the optimal learning algorithm by analyzing past schedule history, thereby improving the accuracy of schedule management. Some or all of the above processing in the learning unit may be performed using generative AI, for example, or without generative AI. For example, the learning unit can input past schedule data into a generative AI and have the generative AI select the optimal learning algorithm.

[0040] The learning unit can weight the learning data based on family lifestyle patterns during the learning process. For example, the learning unit can analyze family lifestyle patterns using generative AI. The learning unit can then weight important tasks based on family lifestyle patterns. It can also weight tasks that are performed frequently. Furthermore, it can weight tasks that are performed at specific times of day. By weighting the learning data based on family lifestyle patterns, the accuracy of learning for important tasks can be improved. Some or all of the above processing in the learning unit may be performed using generative AI, for example, or without generative AI. For example, the learning unit can input family lifestyle pattern data into a generative AI and have the generative AI perform the weighting of the learning data.

[0041] The learning unit can prioritize learning highly relevant schedules by considering the geographical location information of family members during the learning process. For example, the learning unit can analyze the geographical location information of family members using generative AI. The learning unit can prioritize learning nearby events and tasks based on the geographical location information of family members. It can also prioritize learning commuting and school schedules. Furthermore, the learning unit can prioritize learning tasks that take place in specific areas. This allows for efficient schedule management by prioritizing the learning of highly relevant schedules by considering the geographical location information of family members. Some or all of the above processing in the learning unit may be performed using generative AI, for example, or without generative AI. For example, the learning unit can input family geographical location data into generative AI and have the generative AI perform the learning of highly relevant schedules.

[0042] The learning unit can analyze the family's social media activities and learn relevant schedules during the learning process. For example, the learning unit can use generative AI to analyze the family's social media activities. For example, the learning unit can learn event and gathering schedules based on the family's social media activities. The learning unit can also learn plans with friends and acquaintances. Furthermore, the learning unit can learn schedules based on the family's interests and concerns. This allows for efficient schedule management by learning relevant schedules through the analysis of the family's social media activities. Some or all of the above processing in the learning unit may be performed using generative AI, for example, or without generative AI. For example, the learning unit can input family social media data into a generative AI and have the generative AI perform the learning of relevant schedules.

[0043] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the household tasks. For example, the suggestion unit can use a generative AI to evaluate the importance of household tasks. The suggestion unit can evaluate importance based on factors such as the urgency of the household tasks or the family's priority. The suggestion unit can also provide detailed suggestions for important household tasks. Furthermore, the suggestion unit can provide concise suggestions for low-priority household tasks. This allows for efficient management of household tasks by adjusting the level of detail of suggestions based on the importance of the household tasks. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input household task importance data into a generative AI and have the generative AI adjust the level of detail of the suggestions.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of household chore task when making suggestions. For example, the suggestion unit can classify household chore task categories using a generative AI. For example, the suggestion unit can classify household chore tasks into categories such as cleaning, cooking, and laundry. The suggestion unit can also apply a suggestion algorithm appropriate to each category. For example, the suggestion unit can apply a recipe suggestion algorithm to cooking tasks. Furthermore, the suggestion unit can apply a cleaning method suggestion algorithm to cleaning tasks. Furthermore, the suggestion unit can apply a laundry method suggestion algorithm to laundry tasks. By applying different suggestion algorithms depending on the category of household chore task, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or without using a generative AI. For example, the suggestion unit can input household chore task category data into a generative AI and have the generative AI execute the application of the suggestion algorithm.

[0045] The proposal unit can determine the priority of proposals based on the submission timing of household tasks. The proposal unit can, for example, use a generative AI to evaluate the submission timing of household tasks. The proposal unit can evaluate the submission timing based, for example, on the deadline for household tasks or the family's schedule. The proposal unit can also prioritize proposals for household tasks that are urgent. Furthermore, the proposal unit can prioritize proposals for household tasks with approaching deadlines. This enables efficient management of household tasks by determining the priority of proposals based on the submission timing of household tasks. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input household task submission timing data into a generative AI and have the generative AI determine the priority of proposals.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of household tasks when making suggestions. For example, the suggestion unit can use generative AI to evaluate the relevance of household tasks. The suggestion unit can evaluate relevance based on, for example, the dependencies between household tasks or tasks that should be done simultaneously. The suggestion unit can also suggest highly relevant household tasks together. Furthermore, the suggestion unit can postpone less relevant household tasks. This allows for efficient management of household tasks by adjusting the order of suggestions based on the relevance of household tasks. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input household task relevance data into the generative AI and have the generative AI perform the adjustment of the suggestion order.

[0047] The calculation unit can analyze the usage history of home appliances and cooking utensils during calculations to select the optimal calculation method. For example, the calculation unit can use a generative AI to analyze the usage history of home appliances and cooking utensils. For example, the calculation unit can calculate the optimal task time based on the usage history of home appliances. The calculation unit can also analyze the usage history of cooking utensils to calculate efficient task time. Furthermore, the calculation unit can select the optimal calculation method considering the usage history of home appliances and cooking utensils. This allows for the selection of the optimal calculation method and improvement of task time accuracy by analyzing the usage history of home appliances and cooking utensils. Some or all of the above processing in the calculation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the calculation unit can input the usage history data of home appliances and cooking utensils into a generative AI and have the generative AI select the optimal calculation method.

[0048] The calculation unit can improve the accuracy of calculations based on performance data of home appliances and cooking utensils during calculations. For example, the calculation unit analyzes the performance data of home appliances and cooking utensils using a generative AI. For example, the calculation unit can calculate accurate task times based on the performance data of home appliances. The calculation unit can also calculate efficient task times by considering the performance data of cooking utensils. Furthermore, the calculation unit can improve the accuracy of calculations by analyzing the performance data of home appliances and cooking utensils. As a result, by improving the accuracy of calculations based on the performance data of home appliances and cooking utensils, it is possible to provide more accurate task times. Some or all of the above processing in the calculation unit may be performed using a generative AI, for example, or without a generative AI. For example, the calculation unit can input the performance data of home appliances and cooking utensils into a generative AI and have the generative AI perform the calculation accuracy improvement.

[0049] The calculation unit can select the optimal calculation method by considering the geographical location information of home appliances and cooking utensils during calculation. For example, the calculation unit can analyze the geographical location information of home appliances and cooking utensils using a generative AI. For example, the calculation unit can calculate the optimal task time based on the geographical location information of home appliances. Furthermore, the calculation unit can calculate an efficient task time by considering the geographical location information of cooking utensils. In addition, the calculation unit can analyze the geographical location information of home appliances and cooking utensils to select the optimal calculation method. As a result, by considering the geographical location information of home appliances and cooking utensils, the optimal calculation method can be selected and the accuracy of task time can be improved. Some or all of the above processing in the calculation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the calculation unit can input geographical location data of home appliances and cooking utensils into a generative AI and have the generative AI select the optimal calculation method.

[0050] The calculation unit can improve the accuracy of calculations by referring to relevant literature on home appliances and cooking utensils during calculations. For example, the calculation unit can analyze relevant literature on home appliances and cooking utensils using a generative AI. The calculation unit can calculate accurate task times based on relevant literature on home appliances, for example. The calculation unit can also calculate efficient task times by referring to relevant literature on cooking utensils. Furthermore, the calculation unit can improve the accuracy of calculations by analyzing relevant literature on home appliances and cooking utensils. As a result, by referring to relevant literature on home appliances and cooking utensils, the accuracy of calculations can be improved, and more accurate task times can be provided. Some or all of the above processing in the calculation unit may be performed using a generative AI, for example, or without a generative AI. For example, the calculation unit can input data on relevant literature on home appliances and cooking utensils into a generative AI and have the generative AI perform the calculation accuracy improvement.

[0051] The maintenance proposal unit can analyze the usage history of home appliances and cooking utensils to select the optimal proposal method when making maintenance proposals. For example, the maintenance proposal unit can use generative AI to analyze the usage history of home appliances and cooking utensils. The maintenance proposal unit can make optimal maintenance proposals based on the usage history of home appliances. Furthermore, the maintenance proposal unit can analyze the usage history of cooking utensils to make efficient maintenance proposals. In addition, the maintenance proposal unit can select the optimal proposal method considering the usage history of home appliances and cooking utensils. As a result, by analyzing the usage history of home appliances and cooking utensils, the optimal maintenance proposal method can be selected and the accuracy of maintenance can be improved. Some or all of the above processing in the maintenance proposal unit may be performed using generative AI, for example, or without using generative AI. For example, the maintenance proposal unit can input the usage history data of home appliances and cooking utensils into the generative AI and have the generative AI select the optimal proposal method.

[0052] The maintenance proposal unit can improve the accuracy of its proposals based on performance data of home appliances and cooking equipment. For example, the maintenance proposal unit can analyze the performance data of home appliances and cooking equipment using a generative AI. The maintenance proposal unit can make accurate maintenance proposals based on the performance data of home appliances. In addition, the maintenance proposal unit can make efficient maintenance proposals by considering the performance data of cooking equipment. Furthermore, the maintenance proposal unit can improve the accuracy of its proposals by analyzing the performance data of home appliances and cooking equipment. As a result, by improving the accuracy of proposals based on the performance data of home appliances and cooking equipment, more accurate maintenance proposals become possible. Some or all of the above processing in the maintenance proposal unit may be performed using a generative AI, for example, or without using a generative AI. For example, the maintenance proposal unit can input the performance data of home appliances and cooking equipment into a generative AI and have the generative AI perform the improvement of proposal accuracy.

[0053] The maintenance proposal unit can select the optimal proposal method when making maintenance proposals, taking into account the geographical location information of home appliances and cooking equipment. For example, the maintenance proposal unit can analyze the geographical location information of home appliances and cooking equipment using a generative AI. The maintenance proposal unit can make optimal maintenance proposals based on the geographical location information of home appliances. Furthermore, the maintenance proposal unit can make efficient maintenance proposals by taking into account the geographical location information of cooking equipment. In addition, the maintenance proposal unit can analyze the geographical location information of home appliances and cooking equipment to select the optimal proposal method. As a result, by taking into account the geographical location information of home appliances and cooking equipment, the optimal maintenance proposal method can be selected and the accuracy of maintenance can be improved. Some or all of the above processing in the maintenance proposal unit may be performed using a generative AI, for example, or without using a generative AI. For example, the maintenance proposal unit can input geographical location data of home appliances and cooking equipment into a generative AI and have the generative AI select the optimal proposal method.

[0054] The maintenance proposal unit can improve the accuracy of its proposals by referring to relevant literature on home appliances and cooking equipment when making maintenance proposals. For example, the maintenance proposal unit can analyze relevant literature on home appliances and cooking equipment using a generative AI. The maintenance proposal unit can make accurate maintenance proposals based on relevant literature on home appliances, for example. The maintenance proposal unit can also make efficient maintenance proposals by referring to relevant literature on cooking equipment. Furthermore, the maintenance proposal unit can improve the accuracy of its proposals by analyzing relevant literature on home appliances and cooking equipment. As a result, by referring to relevant literature on home appliances and cooking equipment, the accuracy of the proposals can be improved, enabling more accurate maintenance proposals. Some or all of the above processing in the maintenance proposal unit may be performed using a generative AI, for example, or without using a generative AI. For example, the maintenance proposal unit can input data on relevant literature on home appliances and cooking equipment into a generative AI and have the generative AI perform the task of improving the accuracy of the proposals.

[0055] The sharing unit can select the optimal sharing method by referring to the family's past sharing history when sharing. The sharing unit can, for example, analyze the family's past sharing history using a generative AI. The sharing unit can, for example, propose the optimal sharing method based on the family's past sharing data. The sharing unit can also analyze past sharing history and select an efficient sharing method. Furthermore, the sharing unit can select the optimal sharing method by considering the family's past sharing history. This makes it possible to select the optimal sharing method and enable efficient sharing by referring to the family's past sharing history. Some or all of the above processing in the sharing unit may be performed using a generative AI, for example, or without a generative AI. For example, the sharing unit can input the family's past sharing history data into a generative AI and have the generative AI select the optimal sharing method.

[0056] The sharing unit can select the optimal sharing method when sharing, taking into account the family's device information. For example, the sharing unit can analyze the family's device information using a generative AI. If the family is using a smartphone, the sharing unit can provide a sharing method that is appropriate for the screen size. If the family is using a tablet, the sharing unit can provide a sharing method optimized for a larger screen. Furthermore, if the family is using a personal computer, the sharing unit can provide a sharing method that includes detailed information. This allows for efficient sharing by selecting the optimal sharing method while considering the family's device information. Some or all of the above processing in the sharing unit may be performed using a generative AI, or not. For example, the sharing unit can input family device information data into a generative AI and have the generative AI select the optimal sharing method.

[0057] The sharing unit can select the optimal sharing method by referring to the family's calendar information when sharing. The sharing unit can, for example, analyze the family's calendar information using a generative AI. The sharing unit can, for example, propose the optimal sharing method based on the family's calendar information. The sharing unit can also analyze the calendar information and select an efficient sharing method. Furthermore, the sharing unit can select the optimal sharing method by considering the family's calendar information. As a result, by referring to the family's calendar information, the optimal sharing method can be selected, enabling efficient sharing. Some or all of the above processing in the sharing unit may be performed using a generative AI, for example, or without a generative AI. For example, the sharing unit can input family calendar information data into a generative AI and have the generative AI select the optimal sharing method.

[0058] The sharing unit can analyze the family's social media activity and select the optimal sharing method when sharing. For example, the sharing unit can use generative AI to analyze the family's social media activity. The sharing unit can, for example, propose the optimal sharing method based on the family's social media activity. Furthermore, the sharing unit can analyze social media activity and select an efficient sharing method. In addition, the sharing unit can select the optimal sharing method considering the family's social media activity. This allows for efficient sharing by analyzing the family's social media activity and selecting the optimal sharing method. Some or all of the above processing in the sharing unit may be performed using, for example, generative AI, or without generative AI. For example, the sharing unit can input family social media activity data into a generative AI and have the generative AI select the optimal sharing method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The household management system may also include an energy management unit that acquires family energy consumption data and proposes energy-efficient household tasks. The energy management unit, for example, acquires energy consumption data from smart meters and energy monitoring devices and evaluates the energy efficiency of household tasks. For example, the energy management unit can propose household tasks that avoid high-energy consumption times. It can also recommend the use of energy-efficient appliances and tools. Furthermore, the energy management unit can provide energy-saving advice based on family energy consumption data. This enables the proposal of energy-efficient household tasks, thereby reducing household energy consumption.

[0061] The household management system can also include a nutrition management department that analyzes the family's eating history and proposes menus that take nutritional balance into consideration. For example, the nutrition management department can acquire family eating history data and evaluate nutritional balance. If, for example, the family's nutritional balance is unbalanced, the nutrition management department can propose a balanced menu. The nutrition management department can also propose menus that take into account the family's preferences and allergy information. Furthermore, the nutrition management department can propose menus that use seasonal ingredients or ingredients containing specific nutrients. This makes it possible to propose menus that take the family's nutritional balance into consideration and supports a healthy diet.

[0062] The household management system can also include a sleep management unit that acquires family sleep data and makes suggestions to improve sleep quality. The sleep management unit can, for example, acquire sleep data from smartwatches or sleep trackers to monitor the family's sleep patterns. If, for instance, a family member's sleep quality is poor, the sleep management unit can suggest ways to create a more relaxing environment. Conversely, if a family member's sleep quality is high, the sleep management unit can provide advice on maintaining it. Furthermore, based on the family's sleep data, the sleep management unit can suggest optimal bedtimes and wake-up times. This can improve the family's sleep quality and support a healthier lifestyle.

[0063] The household management system may also include a mobility management unit that acquires family movement data and makes suggestions to improve travel efficiency. For example, the mobility management unit acquires GPS data and traffic information from smartphones and analyzes family movement patterns. The mobility management unit can, for example, make suggestions to optimize commuting or school routes. It can also suggest the most suitable mode of transportation to shorten family travel time. Furthermore, based on family movement data, the mobility management unit can make suggestions to avoid congestion. This improves family travel efficiency and saves time.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The learning unit learns the daily schedules of all family members. For example, it uses generative AI to analyze the schedules of all family members and understand each individual's plans. The learning unit can collect calendar information from all family members and learn their schedules. It can also analyze the social media activities of all family members and learn their schedules. Step 2: The suggestion unit proposes efficient division and start times for household tasks based on the schedule learned by the learning unit. For example, it can use generative AI to optimize the division of household tasks and propose start times for tasks considering the schedules of all family members. It can also determine the priority of household tasks and propose efficient division. Step 3: The calculation unit calculates task time based on the home appliances, cooking utensils, and cleaning tools owned. For example, it calculates task time based on the performance data of home appliances, and calculates task time by analyzing the usage history of cooking utensils. It can also calculate task time considering the geographical location information of home appliances and cooking utensils. Step 4: The maintenance proposal unit proposes the optimal usage method and maintenance schedule based on the task time calculated by the calculation unit. For example, it proposes a maintenance schedule based on the frequency of use of home appliances, and a maintenance schedule that takes into account the performance data of cooking appliances. It can also propose a maintenance schedule by referring to relevant literature on home appliances and cooking appliances.

[0066] (Example of form 2) The household chore management system according to an embodiment of the present invention is a system for understanding each individual's schedule within the household and, based on that, for optimal distribution and scheduling of household chores. This household chore management system is provided through a smartphone / tablet app and a personal computer website, and a generating AI learns the daily schedules of all family members and proposes efficient distribution of household chore tasks and start times. For example, the household chore management system's generating AI understands all household chore tasks and daily schedules in a conversational format and proposes distribution and adjustments. The household chore management system also calculates the time required for cooking and cleaning tasks based on owned home appliances, cooking utensils, and cleaning tools, and proposes optimal usage methods and maintenance schedules. Furthermore, the household chore management system links with an e-commerce site to propose home appliances and tools suited to each household, aiming to generate revenue by leading to purchases. It stimulates purchasing intent by proposing efficient household chore methods using the latest home appliances. The household chore management system provides menu suggestions, checks ingredient inventory, and allows online purchase of necessary ingredients. The household chore management system also allows family members to share schedules and chore divisions through the app. As an option, the system also offers suggestions for recommended bedtimes and wake-up times, exercise time and methods, optimal route planning using AI-generated data during travel, and suggestions for modes of transportation (car, bicycle, public transport, etc.). This allows for the optimization of daily tasks such as commuting, going to school, and shopping. The household management system efficiently manages all household tasks and significantly reduces the burden of housework through the use of the latest appliances and tools. Furthermore, by optimizing daily travel such as commuting, going to school, and shopping, it enables a more efficient and comfortable life. It also allows for more time for family communication and relaxation.

[0067] The household management system according to this embodiment comprises a learning unit, a suggestion unit, a calculation unit, and a maintenance suggestion unit. The learning unit learns the daily schedules of all family members. The learning unit analyzes the schedules of all family members using, for example, generative AI, and understands each individual's schedule. The learning unit can, for example, collect calendar information of all family members and learn their schedules. The learning unit can also analyze the social media activities of all family members and learn their schedules. The suggestion unit proposes efficient distribution and start timing of household tasks based on the schedule learned by the learning unit. The suggestion unit optimizes the distribution of household tasks using, for example, generative AI. The suggestion unit can, for example, consider the schedules of all family members and propose start timings for household tasks. The suggestion unit can also determine the priority of household tasks and propose efficient distribution. The calculation unit calculates task time based on owned home appliances, cooking utensils, and cleaning tools. The calculation unit calculates task time based on, for example, performance data of home appliances. The calculation unit can, for example, analyze the usage history of cooking utensils and calculate task time. Furthermore, the calculation unit can also calculate task time considering the geographical location information of home appliances and cooking utensils. The maintenance proposal unit proposes optimal usage methods and maintenance schedules based on the task time calculated by the calculation unit. For example, the maintenance proposal unit proposes a maintenance schedule based on the frequency of use of home appliances. For example, the maintenance proposal unit can propose a maintenance schedule considering the performance data of cooking utensils. In addition, the maintenance proposal unit can propose a maintenance schedule by referring to relevant literature on home appliances and cooking utensils. As a result, the household management system according to the embodiment can efficiently manage the daily schedules of all family members and optimize the distribution and start timing of household tasks.

[0068] The learning unit learns the daily schedules of all family members. For example, it uses generative AI to analyze the schedules of all family members and understand each individual's plans. Specifically, the generative AI collects calendar information from all family members and analyzes each individual's plans in detail. This calendar information includes work appointments, school events, and personal appointments, and the unit learns each individual's schedule based on this information. The learning unit can also learn schedules by analyzing the social media activities of all family members. Social media activities include posts, event participation information, and location information, and by analyzing this data, it understands each individual's behavioral patterns and plans. Furthermore, the learning unit can accumulate past schedule data from all family members and learn long-term trends and patterns. For example, it can analyze behavioral patterns on specific days of the week or time slots to help predict future schedules. As a result, the learning unit can gain a detailed understanding of the schedules of all family members and provide a foundation for efficient household management.

[0069] The suggestion unit proposes efficient division and start times for household tasks based on schedules learned by the learning unit. For example, the suggestion unit optimizes the division of household tasks using generative AI. Specifically, the generative AI considers the schedules of all family members and distributes household tasks in a way that minimizes each individual's free time and burden. For example, members who have time to do chores in the evenings on weekdays are assigned to prepare dinner and clean, while members who have time on weekends are assigned to do laundry and shopping. The suggestion unit can also determine the priority of household tasks and propose efficient division. For example, it prioritizes assigning urgent and important tasks to ensure that household chores are completed efficiently. Furthermore, the suggestion unit can collect feedback from all family members and continuously improve its suggestions. For example, it evaluates satisfaction with the division of household tasks and the difficulty of execution, and reflects this in future suggestions. As a result, the suggestion unit can propose efficient division and start times for household tasks based on the schedules of all family members, thereby reducing the burden of household chores.

[0070] The calculation unit calculates task times based on the home appliances, cooking utensils, and cleaning tools owned. For example, the calculation unit calculates task times based on the performance data of home appliances. Specifically, it collects performance data for appliances such as washing machines, dishwashers, and vacuum cleaners and accurately calculates the time required for each task. For example, it calculates the time required for washing clothes and washing dishes based on the washing cycle time of a washing machine or the washing time of a dishwasher. The calculation unit can also analyze the usage history of cooking utensils and calculate task times. For example, it analyzes the usage history of ovens and microwave ovens to predict the time required for cooking. Furthermore, the calculation unit can also calculate task times considering the geographical location information of home appliances and cooking utensils. For example, it considers the location of the vacuum cleaner charging station or the storage location of cooking utensils and calculates the total task time, including travel time from the start to the end of the task. As a result, the calculation unit can accurately calculate task times based on the home appliances, cooking utensils, and cleaning tools owned, supporting efficient household management.

[0071] The Maintenance Proposal Department proposes optimal usage methods and maintenance schedules based on task times calculated by the Calculation Department. For example, the Maintenance Proposal Department proposes maintenance schedules based on the frequency of use of home appliances. Specifically, it analyzes the frequency of use of appliances such as washing machines, dishwashers, and vacuum cleaners and proposes appropriate maintenance timings. For example, it notifies users of the timing for cleaning washing machine filters or internal cleaning of dishwashers. The Maintenance Proposal Department can also propose maintenance schedules considering the performance data of cooking appliances. For example, it proposes the timing for internal cleaning of ovens or replacement of microwave filters. Furthermore, the Maintenance Proposal Department can propose maintenance schedules by referring to relevant literature on home appliances and cooking appliances. For example, it proposes optimal maintenance methods and schedules based on manufacturer instruction manuals and expert advice. In this way, the Maintenance Proposal Department can propose optimal usage methods and maintenance schedules for home appliances and cooking appliances, supporting increased efficiency in household chores and extending the lifespan of equipment.

[0072] The suggestion department can propose home appliances and tools in conjunction with the e-commerce site, leading to purchases. For example, the suggestion department can use generative AI to suggest home appliances and tools. For example, the suggestion department can suggest the most suitable home appliances and tools based on family schedules and household tasks. Furthermore, the suggestion department can also suggest home appliances and tools based on data from the e-commerce site. This allows for increased purchasing intent and monetization through suggestions for home appliances and tools. Some or all of the above-mentioned processes in the suggestion department may be performed using generative AI, or not. For example, the suggestion department can input data from the e-commerce site into the generative AI and have the generative AI execute suggestions for home appliances and tools.

[0073] The suggestion unit can suggest menus, check ingredient inventory, and enable online purchase of necessary ingredients. For example, the suggestion unit can use generative AI to suggest menus. For example, the suggestion unit can suggest menus considering family preferences and nutritional balance. The suggestion unit can also check ingredient inventory based on sensors in the refrigerator or manual input. Furthermore, the suggestion unit can be linked to e-commerce sites to enable online purchase of necessary ingredients. This makes meal preparation more efficient through menu suggestions and online ingredient purchases. Some or all of the above processes in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input sensor data from the refrigerator into the generative AI and have the generative AI perform ingredient inventory checks.

[0074] The suggestion unit can make suggestions regarding recommended bedtime and wake-up times, exercise time and methods, etc. For example, the suggestion unit can use a generative AI to suggest recommended bedtime and wake-up times. For example, the suggestion unit can suggest bedtime and wake-up times based on the family's lifestyle rhythm and health guidelines. The suggestion unit can also make suggestions regarding exercise time and methods. For example, the suggestion unit can suggest exercise time and methods considering the family's fitness level and health guidelines. In this way, it can support the health of the family by suggesting healthy lifestyle habits. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or without using a generative AI. For example, the suggestion unit can input family lifestyle rhythm data into a generative AI and have the generative AI make suggestions regarding recommended bedtime and wake-up times.

[0075] The proposal unit can generate optimal route plans using a generation AI during travel and propose modes of transportation. For example, the proposal unit generates optimal route plans using a generation AI. For example, the proposal unit can propose optimal routes based on traffic information and geographical information. The proposal unit can also propose modes of transportation. For example, the proposal unit can propose modes of transportation such as public transport, private cars, bicycles, and walking. This can optimize daily travel such as commuting, going to school, and shopping. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input traffic information data into a generation AI and have the generation AI generate optimal route plans.

[0076] The household management system according to this embodiment includes a shared unit, which shares schedules and responsibilities among family members via an app. The shared unit can share schedules and responsibilities among family members via an app. The shared unit can share schedules, for example, using a smartphone or tablet app. The shared unit can, for example, centrally manage and update the schedules of all family members in real time. The shared unit also has a function to notify changes or additions to the schedule. This facilitates communication among family members by sharing schedules and responsibilities. Some or all of the above-described processes in the shared unit may be performed using, for example, a generation AI, or without a generation AI. For example, the shared unit can input schedule data for all family members into a generation AI and have the generation AI perform schedule sharing.

[0077] The learning unit can estimate the emotions of family members and adjust the learning frequency of the schedule based on the estimated emotions. The learning unit estimates the emotions of family members using an emotion estimation function, for example, using an emotion engine or generative AI. The learning unit can estimate emotions based on, for example, facial recognition or voice analysis of family members. The learning unit can also adjust the learning frequency of the schedule based on the estimated emotions. For example, if family members are feeling stressed, the learning unit can reduce the learning frequency and increase the time they can relax. Conversely, if family members are relaxed, the learning unit can increase the learning frequency and create a more detailed schedule. Furthermore, if family members are busy, the learning unit can adjust the learning frequency to allow them to focus on important tasks. This reduces stress and enables efficient schedule management by adjusting the learning frequency of the schedule according to the emotions of family members. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input family emotion data into a generative AI and have the generative AI adjust the learning frequency of the schedule.

[0078] The learning unit can analyze a family's past schedule history and select the optimal learning algorithm. For example, the learning unit can use generative AI to analyze past schedule history. For example, the learning unit can collect past schedule data of the family and identify frequently performed tasks. The learning unit can also select a learning algorithm that takes seasonal variations into account. Furthermore, the learning unit can select the optimal learning algorithm for specific days of the week or time slots. This allows for the selection of the optimal learning algorithm by analyzing past schedule history, thereby improving the accuracy of schedule management. Some or all of the above processing in the learning unit may be performed using generative AI, for example, or without generative AI. For example, the learning unit can input past schedule data into a generative AI and have the generative AI select the optimal learning algorithm.

[0079] The learning unit can weight the learning data based on family lifestyle patterns during the learning process. For example, the learning unit can analyze family lifestyle patterns using generative AI. The learning unit can then weight important tasks based on family lifestyle patterns. It can also weight tasks that are performed frequently. Furthermore, it can weight tasks that are performed at specific times of day. By weighting the learning data based on family lifestyle patterns, the accuracy of learning for important tasks can be improved. Some or all of the above processing in the learning unit may be performed using generative AI, for example, or without generative AI. For example, the learning unit can input family lifestyle pattern data into a generative AI and have the generative AI perform the weighting of the learning data.

[0080] The learning unit can estimate the emotions of family members and prioritize training data based on the estimated emotions. The learning unit estimates the emotions of family members using an emotion estimation function, for example, using an emotion engine or generative AI. The learning unit can estimate emotions based on, for example, facial recognition or voice analysis of family members. The learning unit can also prioritize training data based on the estimated emotions. For example, if family members are feeling stressed, the learning unit can prioritize tasks that help them relax. If family members are relaxed, the learning unit can prioritize tasks that are efficient. Furthermore, if family members are busy, the learning unit can prioritize important tasks. This allows for more appropriate schedule management by prioritizing training data according to family members' emotions. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input family emotion data into a generative AI and have the generative AI determine the priority of training data.

[0081] The learning unit can prioritize learning highly relevant schedules by considering the geographical location information of family members during the learning process. For example, the learning unit can analyze the geographical location information of family members using generative AI. The learning unit can prioritize learning nearby events and tasks based on the geographical location information of family members. It can also prioritize learning commuting and school schedules. Furthermore, the learning unit can prioritize learning tasks that take place in specific areas. This allows for efficient schedule management by prioritizing the learning of highly relevant schedules by considering the geographical location information of family members. Some or all of the above processing in the learning unit may be performed using generative AI, for example, or without generative AI. For example, the learning unit can input family geographical location data into generative AI and have the generative AI perform the learning of highly relevant schedules.

[0082] The learning unit can analyze the family's social media activities and learn relevant schedules during the learning process. For example, the learning unit can use generative AI to analyze the family's social media activities. For example, the learning unit can learn event and gathering schedules based on the family's social media activities. The learning unit can also learn plans with friends and acquaintances. Furthermore, the learning unit can learn schedules based on the family's interests and concerns. This allows for efficient schedule management by learning relevant schedules through the analysis of the family's social media activities. Some or all of the above processing in the learning unit may be performed using generative AI, for example, or without generative AI. For example, the learning unit can input family social media data into a generative AI and have the generative AI perform the learning of relevant schedules.

[0083] The suggestion unit can estimate the emotions of family members and adjust the way suggestions are expressed based on those estimated emotions. For example, the suggestion unit estimates family members' emotions using an emotion estimation function, such as an emotion engine or generative AI. The suggestion unit can estimate emotions based, for example, on facial recognition or voice analysis of family members. Furthermore, the suggestion unit can adjust the way suggestions are expressed based on the estimated emotions. For example, if family members are stressed, the suggestion unit can provide simple and easy-to-understand suggestions. If family members are relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if family members are busy, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are expressed according to family members' emotions, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without using generative AI. For example, the suggestion unit can input family emotion data into a generative AI and have the generative AI adjust the way suggestions are expressed.

[0084] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the household tasks. For example, the suggestion unit can use a generative AI to evaluate the importance of household tasks. The suggestion unit can evaluate importance based on factors such as the urgency of the household tasks or the family's priority. The suggestion unit can also provide detailed suggestions for important household tasks. Furthermore, the suggestion unit can provide concise suggestions for low-priority household tasks. This allows for efficient management of household tasks by adjusting the level of detail of suggestions based on the importance of the household tasks. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input household task importance data into a generative AI and have the generative AI adjust the level of detail of the suggestions.

[0085] The suggestion unit can apply different suggestion algorithms depending on the category of household chore task when making suggestions. For example, the suggestion unit can classify household chore task categories using a generative AI. For example, the suggestion unit can classify household chore tasks into categories such as cleaning, cooking, and laundry. The suggestion unit can also apply a suggestion algorithm appropriate to each category. For example, the suggestion unit can apply a recipe suggestion algorithm to cooking tasks. Furthermore, the suggestion unit can apply a cleaning method suggestion algorithm to cleaning tasks. Furthermore, the suggestion unit can apply a laundry method suggestion algorithm to laundry tasks. By applying different suggestion algorithms depending on the category of household chore task, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or without using a generative AI. For example, the suggestion unit can input household chore task category data into a generative AI and have the generative AI execute the application of the suggestion algorithm.

[0086] The suggestion unit can estimate the emotions of family members and adjust the length of the suggestion based on the estimated emotions. The suggestion unit estimates the emotions of family members using an emotion estimation function, for example, using an emotion engine or generative AI. The suggestion unit can estimate emotions based, for example, on facial recognition or voice analysis of family members. The suggestion unit can also adjust the length of the suggestion based on the estimated emotions. For example, if family members are feeling stressed, the suggestion unit can make short, to-the-point suggestions. If family members are relaxed, the suggestion unit can make detailed suggestions. Furthermore, if family members are busy, the suggestion unit can make concise suggestions. By adjusting the length of the suggestion according to the emotions of family members, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input family emotion data into generative AI and have the generative AI adjust the length of the suggestion.

[0087] The proposal unit can determine the priority of proposals based on the submission timing of household tasks. The proposal unit can, for example, use a generative AI to evaluate the submission timing of household tasks. The proposal unit can evaluate the submission timing based, for example, on the deadline for household tasks or the family's schedule. The proposal unit can also prioritize proposals for household tasks that are urgent. Furthermore, the proposal unit can prioritize proposals for household tasks with approaching deadlines. This enables efficient management of household tasks by determining the priority of proposals based on the submission timing of household tasks. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input household task submission timing data into a generative AI and have the generative AI determine the priority of proposals.

[0088] The suggestion unit can adjust the order of suggestions based on the relevance of household tasks when making suggestions. For example, the suggestion unit can use generative AI to evaluate the relevance of household tasks. The suggestion unit can evaluate relevance based on, for example, the dependencies between household tasks or tasks that should be done simultaneously. The suggestion unit can also suggest highly relevant household tasks together. Furthermore, the suggestion unit can postpone less relevant household tasks. This allows for efficient management of household tasks by adjusting the order of suggestions based on the relevance of household tasks. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input household task relevance data into the generative AI and have the generative AI perform the adjustment of the suggestion order.

[0089] The calculation unit can estimate the emotions of family members and adjust the task time calculation method based on the estimated emotions. The calculation unit estimates the emotions of family members using an emotion estimation function, for example, using an emotion engine or generative AI. The calculation unit can estimate emotions based on, for example, facial recognition or voice analysis of family members. The calculation unit can also adjust the task time calculation method based on the estimated emotions. For example, if family members are feeling stressed, the calculation unit can shorten the task time. Also, if family members are relaxed, the calculation unit can calculate a more detailed task time. Furthermore, if family members are busy, the calculation unit can adjust the task time so that they can concentrate on important tasks. In this way, by adjusting the task time calculation method according to the emotions of family members, a more appropriate task time can be provided. Some or all of the above processing in the calculation unit may be performed using, for example, generative AI, or without using generative AI. For example, the calculation unit can input family emotion data into the generative AI and have the generative AI perform the adjustment of the task time calculation method.

[0090] The calculation unit can analyze the usage history of home appliances and cooking utensils during calculations to select the optimal calculation method. For example, the calculation unit can use a generative AI to analyze the usage history of home appliances and cooking utensils. For example, the calculation unit can calculate the optimal task time based on the usage history of home appliances. The calculation unit can also analyze the usage history of cooking utensils to calculate efficient task time. Furthermore, the calculation unit can select the optimal calculation method considering the usage history of home appliances and cooking utensils. This allows for the selection of the optimal calculation method and improvement of task time accuracy by analyzing the usage history of home appliances and cooking utensils. Some or all of the above processing in the calculation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the calculation unit can input the usage history data of home appliances and cooking utensils into a generative AI and have the generative AI select the optimal calculation method.

[0091] The calculation unit can improve the accuracy of calculations based on performance data of home appliances and cooking utensils during calculations. For example, the calculation unit analyzes the performance data of home appliances and cooking utensils using a generative AI. For example, the calculation unit can calculate accurate task times based on the performance data of home appliances. The calculation unit can also calculate efficient task times by considering the performance data of cooking utensils. Furthermore, the calculation unit can improve the accuracy of calculations by analyzing the performance data of home appliances and cooking utensils. As a result, by improving the accuracy of calculations based on the performance data of home appliances and cooking utensils, it is possible to provide more accurate task times. Some or all of the above processing in the calculation unit may be performed using a generative AI, for example, or without a generative AI. For example, the calculation unit can input the performance data of home appliances and cooking utensils into a generative AI and have the generative AI perform the calculation accuracy improvement.

[0092] The calculation unit can estimate the emotions of family members and adjust the display method of the calculation results based on the estimated emotions. The calculation unit estimates the emotions of family members using an emotion estimation function, for example, using an emotion engine or a generative AI. The calculation unit can estimate emotions based, for example, on facial recognition or voice analysis of family members. Furthermore, the calculation unit can adjust the display method of the calculation results based on the estimated emotions. For example, if a family member is feeling stressed, the calculation unit can provide a simple and easy-to-understand display method. Also, if a family member is relaxed, the calculation unit can display detailed calculation results. Furthermore, if a family member is busy, the calculation unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the calculation results according to the emotions of family members, a more appropriate display becomes possible. Some or all of the above processing in the calculation unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the calculation unit can input family emotion data into a generative AI and have the generative AI perform the adjustment of the display method of the calculation results.

[0093] The calculation unit can select the optimal calculation method by considering the geographical location information of home appliances and cooking utensils during calculation. For example, the calculation unit can analyze the geographical location information of home appliances and cooking utensils using a generative AI. For example, the calculation unit can calculate the optimal task time based on the geographical location information of home appliances. Furthermore, the calculation unit can calculate an efficient task time by considering the geographical location information of cooking utensils. In addition, the calculation unit can analyze the geographical location information of home appliances and cooking utensils to select the optimal calculation method. As a result, by considering the geographical location information of home appliances and cooking utensils, the optimal calculation method can be selected and the accuracy of task time can be improved. Some or all of the above processing in the calculation unit may be performed using a generative AI, for example, or without using a generative AI. For example, the calculation unit can input geographical location data of home appliances and cooking utensils into a generative AI and have the generative AI select the optimal calculation method.

[0094] The calculation unit can improve the accuracy of calculations by referring to relevant literature on home appliances and cooking utensils during calculations. For example, the calculation unit can analyze relevant literature on home appliances and cooking utensils using a generative AI. The calculation unit can calculate accurate task times based on relevant literature on home appliances, for example. The calculation unit can also calculate efficient task times by referring to relevant literature on cooking utensils. Furthermore, the calculation unit can improve the accuracy of calculations by analyzing relevant literature on home appliances and cooking utensils. As a result, by referring to relevant literature on home appliances and cooking utensils, the accuracy of calculations can be improved, and more accurate task times can be provided. Some or all of the above processing in the calculation unit may be performed using a generative AI, for example, or without a generative AI. For example, the calculation unit can input data on relevant literature on home appliances and cooking utensils into a generative AI and have the generative AI perform the calculation accuracy improvement.

[0095] The maintenance suggestion unit can estimate the emotions of family members and adjust the method of suggesting maintenance schedules based on the estimated emotions. The maintenance suggestion unit estimates the emotions of family members using an emotion estimation function, for example, using an emotion engine or generative AI. The maintenance suggestion unit can estimate emotions based, for example, on facial recognition or voice analysis of family members. Furthermore, the maintenance suggestion unit can adjust the method of suggesting maintenance schedules based on the estimated emotions. For example, if family members are feeling stressed, the maintenance suggestion unit can provide simple and easy-to-understand maintenance suggestions. If family members are relaxed, the maintenance suggestion unit can provide detailed maintenance suggestions. Furthermore, if family members are busy, the maintenance suggestion unit can provide concise maintenance suggestions. In this way, by adjusting the method of suggesting maintenance schedules according to family members' emotions, more appropriate maintenance suggestions become possible. Some or all of the above processing in the maintenance suggestion unit may be performed using, for example, generative AI, or without using generative AI. For example, the maintenance suggestion unit can input family emotion data into generative AI and have the generative AI perform the adjustment of the maintenance schedule suggestion method.

[0096] The maintenance proposal unit can analyze the usage history of home appliances and cooking utensils to select the optimal proposal method when making maintenance proposals. For example, the maintenance proposal unit can use generative AI to analyze the usage history of home appliances and cooking utensils. The maintenance proposal unit can make optimal maintenance proposals based on the usage history of home appliances. Furthermore, the maintenance proposal unit can analyze the usage history of cooking utensils to make efficient maintenance proposals. In addition, the maintenance proposal unit can select the optimal proposal method considering the usage history of home appliances and cooking utensils. As a result, by analyzing the usage history of home appliances and cooking utensils, the optimal maintenance proposal method can be selected and the accuracy of maintenance can be improved. Some or all of the above processing in the maintenance proposal unit may be performed using generative AI, for example, or without using generative AI. For example, the maintenance proposal unit can input the usage history data of home appliances and cooking utensils into the generative AI and have the generative AI select the optimal proposal method.

[0097] The maintenance proposal unit can improve the accuracy of its proposals based on performance data of home appliances and cooking equipment. For example, the maintenance proposal unit can analyze the performance data of home appliances and cooking equipment using a generative AI. The maintenance proposal unit can make accurate maintenance proposals based on the performance data of home appliances. In addition, the maintenance proposal unit can make efficient maintenance proposals by considering the performance data of cooking equipment. Furthermore, the maintenance proposal unit can improve the accuracy of its proposals by analyzing the performance data of home appliances and cooking equipment. As a result, by improving the accuracy of proposals based on the performance data of home appliances and cooking equipment, more accurate maintenance proposals become possible. Some or all of the above processing in the maintenance proposal unit may be performed using a generative AI, for example, or without using a generative AI. For example, the maintenance proposal unit can input the performance data of home appliances and cooking equipment into a generative AI and have the generative AI perform the improvement of proposal accuracy.

[0098] The maintenance suggestion unit can estimate the emotions of family members and determine the priority of the maintenance schedule based on those estimated emotions. The unit estimates family members' emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, the unit can estimate emotions based on facial recognition or voice analysis of family members. Furthermore, the unit can determine the priority of the maintenance schedule based on the estimated emotions. For example, if family members are stressed, the unit can prioritize important maintenance tasks. If family members are relaxed, the unit can propose a detailed maintenance schedule. Additionally, if family members are busy, the unit can prioritize important maintenance tasks and postpone other tasks. This allows for more appropriate maintenance management by prioritizing the maintenance schedule according to family emotions. Some or all of the above-described processes in the maintenance suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the maintenance suggestion unit can input family emotion data into a generative AI and have the generative AI determine the priority of the maintenance schedule.

[0099] The maintenance proposal unit can select the optimal proposal method when making maintenance proposals, taking into account the geographical location information of home appliances and cooking equipment. For example, the maintenance proposal unit can analyze the geographical location information of home appliances and cooking equipment using a generative AI. The maintenance proposal unit can make optimal maintenance proposals based on the geographical location information of home appliances. Furthermore, the maintenance proposal unit can make efficient maintenance proposals by taking into account the geographical location information of cooking equipment. In addition, the maintenance proposal unit can analyze the geographical location information of home appliances and cooking equipment to select the optimal proposal method. As a result, by taking into account the geographical location information of home appliances and cooking equipment, the optimal maintenance proposal method can be selected and the accuracy of maintenance can be improved. Some or all of the above processing in the maintenance proposal unit may be performed using a generative AI, for example, or without using a generative AI. For example, the maintenance proposal unit can input geographical location data of home appliances and cooking equipment into a generative AI and have the generative AI select the optimal proposal method.

[0100] The maintenance proposal unit can improve the accuracy of its proposals by referring to relevant literature on home appliances and cooking equipment when making maintenance proposals. For example, the maintenance proposal unit can analyze relevant literature on home appliances and cooking equipment using a generative AI. The maintenance proposal unit can make accurate maintenance proposals based on relevant literature on home appliances, for example. The maintenance proposal unit can also make efficient maintenance proposals by referring to relevant literature on cooking equipment. Furthermore, the maintenance proposal unit can improve the accuracy of its proposals by analyzing relevant literature on home appliances and cooking equipment. As a result, by referring to relevant literature on home appliances and cooking equipment, the accuracy of the proposals can be improved, enabling more accurate maintenance proposals. Some or all of the above processing in the maintenance proposal unit may be performed using a generative AI, for example, or without using a generative AI. For example, the maintenance proposal unit can input data on relevant literature on home appliances and cooking equipment into a generative AI and have the generative AI perform the task of improving the accuracy of the proposals.

[0101] The sharing unit can estimate the emotions of family members and adjust the method of sharing schedules based on the estimated emotions. The sharing unit estimates the emotions of family members using an emotion estimation function, for example, using an emotion engine or generative AI. The sharing unit can estimate emotions based on, for example, facial recognition or voice analysis of family members. Furthermore, the sharing unit can adjust the method of sharing schedules based on the estimated emotions. For example, if family members are feeling stressed, the sharing unit can provide a simple and easy-to-understand method of sharing. If family members are relaxed, the sharing unit can share a detailed schedule. Furthermore, if family members are busy, the sharing unit can provide a concise method of sharing. This allows for more appropriate sharing by adjusting the method of sharing schedules according to family members' emotions. Some or all of the above processing in the sharing unit may be performed using, for example, generative AI, or without generative AI. For example, the sharing unit can input family emotion data into a generative AI and have the generative AI adjust the method of sharing schedules.

[0102] The sharing unit can select the optimal sharing method by referring to the family's past sharing history when sharing. The sharing unit can, for example, analyze the family's past sharing history using a generative AI. The sharing unit can, for example, propose the optimal sharing method based on the family's past sharing data. The sharing unit can also analyze past sharing history and select an efficient sharing method. Furthermore, the sharing unit can select the optimal sharing method by considering the family's past sharing history. This makes it possible to select the optimal sharing method and enable efficient sharing by referring to the family's past sharing history. Some or all of the above processing in the sharing unit may be performed using a generative AI, for example, or without a generative AI. For example, the sharing unit can input the family's past sharing history data into a generative AI and have the generative AI select the optimal sharing method.

[0103] The sharing unit can estimate the emotions of family members and determine sharing priorities based on those estimated emotions. The sharing unit estimates family members' emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the sharing unit can estimate emotions based on facial recognition or voice analysis of family members. Furthermore, the sharing unit can determine sharing priorities based on the estimated emotions. For example, if a family member is stressed, the sharing unit can prioritize sharing important schedules. If a family member is relaxed, the sharing unit can share detailed schedules. Additionally, if a family member is busy, the sharing unit can prioritize important schedules and postpone other tasks. This allows for more appropriate sharing by determining sharing priorities according to family members' emotions. Some or all of the above processing in the sharing unit may be performed using, for example, generative AI, or without generative AI. For example, the sharing unit can input family emotion data into a generative AI and have the generative AI determine sharing priorities.

[0104] The sharing unit can select the optimal sharing method when sharing, taking into account the family's device information. For example, the sharing unit can analyze the family's device information using a generative AI. If the family is using a smartphone, the sharing unit can provide a sharing method that is appropriate for the screen size. If the family is using a tablet, the sharing unit can provide a sharing method optimized for a larger screen. Furthermore, if the family is using a personal computer, the sharing unit can provide a sharing method that includes detailed information. This allows for efficient sharing by selecting the optimal sharing method while considering the family's device information. Some or all of the above processing in the sharing unit may be performed using a generative AI, or not. For example, the sharing unit can input family device information data into a generative AI and have the generative AI select the optimal sharing method.

[0105] The sharing unit can select the optimal sharing method by referring to the family's calendar information when sharing. The sharing unit can, for example, analyze the family's calendar information using a generative AI. The sharing unit can, for example, propose the optimal sharing method based on the family's calendar information. The sharing unit can also analyze the calendar information and select an efficient sharing method. Furthermore, the sharing unit can select the optimal sharing method by considering the family's calendar information. As a result, by referring to the family's calendar information, the optimal sharing method can be selected, enabling efficient sharing. Some or all of the above processing in the sharing unit may be performed using a generative AI, for example, or without a generative AI. For example, the sharing unit can input family calendar information data into a generative AI and have the generative AI select the optimal sharing method.

[0106] The sharing unit can analyze the family's social media activity and select the optimal sharing method when sharing. For example, the sharing unit can use generative AI to analyze the family's social media activity. The sharing unit can, for example, propose the optimal sharing method based on the family's social media activity. Furthermore, the sharing unit can analyze social media activity and select an efficient sharing method. In addition, the sharing unit can select the optimal sharing method considering the family's social media activity. This allows for efficient sharing by analyzing the family's social media activity and selecting the optimal sharing method. Some or all of the above processing in the sharing unit may be performed using, for example, generative AI, or without generative AI. For example, the sharing unit can input family social media activity data into a generative AI and have the generative AI select the optimal sharing method.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The household management system can also include a health management unit that acquires family health data and adjusts the distribution of household tasks based on their health status. The health management unit, for example, acquires heart rate and sleep data from smartwatches and fitness trackers to monitor the family's health. If a family member is in poor health, the health management unit can assign less burdensome tasks. Conversely, if a family member is in good health, it can assign more tasks. Furthermore, the health management unit can suggest exercise and rest times based on the family's health status. This enables flexible distribution of household tasks according to the family's health, supporting a healthy lifestyle.

[0109] The household chore management system can also include a hobby suggestion section that proposes chore tasks considering the family's hobbies and interests. For example, the hobby suggestion section could suggest chore tasks that can be done in an enjoyable way based on the family's hobbies and interests. For instance, it could suggest new recipes to a family member who enjoys cooking, or new cleaning methods to a family member who likes cleaning. Furthermore, the hobby suggestion section could gamify chore tasks based on the family's interests, making chores more fun. In addition, the hobby suggestion section could provide information on events and workshops related to the family's hobbies. This allows for more enjoyable chore tasks and can boost family motivation.

[0110] The household management system may also include an energy management unit that acquires family energy consumption data and proposes energy-efficient household tasks. The energy management unit, for example, acquires energy consumption data from smart meters and energy monitoring devices and evaluates the energy efficiency of household tasks. For example, the energy management unit can propose household tasks that avoid high-energy consumption times. It can also recommend the use of energy-efficient appliances and tools. Furthermore, the energy management unit can provide energy-saving advice based on family energy consumption data. This enables the proposal of energy-efficient household tasks, thereby reducing household energy consumption.

[0111] The household management system can also include a communication support unit that acquires family communication data and facilitates smoother communication. For example, the communication support unit analyzes messages and call history between family members to evaluate the frequency and content of communication. If communication between family members is insufficient, the unit can suggest ways to promote communication. Furthermore, if communication between family members is smooth, the unit can suggest activities to further promote deeper communication. In addition, the communication support unit can consider family emotional data and suggest communication at the appropriate time. This can facilitate smoother communication among family members and strengthen relationships within the household.

[0112] The household management system can also include a stress management unit that monitors family stress levels and suggests household tasks to reduce stress. The stress management unit can acquire stress level data from, for example, smartwatches or stress monitoring devices and evaluate the family's stress levels. For instance, if the family's stress levels are high, the stress management unit can suggest relaxing household tasks. Conversely, if the family's stress levels are low, it can suggest efficient household tasks. Furthermore, the stress management unit can suggest relaxation activities based on the family's stress levels. This allows for the suggestion of household tasks tailored to the family's stress levels, thereby reducing stress within the home.

[0113] The household management system can also include a nutrition management department that analyzes the family's eating history and proposes menus that take nutritional balance into consideration. For example, the nutrition management department can acquire family eating history data and evaluate nutritional balance. If, for example, the family's nutritional balance is unbalanced, the nutrition management department can propose a balanced menu. The nutrition management department can also propose menus that take into account the family's preferences and allergy information. Furthermore, the nutrition management department can propose menus that use seasonal ingredients or ingredients containing specific nutrients. This makes it possible to propose menus that take the family's nutritional balance into consideration and supports a healthy diet.

[0114] The household management system can also include a sleep management unit that acquires family sleep data and makes suggestions to improve sleep quality. The sleep management unit can, for example, acquire sleep data from smartwatches or sleep trackers to monitor the family's sleep patterns. If, for instance, a family member's sleep quality is poor, the sleep management unit can suggest ways to create a more relaxing environment. Conversely, if a family member's sleep quality is high, the sleep management unit can provide advice on maintaining it. Furthermore, based on the family's sleep data, the sleep management unit can suggest optimal bedtimes and wake-up times. This can improve the family's sleep quality and support a healthier lifestyle.

[0115] The household management system may also include a mobility management unit that acquires family movement data and makes suggestions to improve travel efficiency. For example, the mobility management unit acquires GPS data and traffic information from smartphones and analyzes family movement patterns. The mobility management unit can, for example, make suggestions to optimize commuting or school routes. It can also suggest the most suitable mode of transportation to shorten family travel time. Furthermore, based on family movement data, the mobility management unit can make suggestions to avoid congestion. This improves family travel efficiency and saves time.

[0116] The household management system can also include an entertainment suggestion unit that acquires family entertainment data and makes suggestions for relaxation. For example, the entertainment suggestion unit acquires family viewing and music playback history and suggests entertainment for relaxation. It can suggest movies or music that the family can relax to. Furthermore, the entertainment suggestion unit can suggest new entertainment content based on family preferences. In addition, it can consider family emotional data and suggest entertainment at the appropriate time. This can support family relaxation and reduce stress.

[0117] The household management system can also include a learning support unit that acquires family learning data and makes suggestions to improve learning efficiency. For example, the learning support unit can analyze the family's learning history and learning style to suggest optimal learning methods. It can also suggest an efficient learning schedule based on the family's learning progress. Furthermore, it can suggest learning materials and resources tailored to the family's learning style. In addition, it can consider the family's emotional data to make suggestions to increase learning motivation. This can improve the family's learning efficiency and support effective learning.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The learning unit learns the daily schedules of all family members. For example, it uses generative AI to analyze the schedules of all family members and understand each individual's plans. The learning unit can collect calendar information from all family members and learn their schedules. It can also analyze the social media activities of all family members and learn their schedules. Step 2: The suggestion unit proposes efficient division and start times for household tasks based on the schedule learned by the learning unit. For example, it can use generative AI to optimize the division of household tasks and propose start times for tasks considering the schedules of all family members. It can also determine the priority of household tasks and propose efficient division. Step 3: The calculation unit calculates task time based on the home appliances, cooking utensils, and cleaning tools owned. For example, it calculates task time based on the performance data of home appliances, and calculates task time by analyzing the usage history of cooking utensils. It can also calculate task time considering the geographical location information of home appliances and cooking utensils. Step 4: The maintenance proposal unit proposes the optimal usage method and maintenance schedule based on the task time calculated by the calculation unit. For example, it proposes a maintenance schedule based on the frequency of use of home appliances, and a maintenance schedule that takes into account the performance data of cooking appliances. It can also propose a maintenance schedule by referring to relevant literature on home appliances and cooking appliances.

[0120] 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.

[0121] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the learning unit, proposal unit, calculation unit, maintenance proposal unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns the daily schedules of all family members. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes efficient distribution and start timing of household tasks based on the learned schedule. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates task time based on owned home appliances, cooking utensils, and cleaning tools. The maintenance proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal usage methods and maintenance schedules based on the calculated task time. The sharing unit is implemented by the control unit 46A of the smart device 14 and shares schedules and tasks among family members through an app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0127] 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.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0129] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] 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.

[0131] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the learning unit, proposal unit, calculation unit, maintenance proposal unit, and sharing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns the daily schedules of all family members. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes efficient distribution and start timing of household tasks based on the learned schedule. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates task time based on owned home appliances, cooking utensils, and cleaning tools. The maintenance proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal usage methods and maintenance schedules based on the calculated task time. The sharing unit is implemented by the control unit 46A of the smart glasses 214 and shares schedules and task distribution among family members via an app. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0143] 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.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0145] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] 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.

[0147] 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.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the learning unit, proposal unit, calculation unit, maintenance proposal unit, and sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns the daily schedules of all family members. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes efficient distribution and start timing of household tasks based on the learned schedule. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates task time based on owned home appliances, cooking utensils, and cleaning tools. The maintenance proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal usage methods and maintenance schedules based on the calculated task time. The sharing unit is implemented by the control unit 46A of the headset terminal 314 and shares schedules and task distribution among family members through an app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0159] 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.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0161] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] 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.

[0163] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] 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.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the learning unit, proposal unit, calculation unit, maintenance proposal unit, and sharing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns the daily schedules of all family members. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes efficient distribution and start timing of household tasks based on the learned schedule. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates task time based on owned home appliances, cooking utensils, and cleaning tools. The maintenance proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal usage methods and maintenance schedules based on the calculated task time. The sharing unit is implemented by the control unit 46A of the robot 414 and shares schedules and task distribution among family members through an app. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] 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.

[0174] Figure 9 shows the 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.

[0175] 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.

[0176] 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.

[0177] 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, and motorcycles, 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 based, for example, 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.

[0178] 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."

[0179] 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.

[0180] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] 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 other things 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.

[0190] 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.

[0191] (Note 1) The learning section is for learning the daily schedules of all family members, Based on the schedule learned by the learning unit, the proposal unit proposes an efficient distribution and start timing for household tasks. A calculation unit that calculates task time based on the home appliances, cooking utensils, and cleaning tools owned, The system includes a maintenance proposal unit that proposes the optimal usage method and maintenance schedule based on the task time calculated by the calculation unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose home appliances and tools in conjunction with our e-commerce site, leading to purchases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We provide menu suggestions, check ingredient inventory, and enable online purchase of necessary ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We will provide suggestions regarding recommended bedtime and wake-up times, as well as exercise time and methods. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We generate optimal route suggestions using AI during travel and propose modes of transportation. The system described in Appendix 1, characterized by the features described herein. (Note 6) Equipped with shared areas, The aforementioned shared section allows family members to share schedules and responsibilities through an app. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, It estimates the emotions of family members and adjusts the learning frequency of the schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, Analyze the family's past schedule history to select the optimal learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, During learning, the learning data is weighted based on the family's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, The system estimates family members' emotions and prioritizes training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, When learning, the system prioritizes learning schedules that are highly relevant, taking into account the geographical location of family members. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During learning, analyze family social media activity and learn related schedules. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, Estimate the family's emotions and adjust the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, adjust the level of detail based on the importance of the household tasks. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of household chore task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, Estimate the family's emotions and adjust the length of the suggestion based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when household tasks are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of household tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The calculation unit, Estimate family members' emotions and adjust the task time calculation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The calculation unit, During calculation, the usage history of home appliances and cooking utensils is analyzed to select the optimal calculation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit, During calculations, the accuracy of the calculations is improved based on performance data of home appliances and cooking equipment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The calculation unit, It estimates the emotions of family members and adjusts how the calculation results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The calculation unit, During calculations, the optimal calculation method is selected by considering the geographical location information of home appliances and cooking utensils. The system described in Appendix 1, characterized by the features described herein. (Note 24) The calculation unit, During calculations, we refer to relevant literature on home appliances and cooking utensils to improve calculation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned maintenance proposal unit, We estimate the emotions of family members and adjust the suggested maintenance schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned maintenance proposal unit, When proposing maintenance, we analyze the usage history of home appliances and cooking equipment to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned maintenance proposal unit, When making maintenance proposals, we improve the accuracy of our suggestions based on performance data of home appliances and cooking equipment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned maintenance proposal unit, Estimate family members' emotions and prioritize maintenance schedules based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned maintenance proposal unit, When proposing maintenance services, we select the most suitable proposal method by considering the geographical location information of home appliances and cooking equipment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned maintenance proposal unit, When making maintenance proposals, we refer to relevant literature on home appliances and cooking equipment to improve the accuracy of the proposals. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned shared portion is, Estimate family members' emotions and adjust how schedules are shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shared portion is, When sharing, refer to the family's past sharing history to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shared portion is, It estimates family members' emotions and determines sharing priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned shared portion is, When sharing, select the optimal sharing method considering the family's device information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned shared portion is, When sharing, refer to family calendar information to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shared portion is, When sharing, analyze family members' social media activity to select the optimal sharing method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The learning section is for learning the daily schedules of all family members, Based on the schedule learned by the learning unit, the proposal unit proposes an efficient distribution and start timing for household tasks. A calculation unit that calculates task time based on the home appliances, cooking utensils, and cleaning tools owned, The system includes a maintenance proposal unit that proposes the optimal usage method and maintenance schedule based on the task time calculated by the calculation unit. A system characterized by the following features.

2. The aforementioned proposal section is, We propose home appliances and tools in conjunction with our e-commerce site, leading to purchases. The system according to feature 1.

3. The aforementioned proposal section is, We provide menu suggestions, check ingredient inventory, and enable online purchase of necessary ingredients. The system according to feature 1.

4. The aforementioned proposal section is, We will provide suggestions regarding recommended bedtime and wake-up times, as well as exercise time and methods. The system according to feature 1.

5. The aforementioned proposal section is, We generate optimal route suggestions using AI during travel and propose modes of transportation. The system according to feature 1.

6. Equipped with shared areas, The aforementioned sharing function allows family members to share schedules and responsibilities through an app. The system according to feature 1.

7. The aforementioned learning unit, It estimates the emotions of family members and adjusts the learning frequency of the schedule based on those estimated emotions. The system according to feature 1.

8. The aforementioned learning unit, Analyze the family's past schedule history to select the optimal learning algorithm. The system according to feature 1.

9. The aforementioned learning unit, During learning, the learning data is weighted based on the family's lifestyle patterns. The system according to feature 1.

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